Data-Based Intelligence Demands Critical Thinking

In Big Data by Marina ErulkarLeave a Comment

Data-Based Intelligence Demands Critical ThinkingTechnology supports the collection of data from a myriad of sources: websites, real life, social, on and on. That data has so much potential for improving our understanding of customers’ needs and their decision processes. Because of the opportunity to improve targeting, speed the purchase cycle, and build customer relationships, all that collected data promises powerful competitive advantage.

To achieve the promise, however, we must engage with the data. We must extract meaning. And yes, we must market differently.

Let’s use a familiar example: new movers. Marketers have a long interest in identifying new movers and for good reason—there is tremendous revenue potential across a spectrum of highly predictable categories all resulting from a move.

Let’s talk specifics:

  • Approximately 1 million U.S. households moved in 2015, according to Epsilon’s 2016 New Mover Report
  • On average, those households spent $9,000 on goods and services
  • 2015 spend from this group was approximately $136 billion

Under the common practice, movers submit their notification to various sources, including utility companies and the USPS. Those sources sell the notification data, and after time required to aggregate and normalize the list, it is delivered to marketing subscribers.

And then the race is on! Marketers scramble to deliver their messages, hoping to capture their share of $136 billion.

There is a critical behavioral fact that is routinely overlooked and misapplied:

Most purchase decisions are made prior to the move.

This insight should direct us from an analytic and a marketing perspective. By the time we start running, the race is already lost.

Movers make most purchase decisions prior to their move—they are simply transacting when our marketing campaigns begin. In short, we can only hope to undo decisions made in the months prior.

The cost of delay is tremendous. We routinely leave $136 billion in play every year. And ironically, we commit enormous ongoing effort trying to generate the very opportunity we routinely neglect when we don’t know customer behavior, fail to exploit our data, and don’t create intelligence.

Delaying ensures increased competitive pressure as every other company (also late to the race) is vying for their share of $136 billion, and is relying on the same list.

When we are awash in data, why do we wait for formal notification?

Here are a few ways to clear this very costly barrier:

  1. Question Existing Practices: The new mover example confirms that old habits die hard. Marketers fail to see the advantage they can create from data, and instead follow a practice that has been in place for decades.

Again, by delaying action, marketers tacitly opt for less return from their efforts, and greater competition for movers’ spend.

  1. Extract Meaningful Intelligence: We are not interacting with data to exploit its full potential.

In the case of the new mover example, there is tremendous opportunity to model collected data to identify predictors: behavior and/or patterns that indicate a move is imminent.

Once identified, marketers have an advantage. Their messages and promotions can be delivered at the right time: when customers are assessing options and making purchase decisions.

  1. Rethink Campaign Reliance: Technology has shifted power from companies to consumers. Consumers can now access information, including competitive features and pricing, anytime anywhere.

Yet, we continue to execute campaigns on the marketer’s schedule. If our objective is to gain brain space and wallet share in the moment of opportunity, it must be on the consumer’s schedule.

Happily, the technology that collects our data serves as ideal platforms to reflect our new understanding and urgency.

Again, intelligence derived from data creates a real right-time opportunity. If we can recognize a prospective mover, we should begin messaging immediately—not after the campaign is set to execute.

  1. Mandate Critical Thinking: New data, and the resulting intelligence applied to new practices, demand new critical thinking. The unfortunate truth is that intelligence does not immediately jump out of results—an intelligent marketer must be present and actively engaged.

Marketers necessarily have to know their business, their customers, and their data to have optimal impact. Superficial interaction may drive near-term results, but will not sustain relationships long term.

There is an important point of distinction to be made. Marketing to a likely new mover is fairly safe. If, for example, modeling reveals certain identifiable behavioral trends by new movers, proactive messaging may begin to sell them furniture, insurance, and financial services, all to gain quick and certain competitive advantage. That marketing demonstrates an understanding of the customer and recognition of their new need. It reinforces the relationship.

And there is minimal cost or repercussion to being wrong.

However, there is another new consideration for the marketer applying analytic intelligence:

  1. Ask, “Should we?”: In a 2012 New York Times Magazine article, “How Companies Learn Your Secrets,” Charles Duhigg tells of a rightly irate father complaining to a national retailer who had been marketing baby clothes and cribs to his teenage daughter.

The retailer had created a model that identified purchase behavior that preceded the formal notification on the baby registry. They correctly identified the teenager’s condition and set about marketing to her. (Her parents did not know of their daughter’s pregnancy, and the retailer’s marketing forced her disclosure. Wow.)

Clearly, the retailer hoped to accomplish the very objective promoted here with the new mover example: exploit data, recognize need, move quickly, capture share.

In this case, the retailer generated accurate intelligence, but the application is entirely questionable. There are so many personal and private considerations with a new baby. In the best of times, families should decide who is told and when. When conditions are complicated, families could grapple with whether they tell anyone.  It certainly is not the retailer’s option to make the announcement, directly or indirectly.

This new baby example is powerful input to a discussion of marketing ethics. (And that discussion should include marketing maternity to a teenager.)

Beyond the theoretical discussion, there is a more practical consideration: data and the intelligence derived from it is intended to bring us closer to our customer—to understand their needs, what they value, and what they will purchase.

The new mover example is one that achieves those objectives: recognize when your customer zags after years of zigging, and market to the new need.

The same practice goes terribly awry when there is no appreciation for the customer, the possible personal circumstances—and when the company is blinded by quick, incremental upside. The objective of capturing wallet share likely ruined a significant number of customer relationships in the retailer to teenager example.

The marketer must necessarily be an active, critical thinker with a deep understanding of their business, their data, and their customer. We absolutely must think differently to understand the meaning—derived from data and not—of the intelligence we create.

And we must demonstrate respect for what we learn from data-driven intelligence in order to optimize a relationship—not just maximize a transaction.

That is simply good marketing.

Photo Credit: Universidad Politécnica de Madrid via Compfight cc

Marina is an executive who acquires customers, grows margins, and achieves financial objectives with unique, integrated expertise: analytics, marketing, and technology.

She is keenly able to extract and exploit meaningful intelligence from analytics and market research and apply those insights to create opportunities or address issues. 

As a customer-centric, big data commander, Marina has conceived and executed analytic-based strategies that doubled revenue (without adding proportionate resources), extended existing services into new industries to support aggressive revenue goals, and redefined customer segmentation which reduced costs by 25% while driving up revenue 30%--and changed the company’s marketing model.

She has contributed her expertise to industries including software, technology, financial services, telecommunications, retail, pharmaceutical, non-profit, and professional services with all sizes of companies—from startups to global enterprises.

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