On January 23, 2019, Whitney Lundeen, founder of Sonnet James, a maker of dresses for active mothers, appeared on the Shark Tank in hopes of securing a business partner and financial investor to contribute $350,000 in exchange for 25% equity in her company, a $1.4 million business valuation.
Sara Blakely, a Shark Tank visiting judge and investor who likes to invest in female entrepreneurs, noted that Sonnet James produces dress designs similar to many other producers, but accepted Whitney's deal for $350,000 in exchange for 25% of her company because she liked Whitney very much.
Kevin O’Leary, a regular Shark Tank judge complimented Whitney on a job well done and said that he felt her presentation was the best clothing presentation they have seen over 10 seasons of Shark Tank.
Whitney is a single mom of two boys who developed the Sonnet James line of women’s dresses to allow moms to have one outfit for every occasion. Whether they are in a business meeting, out to dinner, on the playground or riding bikes with their kids, women would have an option for clothing that is comfortable and fashionable for all occasions. Not only has Sonnet James introduced a high-quality product into the mothers market, but they have also created a network of moms which is very important for brand awareness and advocacy.
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As Whitney explained to the Sharks, the idea for her company came about when she got tired of mom-look staples like yoga pants and wanted to come up with something fashionable, but easy to clean. Lundeen elaborated.
“I was going through a difficult time in my life, and so I had this idea of making a dress that my mom could have worn that could have reminded her to play with me when I was little. And I said, ‘Alright, this year, I’m going to take the idea, and I’m going to teach myself how to sew, and I’m going to pattern draft.’ And every night, I would pretty much sit on the kitchen floor crying, trying to teach myself how to do two things I didn’t know how to do.”
The Sharks asked Lundeen to explain what she meant by the idea of the dress reminding her mom to play with her. Holding back tears, Lundeen says that she had a challenging childhood that included some abuse. She explained.
“My parents did the best they could with what they had. I found when I became a mom, I couldn’t engage with my kids as much as I wanted to. And I wanted something that could help me be the mother that I had always wanted to be, and something that could remind me every day when I put it on what my priorities were.”
Whitney wanted something fashionable, yet durable enough for rough and tumble play. She made her own dresses out of four-way stretch modal spandex. On New Year’s Day, 2013, Lundeen resolved to make her own dress line and she designed 12 different styles of what would become Sonnet James dresses. Next she sourced fabrics and started making dresses for moms.
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She got her business going back in August 2013 with a successful Kickstarter campaign that raised $58,245 and provided proof of product-market fit. That’s when Sonnet James became a real business. The dresses are playful and durable. They have the classic lines of designer dresses, but they’re safe for “play clothes.” Sonnet James designs cost between $100-$160 and come with a three-day, no hassle try-on guarantee.
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Whitney developed the Sonnet James "playtime" dress line to remind her to be a mom who is present and plays with their kids while feeling confident and put together. The dresses in the Sonnet James line are all washable and made of quality spandex fabric that has stretch and compression so that it retains its shape throughout washing cycles. Whitney has designed all of the dresses in her line as well as taught herself how to sew and how to design and create a website.
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Sonnet James operates a direct-to-consumer (D2C) revenue model through their website (see below) and had $1.2 million in sales at the end of 2018 with a 75% product margin. The average order size is 2 dresses and their rate of return is 23% which is below the retail average of 30%.
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Sonnet James dresses are made in San Francisco, from fabric produced in New York City and printed in Los Angeles. As part of a community of supportive women, it is important to Whitney that the fabric and the production house work environment are a healthy place for the seamstresses to work. As she told The Hive Magazine, even though she may pay more for the production and thereby make less profit, she wants to maintain a close, one-to-one relationship with the production end of her business. It is “important to me to feel good about where these dresses were being made.” And what is her advice for other mothers who might want to start their own business? Despite all the snags, fears, problems and long, hard hours of work, Whitney says simply: “Just do it!”
So how well has Sonnet James done since the Shark Tank investment by Sara Blakely? Sonnet James is still in business and still designs out of Whitney's home in Palo Alto. As of June 2021, Sonnet James is thriving and reported revenues of $6 million.
COMMENTARY: Sonnet James is a classic example of an entrepreneurial success story driven by incredible passion and drive and very little capital. Sara Blakely, the Shark Tank investor who accepted Whitney Lundeen's offer of a 25% share of her young company, noted that Sonnet James dresses were not much different than those made by other garment manufacturers. So what is the secret to Sonnet James success? The answer: POSITIONING, STORYTELLING and MADE IN USA
Sonnet James does not owe its success to being first-to-market, product differentiation, lowest cost producer, or even great apparel designs. Instead, Sonnet James has succeeded where others have failed in a very crowded women's apparel market through crafty storytelling, product positioning and manufacturing in the USA. Whitney Lundeen has positioned Sonnet James as a designer of "playtime" dresses for moms -- a new women's apparel category. Whitney's successful appearance on the Shark Tank is a marketing storyteller's dream. A true "rags-to-riches" story. Whitney's success post-Shark Tank has produced a lot of "free" publicity which is worth its weight in gold.
I don't know how much Sonnet James spends on paid advertising, but going from $1.2 million in revenues at the end of 2018 to $6 million by June 2021 can't all be due to positioning, crafty storytelling or manufacturing in the USA. The "Made in USA" label probably contributes to Sonnet James success for several reasons:
Job Creation - Made in he USA means creating jobs right here in the good ole USA.
Quality - Made in the USA has always stood for better quality compared to goods made in China, India or other Asian countries. Economists at The Boston Consulting group found that 60 percent of Chinese consumers are willing to pay more for products labeled “Made in the USA” than for those labeled “Made in China.”
Better For Families - Products made in America are better for consumers because they must follow American consumer protection laws and safety standards. Many foreign countries have far less extensive product safety standards than those in the United States, frequently leading to recalls and safety issues.
Addressing Poor Conditions - Many countries do not enforce the same worker safety and child protection controls of Western countries. It can be hard for companies to compete on cost with regimes willing to exploit their own people. You’re supporting a higher standard of working conditions when you buy American-made.
S0nnet James has identified or created a new category or "Blue Ocean" as described in the book Blue Ocean Strategy in the women's apparel market: "playtime" apparel for women. In 2020, there were 15.6 million single mothers living with young children in he USA. There are presently 43.5 million mothers (ages 15 to 50) in the USA. The COVID pandemic, inflation and the Ukraine war with Russia has put a brake on U.S. birth rates. Whether birth rates will continue to decline remains to be seen, but something that Sonnet James needs to keep a careful eye on going forward.
Courtesy of an article dated February 14, 2019 appearing in Business2Community, a tweet dated January 23, 2019 appearing in Twitter, an article dated February 3, 2019 appearing in Tiny Beans, an article dated May 2019 appearing in the Shark Tank Blog, an article dated January 14, 2019 appearing in Shark Tank Products, an article dated April 21, 2021 appearing in Infoplease and an article dated August 28, 2017 appearing in Made In America Movement
What separates a jaw-dropping unicorn business concept ready for seed funding injections from an amateur-hour startup pitch no one takes seriously?
It might not be your first guess, but for me it is definitely a clear and concise TAM formula.
The potential of a product or service can be a strong selling point to show investors and stakeholders, especially if you have an actionable business plan that will complete the vision and alignment you’ll establish for your growth.
Therefore, calculating the Total Addressable Market of your business is your first step in creating a successful business. Let’s find out how you can do that together:
What is Total Addressable Market?
Total Addressable Market or TAM refers to the total market demand for a product or service; the overall revenue opportunity available in a marketplace for a product or service. In other words, it’s the eagle-eye view of where any new product or service will be introduced. Regardless of what we’re talking about and in every context, TAM is arguably the single most important piece of information in terms of understanding market potential.
Here, have a look at this simple diagram:
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As you can see TAM is the overarching big picture – the TOTAL market potential.
From here, you should have a firm grasp on why this calculation is THE calculation investors are going to inspect first.
Why Are Proper TAM Calculations Invaluable?
In reality, reaching a proper TAM calculation is a worthwhile team-enlightening process in itself.
Along the way, as you ‘Narrow the Tam Funnel’ you’re going to define a number of other critical metrics, such as:
Fundamental market data you should have anyway – i.e. potential size.
A formula to attract and convert investors.
Thorough competitor data, broken down in relation to your venture – market fit.
Data-driven and formalized target customer information.
The spectrum of currently available similar products/services.
The major upward and downward trends influencing your TAM.
If your market, product, and service analysis is weak, your TAM will be weak. If your TAM is weak, so is your SAM and SOM. This is pretty much always going to lead to monstrous blunders across the board.
Before jumping into that, let’s include our two new key terms and some definitions.
TAM – Upon reaching 100%, with every individual in your chosen geography using in your product or service, you become the proud owner of a market monopoly.
SAM – Serviceable Available Market: This is your slice(s) of the market. The total segment of TAM you can feasibly or reasonably target. For example, your New York pizza shop’s 1-10% of the pie-lover TAM within twenty miles of your prime real estate.
SOM – Serviceable Obtainable Market: Out of that SAM, this is the more refined slice of the TAM you can hope to achieve within the first 1-3 years of opening your doors or trying a new form of pizza in the local market; app, etc. For example, if you don’t sell thick crust for some reason, your SAM and SOM will need to continue narrowing.
Another crude example could be the worldwide transportation market, which is gigantic – over $5 trillion. Imagine having a monopoly there… Fortunately, an unlikely scenario. Even for Uber, they were initially targeting the Taxi & Limo Sector (SAM), coming way down to a smidgen over $4 billion. Then the SOM they aimed for within the first 5 years was around $1 billion.
From $5 trillion, to $1 billion.
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Their TAM refinement didn’t stop there though, as in their original pitch deck they were only targeting a certain area of one city. If that went well, the next step was a second major city, and so on.
