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AI converts consumer analytics into potent sales weapons

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This article is part of our Essential Guide: Customer data management tools shape personalization, boost CX

Consumer behavior analytics makes the unpredictable predictable

With AI's arsenal of machine learning, deep learning and NLP, consumer analytics can take some of the unpredictability out of predicting buying patterns and up conversion rates.

As long as there has been a marketplace, merchants have struggled to know and understand the mindset of their customers. The digital marketplace today is bursting at the seams with information about customers, but parsing what's going on in their heads, let alone anticipating their next move, is still difficult to define.

No doubt plenty of tools are geared toward reading customers' minds. From analytics to artificial intelligence and machine learning to natural language processing, companies have never had it so good when it comes to discerning consumer gestalt. Aside from the proper tools, the real question now centers on framing the desired goals and roadblocks to getting there, then choosing the right methodology to achieve the desired outcome.

At the core of sales and marketing are key questions when it comes to consumer behavior analytics: How can we find more consumers who want to buy our products and services? How can we get customers to continue buying them? More specifically, which consumers are likely to buy which of our products and services? And what will keep our customers loyal to our brand?

Answering these questions requires combing through lakes of data to first grasp, in considerable detail, a general understanding of consumer behavior, then to target populations of consumers. That analysis is essential to gaining insight into the differences between target groups, what attracts and engages members of those groups and, once they become customers, what keeps them coming back.

"That is essentially what marketing is about -- anticipating people's needs and future behavior," Cognitiv CEO Jeremy Fain wrote in a Forbes article last year. "Thanks to the rise of big data and the development of advanced machine learning technologies, including deep learning, predicting human behavior more accurately has finally become possible."

Predicting success

It can be tough getting there, but once consumer behavior analytics is performed, all kinds of useful insights come to light. And companies that have succeeded in gaining precious insights into their customers have stories to tell.

Netflix is a pacesetter in the field. The media services provider has fine-tuned its customer portraiture so precisely that its recommendation engine drives 80% of subscriber content selections, accounting for $1 billion in annual savings due to customer retention, according to a Business 2 Community newsletter last March. Amazon has likewise used AI to optimize its delivery process with anticipatory shipping -- predicting which customers will buy which products at particular times -- to ensure that the right products are stocked in the right locations.

Predicting consumer behavior patterns

However, most companies trying to deploy AI to consumer behavior analytics don't get those kinds of results and even miss their sales forecasts by a significant amount.

Defining consumer behavior

More than a decade into the era of digital sales and marketing, the substance of consumer behavior data is well understood, even if difficult to process. The factors that influence consumer behavior are critical to predictive methodology:

  • Demographics include the most common parameters for defining a segmented or targeted audience for a particular sales initiative, such as age, gender, income and other metadata. Perhaps difficult to collect, yet more useful, is abstract information like opinions, beliefs, values, traditions, goals and peer groups that helps determine consumer behavior.
  • Marketing includes promotional campaigns touting brand, products and pricing that are directed at consumers through various channels of communication.
  • Since consumers don't live in a vacuum, their patterns of living emerge in the context of the world around them. Environmental factors include the current economic climate's influence on purchasing habits, cultural trends and, of course, technology, which is increasingly tied to context in product presentations.
  • Mining social media data from sources like Facebook, Twitter and LinkedIn can provide insight into consumer brand and product preferences. "Social listening has been very helpful in contextualizing marketing beyond sales data," Black Swan Data CEO Steve King told The Wall Street Journal last November. "Now, we're beginning to see patterns that can help us forecast consumer behavior 6-12 months ahead of time."

All this data from various sources and channels feeds into machine learning and passes repeatedly through analytical software to detect patterns through data models and predictive algorithms. Once tested and deployed, the algorithm accepts new data and renders predictions of customer behavior and outcomes. The more the algorithm is used, the better it gets at predictions.

Looking inward

Segmenting consumers into precise subgroups for optimum targeting is key in applying AI to sales and marketing efforts.

When applying consumer behavior analytics, some old-fashioned business analytics can provide a strong understanding of how the company itself performs in predicting how customers will respond to the company -- a sort of post-game analysis of corporate sales and marketing efforts. Depending on whether sales rise or fall, factors to analyze include the apparent sales drivers and how they're measured, how the rest of the market is performing at the time and how and why sales vary region to region.

Just as there's plenty of useful data to be evaluated with machine learning on the consumer side, there's a lot of tangible information readily available on the business side to evaluate before ramping up AI for sales and marketing campaigns. And that data is too often overlooked -- for example, the Consumer Price Index, disposable personal income, real average hourly earnings and consumer sentiment.

Looking even deeper

Beyond consumer behavior analytics and machine learning is deep learning. The inputs for machine learning are the same as for deep learning as is the process of training a predictive model. But deep learning is based on layered neural networks, similar to those found in the human brain. While conventional machine learning can detect human behavior patterns, deep learning is so sensitive that it can detect patterns within patterns.

"Marketing automation powered with AI … [ensures that] the right message gets to the right person at just the right time," wrote Reshu Rathi, inbound marketing manager at Netcore Solutions, in an article posted on Enterpreneur.com. "But deep learning can take it a notch higher. It takes into account customer taste, personal preferences, spending patterns and even micro preferences combined with external factors, like weather, to send highly customized and more relevant suggestions to their customers."

With deep learning, for example, it's possible to train a predictive model on all the actions of last month's customers and produce an algorithm that can predict which consumers will like a particular product. We can add information about their buying habits and train the model to predict the likelihood that potential customers will buy the product and thus improve conversion rates.

Simplifying demographics

Segmenting consumers into precise subgroups for optimum targeting is key in applying AI to sales and marketing efforts. But consumer segmentation, typically based on conventional demographics, can transform into something very different when AI is applied effectively. Rather than lump consumers into buckets based on, say, age or gender, AI can make a quantum leap beyond those upfront factors and move directly to the back of the book for answers. So the only demographic that really matters is simply consumers who will buy our product.

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In addition to internally created factors, what external factors do you include when feeding consumer analytics processes?
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