SPSS - UK Customers

Sofmap

Modelling customers’ behaviours boosts Web site traffic and increases sales

The situation

Sofmap Company, Ltd, Tokyo, is one of Japan's top personal computer and software retailers, with 40 retail stores located throughout the country.

The challenge

Sofmap managers believed that many of their customers had difficulty making hardware and software purchasing decisions, which was hindering online sales.

The solution

Sofmap used SPSS Inc.'s IBM SPSS Modeler* data mining solution to build an engine that recommends appropriate products based on customers' profiles, which are themselves based on information gathered during the online registration process and from past transactions.

We selected IBM SPSS Modeler because it enables users without computer programming knowledge to develop very powerful analytical solutions. This approach is so much more efficient than having 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.
Nobuyuki Matsuda
Strategic Planning Manager
Sofmap

The results

Sofmap first began selling computers over the Internet in 1995, establishing the ‘Sofmap Virtual Store’ to supplement ‘Sofmap Hyper’, their mail order catalogue. Over the years, the company's e-commerce sales had 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 personalised recommendations to their online customers. "We already had a large database with information on over two million customers," says Nobuyuki Matsuda, strategic planning manager for Sofmap. "We wanted to analyse the demographics, individual characteristics and purchase patterns of these customers 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 created a buzz among users.

Sofmap needed to quickly and easily mine the information that was buried in its customer and transactional databases. The company wanted a software package that its marketing staff, who have the best understanding of customers’ behaviours, 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 project.

IBM SPSS Modeler’s ease of use enabled Sofmap's marketers to analyse transaction data and information from their customer database, and generate the business rules used by the recommendation engine.

"We selected IBM SPSS Modeler because it enables users without computer programming knowledge to develop very powerful analytical solutions," explains Matsuda. "This approach is so much more efficient than having 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, including:

Demographic information such as age, sex and occupation
Survey data collected from previous events and seminars
Purchasing information from over-the-counter sales
Transaction data from Sofmap.com, including both page views and purchasing data.

Using IBM SPSS Modeler to analyse this information, the marketing staff clustered customers based on what they called a ‘digital lifestyle model’. The model was then used to construct both the business rules and the recommendation engine, as well as to personalise the Web site for returning customers.

The engine works by comparing a customer's profile, which was registered on their My Sofmap page, to the pre-defined digital lifestyle model. Recommendations are made on the basis of the match between a specific digital lifestyle and the customer’s profile. For example, the engine can:

Select news articles and featured products to be displayed on the customer's personal Sofmap home page
Select specific products to highlight when the customer visits a particular product section
Recommend complementary products when a customer makes a purchase.

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.

Page views increased 67 percent per month

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.

Profits tripled as sales increased

Even more importantly, sales have significantly increased since the recommendation engine went live, driven by the fact that consumers purchased many more items.

Before it went live, 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.

Interested in modelling customers’ behaviours? Download the Sofmap PDF here.

*IBM SPSS Modeler, formerly called Clementine®, is part of SPSS Inc.’s Predictive Analytics Software portfolio.

Customer Success Stories