You can also take huge six and seven-figure TAMS and break them down into the different sub-sectors.
For example, clothing. Here are three TAM-SAM-SOM diagrams showing different pathways to approach along the supply chain.
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Right, with a basic foundation and core terms all laid out, let’s get to the soft gooey center in the middle of today’s subject.
Your Goal
Your goal when ironing out the TAM for a product or service is to make this calculation the most educated, de-risked (as close to the truth), and data-driven estimation possible. And yes, it’s nearly always going to be an estimation rather than a fixed concrete number, because TAMs are often projections.
How to Calculate Total Addressable Market (3 Ways)
The Top-Down Approach: Not much calculating in this approach, other than coming to terms with the overall big-picture number without any real precision.
The Bottom-Up Approach: Using as many sources as possible to start with the smallest, most refined picture of the market you can hope to capture (ex: the Central SF area like Uber). So, starting with your highly-specific SOM and working your way up.
Value Theory Crafting: Using highly-educated, but nonetheless subjective or less reliable sources of data, to influence your calculation. Conjecture, opinions, base statistics, buyer WTP numbers (more on this below), etc.
“The best TAM formulations are the result of using all three methods, combined with studious, prudent due diligence.”
Total Addressable Market (TAM) Formula
In all of its glory, this is as simple as it gets.
[TAM = # of Customers x Pricing]
From leveraging distinguished industry and Wall Street-based analysts (paid and free), to using a huge assortment of publically available market and competitor data, there’s no excuse not to roll up your sleeves and do your homework – TAMwork.
Let’s take a look at these two variables.
Customers
Geography: Where is the overall market (TAM), along with your SAM and SOM? Are we talking ecommerce? How many countries are we targeting, and where are your customers within them? Be specific, specific, specific!
Relevancy: Why are these your customers? And no, you can’t count a competitor’s customers or users as your own.
Testing?: How do you know these are your customers? Be sure you’re ‘showing your work’ and proving these assumptions as best you can. Show the logic.
Your Pricing
Pricing Basis: What is the basis for your pricing? How are you coming to these conclusions? Again, show how the math plays out step by step.
Units of Measurement: Depending on your situation, this can be a great number of things. The point is to be ultra-definitive with how your pricing is structured.
Customer WTP: This is the basic number your customers or users are Willing To Pay. Typically WTP numbers are based on competitors so they aren’t really sources for YOUR pricing, since you are not your competitors. But, they can help paint a market picture.
Congratulations, you’ve now swept through the pillars of TAM, why it’s important, and how to properly calculate it.
Let’s finish this off by looking at the most common mistakes inexperienced startups and entrepreneurs make as they set out to tackle the numbers.
Some of this will be repetitive, but that’s a good thing. Stamp these details into your business mind!
Common TAM Calculation Mistakes
Inexperienced startups in the pre-seed, seed, and Series A funding round are where to look for pricing metrics that are WAY off.
It’s so common, yet they NEED to nail these concepts.
How?
Why?
Most often it’s a mixture of being too swept up into the product or business concept itself without crunching numbers or having no one who is really considered a number cruncher on the team.
For investors, the first thing they want to KNOW, is how de-risked the numbers are and what kind of feasible ROI they can expect in the short and medium-term. At the end of the day, that’s where the rubber meets the road. No matter how mind-shatteringly-insane your new product concept might seem, if these numbers:
Don’t exist, or,
Do exist, but they’re obviously way off,
Then you’re going to be sent back to the drawing board, heads hung low.
Nobody wants that, so here are the big three mistakes to steer clear of.
1) Not Proving the Math – Going Top Down Only
“Listen, it’s a no brainer. We’re going to waltz in and nab up at least 3% of this $375 BILLION Market. Yep, that’s right folks, grab your wallets because we’re talking $11.5 BILLION smackaroos. You’ve got to get in on the ground floor before this puppy REALLY takes off!”
That’s what a corny top down-only pitch sounds like. Experienced investor eyes begin glazing over instantly, waiting for you to dial things down and prove your TAM.
How EXACTLY do you plan to enter into and capture this 3% of the market?
How EXACTLY do you figure these are all your customers when the new product or service doesn’t exist yet?
Investors will begin firing off questions, testing each and every assumption that comes baked into that $11.5 billion top-down number you threw out. If they have to do your math for you, that’s a bad sign, and it’s likely your TAM will fall… dramatically.
What if your team is left standing there, realizing you were focusing on a product that had a TOTAL product-lifespan value of only $11.5 MILLION?
What if it’s only $1.5?
#2) Basing Your Math on Competitor TAMs
“We’ve created the absolute best accessory for jeep owners in the known universe, and the jeep owner market worldwide is…cosmic.”
Those aren’t your jeeps and those aren’t your customers.
Don’t base any of your math on competitor numbers, because it’s sloppy and you end up with a horribly misleading formula.
What kinds of jeeps are ideal for your accessory?
What country or countries will you choose to target first?
What specific kind of jeep owner are you REALLY targeting?
Starting to see how powerful this calculating ability is and why shows like old school ‘Shark Tank’ or ‘The Apprentice’ have become so popular (and educational)?
#3) Mistaking ‘Market Pain’ for TAM
“Smoking is a gigantic problem that in total creates X trillion….”
“Too many bicyclists are getting hit in big cities because…”
Problems are not TAMs.
If you attempt to base your math on the size of a social pain, rather than the response to your solution within a specific marketplace impacted by the problem, you’re starting off on the wrong foot.
By avoiding these three mistakes, you can stay on the right path and provide a FAR more de-risked formula to base decisions, time, and money on.
Conclusion
Before you take any action on a new product or service venture, know the TAM… be the TAM.
Projecting revenue growth using huge numbers is fun, but to attract genuine investor interests, or get serious stakeholders on board, or really just have a real shot at success, you need to REALLY see what is possible. Let your TAM + SAM + SOM calculations serve as your guiding light, always keeping your financials firmly rooted in reality, not steeped in hyperbole or hype.
COMMENTARY: This is one of the best discussion of how to calculate addressable market based on three market measures: TAM, SAM AND SOM.
I have had to make addressable market estimates based on using different variables and assumptions. Keep in mind that you are not shooting for absolute accuracy when you conduct your calculations. Addressable market is a moving target. You could get three different people and you would obtain three different estimates for addressable market.
In calculating TAM, SAM and SOM you should include what stage in the market life cycle your firm presently occupies as depicted in he following graphic:
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I would recommend that you calculates three different addressable market estimates. Most Likely, Least Likely and Average of Most Likely and Least Likely for TAM, SAM and SOM. If you are a startup base your revenue projections on your Serviceable Obtainable Market (SOM) over the next three years. SOM is the portion of your Serviceable Addressable Market (SAM) that you believe you can actually grab based on your estimated costs to acquire new customers, churn rates and financial resources available to attract new customers.
TIP: In estimating SOM, aim for the "low hanging fruit" first and work up from there. If you are launching a new product, your existing customers will be the easiest and least costly to convert to the new product. Existing customers are your early adopters, and most likely to covert because of the brand loyalty and trust you have built over the years.
Hope the above helps.
Courtesy of an article dated February 23, 2021 appearing in UserGuidingBlog
Data-driven marketing is the approach of optimising brand communications based on customer information. Data-driven marketers use customer data to predict their needs, desires and future behaviours. Such insight helps develop personalised marketing strategies for the highest possible return on investment (ROI).
Business intelligence and analytics are competitive necessities in today’s fast-moving, data-driven business world. In fact, more than half of organizations across industries are using business intelligence in some capacity to drive important initiatives like new revenue streams and increased efficiency.
Yet there remains a gap between those who use it and those who use it successfully. A recent survey conducted by Sisence found that 94% of respondents felt data was critical to their ongoing strategy, but only 24% believed their companies were using it effectively. So where is the disconnect?
One key factor to keep in mind is that while data is a powerful tool, it’s not universally applied. Organizations must understand the types of business intelligence and analytics solutions that exist and apply them in ways that add the highest strategic value to their companies.
We’ve put together this quick guide to help you understand exactly what business intelligence entails, the different ways companies can leverage it, and how to choose the tools that make the most sense for your business.
Quick Takeaways
Business intelligence and analytics provides data-driven insights at a level and scope not capturable by the human brain alone.
Companies leverage business intelligence in several critical ways including to identify problems earlier, improve operational efficiency, and refine marketing strategies with deep customer insights.
Organizations should always choose business intelligence and analytics solutions with their specific priorities and needs first in mind.
What is business intelligence and why is it important?
Business intelligence (BI) involves the use of technology tools that enable data-driven decision making and actionable insights to help companies improve performance and identify new opportunities. While business intelligence has been around for a few decades, it has recently reached new capability levels with the emergence of AI and machine learning.
An important component of business intelligence is its data visualization capabilities, which allow teams to take large and complex datasets and present them in digestible ways that make them usable for driving strategy.
Also of note is the difference between business intelligence and analytics. Business intelligence aims to leverage past and current data to inform real-time decision making and strategies. Analytics uses past data to explain current data, and it uses both past and current data to make predictions about what will happen in the future.
In other words, business intelligence involves looking at data to see what it’s saying and make decisions accordingly. Analytics dives deeper to understand why the data says what it says, and uses those deeper insights to forecast what’s likely to happen next. Both business intelligence and analytics are critical to gleaning complete insights.
Business intelligence and analytics are important because they augment traditional business decision making in a number of ways. First, they provide the necessary data insights for managers to make informed decisions. In today’s world, any company making decisions strictly from gut feelings or firsthand observations are going to fall behind.
Business intelligence also allows organizations to expand data insights beyond the c-suite and make them part of everyday business operations. When employees have access to the level of insight business intelligence and analytics can provide, they’re able to make better decisions, be more productive, and add more strategic value to the company in their roles.
How do companies leverage business intelligence and analytics?
