Sofmap Company, Ltd., Tokyo, is one of Japan's top personal computer and software retailers with 40 retail stores located throughout the country.
Sofmap managers believed that many of their customers had difficulty making hardware and software purchasing decisions, which was hindering online sales.
Sofmap used SPSS Inc.'s Clementine data mining solution to build an engine that recommends appropriate products based on customers' profiles, which are based on information gathered during the online registration process and from past transactions.
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Sofmap first began selling computers over the Internet in 1995 - establishing the "Sofmap Virtual Store" to supplement "Sofmap Hyper," their mail order catalog. Over the years, the company's e-commerce sales have grown dramatically. However, Sofmap executives believed that they were losing sales from less computer-savvy individuals who didn't have the expertise to select products that met their needs.
The company addressed this problem by developing a recommendation engine that provided personalized recommendations to their online customers. "We already had a large database with information on over two million customers," said Nobuyuki Matsuda, strategic planning manager for Sofmap. "We wanted to analyze the demographics, individual characteristics and purchase patterns of these customers in order to provide visitors with information that will help them make wise purchases."
The recommendation engine, the first of its kind in the Japanese market, attracted a large amount of publicity and buzz among users.
Sofmap needed to quickly and easily mine the information that was buried in the customer and transactional databases. The company wanted a software package that their marketing staff, who have the best understanding of customer behavior, could use to perform their own analysis. Sofmap knew this would save time and money by eliminating the need to have programmers, who were already very busy, involved in the early stages of the project.
Clementine's ease of use enabled Sofmap's marketers to analyze transaction data and information from their customer database and generate the business rules used in the recommendation engine.
"We selected Clementine because it enables users without computer programming knowledge to develop very powerful analytical solutions," explained Matsuda. "This approach is so much more efficient than to have the marketers communicate the goals of the analysis to the programmers. Now the marketers can do the entire job themselves, which saves a considerable amount of time."
Sofmap marketers collected a wide range of information for use in building models of purchasing behavior including:
Using Clementine to analyze this information, the marketing staff clustered customers based on what it called a "digital lifestyle model." The model was then used to construct both the business rules and the recommendation engine, as well as to personalize the site for returning customers.
The engine works by comparing the customer's profile, registered on their "My Sofmap" page, to the pre-defined digital lifestyle model. Based on what digital lifestyle model the profile matches up with, recommendations are made. For example, the engine can:
The marketers also identified their most profitable customers, based on shopping frequency and purchase size. This enabled the new recommendation engine to focus on these customers in particular.
We selected Clementine because it enables users without computer programming knowledge to develop very powerful analytical solutions.
Nobuyuki Matsuda
Strategic Planning Manager
Sofmap
During the first month that the recommendation engine was available, site traffic increased from a typical 18 million page views per month to a new sustained level of more than 30 million views per month. Sofmap managers said that the rise in traffic could be almost entirely attributed to the new recommendation engine.
Overall, the recommendations increased the "stickiness" of the site from 7.8 to 15 page views per session.
Even more important is that sales have significantly increased since the recommendation engine went live, driven by the fact that consumers purchased many more items.
Prior to the go-live date, sales at Sofmap.com saw an annual growth rate of approximately 270 percent. After the recommendation engine went live, the growth rate immediately jumped to 320 percent. According to Sofmap, achieving this substantial increase in sales without any additional promotional expenditure has tripled the profitability of the site.
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