Last month, we outlined some common techniques for taking advantage of Best-Practices Marketing Database Content. Two examples were customer clustering and product affinity analysis. This month, we provide some examples of marketing tactics that are supported by these techniques. Perhaps they will stimulate a brainstorming session or two at your company!
First, identify targets suitable for focused, data-driven marketing. For example:
- High-potential customers, inquirers and prospects.
- Conversely, at-risk and lost customers.
- New ship-to customers such as gift recipients, including their locations.
- Top-selling products and services.
Then, take action on these targets, mixing and matching promotional channels as appropriate. For example, consider what can be done with a list of top-selling products:
First, identify the top sellers for each existing customer cluster. Then, for each customer within a cluster, tailor a promotional sequence to focus on a product or two that has not been purchased - but logically might have been. One such application is to identify and take advantage of "missing demand," in the form of the "primary" products and services that are logically related to observed customer demand for consumables and accessories.
Assume, for example, that your firm sells printers and cartridges. What if a customer is purchasing nothing but cartridges from you? By definition, some other company is benefiting from the underlying printer purchases. It probably makes sense to try to rectify the situation going forward!
Fine-tune mass-market promotions. For example, a national retailer used its Marketing Database to determine the zones (geographic areas) for which newspaper inserts ("FSI's) could be profitably distributed. Customer revenue was calculated by zone, and then compared with the cost of insertion for each of the same zones. Armed with this information, FSI frequency was increased in high-value areas, and reduced or eliminated in low-value areas.
Optimize merchandise adjacencies in promotional pieces, websites and stores. For example, a national retailer commissioned a product affinity analysis in which it was discovered that luggage and home office merchandise often are purchased by the same people. However, the two categories were far apart from each other on the store floors. Inspired by this finding, the retailer reconfigured the floor layouts of over 200 stores so that luggage and home office merchandise would be positioned next to each other.