There are many ways to leverage business intelligence and analytics in your organization. Here are some of the most common and valuable ways to do it:
Identify potential problems sooner – Data insights allow organizations to identify worrisome incidents earlier (like a dip in sales or revenue, a drop in retention, or prospect fall-off at a specific point in the customer journey) and address them sooner and more quickly, before they turn into larger issues.
Make better decisions – Data makes decisions more informed. Business intelligence and analytics, in short, go beyond the capacity of our human minds and help us make decisions that account for larger trends and insights we just couldn’t capture without technology. For instance, it would be great for a telecom company to know the future potential of 5G so that they can come up with competitive tactics and pricing strategies.
Improve operational efficiency – BI tools help companies make smart strategic decisions about the future, but they also offer insight into daily operations that help companies make adjustments to increase productivity and daily performance.
Increase marketing effectiveness – Modern technology has given us unprecedented insights into consumer and customer behavior, and business intelligence and analytics tools are critical to accessing them. For example, Zoom took to new age marketing methods to reach consumer households in a saturated video conferencing market.
See larger trends – With BI tools, companies can access and analyze bigger data sets and see larger market and industry trends. These insights contribute to smarter competitive analyses and a better understanding of market position.
Choosing business intelligence and analytics solutions
So how do you choose the right BI and analytics solutions for your business? First, it’s important to know what’s out there. Business intelligence tools have a variety of capabilities, including data mining, data visualizations, text mining, KPI reporting and more. Most top business intelligence solution providers package all or most of these capabilities into one tool so that companies can use it in ways that benefit them.
To choose the tool that’s best for you, there’s one key strategy to keep in mind: think first about your company’s priorities and needs, then choose the product that offers the highest value in those specific areas. Simply choosing the solution with the most features or the highest brand awareness is likely not going to add the most possible value to your BI and analytics efforts.
Here’s an overview of the top business intelligence and analytics solutions on the market right now:
COMMENTARY: As you can readily see from the above post, business intelligence and data analytics are essential in order to develop valuable insights about consumers (and business leaders) and management the entire marketing processes of a data-driven organization.
Business intelligence is key to monitoring business trends, detecting significant events, and getting the full picture of what is happening inside your organization thanks to data. It is important to optimize processes, increase operational efficiency, drive new revenue, and improve the decision-making of the company.
A marketing strategy is a document that answers the big questions—what you offer, who your audience is, what your company stands for, brand guidelines, what niche or industry you play in, and who your main competitors are. It can include less or more information than that, but its purpose is to be a sort of lighthouse, helping you keep your marketing focused on the bigger picture.
Marketing strategies are always rooted in what your overarching business goals are. If your business needs to increase revenue by 25%, then your marketing strategy would explain how marketing will help achieve those goals from a high-level point of view.
When writing a marketing strategy, do not—I repeat—DO NOT go into uber-specific marketing tactics. That’s for later. On the flip side, you should be asking the right kinds of questions.
What Is a Marketing Plan?
Your marketing plan, based on your marketing strategy, dives into the specifics—what channels and tactics you’re using, which segment of the audience you’re targeting, when initiatives are occurring, and how you’ll measure success.
Compared to your marketing strategy, your marketing plan is more about which tactics, campaigns, initiatives, and promotions you’ll run in a certain amount of time. Marketing plans can range from three months to a year, and should remain semi-flexible in case marketing needs to shift priorities.
MARKETING STRATEGY VS. MARKETING PLAN
To help visualize all of this, we’ve created a graphic that explains the core differences between a marketing strategy and a marketing plan.
In our experience, the marketing strategy shouldn’t change as much as your marketing plan. If it does, it’s almost impossible to gain any real traction or measure the impact of your marketing on the business. That said, you’ll probably need to revisit your marketing strategy each year, whereas you’ll be executing against your marketing plan regularly.
At this point, you might be thinking, “this seems like A LOT.” It is, which is why strategies and plans are often broken down into smaller disciplines within marketing (think: content, customer acquisition, customer retention, and more). Let’s look at an example.
An Example in Action: Content Marketing
Most marketers are familiar with content marketing, so let’s use it as an example. Like I mentioned earlier, strategies address high-level issues whereas plans show how you’re going to execute.
CONTENT MARKETING STRATEGY
In this case, your content marketing strategy would answer those big questions discussed above. Questions like:
Who should we be writing to?
How will content marketing support our business goals?
How much money can we spend on creating content?
Who are our content competitors?
What tone do we want to come across in our content?
The role of the content marketing strategy isn’t to point out every single topic you’re going to write about or even what your specific KPIs for the upcoming year are going to be. Instead, it’s supposed to give context to why you’re investing in content marketing, who you’re targeting, what content mediums you’re going to use (generally), and more.
CONTENT MARKETING PLAN
Once you have your strategy set, your content marketing plan should explain how you’re going to attack the problem. This is where you’ll document content marketing goals and KPIs, what resources you plan on creating, when you plan on creating them, who will be involved in the process, how you’ll be promoting each piece of content, and more.
There’s more to content marketing plans than an editorial calendar, but your editorial calendar is a huge part of your plan. It tells everyone what topics your content is addressing, where it’s being published, when it’s being published, which channels you’ll promote it through, and the goals it should be aiming to hit.
Tips for Creating a Stellar Marketing Strategy or Plan
Whether you’re focusing on the overarching strategy or getting into the fine details, there are a few things you need to keep in mind throughout the journey that will help produce something of real value for your organization or team.
TIP #1: DOCUMENT YOUR PLAN/STRATEGY
This sounds obvious, but it is oh-so important. Enter impactful stat—marketers who document strategy are 538% more likely to report success than those who don’t. That’s a real number. Write your plan down. Even if you don’t think it’s 100% correct.
As marketers, we want so desperately to get everything right that it often has the opposite effect—we’re paralyzed. You can go back and adjust your plan or strategy as necessary, but it starts with actually having a plan or strategy. Plus, without a documented vision for the future, it’s much harder to gain buy-in from your co-workers and miscommunication ensues.
TIP #2: COLLABORATE WITH OTHERS
You don’t have to do this alone. Nor should you. Even if you’re legitimately the only person in your marketing department, you can ask your marketing friends at different companies for some insight. If all your friends sadly work in sales (jk, jk) you can even gain some perspective from co-workers in different departments. They might not give you the best “marketing” ideas, but they might ask good questions and prove to be good sounding boards.
There are tons of positives when it comes to collaborating with others. A few I like are gaining new perspectives, considering other ways to solve the problem at hand, gaining feedback, and the efficiencies that come with asking people that may have done something similar in the past.
TIP #3: MAKE IT CLEAR. MAKE IT REALISTIC.
There’s no problem shooting for the moon. But if you create a marketing plan or strategy that requires 10 people to execute and you only have two sad interns, then it’s not all that helpful. You’ll just end up with a ton of ideas but no idea which ones to work through.
A wise person once said, “The essence of strategy is choosing what not to do.” Try your best to paint a clear picture that’s based in reality.
Don’t Let the Scary Marketing “Strategies” Overwhelm You
To play on even more cliches, “A journey of a thousand miles begins with a single step.” Try your best to avoid paralysis by analysis, and instead tackle your strategy or plan one step at a time.
And if you don’t think documenting your marketing plan or strategy is important, think again. Remember: marketers who document strategy are 538%(!) more likely to report success than those who don’t.
Finally, one last reminder that for those who can't wait to dig in and start planning, we created a marketing toolkit that allows you to work through building your very own marketing strategy. Happy planning!
Courtesy of an article dated September 21, 2020 appearing in Element Three Blog
Over the last two decades of building and running businesses, and the last couple of years working full time with dozens of startup founders and CEOs on their strategies and funding plans in my consultancy business, I have observed that there are a common set of reasons that startups struggle and fail, and a consistent set of factors that make startup companies successful.
I wondered if my observations were supported by hard data, and my curiosity around startup success and failure eventually got the best of me. I decided to do some in-depth investigation around this topic. I wondered if there were any research studies that showed why startups succeed and fail? I found several articles that were filled with unsubstantiated opinions and a few sources that had really great hard research around the topic.
Why do companies fail?
According to an article in FastCompany, "Why Most Venture Backed Companies Fail," 75 percent of venture-backed startups fail. This statistic is based on a Harvard Business School study by Shikhar Ghosh. In a study by Statistic Brain, Startup Business Failure Rate by Industry, the failure rate of all U.S. companies after five years was over 50 percent, and over 70 percent after 10 years.
This study also asked company leadership the reason for business failure, giving a list of four main reasons for failure with sub-categories below those. They also gave a list of 12 leading management mistakes. It is worth checking out the details. This research-based analysis confirmed some of my observations. I bracket the Statistic Brain finding into seven key reasons for that entrepreneurs experienced business failure:
Lack of focus
Lack of motivation, commitment and passion
Too much pride, resulting in an unwillingness to see or listen
Taking advice from the wrong people
Lacking good mentorship
Lack of general and domain-specific business knowledge: finance, operations, and marketing
Raising too much money too soon
All of these focus on the decision-making of the entrepreneur and general business knowledge.
In another study, CB Insights looked at the post-mortems of 101 startups to compile a list of the Top 20 Reasons Startups Fail. The focus was on company level reasons for failure. I think this list is instructive, but each of these reasons for failure is due to a failure in leadership at some level. The top nine most significant from this study are:
No market need
Ran out of cash
Not the right team
Got outcompeted
Pricing/cost issue
Poor product
Need/lack business model
Poor marketing
Ignore customers
Notice that all of these are business- and team-related issues, even the ones that relate to the product. Issues like there are always tied to leadership and the leader’s ability to build a strong team and drive a business model and business thought process and discipline. Also, keep in mind, if running out of money is the ultimate reason for failure, there are always other factors that cause this result.
Why do startups succeed?
