HOW MARKETERS ARE USING DATA FROM SOCIAL NETWORKS INSTEAD OF JUST DEMOGRAPHICS TO TARGET CONSUMERS

Jamie Beckland is a Digital and Social Media Strategist at Janrain where he helps Fortune 1000 companies integrate social media technologies into their websites to improve user acquisition and engagement. He has built online communities since 2004. He tweets as @Beckland.

Marketers have built a temple that needs to be torn down. Demographics have defined the target consumer for more than half a century — poorly. Now, with emerging interest graphs from social networks, behavioral data from search outlets and lifecycle forecasting, we have much better ways of targeting potential customers.

The rise of mass-produced consumer goods also brought the rise of mass-market advertising. In the 1950s and 1960s, the goal of television was to aggregate the most possible eyeballs for advertisers. In order to convince consumers that an advertising message was relevant to them, consumers had to buy the idea that they were just like everyone else.

Marketers created that buy-in by bucketing people into generations. When you lump 78 million people into one group called “Baby Boomers,” it’s much easier to sell them stuff, especially when consumers accepted their generational classification.

But now, that entire system has broken down. The year that someone was born will not tell you how likely he is to buy your product.

Fragmentation is now the norm because the pace of change is accelerating. Generations have been getting smaller because there are fewer unifying characteristics of young people today than ever before:

 

With the recent rise of the social web, people self-select into groups so small, so fragmented, and so temporal, that no overarching top-down approach could be successful at driving marketing performance.

Marketers have responded by adding more demographic information to the mix, but even that is a losing battle. I worked with one client who was introducing a technology product, and had identified a target market of “connected consumers.” Connected consumers were 34-55, had a household income over $120k, and read technology publications regularly. This target market represented 14 million consumers.

They were targeting 14 million consumers to sell 50,000 units — that means they were hoping for 3.5 sales for every 1,000 people with whom they connected through their marketing.

What if, instead, you could get 500 sales from every 1,000 people you marketed to?

It’s possible through psychographic profiling. Psychographics look at the mental model of the consumer in the context of a customer lifecycle. Amazon.com has long been a leader in this space, through innovations like “recommended products” and “users like me also bought.” Its algorithms have learned to predict its users, and what they are interested in. And now, there are a number of tools that any business can use to leverage psychographics.

Here’s how a psychographic profile might look different from a traditional marketing profile target for a childcare provider:

Psychographics provide much more useful information about users. There are multiple data sources making this possible today.

• Social profile data 
• Behavioral data
• Customer lifecycle data

 can now finally be leveraged to contact people who are ready to buy.

Social Profile Data

Profile data from social networks consist of all the fields users grant permission for brands to use on their behalf. Most things that users track on social networks can be leveraged to create a closer relationship with a customer. Fields like:

• Relationship status 
• Alma mater 
• Interests
• Occupation 

can all be managed through social profile data management tools.

Social profile data is the critical cornerstone of psychographic insights. The level of nuance and insight provided by social data, when compared to standard demographics, is the difference between performing surgery with a scalpel or a butter knife. Previously unimaginable questions are now routine:

• Are customers who kayak more likely to buy water shoes than those who canoe?
• Who is more likely to spend over $100 on an order: Seattle Seahawks fans or Seattle Mariners fans?
• Are your customers more likely to purchase when they move across the state or across the country?

In addition, companies such as GraphEffect are measuring purchase intent by doing semantic analysis on Facebook status updates. This type of qualitative analysis can move users into specific marketing funnels from their very first online experience with your brand.

Behavioral Data

Retargeting advertising messages is gaining popularity among marketers, but its very success has jeopardized its effectiveness. Ads that follow users around the web have been implemented — usually poorly. Every ad network quickly incorporated the ability to place cookies in users’ browsers, and display specific ads to them any time they visit a site that’s part of their networks.

The next generation of ad targeting will focus more on telling the customer a story over time, based on specific behavior triggers. That means ad networks and clickstream data aggregators will work together to trigger when a customer moves forward in a mental model toward a purchase event.

Site content and product recommendations will also be informed by clickstream analysis. Companies such as RichRelevance, Certona, Baynote and Monetate all offer the ability to personalize information to specific visitors based on their behavior. Leveraging those alongside a payload of social profile data can turbocharge those services from the first moment a new user visits a site.

