Targeted Marketing: To Infinity … and Beyond

Stephen DeAngelis

December 17, 2019

On a clear night in the countryside, you can look up and see a star-studded sky. As impressive as the night sky can be, the Hubble telescope has opened our eyes to the mind-boggling truth that an endless array of galaxies containing an innumerable number of stars lie outside our field of vision. Chief Marketing Officer Lance Porigow (@lporigow) likens the galaxies, and the stars they contain, to marketing segments and individual consumers. “In a fundamental way,” he writes, “a target market is like a galaxy. Both seem like comprehensive, discrete entities, complete unto themselves, until we look closely. But ultimately, a galaxy is an aggregation of micro-phenomena — stars, planets, comets — each with its own discrete existences and properties. This is true of target markets as well. A target market is a personification of the audience we think is most likely to buy our product. But the reality is that all members of this ‘market’ have unique characteristics, and they often respond to different stimuli.”[1] The only way to “see” individual consumers, even within a marketing segment, is by leveraging data.

Murali Nadarajah, Head of Big Data and Analytics for Xchanging, believes big data and cognitive computing systems can provide insights so granular they can create a “segment of one.” He explains, “Traditionally, companies have marketed products with a specific demographic in mind. This approach groups people into buckets and develops specific marketing programs for each segment. While this can be an effective approach, it doesn’t take into account potential customers who may not fall in the predetermined buckets. Through machine learning, businesses can see people beyond generic segments and target them as individuals — creating a ‘segment of one’.”[2] Even if companies can create segments of one, target markets (i.e., larger consumer segments) still have value.

The value of segmentation

Paul Laughlin (@LaughlinPaul), a self-described customer insight enthusiast, writes, “It could be argued that the combination of IoT and Cognitive learning power … and the enablement of true 1:1 personalization (at last!) is sounding the death-knell for segmentation.”[3] Mark Twain was once asked by a reporter about rumors he was in ill-health. In response, Twain wrote, “I have even heard on good authority that I was dead. … The report of my death was an exaggeration.”[4] According to Laughlin, the same can be said about segmentation. He writes, “Segmentation will still be required, at least to the medium term. This gives marketers and data scientists a clear window to improve, optimize and eventually replace their segmentation approaches, i.e., to evolve alongside both the cognitive tools and the customer appetite/expectation for digitally enabled personal experiences.” Some notable companies have even decided that segmentation is a better approach than 1:1 personalization because of the unintended consequences that can result from getting “too personal.”

Laughlin makes a distinction between market segmentation and customer segmentation. He explains, “Market segmentation operates at demographic or geographic levels, which when combined with researched psychographic and behavioral data creates distinct groups or personas that bring clarity a company’s product and marketing strategies. Market segmentation can be completely anonymous, merely referring to categories of product or types/personas of customers rather than any individuals. … Customer segmentation has many similar elements to market segmentation but approaches it in the other direction, i.e., starting with your own database of prospects, customers and ex-customers. Typically, a customer segmentation project starts with the behavioral and value data available, which is ever-increasing and the best companies are surfacing and including their ‘dark data’. It groups customers along specified dimensions (e.g., ‘needs’ and ‘value’), and then looks for geodemographic and attitudinal discriminators to refine the parameters for segment allocation, with an expectation that all customers on the database will be reliably allocated to a segment.” The most important point to make is that segmentation still has a place as does the development of personas.

The value of personalization

“If we really want to understand either a target market or a galaxy,” Porigow writes, “we need to zoom in and focus on the individual objects and phenomena that compose it.” He adds, “Advances in tracking, ID resolution and data science allow us to expand and refine our vision of our audiences, enabling us to understand — and thus better serve — the entire range of people who might be interested in what we have to offer. In this way, we significantly expand our potential universe of consumers, while improving every communication and interaction.” Advanced analytics capabilities found in most cognitive computing platforms can provide valuable marketing insights. For example, the Enterra Shopper Marketing and Consumer Insights Intelligence System™ can leverage all types of consumer data to provide high-dimensional consumer, retailer, and marketing insights.

Porigow continues, “Now that computational processing power and data science techniques can simultaneously make sense of the thousands — or millions, or billions — of data points we have about the members of our audience, we have opened up a much more robust form of marketing intelligence; one where there are really no limits to whom we market our products to. This enables us to move past the limitations of targeting to a handful of segments.” Marketer Marc Mathies (@MathiesMarc) asserts, “Today, consumers expect personalized experiences and will choose, recommend and pay more for a brand when advertisements are targeted.”[5] He also stresses targeted marketing is not as easy as some people make it appear. “It can be difficult,” he writes, “for Marketing and Advertising experts to refine the messaging accuracy and consistency needed to reach consumers and drive the highest possible return on ad spend.” In today’s omnichannel world, he suggests marketers need to study the data, prepare appropriate content, and modify messages as needed. He explains:

Study: Understanding Key Data Ingredients. “When creating [targeted] messages, quality and accurate data is imperative to understanding the complete 360-degree view of an individual consumer. However, this often doesn’t happen. Forrester Consulting states that 65% of marketers have concerns about the quality of their data. The critical first step for successful Omnichannel Marketing is identifying and surveying the complete data pool.”

Prepare: Messaging and Placement. “As Omnichannel Marketing campaigns seek to provide consumers with connected experiences across the entire customer journey, it’s important to ensure messages are relevant and reaching consumers through the proper channels. While the same messaging and channel ‘mix’ might not be right for all consumers, it’s important to understand individual target audiences to determine the best strategy for reaching them.”

Modify: Adjusting to the Dynamic Nature of Consumer Data. “Finally, it’s important to understand consumer data is dynamic. What’s true about a consumer is constantly changing, so crafting a data strategy from compiled data snapshots will not suffice. As consumer affiliations, loyalty and interests change, brands need to keep pace to ensure they have the right data ‘ingredients’ for reaching customers.”

Cognitive platforms can help marketers stay on top and in tune with consumers since they constantly learn. Porigow concludes, “The time to experiment with marketing to many audiences is now. By doing so, we can more deeply understand and better communicate with an entire human galaxy of individuals who find value in our brand.” As Buzz Lightyear would say, “To infinity … and beyond.”

Footnotes
[1] Lance Porigow, “The End of Targeting As We Know It,” Media Post Agency Daily, 13 November 2019.
[2] Murali Nadarajah, “Machine Learning and the Great Data Analytics Shake-Up,” Information Management, 2 March 2016.
[3] Paul Laughlin, “Customer Segmentation in a Cognitive Computing age,” Customer Insight Leader, 24 October 2017.
[4] Emily Petsko, “Reports of Mark Twain’s Quote About His Own Death Are Greatly Exaggerated,” Mental Floss, 2 November 2018.
[5] Marc Mathies, “Omnichannel Targeting Is All About Effective Data Mixology,” MarTech Series, 29 October 2019.