Next, I looked for sources of information of why businesses were successful. I found some good research from Harvard Business School, Performance Persistence in Entrepreneurship, which suggest that serial entrepreneurs that have prior success are more likely to have success, and that the best VCs are good at picking serial entrepreneurs. However, that really didn’t answer my question about the qualities of the entrepreneur.
The best comprehensive research that helped to answer the “reasons for success” question that I could find was from The Ecommerce Genome by Compass in their Startup Genome report, which looked at 650 internet startups. Although this research is tech industry specific, I still think it is very instructive. The report stated 14 indicators of success. Some of the 14 were a bit redundant, but you should review the report yourself. This analysis also confirmed some of my observations. I bracketed these 14 indicators into nine key factors for success:
Founders are driven by impact, resulting in passion and commitment
Commitment to stay the course and stick with a chosen path
Willingness to adjust, but not constantly adjusting
Patience and persistence due to the timing mismatch of expectations and reality
Willingness to observe, listen and learn
Develop the right mentoring relationships
Leadership with general and domain specific business knowledge
Implementing “Lean Startup” principles: Raising just enough money in a funding round to hit the next set of key milestones
Balance of technical and business knowledge, with necessary technical expertise in product development
Are the reasons for success the opposite of those for failure?
There are things that you must possess to be a successful entrepreneur, but they won't guarantee success. That said, it stands to reason that if you fixed the reasons for business failure, you would at least improve your chances of success. So, I decided to look at the side-by-side comparison of the reasons for failure and the factors for success.
If you look at both the reasons for failure and the factors for success, it is clear that commitment to a plan is key. This, of course, implies having a plan. This does not mean that you are completely inflexible, but you can stay the course. This is why the most successful companies have one or two pivots. I do not think that every little business adjustment or fine-tuning as a pivot.
A true pivot is a change in course of direction that results in a material change in the product-market strategy. It could be along the product axis or the market axis, but it has to be enough of a change that it really requires an adjustment in strategy and a corresponding adjustment in resource allocation. At least, that’s my definition. Passion and motivation are the obvious factors. Every entrepreneur, business coach, consultant, advisor, newscaster, investor and industry analyst talks about passion. Steve Jobs is quoted all the time about this. It’s probably become too cliché and overused at this point.
What I like about this analysis is that it goes to the root of the passion. People that are successful believe in what they are doing. The successful entrepreneur feels that they can make an impact and a difference in the world. There is so much inertia and negativity around getting a startup off the ground, much less getting it to “escape velocity,” that if you don’t have this deep-seated commitment to making an impact, you will surely give up. Successful entrepreneurs are competitive. They play to win, and they hate to lose. This trait may show-up differently with different personality types, but I have never met a successful entrepreneur that doesn’t have a competitive spirit and a will to win.
The next two things go hand-in-hand. I kept them separate since I think mentorship is so important, and it has played such a huge role in my career success. Just because you are willing to learn does not mean that you are willing to seek a mentor and listen to their guidance. By the way, I’m not advocating that you take every piece of advice and guidance from your mentors, but if you have selected strong mentors that have significant domain, technical or business expertise, you should at least consider thoughtfully consider what they have to say. Otherwise, why have them around as a mentor? It gets to humility. It’s one of those things when you think you have it, you don’t.
Successful startups are businesses. It therefore stands to reason that you need to establish and implement solid fundamental business principles and practices to improve your chances of success. Many technical founders fall in love with their product idea and consciously or unconsciously believe that if they build a better mousetrap, the world will beat a path to their door. However, both the success and failure studies show that you need leadership in the company with general and domain-specific business knowledge to be successful. Of course, you also need to have strong technical expertise in your chosen product development area.
Does this mean that a technical founder cannot be successful as a CEO? No, it doesn't. Look at Dr. Irwin Jacobs, the co-founder and founding CEO of Qualcomm, as a classic example. Dr. Jacobs is a brilliant engineer and former professor at MIT. However, he also has a brilliant business mind and a lot of business knowledge. Prior to Qualcomm, Dr. Jacobs ran another company, MA-Com, so he had experience running a company. He also surrounded himself with a strong management team. There are many other examples of this success formula, but there are far more where there is a seasoned businessperson who has domain expertise leading the company, and a strong technical team driving product development. Steve Jobs (Apple, NeXT, and Pixar) is the classic example as a business-oriented founder. Meg Whitman (eBay) and Eric Schmidt (Google) are great examples of CEOs who were brought into companies at an early stage to complement an exceptional team of technical founders.
Finally, having a clear and realistic idea of how long things take, setting intermediate milestones for every 12 to 18 months, and raising just enough money it to get to the next set of key milestones, is not only important to capital efficiency, it is also important for success.
How do I become a member of the $100 million club?
Interestingly, according to the Kauffman Institute, in its article The Constant: Companies that Matter, the pace at which the United States produces $100-million companies has been stable over the last 20 years despite changes in the economy. The study sates, “Anywhere from 125 to 250 companies per year (out of roughly 552,000 new employer firms) are founded in the United States that reach $100 million in revenues.” My former company, Entropic, achieved this status. How do you become part of that club? You need some luck and a good sense of timing. However, as said by the Roman philosopher Seneca, “Luck is what happens when preparedness meets opportunity.”
Beyond that, you need a plan, persistence, perseverance, a willingness to be flexible, and a world-class team. You also need to be frugal, bright, and cultivate strong mentors. The best way know to do all these things well and efficiently is to follow a systematic process where you plan, commit, track results, promote accomplishments and raise the necessary capital, or "fuel in the tank," to drive the growth of your startup.
Plan. Commit. Win.
COMMENTARY: As a consultant it always pains me when a startup client launches successfully and gains traction, but never seems to quite "cross the chasm" that all startups encounter, and must cross in order to "get to the next level." Crossing the chasm simply means helping a product, service or technology move from "early adopters" to a larger market segment, sometimes called the "early majority," in the Product Adoption Curve (see below).
Product Adoption Curve
The product adoption curve is a standard model that reflects who buys your products and when.
Think of it as the big picture view of your product adoption. It takes the product lifecycle and considers what happens at different points.
In most product adoption models, there are five distinct stages. Each stage represents an arbitrary amount of time, so what’s most important here is the process as a whole.
Now let’s break this down step by step, stage by stage.
Stage 1. Innovators
The innovators are the first group of people to invest in your product.
This is a unique group. People who buy super early are usually obsessed with technology and want to keep up with the cutting edge of technology. When the first Apple iPhone was first launched on July 29, 2007, the innovators were the very first to buy the iPhone.
What’s most important about the innovators group is its size. You might have noticed that it’s small. That’s completely normal.
This is why you might only get a few sales immediately after you launch. You’ll typically get about 2.5% of your total sales from innovators.
The Innovators
Stage 2. Early Adopters
At some point, you’ll see a swell in sales, and you’ll start to get a steadier conversion rate.
This is probably because the early adopters have arrived.
Like innovators, early adopters tend to be ahead of everyone else, willing to test the waters.
Early Adopters
Although early adopters are similar to innovators, there are some important differences.
It could be the case that early adopters have purposely waited to buy your product.
Whereas innovators are fine with rushing in and testing out something new, early adopters are a bit more hesitant. They still want to try something new, but they want a few reviews to consult.
Then again, it could be the case that they just found out about your product.
Expect your percentage of adoption to go up to about 13.5% or so.
Stage 3. Early Majority
Here’s when your product really gets some momentum going.
You’ve got a good amount of sales from innovators and early adopters. At this point, usually an even larger group sweeps in and gives you a heck of a lot more sales. Specifically, about 34%.
The people in the early majority are usually pragmatic and will only buy something once it’s been road-tested (at least a little bit) and has proven its value.
Early Majority
This is the beginning of your product’s peak. Maybe it’s gained traction with more marketing or word of mouth.
Stage 4. Late Majority
At stage 4, your product has been out for a while, and there’s widespread use.
However, there are still some people who are a bit skeptical of your product. Once they’ve put their worries to rest, they buy your product, and these people are usually in the late majority or laggards.
Late Majority
At some point during the early or late majority phase, you’ll have your peak where you get more sales than ever, and your product is at the height of its popularity.
Interestingly, in terms of adoption rates, the early and late majorities are usually roughly equal, around 34%.
Stage 5. Laggards
These are the people who buy your product after all the hype has died down. Sometimes, laggards purchase a product years after it’s been released.
Laggards might be extreme skeptics or people who have only heard about your product a long time after you launched it. Whatever the reason, these people don’t buy until much later in the product lifecycle.
The Laggards
Surprisingly, this is a pretty big group. 16% of your product adoption will come from laggards.
Try to wrap your head around the fact that laggards have a higher adoption rate than early adopters.
Change Your Marketing as Your Product Ages
At each stage of the product adoption curve, it’s likely there’s going to be certain demographics buying your product.
For example, innovators are more likely to buy on impulse, while buyers in the late majority will do lots of research before purchasing.
And as your product gets older, it will become more well-known. So you might start out with a product no one knows but end up with a product everyone and their brother has heard of.
Given these facts, consider changing your marketing messages as your product ages.
The Apple iPhone Marketing Messages Over Time
The marketing of each successive version of the Apple iPhone illustrates how Apple changed its marketing message to appeal to innovators, early adopters, early majority, late majority and laggards.
A commercial for iPhone 2showed off a lot of the hip new features: music, email, and Internet browsing, to name a few.
iPhone 2 TV Commercial
This obviously appealed to a younger, more tech-savvy audience.
Then in 2010, three years after the first iPhone launched in 2007, the iPhone 4 came out with a commercial that featured two grandparents celebrating their granddaughter’s graduation:
iPhone 4 TV Commercial
Apple wanted to show that even grandparents (who may not have understood smartphones back in 2010) could benefit from the iPhone. This is important because older consumers are typically late adopters.
Apple’s strategy was clear: Begin by showcasing all the bells and whistles, then open up the audience to include more types of customers.
