Segmentation is a foundational tool for gaining insights into your market and customers to identify business opportunities, improve the targeting and relevancy of communications, and inform product
and service development. This has increasingly become the case as digital technologies fuel the growth of customer data, and tech-savvy consumers continue to opt out of mass-marketing
campaigns.
In today's increasingly fragmented marketplace, being able to identify new market opportunities and capitalize on them through personalization and relevance is paramount to
meeting consumer demand. Yet many companies continue to struggle to achieve the value that segmentation promises or gain enterprise-wide acceptance in order to drive more impactful initiatives. What
should organizations do to gain deeper insights into the marketplace and existing customers to ensure that their marketing initiatives will deliver the anticipated return on investment (ROI)?
Businesses should consider advanced segmentation techniques by employing artificial intelligence technology.
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The Lotto Effect
First, consider a complex
“wrinkle” in customer segmentation that often goes unnoticed. Take a typical customer segmentation effort today -- a company wants to identify customer segments with significant growth
potential for a particular product. To accomplish this, it captures 50 customer attributes such as age, gender, location, account tenure and other customer-level information. From this relatively
small set of attributes, an analyst will need to identify the “best” six attributes to base the segmentation upon -- a surprisingly complex endeavor.
Selecting the best six out of
fifty is akin to playing a lottery -- 50 numbers… pick six… 15 million ways to lose. This wrinkle quickly grows into a chasm of complexity with the impact of Big Data. In fact, a more
reasonably sized dataset may have 500 attributes, which would result in over 21 trillion segmentations to evaluate. Not great lottery odds.
A new way to play the game
Traditional segmentation approaches rely on the analyst to determine the best set of attributes facing these odds, and also attempts to decipher what the “best” attributes mean. It's no
wonder that segmentations based on these approaches often lose their relevance within six months. The lottery effect, although it initially seems like an added obstacle, in fact provides the big data
landscape to search for the optimal segmentation. However, a more cutting-edge approach is required to take advantage of it, which is best addressed by artificial intelligence.
This type
of approach finds the optimal segmentation among an endless number of attributes -- and more importantly, bases it on business objectives that are embedded directly into the technology. The latter
ensures that the optimal segmentation remains aligned to the core business objectives that drove the need for it in the first place. One of the key values of leveraging artificial intelligence
technologies is that this provides a unique approach that connects advanced analytics to business goals. This is critical because objectives will vary for each company depending on their brand,
market, and priorities, which is often reflected in three core areas.
• Who to focus on -- resulting segments must be highly differentiated across core value dimensions such as
revenue, cost, profitability, NPS, and other aspects to guide investment strategies
• What to offer and what to say – each segment needs a rich, unique and comprehensive profile
to inform product, offering and messaging strategies
• Where to find them -- for the solution to be actionable, the segmentation often must be developed so that it can be applied
directly to a customer database or prospect universe for targeting purposes.
With the advent of Big Data and more advanced analytics, customer segmentation techniques based on traditional
approaches will fast become obsolete in today's omnichannel 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 their own customers.