There are two contrasting approaches to marketing—customer centric and product centric. Product-centric marketing focuses on what’s being sold; campaigns are organized by product. Currently, product-centric marketing dominates the marketing landscape. But customer-centric marketing, which begins with analyzing customer behavior and deciding which customers to target, is a powerful and underutilized alternative that is key to building customer loyalty.
Both approaches are useful. When a company introduces a new product or its warehouse is overflowing with inventory, marketers roll out product-centric direct marketing campaigns featuring those products. Customer-centric marketing is a newer skill with far fewer practitioners and a less well understood methodology, despite its many advantages. Here are the components of that methodology.
Targeting
If the focus is on the customer, the place to start is deciding which customers to target. The product-centric extreme is a “spray-and-pray” campaign targeting everyone with the same message and offer. More sophisticated segment targeting can be either product centric (find potential buyers for our new widgets) or customer centric (target customers who have recently visited our website).
The gold standard in customer-centric targeting is micro-targeting. Here you use customer analytics, typically based on transactional data, to ferret out appropriate customers. Your filters, applied at the individual customer level, might be the following: the probability of the customer making a purchase in the next few weeks a change in their wait time between purchases, a minimum expected amount from a transaction, a trigger based on a changing risk score, or something as simple as their recency. Usually targeting is a combination of several of these metrics.
Whatever criteria you use to determine which customers to target, the underlying customer-centric method is to use metrics associated with a customer’s behavior. Who gets targeted is based on a customer’s state (ready to purchase, tending toward defection, buying narrowly, etc.), not on what your company wants to sell him or her.
Offers
Once a customer is targeted your offers to that customer won’t be customer centric unless the offers are based on what your analytics tells you about which products will be of interest to each customer. If you don’t know the purchase propensities of an individual customer, it’s impossible to make a customer-centric offer. This means calculating propensities for both upsell (selling more of what was previously purchased) and cross-sell (product categories not yet represented in the customer’s purchase history).
The challenge here is calculating these individual propensities. You need to develop a large set of customer/product propensities, one for every customer/product combination. For practical purposes, rather than doing this at the SKU level, it makes more sense to do it at some midlevel in your product category hierarchy. For instance, you should create offers for men’s sport shoes, not for a particular size and color of a certain style of loafer.
You also need to ensure that your analytics methodology is capable of delivering the information that you need. The most commonly used methodology, Recency-Frequency-Monetary Value (RFM), has no product information in its set of input variables and, consequently, is unable to predict purchase propensities for particular products at the individual customer level. It is essentially worthless for calculating purchase propensities, especially for cross-sell.
Messages
After deciding whom to target and what to offer, you need to consider what to say to your customer. Irrelevant messages are a big turn-off for many customers. But constructing individual messages is probably not feasible when the customer count goes beyond 100, let alone 100,000 or one million. Consequently, while offers can be at the customer level, and thus be truly customer-centric, messages (such as a discount) are often at the segment level.
If your segmentation is based on customer-oriented metrics, such as customer value or the risk of a customer’s defection, then the resulting segments are customer centric and you can create a customer-centric message. For example, you can offer different discount levels to customers depending on their segment membership. Other segment-appropriate messages might be a “welcome back” for a returning customer or an extra incentive to a one-time buyer to influence that crucial second purchase.
Personalization and customization
Marketers who think they’re crafting a customer-centric strategy are often only using one of these two simpler techniques. Personalization means referring to a customer by name, as in “Dear Susan.” You only have to receive one poorly executed personalized message greeting you as “Dear first name” to know how superficial this is. Still, personalization works for attention, cosmetics, and as a foundation for customization.
Customization has more value. A postcard campaign customized to show a local image dependent on the recipient’s ZIP Code can be worthwhile customer-centric messaging, however understated. Segment-specific messaging is another form of customization. But neither personalization nor customization approaches the relevance and effectiveness of truly individualized communications, where the target customers are chosen based on their behaviors and the offers are based on individual purchase propensities.
Customer-centric loyalty
Customer loyalty is an individual metric, a different number for each different customer. Companies want to treat the customers in their loyalty programs as individuals, and many of those customers demand that kind of treatment. Soon all customers will do so, too. It follows directly that marketing to those customers must be customer centric.
Slightly less obvious, but an absolute requirement, is that customer-centric marketing depends on customer-level analytics. You might create customer-centric messages without customer-level analytics, but not customer-centric offers. Micro-targeting is impossible without customer-level analytics.
Market leaders are blazing a clear path: using customer-level analytics to enable customer-centric marketing; and using that customer-centricity to build customer loyalty. Today, the marketing tools are there for all size companies ready to embrace this powerful methodology.