In the same way, you should think about what your marketing should look like at each stage of the product adoption curve.
For example, when the innovators and early adopters come rolling in, your marketing should clearly describe the value and benefits of your product.
Later on, perhaps in the late majority stage, you can utilize customer testimonials and reviews. This can help address the skepticism that later adopters typically have.
Think about addressing the common questions that each group has, innovators will ask themselves what’s so unique about your product, while the early majority wants to know what other people think about your product and why it’s useful.
Thinking like this can completely change your marketing. By sending a customized message every step of the way, you’ll battle objections and questions head-on.
Know How to Overcome The Chasm
In most product adoption curves, there’s a point that can make or break the success of the product.
It’s called the chasm. It’s the point between the early adopterstage and the early majority stage.
The Chasm
As the chart above represents, crossing the chasm means breaking into the mainstream market. It’s one of the most difficult aspects of product adoption, but it’s one of the most important aspects to get right. There’s even a bestselling book on the topic––Crossing the Chasm.
Crossing the chasm is particularly tough to do for a few reasons. One reason is that as your product ages and grows, your audience will have higher expectations. Specifically, your potential customers will want increasingly better reasons to buy your product. You have to be ready to meet these demands throughout your product’s lifecycle, but it’s especially important in getting past the chasm.
As impulse buyers, the innovators and early adopters didn’t need huge reasons to buy your product. But to get the early majority to convert, that’s exactly what you’ll need. You have to think about your branding and not just your product. You have to offer value and not just features.
Another reason for the difficulty is the possible necessity of pivoting. In other words, to cross the chasm you may need to take a new angle for your campaign. Early on, you may be hedging on the idea behind your product. Early adopters are cool with that, but the early majority wants consistency. In other words, to cross the chasm you may need to take a new angle for your campaign. Early on, you may be hedging on the idea behind your product. Early adopters are cool with that, but the early majority wants consistency.
The Chasm
If you’re at the chasm right now, you might need to pivot yourself or even improve your product.
Don’t Forget The Laggards
You can’t stop after your product has hit its pinnacle and is riding the waves of success. It's important to remember, the second largest adoption group is laggards, coming in at 16%. A lot of people will be buying your product well after the hype dies down, and you can’t forget or alienate this audience.
Laggards are often skeptics, so at the end of your product lifecycle, your marketing should be laser-focused on overcoming objections. Think about it––you’re marketing to people who resist change and may not even want to be a customer. They’re going to need awesome reasons to invest in your brand. (A slew of positive testimonials, reviews, and press mentions will come in handy for this.)
Time also plays an important role. Think back to the iPhone example; sure, older folks are commonly seen with iPhones, but it’s been a decade since the device’s initial release. It might take a lot of time and exposure to your brand for laggards to adopt your brand.
Finally, you’ll also need to brace for the declining sales that inevitably occur at the end of the product life cycle. If your brand is experiencing one or more of these symptoms (see below) listed in the Product Life Cycle chart, its time to evaluate whether you can extend its life by introducing an improved version, replace the product with an entirely new product or dump the brand or line entirely.
Product Life Cycle
Courtesy of an article appearing in September 2014 issue of Entrepreneur and an article dated October 23, 2017 appearing in The Daily Egg and an article dated October 23, 2017 appearing in The Daily Egg
Digitally savvy consumers know there’s an abundance of choices when it comes to purchases. With high expectations, most will seek out appealing items with little regard to brand loyalty. Churn and attrition are at an all-time high.
The response for organizations sounds simple enough: Provide a consistently good, engaging customer experience, optimize it on a variety of devices and deliver it when customers want it. Why has it been so hard for organizations to do this?
The answer starts with the way companies operate on the back end. With multiple organizational silos, no online/offline data synthesis, rigid customer databases and other inflexible legacy systems, organizations only have a piecemeal view of the customer. It’s hard to take advantage of all the existing corporate customer data that’s available, much less the rich variety of external data. As a result, marketing efforts are fragmented. Communications are inconsistent and ineffective. And revenue growth is hindered.
By taking a technological approach that synchronizes marketing processes with the customer journey across multiple channels, organizations can achieve great results – in terms of revenue, customer advocacy and loyalty. First, they need to get a panoramic view of each customer. Then they can understand and anticipate customer behavior; orchestrate the next best action across any channel; and accurately measure results to inform future actions.
SAS recommends that you connect your marketing efforts with all the relevant data from customer interactions as well as back-end operations. Then, through advanced customer and marketing analytics, you can deliver an integrated, omnichannel experience and truly compelling content. By responding to your customers on their terms – right content, right time, right device – you can keep them coming back for more and raise their value to your business.
Step 1: Synchronize Marketing Processes Based on a Comprehensive Understanding of the Customer
When marketing departments, call centers, service operations and merchandisers operate independently based on their own distinct views of the customer, both customer engagement and marketing efforts suffer.
Consider a scenario where a customer’s browsing history (showing his preferences or inferred interests) is in one database while offline point-of-sale data about the customer is in another database. If these databases are not connected, there’s a good chance you will have less relevant interactions with that customer than what the customer expects. Or you may see the “echo effect,” where you reach the customer through one channel but he responds through a different one – leaving you unsure how to attribute the response or plan your next offer.
Many organizations don’t use their existing corporate data to the fullest extent. They overlook opportunities to enrich customer data with information from service records, operations or contact centers. Many also fail to use external data sufficiently, missing chances to broaden their understanding of the customer with data from social media, open data, third-party data, etc. In an ad sales scenario, these proprietary data sets can present a unique differentiator in the marketplace and enable you to create highly targeted campaigns for your advertisers.
SAS Customer Intelligence solutions provide a panoramic view of the customer by consolidating all first-, second- and third-party data. From digital data to CRM information and call center records, SAS captures, integrates and transforms disparate data sources, breaking down multiple customer data silos. Built-in data management capabilities ensure that you can use your data effectively to engage customers, and boost ad sales. Use SAS to:
Improve data quality where the data resides, regardless of whether it’s in a marketing or operational system. SAS profiles, standardizes, monitors and verifies data without moving it, which creates significantly faster, more secure processes. So you can speed up many marketing processes to run in real time and near-real time instead of weeks and months.
Access the data you need, no matter where it’s stored – from legacy systems to Hadoop. You can create data management rules once and reuse them, for a standard, repeatable method of improving and integrating data – without additional costs.
Be confident that your data is reliable and ready to use for analytics, whether you’re doing segmentation, content recommendations, next best offer, retention or lifetime value scores.
Create a panoramic view of the subscriber that connects all touch points, contact history and online/offline interactions.
Step 2: Understand Customer Behavior and Fuel Content Engagement
Content is core to enticing and keeping consumers. You can attract the right customers by optimizing your content. But it’s just as important to optimize the customer’s overall experience. Using advanced techniques like text and predictive analytics, you can improve search engine optimization (SEO) for digital content, quickly categorizing content and text mining words, phrases and topics for customers.
Beyond SEO, you can profile and segment customers based on their historical behavior, profitability and lifetime value. Through a range of predictive analytic models, including affinity analysis, response modeling and churn analysis, you’ll know whether it’s a good move to combine digital and print subscriptions. You’ll recognize which content merits a fee versus which content you can monetize without a paywall.
To keep your marketing efforts fresh, you’ll need to continually supply models with updated data as you interact with customers and prospects. For example, your models should include purchase transaction data, online data from website users, direct marketing response data and more.
Figure 1 - Decision Tree to quickly idenify variables that can best predict iPad usage and high versus low user populations (Click Image To Enlarge)
Through advanced analytics, you can use these models to predict behavior and:
Identify how different customer segments are most likely to respond to specific content, campaigns or marketing actions. Your approach will be based on analytically driven, granular segmentation of both known and unknown customers.
Reach the target population that’s most likely to respond positively to certain content, campaigns and other marketing activities. With predictive modeling, you can understand and predict the behavior of each targeted group.
Improve economic outcomes using optimization to make the most of each individual customer communication. Take into account resource and budget constraints, contact policies, the likelihood of customers responding, and more.
Step 3: Automate and Synchronize Customer Engagement Across Channels
Once you’ve determined which analytics approach is best, you’ll need to automate your engagement activities with customers. SAS Marketing Automation helps you to quickly define target segments, prioritize selection rules, choose appropriate communication channels, schedule and execute campaigns, analyze results, and make adjustments to improve future campaign performance.
Use SAS to orchestrate data-driven marketing activities across all of your channels. So you’ll be able to present customers with the best, most profitable offers to keep them engaged or to win them back from competitors. Analyze – in real time – how people get to your site and what they do while there. Then present them with engaging content at precisely the right moment. Use SAS to:
Build an omnichannel marketing environment so you can align outbound and inbound marketing tactics across all channels.
Develop event-triggered campaign tactics to ensure timely, relevant marketing strategies.
Know the next best action to take for each customer by incorporating analytics into your marketing execution efforts.
Track the effectiveness of all marketing activities and monitor campaign results in real time.
Reduce your reliance on IT for campaign creation and deployment with an easy-to-use interface.
With a complete view of the customer, a deep understanding of behavior and automated engagement efforts, you’ll be able to make decisions that resonate for customers and invigorate your marketing efforts. For example, if you know a customer checks email every Friday, you’ll send her an email on Friday – because you’ll know that’s the best way to reach her. You’ll also be able to decipher between premium content versus content that should be free. You’ll know what will hook your customers, whether they’re using your services for the second time or the hundredth time.
Today’s customers demand value and expect a consistent experience regardless of the channel or device they’re using. SAS positions you to meet these ultra-high customer expectations at every touch point.
