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Taking a fresh look at customer segmentation: Winning the lotto

Artificial intelligence technologies are driving next-generation customer segmentation to improve relevance and targeting.

Overview

Knowing who customers are—and what they want—to get the right message to the right customer at the right time. This is key for companies to improve the targeting and relevance of communications, guide product and service development, and remain competitive in a today’s multichannel marketplace. And results demand customer segmentation.

While companies recognize the value of customer segmentation, many have yet to explore how segmentation is advancing and evolving. And they cannot afford to be left behind in a world of big data, advanced analytics and customer-centric marketing. Why? Because customer segmentation must be more robust and more compelling than ever to meet the needs of the more informed and tech-savvy buyer.

How, then, can companies achieve the value that segmentation promises? What’s needed are more advanced segmentation approaches that work across multiple dimensions, handling infinite numbers of attributes, factoring in business objectives and ensuring that the resulting solution is aligned with these objectives. With artificial intelligence technology, next-generation segmentation has arrived.

Background

To understand the promise of next-generation customer segmentation, it is helpful to explore an example that uncovers the limitations of current approaches. It highlights something that we call “the lotto effect.”

Imagine this scenario. A company wants to identify customer segments with significant growth potential for a particular product. It has captured 50 customer attributes such as age, gender, location, account tenure and other customer level information. From this relatively small set of attributes, the company wants to identify the “best” six attributes to base the segmentation on. But selecting the best six out of 50 is akin to playing a lottery—50 numbers… pick 6… with more than 15 million ways to lose.

With more data creating more sources and attributes to consider, the challenge only grows. Doubling the size of that same dataset doesn’t just double the possibilities. In fact, it expands possible options from 15 million to more than 1 billion—a more than 6,000 percent increase. Traditional analytic methods require the person developing the segmentation, who is often a statistician, to determine the best set of attributes facing these odds. It’s no wonder that segmentation based on traditional approaches often lose their relevance within six months. Advanced segmentation approaches,however, are able to handle infinite attributes while factoring in business objectives.

Analysis

Artificial intelligence technology can help companies move past the lotto effect—and hit the jackpot when it comes to customer segmentation. In fact, it provides much needed flexibility so companies are no longer faced with a segmentation strategy that only reflects a single dimension of customer information.

Next-generation segmentation can balance customer dimensions and business objectives while addressing the three fundamental issues of segmentation strategy:

  1. Who to focus on? The resulting segments must be highly differentiated across core value dimensions such as revenue, cost, profitability, tenure and other aspects to guide investment strategies.

  2. What to offer and what to say? Each segment needs a rich, unique and comprehensive profile to inform product, offer and messaging strategies.

  1. Where to find them? In order for the solution to be actionable, the segmentation must be developed so that it can be applied to a customer database or target customers in a mass marketing campaign.

Artificial intelligence technologies can be adapted specifically to address these issues. By using advanced data mining techniques, these technologies can find optimal solutions based on business objectives and insight quality that can be acted on. The result? A unique approach that aligns advanced analytics with business goals for strategies that drive significant market impact—and that has the flexibility to accommodate change as needed.

Download the full article to see artificial intelligence technology in action in Accenture’s OptiCluster solution. This opens a new window.Download the full article to see artificial intelligence technology in action in Accenture’s OptiCluster solution.

Recommendations

Segmentation strategies vary across companies, depending on the brand, market and business priorities. By leveraging artificial intelligence technologies, analytics can now be aligned with unique business objectives to determine the optimal segmentation. Through the more precise narrowing of the “best” attributes, segment profiles will be both richer and more comprehensive. And marketing strategies based on next-generation customer segmentation will be more effective in personalizing and targeting offerings and messaging to relevant customers.

With the advent of big data and advanced analytics, customer segmentation techniques based on traditional approaches are increasingly obsolete today’s multi-dimensional world. Not only do companies risk wasting their investment on the segmentation effort—but more importantly—they risk being misled on where value exists in the market and among customers.