Using “Digital Footprints” to Predict Consumer Motivation Online

digital footprints

Modern marketers must become data-driven detectives.

If you want to boost customer loyalty, customer engagement and market growth, the answer is in the data, and the closer it is to real time, the better. In today’s hyper-competitive economy, moments of truth may literally be fleeting moments.

In the course of my research work with Forbes Insights, I have had the opportunity to speak with Dr. Ravi Dhar about the opportunities for companies to understand what is behind such real-time analysis of consumer behavior. Dhar, who is professor of management and marketing, and director of the Center for Customer Insights at the Yale School of Management, has been watching these trends for some time.

At Forbes Insights, we explored the trends shaping consumer-driven data analytics. One survey of 331 senior executives found that data-driven marketing has delivered demonstrable results in terms of customer loyalty, customer engagement and market growth. In addition, organizations that are “leaders” in data-driven marketing report far higher levels of customer engagement and market growth than their “laggard” counterparts. Growth and commitment to data-driven marketing are on the rise, with most organizations planning to step up their efforts. However, about half of executives admit their efforts are lagging or are siloed across their enterprises. A majority are now collecting demographic data on customers, but most other data types remain uncaptured.

Social Media Advertisement Connection ConceptA case for data integration in customer behavior

In addition, a more recent survey of 308 senior executives finds predictive analytics have become an important tool in understanding and preparing for consumer behavior. Predictive analytics is on most marketing agendas. However, the journey has only begun. Only 13 percent considered themselves to be highly advanced with the technology. The vast majority of organizations, 82 percent, intend to increase the role of predictive analytics in their marketing processes over the next 12 months. In addition, an overwhelming majority of executives with experience in predictive analytics (86 percent) indicate the technology has already delivered a positive return on investment.

Data-driven analysis of consumer behavior used to be limited to consumer-focused companies such as banks and telecommunications companies, Dhar says. These capabilities are now opening up to a broader array of businesses, seeking to “connect the dots” of data that stream from advertising results, sales data, production data, and even social media.

“The integration of the dots is more important than the dots themselves,” he says. The key is for marketers to “understand what they see in social media, what customers bought in a store, and what other media did they see to get the overall understanding of how media consumption drives both online and offline purchases.”

What data can’t answer

However, while data is now highly effective in determining various aspects of consumer behavior, there’s still a missing piece, Dhar says. “It’s about who buys it, when did they buy it, where they buy it, and what did they buy. Data is good at telling all these. But the data can’t tell you by itself the ‘why, or why not.’”  Understanding this fifth “why” requires “good managerial understanding,” Dhar continues.

Companies need a greater understanding of which customers will respond to marketing and advertising, and be able to apply their efforts directly as required. The key is to develop algorithms, or methodologies, to understand which customers will respond to given promotions, and which won’t – and be able to respond in real time.

Perhaps the most pronounced transformation that the move to a data-driven marketing culture is what Dhar calls “integrated thinking.” This brings together the variety of data now at decision-makers’ disposal – beyond the quantitative data that’s been available for years. This includes digital footprints “left online, browsing patterns and comments.”

The goal is to employ the data to “clearly understand the business question. How do you have a good understanding of what pulls the heartstrings, not the purse strings, of the consumer? If you’re too much caught up in the weeds of the data, you often don’t see the ‘why’s.’ You do need someone who can combine the analytics with a good fundamental understanding of consumer behavior, consumer psychology and consumer motivation.”


About Joe McKendrick

Joe McKendrick is RTInsights Industry Editor and industry analyst focusing on artificial intelligence, digital, cloud and Big Data topics. His work also appears in Forbes an Harvard Business Review. Over the last three years, he served as co-chair for the AI Summit in New York, as well as on the organizing committee for IEEE's International Conferences on Edge Computing. (full bio). Follow him on Twitter @joemckendrick.

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