Figure 2 - A marketing campaign response measurement dashboard (Click Image To Enlarge)
Step 4: Effectively Measure Campaign Performance and Attribution
It’s hard to understate the importance of accurate, useful measurement. Combining SAS Reporting capabilities with SAS Visual Analytics – a visualization and exploration suite built to handle big data – it’s easy to examine the effectiveness of your marketing campaigns and tactics based on your budget and success metrics. Use response attribution modeling to understand the customer’s conversion path, and to know where to assign marketing credit. Then you can create future marketing mix optimization models, test/control strategies, predictive models and marketing campaigns.
With adaptive, agile marketing, you can test your offers and content quickly, on a small scale, and nurture continually richer customer interactions. Then get rapid feedback to show you when and how to modify the customer’s experience to get the most impact. Plus, you’ll have easy access to campaign reports and dashboards so you can track and manage campaigns across all of your channels.
Figure 3 - Campaign and offer performance reports are integrated with revenue metrics and demographic indicators (Click Image To Enlarge)
COMMENTARY:
A New Definition of Data-Driven Marketing
What is data-driven marketing, how can event marketers effectively use it to drive conversions, and why does it matter? For decades marketers were forced to launch campaigns while blindly relying on gut instinct and hoping for the best. That all changed with the digitization of business and an increasingly demanding and digitally connected consumer. Now more than ever, there is a greater urgency to develop data-driven marketing campaigns as organizations have come under increasing pressure to deliver results or ROI for their marketing spend. To be successful in this landscape, a modern marketing campaign must integrate a range of intelligent approaches to identify customers, segment, measure results, analyze data and build upon feedback in real time.
While almost every area in marketing has been folded into the digital marketing ecosystem, in-person events have remained elusive to today’s modern marketer. In fact, when it comes to tracking your marketing efforts and determining which channels provide the best return on investment (ROI), most marketers will agree that results from in-person events are still difficult to track:
69% of marketers say that tracking ROI for events is their primary challenge. (Aberdeen Group)
Only 48% of marketers report having any event ROI metric in place (Regalix)
82% of marketers cannot quantify the data received from attendee interactions at their corporate events (Kissmetrics)
Indeed, events often lag behind other marketing methods by a significant gap, with the success or failure of many events based solely on anecdotal evidence instead of quantitative measurement and logic.
Furthermore, because data-driven marketing produces highly personalized, engagement-focused campaigns for everything from enterprise servers to event apps, consumers are now beginning to expect a high level of personalization with each transaction.
What is data-driven marketing?
Let’s start out by trying to develop a simple definition for a relatively complex concept and practice. Data-driven marketing captures insights and data from a prospect, analyzes and scores the prospect’s data and behavior, and then subsequently triggers marketing actions and campaigns based upon marketing analysis. An appropriate analogy is to think of data-driven marketing from the consumer side in the average online shopping experience. When you purchase an item online, data-driven marketing strategies provide recommendations of complementary products to provide a better overall experience. If you’re looking at airfare rates for your next vacation to Hawaii, a data-driven marketing approach will focus on restaurants around the island with cuisine you regularly Google, potential places to stay based on positive reviews on Facebook, visitor’s guides that reflect your online budget-hunting practices and local activities such as scuba diving, listed on your LinkedIn profile.
By comparison, when you look at data-driven marketing from the marketer’s side, you’ll find a much more complex process. As you are able to obtain and update information on the customer from secondary sources, such as social media sites and web search data, you can create an approach that is customized to their buying behavior, interests, past purchases, web searches, social media posts and similar information. In other words, this approach allows you to optimize your funnel and customize your buyer journey to that particular prospect’s needs. You can also survey prospects to obtain primary sources of data, but be aware that there is often a bias between what individuals or groups claim versus their actual behavior. For example, an event attendee who was ranting about poor service at the luncheon one day may be raving about the closing keynote, leaving you with plenty of praise on the keynote but failing to mention the luncheon on the exit survey. Once you’ve obtained the data you need to make a comprehensive group, you can divide your prospects up into the personas they fit into best. This allows you to customize and personalize your approach, timing, channel and subject matter to optimize the results for each persona group.
The problem many marketers run into at in-person events is that they often don’t have the information they need to determine how to best engage each prospect. The closest option currently available are scans that provide contact information and basic registration information. But scans don’t provide the data you need to track that prospect’s engagement before, during, and after the event to prove the event ROI that particular group of prospects has generated for your company. As an example, at a recent conference, my badge was scanned by a gentleman from a company that prints promotional items. I was looking through the items in his booth to determine if there was anything I could use for our company’s next event. Though the exhibitor could have collected further data from me at the time, it would have been at the cost of other prospects that he could not help while gathering my information. When I returned home from the event, I had several recommendations for items that didn’t meet our needs because the minimum quantity was much too high, the quality wasn’t good enough and the prices were too expensive. The company had my contact information, but didn’t know enough about me or my organization to make appropriate recommendations. A data-driven marketing approach to this in-person event would have drastically improved my experience while increasing the marketer’s Event ROI.
How does data-driven marketing improve your ROI?
If you’re still wondering how data-driven marketing can make a difference to your company, you’re not alone. Though there was a 14% increase in confidence in putting big data to work in marketing departments from 2013 to 2014, with expectations for additional growth, many marketers still don’t know how the additional data provides a solid improvement in ROI or how to use the data to their company’s best advantage. In fact, companies that have implemented data-driven marketing into their marketing toolbox and recorded the results have often seen a 10-20% improvement on their ROI. Like any tool, it must be used correctly and implemented with other tools in your kit, such as using social media data, search analytics, SEO, content targeting and developing better buyer personas.
Why does data-driven marketing make such a big difference? Using the marketing convention example above, if the company had used data-driven marketing techniques to track my information, they would have known my organization was operating on a modest budget. All these factors made their special offer on a tri-fold brochure with a minimum order of 5,000 a very bad fit. Instead of learning more about the client, the company made a suggestion based on what was popular with their clients in general, few of whom had the needs of our organization, and lost a prospective sale. A targeted campaign based on data-driven marketing would have recommended a small-minimum product order that was inexpensive, while offering additional items that would have fit well with our company’s mission.
Click Image To Enlarge
Courtesy of an article titled "How a Data-Driven Approach Can Engage Customers and Boost Marketing Returns" appearing in SAS blog and an article dated May 26, 2016 appearing in Certain
A new study by the Economist Intelligence Unit has just been released that shows how big data is moving from its infancy to “data adolescence,” in which companies are increasingly meeting the challenges of a data-driven world.
The report, called “Big Data Evolution,” details the ways in which companies’ attitudes and activities have changed over the past four years with regards to big data — collecting it, storing it, analyzing it, and using it to make business decisions about strategy.
Data is becoming a corporate asset
The report shows that, since 2011, substantially more companies are treating their data as what it actually is:a strategic corporate asset. The initial excitement about the possibilities presented by big data is morphing into a more strategic approach, defining which data initiatives will have the biggest and most immediate impact.
I refer to this as asking the right questions. Companies are getting a bit savvier, and on the whole, are not asking for more data, but rather the right data to help solve specific problems and address certain issues.
Because of this greater understanding, executives are more likely to report they are making good, fact-based decisions about their data and their business.
In addition, data strategy has been elevated to the C-level, usually centralized with a CIO/CTO or a newly-appointed Chief Data Officer (CDO). Outside that position, executives across the board are more likely to be in charge of their departments’ particular data initiatives and instrumental in putting those resources to use.
Click Image To Enlarge
Click Image To Enlarge
Another important finding of the survey points to a strong correlation between good data management practices and financial success.
Companies with a well defined data policy, are much more likely to report that they are financially competitive with their peers and rivals. In addition, they’re more likely to report that their data initiatives are successful and effective at resolving real business problems.
The reason for this could be that data initiatives are moving out of the realm of theoretical possibility and well into reality, demonstrating the ability to focus on real business problems and provide practical solutions.
Less about volume and velocity than value
One final encouraging trend the report finds is that the “bigness” of big data is starting to wear off. Companies are less focused on the quantity of data they can collect and the speed at which they can access it, and more focused on the value the data can provide for their business.
I strongly believe that the right data is much more important and valuable than simply collecting more data, and the report bears that out.
As technology continues to improve, the “bigness” of big data will become less and less of a factor. Companies are becoming more comfortable with the idea that they will need to scale up to allow the value of data initiatives to reach all sectors of the business, and so they are becoming more comfortable with approximation, agility and experimentation.
From my point of view, all of these are positive signs that big data is moving out of its infant stages and is well on the way to data maturity.
Bernard Marr is a best-selling author, keynote speaker and business consultant in big data, analytics and enterprise performance. His new books are 'Big Data' 'Key Business Analytics'
COMMENTARY: The tone of corporate conversations about big data continues to shift from initial excitement to expecting long-term business impact.
Over the past four years, executives have not only become better educated about the technology behind big data, but have fully embraced the relevance of data to their corporate strategy and competitive success. It could be said that most companies are experiencing their “data adolescence”, increasingly rising to the challenge of executing and delivering against the promise and potential of big data.
What are the hallmarks of this current stage of evolution, and what does the path to “data adulthood” look like from here?
In February 2015, the Economist Intelligence Unit (EIU) conducted a global survey of 550 senior executives sponsored by SAS, to follow up on our 2011 and 2012 executive surveys. By comparing the results, we were able to examine the evolution of companies’ views, capabilities and practices regarding big data as a corporate asset, and explore the future implications as companies continue to mature as strategic data managers.
Additionally, EIU conducted six in-depth interviews with leading corporate big data thought leaders and practitioners. Two of these interviews revisited specific big data–related issues these companies faced beginning in 2011.
Key highlights of the research include the following:
Since 2011, a significantly larger proportion of companies have come to regard and manage data as a strategic corporate asset. The ranks of companies with well-defined data-management strategies that focus on identifying and analysing the most valuable data (referred to here as “strategic data managers”) have swollen impressively since 2011. No longer indiscriminate data collectors or wasters, companies are entering a period when the initial excitement over the possibilities presented by big data gives way to the need to prioritise and develop on data initiatives with the biggest payoff. More companies have ventured further into this stage of their data evolution, and their executives are more likely to feel that they are better at making good, factbased business use of their information.
