Data Mining for Smarter Retailing
Source: www.spss.com
Copyright SPSS, Inc. 2004


Introduction
Retailing is a highly competitive business, subject to many changes and cycles in customer buying preferences. Since the introduction of point-of-sales terminals and universal bar coding, retailers have accumulated a wealth of sales data. More recently the use of credit cards, and especially of store cards (e.g., “frequent shopper” or retail credit cards), makes it possible to relate purchases to particular customers. This wealth of data provides the opportunity for competitive advantage.

Online retailing has provided new opportunities for retailers as well. In addition to collecting information on what customers buy, retailers can collect information on what customers look at during the shopping process. This provides another level of detail for understanding what customers want and meeting these needs more efficiently: Customers are happier because they find what they need while retailers maximize profitability.

The Web also offers new possibilities for merchandising. In brick and mortar operations, placing items next to each other to increase cross-selling usually isn’t physically possible. Changing shelf placement upsets other product affinities and store space is limited and inflexible. With the Web, a virtual store offers endless possibilities for tailoring “shelf space” for each shopper. 

Data mining is the key for taking advantage of these new opportunities it enables organizations to understand and predict customer behavior. This paper highlights data mining myths and truths, offers common retailing applications and explores market basket analysis in detail.


Data mining myths and truths

Myth: data mining is a new way of doing business
Data mining is a natural business process: Data mining isn’t about expensive hardware and even more expensive consulting. In actuality, data mining is straightforward and usually provides quick and substantial payback. Put simply, data mining means finding patterns in your data, which you can use to do your business better. 

At one level, data mining is merely automation of the oldest and most fundamental of business processes: Analyzing what you did in the past and interpreting the results in order to learn how to do better in the future. Yet, at another level this same process can revolutionize retailing by enabling a one-to-one relationship with each and every customer. So whenever individual customers come into the store, we know exactly what products we most want to sell to them in order to build loyalty and profitability. 

Myth: the more data you have, the better your results
Business knowledge is more important than massive data: The key to success in data mining is the quality of the business input. Good data mining is directed by business goals. For example, undirected segmentation of customers is likely to produce illusory or transient segments. One study showed that when a supermarket repeated the same clustering exercise one year later, more than 50 percent of customers had changed segments. Similarly simple associations, such as the well-known “diapers and beer” pattern, even if real, have negligible business value. Before taking any action, the retailer must check out all the other associations with diapers or beer, since any action to leverage this unusual association runs the danger of upsetting many other valuable product associations.

Data needs to be analyzed with a business goal in mind. Classifying customers into “high-margin,” “average-margin,” and “low-margin” segments can provide real business value. Data mining can then be used to find the patterns or “signatures” underlying the high-margin customers, who can then be motivated through appropriate, targeted promotions. It can also identify average- or low-margin people with the most potential to be developed into high-margin accounts.

Myth: data mining is throwing algorithms at data
Business knowledge will suggest how best to treat data: Usually the raw data is not in itself the most fruitful data to mine. We may want to look at purchases per week, frequency, recency or store-specific or category-specific ratios such as average sales per square foot of shelf space or sales up-lift following a promotion. The crucial importance of these derived attributes is well known to the business-savvy executive but not to a data-oriented analyst. So when you are data mining, the quality of business-knowledge input to the project is crucial. To be effective, data mining tools must not distance the business expert from the data, because results require making this business input easy to achieve and understand. 

Data mining in retail
Retailing offers many profitable applications, including customer relationship management (CRM) and e-CRM, store performance analysis, purchasing and stock management and other applications.

CRM and e-CRM:
· Customer profiling
· Customer profitability analysis
· Targeted marketing
· Basket analysis
· Opportunities for up -selling or cross-selling
· Churn prediction

These applications work by data mining basket data when the customer identity is known, usually as a result of e-store tracking or card membership information. Baskets can be analyzed for profitability and successive baskets bought by the same customer can be aggregated to yield a measure of customer profitability. This data can then be mined to find the highest-profit customer profile.

Often high-profit is associated with purchase of certain categories or brands. Other customers can then be scanned to find those with the potential to move into the high-profit category. The relevant products can be offered to these customers via custom online content, a mailing or coupon generated at the checkout.

Data mining can also be used to examine the behavior of customers in the period leading up to an apparent defection, e.g., they’ve stopped coming into the store or dropped an online shopping cart. If we then find other customers – and especially our profitable customers – exhibiting the same behavior, we can take urgent action to reinforce loyalty.

Overall store performance:
· Store (or department revenue or profit) forecasting
· Store performance assessment
· Store site assessment
· Store closure program management

Store performance analysis is performed by building models that predict sales (or profits) based on store, site and demographic inputs. These models can be used to analyze stores by specifying which ones perform above the level predicted by the model and those that fall below. The model can be used to assess the sales from a possible new site to lessen the risk of expansion.

Purchasing and stock management applications:
· Category management
· Promotions planning and analysis
· Demand forecast for individual stock items or categories
· Reliability/timeliness/quality of suppliers


Data mining can analyze and model sales patterns and how they vary with store demographics, time of year, economic climate, weather, promotions and many other factors. If data is available, the uptake of new lines can be modeled with a view to optimizing the life-cycle profitability of a line. The resulting models can be used to make more accurate purchasing decisions, to tune promotions strategies and align these with stock purchase decisions.

Miscellaneous retail applications:
· Human resources: Optimize recruitment, manage staff development and reduce staff turnover
· Credit risk assessment of customers and suppliers
· Equipment reliability modeling and pre-emptive maintenance planning

Performing market basket analysis to increase customer profitability
Selling as many different products as possible to your customers maximizes their value to your business. One way to accomplish this goal is to understand what products or services customers tend to purchase at the same time or later on as follow-up purchases. A common data mining approach for this type of problem is market basket analysis, which is the analysis of transactions or the items purchased by a customer.

Business managers or analysts can use a market basket analysis to plan: 
· e-store layout. Develop custom content such as a list of other items that may be of interest to the customer
· Couponing and discounting. Changing a coupon to feature a second product that a customer might buy would increase sales at no additional promotion expense
· Product placement. Place products that have a strong purchasing relationship close together to take advantage of the natural correlation between the products. Alternatively, place such products far apart to increase traffic past other items
· Timing and cross-marketing. For example, assume that analysis produced a rule that says people who have purchased pet food are three times more likely to purchase pet toys in the time period one-to-three months after the pet food was purchased