Targeted Marketing and Predictive Analytics, Part 1

Stephen DeAngelis

August 29, 2013

Seth Gottlieb believes that using predictive analytics to target customers “is going to feel invasive” as it becomes more common. However, he continues, he is “hoping that predictive analytics will help marketers target their message to receptive customers who can genuinely benefit from the product or service. Maybe this science will even help companies discontinue programs that nobody would want.” [“Predictive Analytics for Marketing,” Content Here, 25 March 2013] There is always going to be the risk of a “creepiness factor” when targeted marketing is employed. But implemented tastefully and ethically, the creepiness factor can be muted. Alex Bulat reminds us that back in 2002, when the movie Minority Report was released, it contained a scene where passengers riding the subway viewed “large wall-sized screens” that showed each rider a different advertisement tailored to their preferences and tastes. “When the film was shot,” he writes, “this type of advertising sounded quite Sy-Fy, the product of a distant future, but today, 11 years later, predictive personalized advertising is absolutely real.” [“Predictive Personalization as a Way to Increase Conversion,” Template Monster Blog, April 2013] Bulat provides this definition of predictive personalization:

“Predictive personalization is defined as the ability to predict customer behavior, needs or wants – and tailor offers and communications very precisely. Social data is one source of providing this predictive analysis, particularly social data that is structured. Predictive personalization is a much more recent means of personalization and can be used well to augment current personalization offerings … [and] discover highly relevant content requiring minimal effort to find. Both bet that people still value content and try to serve up good stuff for those moments in which they have nothing else to do. Both attempt to provide a machine-augmented curated media experience.”

Bulat believes that “predictive personalization improves the quality of our lives”; but, he also understands that privacy concerns are going to temper any consumer enthusiasm for Big Data analytics. In the end, he believes the benefits will outweigh the concerns. He concludes, therefore, “Marketers who see the future in personalized ads should not fear. [Since] predictive advertising helps consumers save money, such ads will be called for and will be really effective.” McKinsey & Company partners Jonathan Gordon, Jesko Perrey, and Dennis Spillecke assert, “Big Data is the biggest game-changing opportunity for marketing and sales since the Internet went mainstream almost 20 years ago.” [“Big Data, Analytics And The Future Of Marketing And Sales,” Forbes, 22 July 2013] They go on to note, however, that many marketers don’t know how to make it happen.

Meta S. Brown, an analytics consultant, writer, and speaker, believes that the best place to start is choosing the right data sets to analyze. [“Selecting Big Data Sources for Predictive Analytics,” SmartData Collective, 8 April 2013] She writes:

“The value of any dataset is determined by the quality of information you can extract from it. The key to value in big data is the detail. In other words, the value of big data is in the small stuff. … The promise of big data is in the details. You want the data to give you the information you’d get if you observed each customer in person. You want to know what each person does. You want to know how each responds to a variety of things – products offered, pricing, presentation, and so on. You only realize value from data if you do something valuable with it.”

So how do you go about selecting the right data sets? Brown suggests that you must first answer the question, “What do you want to accomplish?” She explains:

“You must know what kinds of action you have the option of taking. Can you offer new products, change the selection you offer, or must you work within the bounds of what you have now? Can you develop new ads, new offers? Now, imagine that you have the same goal, and the same options, in a face-to-face situation. What information would you want? Knowing that, you are ready to look for data sources that meet your needs.”

When addressing the topic of where to look, Brown recommends staying close to home. “Start with the data you already own,” she writes. “Your transaction records are a treasure chest of behavioral data.” She continues:

“You know when each transaction takes place, what is purchased, at what price. If you have a loyalty program or house credit card, then you also know who was buying. Your own data is more valuable to you than anything you could buy, and it’s already paid for. And this data is yours alone, giving you a unique information advantage over your competitors. If you do business online, get an understanding of the information collected in your web activity logs. These logs contain revealing details about shopping behavior, including details on the behavior of non-buyers. Only when you’ve thoroughly investigated the possibilities of your internal data sources should you look beyond your walls. Once you have a clear idea of what you want to know, and the limits of your own data, can you shop selectively, and shrewdly, for information that fills in the blanks.”

Gordon, Perrey, and Spillecke agree with Brown that data is important, but they note, “Data on its own … is nothing more than 1s and 0s.” Their research shows that “companies that succeed today do three things well” with that data. The first activity involves analytics.

1. Use analytics to identify valuable opportunities. Successful discovery requires building a data advantage by pulling in relevant data sets from both within and outside the company. Relying on mass analysis of those data, however, is often a recipe for failure. Analytics leaders take the time to develop ‘destination thinking,’ which is writing down in simple sentences the business problems they want to solve or questions they want answered. These need to go beyond broad goals such as ‘increase wallet share’ and get down to a level of specificity that is meaningful.”

It’s clear that many of the questions that Brown suggests should be asked when selecting data are also valuable when it comes to setting up the analytics for that data. The McKinsey partners assert, “Using data to specifically unlock new opportunities requires looking at data in a new way.” Their second recommendation deals with a customer’s path to purchase.

2. Start with the consumer decision journey. Today’s channel-surfing consumer is comfortable using an array of devices, tools, and technologies to fulfill a task. Understanding that decision journey is critical to identifying battlegrounds to either win new customers or keep existing ones from defecting to competitors.”

They indicate that “marketing and sales leaders need to develop complete pictures of their customers so they can create messages and products that are relevant to them.” For more on that topic, read my post entitled “The “Person” is the Most Important Part of Personalization Marketing.” The McKinsey partners conclude, “Personalization can deliver five to eight times the ROI on marketing spend and lift sales 10 percent or more. Becoming ever more effective with this kind of targeting, we believe (and hope), will mean the death of spam.” Their final recommendation is to keep your approach simple. They write:

3. Keep it fast and simple. Data worldwide is growing 40 percent per year, a rate of growth that is daunting for any marketing and sales leader. Companies need to invest in an automated ‘algorithmic marketing,’ an approach that allows for the processing of vast amounts of data through a ‘self-learning’ process to create better and more relevant interactions with consumers.”

Gordon, Perrey, and Spillecke call this “a pivot-point moment for marketing and sales leaders. Those who are able to drive above-market growth, though, are the ones who can effectively mine that gold.” I’ll finish the discussion about how to mine the gold in the next post.