Strategic data management is correlated with strong financial performance. Our survey points to a clear correlation between managing data strategically and achieving financial success. Companies with a well-defined data strategy are much more likely to report that they financially outperform their competitors. In addition, they are more likely to be successful in executing their data initiatives and effectively applying their data and analytics to resolve real and relevant business problems.
Data-strategy ownership has been elevated and centralised, while engagement and demand from the business is at an all-time high. Across industries, data strategy has been elevated and centralised to the C-level, most often with the CIO/ CTO or the newly minted chief data officer (CDO) role. At the same time, senior executives across functions and business units are increasingly in the driver’s seat of their data initiatives, and not just relying on IT leadership to design and execute them.
Data initiatives have moved from theoretical possibilities to focus on solving real and pressing business problems. Companies approach data initiatives today with a clear focus on their purpose—putting business value first. They are much more likely to start by articulating and finding a consensus on the high-priority business problems the organisation will solve by leveraging its data assets. Financial resources available for big data initiatives remain scarce, so there is a pronounced need to prioritise which initiatives to invest in, as well as how to demonstrate the financial return on these investments.
Technical challenges associated with quality, quantity and security persist. Even top performers continue to struggle with a number of technical aspects of big data. These foundational aspects of data management still drown out the more advanced, higher-value-add aspects of data management, such as governance, compliance and converting data into actionable insights.
The future of big data is less about volume and velocity, and more about the value that the business can extract from it.Going forward, companies will have to shift their attention away from the “bigness” of big data and focus on its business value. Data and analytics will be increasingly applied to predict future outcomes and automate decisions and actions. Most importantly, many companies will have to continue to evolve their structure and culture to scale up successful data pilots across the entire organisation. This means becoming more comfortable with approximation, agility and experimentation, and reinventing themselves into a new kind of information-driven, data-centric business—closer to data adulthood.
CIO's Now Consider Big Data Analytics A Game-Changer
Greg Taffet, CIO of U.S. Gas & Electric, when The Economist Intelligence Unit interviewed him back in 2011, said.
“It is going to be a game changer.”
He was referring to fast-moving, real-time “big data”—which, at that time, was a novel buzz word.
In EIU's first comprehensive study in 2011 of how companies perceive and handle big data as a corporate asset, just 9% of survey respondents said data had completely changed the way they do business, while 39% believed data had become an important tool that drives strategic decisions at their organisation. But more than half of executives saw data in less critically important terms.
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As you can see from the above EUI survey, 58% (First two colums for 2015 survey: 14% + 44%) of respondents now see data as a game-changing asset, or at least, an important decision-making tool.
In the EUI 2011 study, four categories of companies were identified based on their level of sophistication of their thinking and strategy vis-à-vis corporate data:
Strategic data managers: companies that have well-defined data-management strategies that focus resources on collecting and analysing the most valuable data;
Aspiring data managers: companies that understand the value of data and are marshalling resources to take better advantage of them;
Data collectors: companies that collect a large amount of data but do not consistently maximise their value; and
Data wasters: companies that collect data, yet severely underuse them.
EUI plotted the four corporate categories and compared 2011 vs 2015 in the following chart:
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The above chart (Figure 2) shows that, in the last four years, companies have advanced along the evolutionary curve and, compared with 2011, many more now have developed a well defined data strategy. The ranks of strategic data managers have swollen impressively (18% vs 33%), and actually showed the only growth among the four categories, while the number of data collectors and wasters is shrinking (28% to 20%).
Further evidence that companies are moving beyond strategy development and are tackling the adoption, or implementation, stage of data evolution is the fact that executives today put more of their valuable data to good use (see Figure 3).
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Alan Feeley, managing director of global shared services at Siemens, a global engineering firm, points out.
“Data and analytics are no longer opportunistic. They are now formal research areas for our company.”
The CIO and CTO Have Now Taken Ownership of Big Data
The ownership of data strategy and the sponsorship of data initiatives have evolved throughout the organisation. Responsibility for the organisation’s data strategy has been elevated and centralised to the C-level, but at the same time, the pull and energy are increasingly coming from the lower levels of the corporate pyramid. Over half of companies surveyed make sure that data are available to employees who need them, and offer the appropriate technology and training programmes. Data strategy has become “everybody’s business”—senior executives across functions and business units are increasingly in the driver’s seat of their data initiatives, instead of relying on the CIO or CTO to design and execute them in a top-down manner.
The vertical migration to centralised leadership of data strategy and strong ownership from the C-suite is an emerging best practice today. Ram Chandrashekar, executive vice-president of operational excellence and IT and president of Asia Pacific and Middle East region at ManpowerGroup, a global human-resources consulting company, says.
“Clearly, a top-down data strategy driven and articulated by the CEO is a critical success factor.”
The EUI Survey data support his observation.
Over the past four years, ownership of corporate data strategy has migrated upwards from executives at the business-unit level to C-suite members—particularly, the CIO. In 2011, 23% of respondents said their CIO is primarily responsible for all data initiatives. This proportion jumped to 30% in 2012, and continued to rise to 39% in 2015 (Figure 7 below).
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A recent appearance in our 2015 survey is the increasingly popular chief data officer (CDO) role. This C-level position was virtually unknown in 2011—limited mostly to government and heavily regulated industries such as banking and insurance following the 2008 financial crisis. In our 2015 survey, some 9% of respondents pointed to their CDO as the custodian of the corporate data strategy and capabilities. Emergence of this role comes at a good time, especially as business executives from across the functional spectrum have become much more technology-literate and involved in the design and execution of their data strategy and initiatives.
Emergence of the Chief Data Officer (CDO)
Increased involvement from the business comes with the challenge of co-ordinating agendas, aligning priorities and communicating effectively with all stakeholders. Mr Chandrashekar of ManpowerGroup says.
“There is strong alignment and articulation at the C-level. People on the frontline, such as sales and operational staff, are also data-driven. The disconnect often happens in the middle, and the challenge is to make the data flow from top to bottom. Engaging the business is critical—data strategy cannot be seen as just a central initiative.”
And few today excel at engaging the business. In the EUI 2015 survey, when asked to rate their company’s competence across different datarelated corporate capabilities, respondents expressed the least confidence in their ability to engage employees across the organisation to use data in day-to-day decision-making (only 26% rated their company as “very competent”, while 22% saw themselves as “not at all competent”). High-quality, consistent engagement across layers of the organisation and among horizontal functional lines is in high demand, and in short supply.
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Enter the CDO. Mr Krishnamurthy of Cognizant Technology Solutions says.
“The CDO has emerged as the embodiment of ‘integrated leadership’.”
He points out that the best-designed CDO roles are focused on three top-level priorities:
Ensuring availability and integrity of data across the organisation;
Driving adoption—from small-scale pilots to company-wide rollouts; and
Driving the monetisation of new data capabilities.
Most importantly, the role is about organisational engagement, brokering between agendas and balancing priorities among big data initiatives. Thus, finding the right senior talent to fill the CDO role can be tricky, as Edd Dumbill, vice-president of marketing and strategy at Silicon Valley Data Science, a big data consulting firm, points out:
“They have to know technology, they have to know the business, and they have to be a political wiz.”
Foundational and talent challenges
Companies have made great strides in embracing data as a strategic asset, making the necessary technology investments, and even beginning to evolve their corporate structure. Centralised leadership allows for better co-ordination in strategy and execution of initiatives. And executives, both on the business side and in IT, are much more focused on deploying their limited resources on top-`priority data projects that extract tangible business value from these investments.
However, significant challenges still plague most companies—and that’s true even for companies with the financial resources. The most daunting challenges companies face relate to three. highly technical and operational aspects of big data—quality, quantity and security (see Figure 8). These are fundamental aspects of data management. Yet companies are far from having resolved them completely and with full confidence, leading to a lack of progress to more advanced, value-added aspects of data management.
In the last four years, the problems posed by the overwhelming amount of data companies can access and collect have only been exacerbated further. In 2011, one in eight companies said they had so much data that they struggled to make sense of them—in 2015 this was nearly one in four companies. And today, more than half of executives (54%) say they probably leverage only half of their valuable data (Figure 3).
Given the sheer volumes, ensuring the integrity and quality of data, and arriving at the proverbial “single source of truth”, are still major problems. And thus, the ultimate challenge of extracting meaningful and actionable business knowledge from data is still a significant one for most companies, even slightly more so for companies that say that they are strong financial performers as they may be more ambitious with their data strategy. But only 16% of companies these cite extracting business insights as a top challenge—for reported poor financial performers, this was 24%. Despite strong or poor financial performance, 33% of all survey respondents continue to struggle with managing the vast amount of data and 41% struggle with maintaining quality (Figure 9).
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On the organisational front, companies have made strides in both creating the right structures and roles, as well as recruiting key talent to enable them to formulate and begin executing their data strategy. However, the talent market in the data and analytics field is still very tight.
This is especially still true in the market for data strategists—executives who are expected to speak the languages of both technology and data science, as well as understand the business, the markets and the customers (see section Paving the way for the CDO). These rare and invaluable executives—the “effective engagers”, as Ms Merkel of Zurich Insurance calls them—are in short supply and high demand. As Mr Feeley of Siemens puts it,
“There’s a war for talent, particularly for people who combine data expertise with domain knowledge.”
In a blog post dated December 8, 2015, in which I wrote about the market for data scientists, I mentioned the serious talent shortage in big data and analytics.
Courtesy of an article dated November 30, 2015 appearing in Forbes and the whitepaper prepared by The Economist Intelligence Unit titled, "Big Data Evolution"
What are the key roles in data science? Here's a look at the various job positions in the data science industry and what they mean.