Customer Lifecycle Data

Social profile data can also be used to predict customer lifecycle. Imagine knowing not only if a customer has children, but the exact ages of those children. In addition, key indicator purchases, like buying diapers for the first time, indicate a customer entering a new lifecycle. Other key indicators, like shipping address changes, first purchases of furniture, or first purchases of substantially higher-value goods can all indicate the start of a new customer mentality and behavior pattern.

These patterns are predictable, so you know the future behavior of high school seniors by looking at the current behavior of college freshmen. By using demographics alone, all high school graduates would be marketed to identically. Using psychographics, we know who is likely to be interested in specific product or content recommendations at a specific time — such as when they actually start their first day of college.

This vision is starting to gain traction among serious marketers. At the 2009 Internet Strategy Forum, Xerox’s VP of Interactive Marketing, Duane Schulz, said that a 1% clickthrough rate was a huge failure — even though it is 10 times the industry average. In his mind, a successful campaign would never waste 99% of its impressions. Using psychographic data, you don’t have to waste any impressions.

We have seen a similar upheaval in marketing before. In the 1960s, marketers who embraced the power of television, broad-based insights into psychology and demographic data created world-class brands and billions of dollars in value. At that time, if you didn’t advertise on TV, you lost. Today’s new tools offer a similar choice: Build a deep understanding of your customer, or risk irrelevance.

COMMENTARY:   I am no stranger to psychographic targeting, which targets consumers by their lifestyle behaviors, interests and values.  My first foray into psychographic targeting began while I was the CFO/marketing head for a tanning salon equipment distributor and tanning salon operator and franchisor.  The problem was to help our tanning salon franchisees to increase their business by identifying and targeting tanning prospects in their local market.  Only about 10% of the U.S. population over 15 years old tans.  Tanning is skewed towards females (70%).  The problem was to identify and target the correct 10% of the consumers in each market that were most likely to tan. We decided to go with the Claritas' (now Nielsen) PRIZM lifestyle segmentation system combines demographic, consumer behavior and geographic data to help marketers identify, understand and target their customers and prospects.  PRIZM profiles consumers into 66 clusters or segments with fancy names, 14 social groups and 11 lifestage groups. Using PRIZM we were able to determine the profiles of all 150,000 tanning salon customers for our 20 tanning salon franchisees.  Armed with this information we are able to identify the dominant profile segments for each franchisee, and target consumers with similar profiles.  The result was a more efficient method for understanding the lifestyle attributes of our tanning salon consumers and targeting them more efficiently and using the most efficient media channel (print publication, direct mail, radio and television).  

One of the pioneers of social psychographic target is Peerset, a marketing and data services company.  Peerset leverages its patented human interest correlations technology to create and sell audiences to brands, agencies and ad technology companies.  

Peerset takes a fundamentally different approach to audience targeting, by understanding consumers and their relationships to brands and products.

1. Peerset identifies the unique interconnected characteristics of the consumers who will be most receptive to advertising. 
2. Peerset finds consumers who match these characteristics, and makes them targetable for advertisers.

The science behind Peerset supports a well-known thesis that human interests tend to cluster together.  They use statistical knowledge of how human interests are related to pinpoint the consumers most likely to have a deep engagement with a product or service.

According to Peerset, other targeting methods miss potential consumers by not utilizing implicit relationships between consumers’ stated interests and interests that are highly correlated with the advertisements. Peerset broadens targeting opportunities by identifying likely connections between advertisements and users.

On June 8, 2010 a patent was issued to Peerset which describes methods whereby an input (e.g., Duran Duran) produces a set of highly related keywords (e.g., Tears for Fears, Erasure) found in the social web as well as methods used to holistically analyze complex user profiles to provide these keyword sets.  Peerset calls this core component of Peerset Audience Targeting their Interest Correlation Analyzer (ICA).

ICA leverages the almost limitless mine of information pertaining to people’s interests found in social media in such places as social networking sites, social bookmarking sites, dating sites, blogs, and the like. The technology uses natural language processing and sentiment detection to analyze sources such as these and extracts words that are associated with anonymous yet unique individuals. Words in specific focus are interests, opinions, characteristics and attitudes about things, services, and Brands. ICA then uses this data to train a neural net to recognize which words tend to occur together across many different profiles. It can thereby come to realize that people who express one or a set of words will tend to share an affinity with other words that commonly cluster together.

Courtesy of an article dated June 30, 2011 appearing in Mashable

 

Comments

[Network Analyzer calibration]

It will be true that ad networks and clickstream data aggregators will work together to trigger when a customer moves forward in a mental model toward a purchase event.

[petsoho]

Wolfpaw Consulting

[petsoho]

Yourwebhoster.eu