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May 13, 2003

Optimizing Customer Insight

When it comes to data, many multichannel retail businesses face an embarrassment of riches. What's missing is timely, actionable insight. Data mining is the centerpiece of an analytics strategy that can deliver business value.

by Usama Fayyad

Continued from Page 1

Equally important, clickstream data tells you who didn't buy your products and why. For example, clickstream data might show that of the customers who left the site without making a purchase, many were searching for shipping information just before they dropped out. You can conclude that you need to make shipping information clearer and more readily available — and you could test this theory and see if higher sales and fewer abandoned shopping carts result.

Retail Uses Of Data Mining

While most online retailers today gather some sort of statistics on the efficacy of their Web site, the majority haven't begun to tap the full potential of data mining. Retailers typically progress through three stages as they come to understand how in-depth mining of customer interaction data can help them meet customer needs and increase profits.

Stage 1. Web analytics consist of gathering Web site statistics that track customers' online behavior: how many hits your site gets, how many pages customers view, the dollar volume of sales, and so forth. This type of feedback can be helpful both for fine-tuning your Web site to better meet your customers' needs and for identifying such factors as which of your products and services generate the highest (or lowest) online revenues.

The problem is that Web site statistics only help you analyze one aspect of customer interaction: their online behavior. The statistics don't capture transactions made through catalog sales or bricks-and-mortar stores; they don't include customer demographics; and they don't provide any way to segment customers. So, while Web site statistics are a good start, they only scratch the surface of the benefits that advanced data-mining techniques can bring to your business. Borrowing the phraseology of Geoffrey Moore's Technology Adoption Model (see Resources), the "late majority" of retailers stop at this stage.

Stage 2. Customer analytics adds depth to understanding customer interactions. This stage, now becoming mainstream within the "early majority" of retail adopters, is where companies gather data from multiple sources, including Web site interactions, transaction data from offline purchases, and demographic data from customer registration forms. A good customer analytics solution bases analyses not on data subsets or high-level aggregations, but on every individual transaction. This approach brings both higher accuracy and the ability to drill down to more detailed views of how your customers interact with your company. The richness of this stage of analytics offers a more holistic view of your customers, with deeper insights into their behaviors, likes, and dislikes.

Good stage 2 customer analytics solutions also include the ability to segment customers according to a variety of criteria and export the results to other programs. That way, as you learn more about the behavior of a particular subset of your customers — say, high-volume purchasers in the 30- to 50-year-old age group — you can target that group with specific marketing campaigns. Such campaigns tend to produce much better results than more general approaches and are also less likely to annoy customers who aren't interested in what you offer.

Stage 3. Optimization, adopted so far only by the retail visionaries, is the most advanced stage of data mining usage and offers the biggest potential payoff. In this stage, sophisticated data-mining algorithms sift through data volumes to discover patterns that may be too subtle for humans to distinguish. The software applications can then automatically apply the insights to optimize customer interactions. In other words, by tailoring recommendations and promotions to the preferences of specific customer groups, you can actually change customer behavior: upselling them to a higher-priced product; crossselling to additional, related products; or even downselling to a lower-priced product in cases where the customer is abandoning a potential purchase because its price is too high. These recommendations, based on data patterns, can produce immediate payoffs in the form of increased sales. Because the marginal cost of these incremental sales are minimal, the contribution to the profit margin can be dramatic since most of the costs associated with each customer have been sunk into the primary effort of driving them to your site or store.

Retailers can derive smarter recommendations through data mining. Recommendations are generally made first to all customers; then to specific segments of customers; and finally to individual customers on a one-to-one basis reflecting knowledge of their preferences. As an example of the first type of recommendation, the data might reveal a connection between customers who buy backpacks and those that buy jeans. The Web site could then display a jeans promotion or link whenever any customers place backpacks in their shopping carts.

More data mining could then allow the retailer to pursue the second type of recommendation. Continuing our example, the data might reveal that customers who buy a particular type of backpack prefer a specific style of jeans. The retailer could arrange the Web site to have a link between these exact styles. Finally, further data mining could enable the third type of recommendation, allowing the retailer to know that particular customers prefer Diesel jeans, and showcase Diesel jeans whenever the site displays a jeans link to that particular customer.

Ultimately, retailers can use data knowledge to make recommendations across sales channels. Multichannel customers are known to have a higher lifetime value than single-channel customers. By marketing to them more effectively, you can further increase their value. For example, some multichannel customers may prefer to use your Web site to view products, but then go to one of your stores to purchase the product (for example, to verify that an item fits). With multichannel customer data analysis, you can discover such a pattern and then bring that knowledge to bear by creating more effective marketing campaigns: for example, by emailing those customers a coupon that they can redeem at a physical store. Conversely, you could leverage knowledge of multichannel customers' offline purchases to tailor promotions that enhance their online shopping.