A data scientist cleans, massages, and organizes Big Data, according to DataCamp in the following infographic.
A database administrator, on the other hand, focuses on backup and recovery, data modeling and design, distributed computing, database systems, and data security.
Another role is statistician. This person "collects, analyzes, and interprets qualitative as well as quantitative data with statistical theories and methods," according to DataCamp.
To find out more about the different roles in data science, click or tap on the infographic.
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COMMENTARY:Data-driven business processes are not a nice-to-have but a need-to-havecapability today. So, if you’re an executive, manager, or team leader, one of your toughest assignments is managing and organizing your analytics and reporting initiative.
The days of business as usual are over. Data generation costs are falling everyday. The cost of collection and storage is also falling. The speed of insight-to-action business requirement is increasing. Systems of Record, Systems of Engagement, Systems of Insight are being transformed with consumerization and digital.
With this tsunami of data and new applications, the bottleneck is clearly shifting from transaction processing to Analytics & Insight-driven “sense-and-respond” Action. This slide from IBM’s Investor Briefing summarizes the data-driven transformation underway in most businesses.
Courtesy of an article dated December 8, 2015 appearing in MarketingProfs and an article dated May 28, 2015 appearing in Ravi Kalakota's Practical Analytics
The huge demand for big data analytics, and scarcity in individuals with the right skills and experience, is driving the starting salaries for data scientists over $200,000, and it is going to get worse
A new species of techie is in demand these days—not only in Silicon Valley, but also in company headquarters around the world. Pascal Clement, the head of Amadeus Travel Intelligence in Madrid, says.
“Data scientists are the new superheroes.”
The description isn’t exactly hyperbolic: The qualifications for the job include the strength to tunnel through mountains of information and the vision to discern patterns where others see none. Clement’s outfit is part of Amadeus IT Holding, the world’s largest manager of flight bookings for airlines, which has more than 40 data scientists on its payroll, including some with a background in astrophysics. The company recently launched Schedule Recovery, a product that tracks delays and automatically rebooks all affected passengers.
A study by McKinsey projects that “by 2018, the U.S. alone may face a 50 percent to 60 percent gap between supply and requisite demand of deep analytic talent.” The shortage is already being felt across a broad spectrum of industries, including aerospace, insurance, pharmaceuticals, and finance. When the consulting firm Accenture surveyed its clients on their big-data strategies in April 2014, more than 90 percent said they planned to hire more employees with expertise in data science—most within a year. However, 41 percent of the more than 1,000 respondents cited a lack of talent as a chief obstacle. Narendra Mulani, senior managing director at Accenture Analytics, says.
“It will get worse before it gets better.”
Many data scientists have Ph.D.s or postdoctorates and a background in academic research, says Marco Bressan, president for data and analytics at BBVA, a Spanish bank that operates in 31 countries and has a team of more than 20 data scientists. He says.
“We have nanotechnologists, physicists, mathematicians, specialists in robotics. It’s people who can explore large volumes of data that aren’t structured.”
So-called unstructured data can include e-mails, videos, photos, social media, and other user-generated content. Data scientists write algorithms to extract insights from these troves of information. But “true data scientists are rare,” says Ricard Benjamins, head of business intelligence and big data at Telefónica, Europe’s second-largest phone company, which employs more than 200 of them. Stan Humphries, chief economist at Zillow, the real estate listings site, says:
“You can find a great developer and a great researcher who has a background in statistics, and maybe you can find a great problem solver, but to find that in the same person is hard.”
Universities are taking note. MIT, where graduate students in physics, astronomy, and biology are fielding offers from outside their chosen fields, is in the process of setting up a dedicated data-science institute. Marilyn Wilson, the university’s associate director for career development, says the center will begin enrolling graduate degree candidates in 2016.
In the U.K., the University of Warwick introduced a three-year undergraduate data-science program last year, which David Firth, the program’s mastermind, says may well be the first of its kind. He says.
“Big Business was complaining about the lack of people. Finance is a major employer, but also large-scale insurers, large online commercial retailers, high-tech startups, and government, which has huge data sets.”
Accenture’s Mulani says he’s tallied some 30 new data-science programs in North America, either up and running or in the works. The University of Virginia began offering a master’s in 2014, as did Stanford. Many of those students may be tempted to drop out before collecting their degree. Margot Gerritsen, director of Stanford’s Institute for Computational & Mathematical Engineering, says.
“Companies are scrambling. We have second- and third-year students getting offered salaries much higher than what I get.”
Starting pay for some full-time jobs is above $200,000, she reports. Summer internships, meanwhile, pay anywhere from $6,000 to $10,000 a month. To make these stints memorable, many employers offer perks such as free meals, complimentary gym memberships, and occasionally temporary housing. Gerritsen says.
“Sometimes you read about students getting abused in internships and working like slaves. We don’t see that.”
The bottom line: McKinsey projects that by 2018 demand for data scientists may be as much as 60 percent greater than the supply.
COMMENTARY: There is no doubt that data scientists are in high demand. There is simply not enough talent to fill the jobs. Why? Because the sexiest job of 21st century requires a mixture of broad, multi-disciplinary skills ranging from an intersection of mathematics, statistics, computer science, communication and business. Finding a data scientist is hard. Finding people who understand who a data scientist is, is equally hard.
Jean-Paul Isson, Monster Worldwide, Inc. says.
“Being a data scientist is not only about data crunching. It’s about understanding the business challenge, creating some valuable actionable insights to the data, and communicating their findings to the business.”
It is very unlikely that you will be able to hire a data scientist who can solve all your data problems. The skill-set presented below is a guide on how the modern data team should be equipped.
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What Makes a Great Data Scientist?
What are the personality traits of today’s data scientists? Do they have the skills needed to support businesses’ changing needs? How can senior management build cohesive teams to drive the most value from big data? Is this fast evolving discipline causing stress to its practitioners? These are some of the questions that SAS Analytics tried to answer when it conducted a survey in 2014 of 596 respondents in the UK and Ireland who identified themselves as part of the data science profession. The survey highlights 10 key personality types as well as some of the challenges data scientists are facing today.
Summary of Findings
Data scientists with “traditional” traits (analytical, logical, technical) make up the largest group (41 per cent), yet a significant proportion of data scientists display other traits, such as strong communication and creativity skills.
We expect a lot from data scientists: to be technically proficient, mathematically agile, business savvy and good at communicating. Yet 55 per cent of data scientists have fewer than three years of experience in the discipline, and more than a quarter are adapting their behaviours to fulfil roles that are not well matched to their skills or work personality profiles.
In most personality types there is a 70:30 split between men and women. The Geeks, the profile with technical bias, strong logic and analytical skills, shows a higher percentage of women at 37 per cent compared to 63 per cent for men.
Data scientists are exhibiting high levels of work-related stress, with a total of 55 per cent of the respondents showing a level of stress, with 1 in 4 male and just under a third of female data scientists being heavily stressed.
Organisations must better identify and define what they need from data scientists. Only then can they build and develop multi-faceted teams with the complementary skills needed to realise the full value of big data.
This SAS survey report looked at the aggregated data of the initial 596 responses, received from 405 men (68 per cent) and 191 women (32 per cent). Respondents’ experience ranged from less than a year to a handful of data science pioneers who have committed more than 21 years to the discipline.
The survey was based on the well-established DISC profiling methodology, which has been used for more than half a century to categorise respondents into a range of personality types with recognised characteristics.
By using DISC, SAS able to create the typical profiles of today’s data scientists, and explore how these compare to skills and personalities required by organisations.
The DISC Personality Profiling Methodology
The DISC methodology has been widely used to assess personality types for decades. This information is often utilised to analyse and build effective teams, improve communication and increase productivity. DISC is based upon a two-axis matrix:
Vertical Axis:Proactive to Reactive
Horizontal Axis:Introvert to Extrovert
This generates four possible combinations – named D, I, S and C. Individuals possess measures of all four of these combinations but in varying amounts. The values for each combination is calculated and plotted on a graph to illustrate the relative strengths of each. These values can be matched to known behavioural characteristics to describe an individual’s personality profile.
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The Data Scientist Personality Mix
The SAS analysis identified ten psychometric profiles evident within the data scientist community, based on the responses to the survey so far. These profiles are characterised by definable patterns that are well known to scientists and were consistently apparent in the survey sample.
The ‘traditional’ traits associated with data scientists – such as technical, analytical and logical skills – still dominate. However, other less technical traits – such as project management, creativity and good communication skills – are also present. Organisations need data savvy individuals who are technically proficient, mathematically minded, business oriented and strong communicators.
It’s unlikely that any individual will have all of the skills required to maximise the value of big data. So it’s important for managers to identify the particular skills needed and build a cohesive team of individuals with complementary skills and traits. As we will see later in the report, failure to do so can result in individuals trying to fulfil roles to which they are not suited – which may lead to stress and burn-out.
Distribution of Personality Types in SAS Survey Sample
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Final Conclusions Drawn From The Survey
There is huge pressure on data scientists to display a range of skills: technical, mathematical, creative, business aptitude and communication. Unsurprisingly, organisations struggle to find this mix of skills in just one person, particularly when the majority of data scientists have only a few years of experience.
It is therefore important for managers to build cohesive, diverse teams that can answer all the needs of the organisation – not just data scientists but also data savvy managers to interpret the insights and transform them into actions. Organisations will need to:
Manage the business’ expectations from data scientists
Find and develop individuals with the appropriate skills and personality traits
Create teams of individuals with complementary skills and experience
Encourage peer to peer support, learning and development
Match training to the business’ requirements.
It’s possible that, as technology develops, there will be less of a weighting towards the more traditional, technical personality traits – while demand for the less frequently found profiles such as the Ground Breakers, the Teachers and the Seekers will grow. As more respondents take part in SAS surveys, it is hoped to be able to explore this interesting development further.
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