I think we can all agree – the days of mass marketing are over. Even if you have a relatively homogenous target market, you should probably spend some time thinking about how you should segment your customers to optimize your marketing efforts.
In an earlier post, I discussed the difference between market segmentation and customer segmentation. Today, lets dive deeper into the realm of customer segmentation.
John Forsyth, former partner at McKinsey & Company, noted in a Harvard Business Review blog that segmentation is the building block of customer centricity, “We see many, many companies saying, 'I want to get more consumer-driven and customer facing. But sometimes organizations dont know how to start. Id say you really start with a basic understanding of your consumers or customers, right? And thats segmentation."
Customer segmentation can be used for many reasons for a given organization, the most obvious today would be for the development of personalized communications and offers and/or different marketing programs by segment. Additional reasons set forth by Bain & Co. include: prioritizing product development efforts, designing distribution strategies and determining product pricing.
So how do businesses start down the segmentation path? First, they need to collect the right customer data. In todays world, the sources of customer data are manifold. For a retailer, shopper specific streams of information can come in through in-store technologies (POS, traffic counter), digital sources (social activity, ecommerce browsing and transactions, digital ad interactions, email, etc.) and third party data providers like Nielsen or Acxiom.
At Manthan, we believe that all data relevant to an individual shopper should be incorporated into a single customer profile to create a baseline of understanding. This may include the data points that fall into the following categories:
- Demographic: Basic customer information, such as age, income, education and gender.
- Lifestyle: Understanding the routines and habits of a customer, such as vacations and memberships.
- Preference: Oftentimes, this includes the stated communication preferences of a customer, such as email opt-in. In other cases, it may include implied customer preferences, such as what sort of channels the customer is most likely to redeem an offer or what day/times the customer is most responsive to a communication.
- Loyalty: Loyalty data generally includes statistics relative to the business loyalty program, such as when the customer joined the program, whether they are active, how many points have been earned and/or redeemed. For companies without defined loyalty programs, this can include a recency, frequency, monetary (RFM) score.
- Behavior. This can include various behaviors, such as browsing, redemption, sentiment or cart abandonment. This also can include customer transactional behavior, such as spend by category and/or brands and spend by price point.
- Value. This is the value an individual shopper represents for the company, whether in terms of revenue or profitability. This can be expressed in terms of historical spend, lifetime value, average spend per visit, etc.
- Predictive. Sometimes, customers should be segmented based on projected activity, such as a propensity to purchase a given item or likelihood of leaving the company for a competitors offering.
I know, seven categories can lead to an overwhelming number of options when starting down the customer segmentation path. The best way to approach customer segmentation is with an objective in mind. What are you trying to achieve by segmenting your customers? Are you trying to maximize marketing lift in terms of revenue? Increase customer retention or merely optimize your email deliverability? As with most customer insights, the marketer should always begin with the business question.
To learn more about customer segmentation strategies or solutions to drive better marketing performance for your business, contact Manthan today.
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