Customer segmentation for a new digital experience

By Wilson Raj, SAS Global Customer Intelligence Director

We’re reaching an era in marketing where grouping customers into segments is considered dated.

Or is it? Fueled by big data and powered by analytics, marketing solutions can now target specific activities to a single customer. This doesn't mean traditional segmentation has reached its expiration date, but what shape will this emerging one-to-one marketing take?

The underlying concept of customer segmentation is straightforward: brands don’t want to treat all their customers the same way. Different groups of customers have different needs and contribute differently to the bottom line. Consequently, marketers need to develop distinct and insightful strategies and messages for a diverse customer base.

Big data and customer analytics makes segmentation all the more valid and enables greater sophistication; however, many organizations struggle with modernizing their segmentation approaches in light of the immense, multi-channel sources of customer intelligence generated each day.

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Your brand fine-tuned to customer segments

You need a benefits-based approach that creates customer segments based on the appeal of product or service benefits to certain groups. You definitely need to know which product benefits are of most interest to customers. But, this method may create groups in which behaviors are dissimilar in terms of brand usage or other quantifiable behaviors. The result is that your message’s effectiveness will be diluted and unclear (or downright confusing) to some members of the segment.

A typical segmentation approach is to focus on brand usage patterns, demographics or media usage behaviors.

For example, consumers who are most concerned with a juice drink’s nutritional value are presumed to be different from other consumers who are seeking a product their toddlers will actually drink. So a marketer may segment the market into families with (or without) young children and develop marketing messages accordingly.

The drawback here is that demographics and brand usage patterns aren’t completely reliable predictors of which benefits are of the greatest appeal to customers. It’s more clustering (observing behaviors) than segmenting (understanding preferences).

Customer segmentation now and next: What should you do?

The tools will tell you the best approach. Customer analytics that incorporate modeling and forecasting will enable you to better understand what each and every customer wants. Here are some things you need to do to inject potency, accuracy and efficiency in your segmentation:

• Integrate downstream and upstream customer data as a continuous process. You can do this now with data management tools and solutions.
• Identify three categories of customer data to build an all-inclusive view of customers’ overt needs, preferences and unmet needs. The three customer data categories for deeper segmentation and analytics:
  • Stated (or structured data) – self-reported customer data, customer databases, CRM systems, transactional systems, etc.
  • Derived (or analyzed data) – customer data through data analytics, modeling, aggregation, etc.
  • Inferred (or unstructured data) – digital data from social web, mobile channels, public domain, etc.
• Create more holistic segmentation models with softer insights based on engagement levels (e.g., downloads, registrations, participation in forums, conversations on social media, etc.), and where consumers are on the customer life cycle. You should be on the lookout for those “moments that matter” and segment accordingly.
• Employ predictive segmentation techniques to provide differentiation based not only on customer needs but also on insights into future reactions to the brand. Predictive segmentation can result in positive responses to the brand such as growth in sales, market share, perception of the brand, or other dependent variables critical to the business.

Don’t overlook big data and privacy

As you rethink segmentation approaches, account for the value of big data and the delicate balance between data privacy and personalization.

Use big data and predictive analytics to create more experiences that matter to a segment. Big data and analytics capability will allow you to fully use the rich data available from the range of new digital touchpoints and turn this into individualized customer interactions. Big box stores are starting to personalize their mobile shopping experience based on the customer’s intent and history.

Be transparent with customers about data privacy and usage. Consumers and employees alike are willing to give up data for value. Data that will help you better segment your customers. Clear and concise disclosure for what data is being used and why, as well as simple user controls to opt in and out of different levels of data sharing, are essential to establishing customer trust.

The upshot: In an era of big data, hyperconnected digital customers and hyperpersonalization, segmentation is the cornerstone of customer insight and understanding across the modern digital business. The question is: Is your segmentation approach antiquated or advanced?