Saturday Co. Ltd

Business challenge

Internet and mobile adoption in China are growing by more than 20 percent per year. To lift sales, Saturday wanted to exploit this trend by adopting an “online-to-offline” (O2O) business model.

Transformation

Saturday uses predictive customer intelligence to analyze customer behavior, sentiments and preferences, and embed these insights into its sales, marketing and product design processes.

Results

Business challenge story

Seizing opportunities with a customer-centric strategy

Over the past three years, China has seen an explosion in the adoption of internet and mobile technologies. Internet usage almost doubled from 247 million internet users in 2012 to 460 million in 2015, a compound annual growth rate of 23 percent. Among women aged between 22 and 40 the figures are even more startling, with compound annual growth of 50 percent.

For fashion companies such as Saturday, these figures highlight a huge opportunity. Although most customers still want to browse and try on shoes and clothing before they make a purchase, well-targeted internet and mobile marketing campaigns can help to attract customers to visit their nearest store. This “online-to-offline” (O2O) sales and marketing strategy is becoming a very popular choice for China’s most forward-thinking retailers.

Mr. Wen Zhu, Project Manager at Saturday, explains: “For fashion retailers to be successful today, it is vital to have a deep understanding of customer psychology, behavior and habits. Consumer preferences are changing faster than ever before, and the shifts between generations—such as customers born pre-1990, post-1990 and post-2000—are very distinct.

“To sell to different groups in different regions with different preferences, we must design and market products and brands that both appeal to the mainstream, and also differentiate Saturday from our competitors. For this reason, we have always invested in specialized market research, as well as involving our sales, marketing, brand management and product design teams in gathering customer insight.”

However, Saturday’s customer insight specialists recognized that the company had gaps in its analytics capabilities, preventing them from fully capitalizing on their research efforts. Different teams relied on different analytical software, and in some cases, the company used tools from third-party social networks and sales websites that limited its ability to download and analyze data.

Mr. Wen Zhu says: “We wanted to reduce our reliance on the knowledge of key individuals such as store managers and brand specialists. We wanted a centralized analytics solution that could take all our customer data, combine it with relevant external data-sets, apply sophisticated predictive modeling techniques, and present the data in a visual, intuitive way that our sales, marketing and product design teams can use easily.”

Transformation story

Harnessing the power of IBM technology

Saturday decided to work with IBM Southeast China to implement IBM® Predictive Customer Intelligence—a solution that helps companies personalize the customer experience. IBM Predictive Customer Intelligence integrates and models data from multiple sources, profiling and segmenting customers based on their buying behavior, web activity, social media presence, demographics, and many other factors. Based on these profiles and segments, it defines “next best actions” based on the derived customer preferences.

Mr. Wen Zhu comments: “We saw this project as a real partnership with IBM, that would deliver mutual benefit for both organizations. As an early adopter of IBM Predictive Customer Intelligence, we would gain access to the latest IBM technology and expert support, helping us explore the possibilities of big data analytics and gain deeper understanding of our customers. At the same time, our experience would help IBM optimize and refine the solution further. Together, we believed we would be greater than the sum of our parts.”

IBM and Saturday divided the project into three phases. The first phase was a micro-consulting engagement to set out a five-year strategic plan for the company—focusing on diversifying into new product categories, increasing the emphasis on online and O2O channels, and leveraging new technologies to enable this. The second phase was to integrate the company’s internal customer and sales data with the IBM Predictive Customer Intelligence solution, and the third involved the integration of external data sources.

“We started by moving our largest brand—Saturday—onto the new platform, and building some models for customer profiling,” explains Mr. Wen Zhu. “Where is the customer from? How high are the heels she likes to wear? What colors does she prefer?

“We have also started performing some sales and market analysis, looking at the time and date of purchases, the regions, and which customers are involved. And we are also doing some semantic analysis of external data sources, such as assessing the sentiment of positive and negative comments on social media and websites. This is a new capability for us—we couldn’t do it before on such a large scale, and it is much more efficient with the IBM solution than trying to do it manually.”

To support these analyses, Saturday is using techniques such as product recommendations and frequency and monetary value models, which are built into the IBM Predictive Customer Intelligence solution.

Mr. Wen Zhu comments: “We could do most of these analyses before using other tools; the difference with IBM Predictive Customer Intelligence is that the data is modeled and the results are produced automatically, so we can monitor changes on a daily or weekly basis. We’re not dependent on one person having time to create a report—the system can provide insight whenever the business needs it. Previously our view of our customers was fragmented across many tools and platforms—now we have one platform that gives us a clear view of everything.”

Results story

Immediate insight into sales, marketing and product design

Saturday is already beginning to see the benefits of IBM Predictive Customer Intelligence—and as it continues to integrate more brands, business systems and data-sets with the solution, it expects these benefits to increase over time.

Mr. Wen Zhu says: “Our marketing team can use the platform to measure the effectiveness of their campaigns by monitoring customers’ responses online: how often a customer makes a comment about one of our campaigns, whether their comments are becoming more positive or negative over time, and even some of the content of what they are writing.

“For example, we recently ran a campaign with a media company, where their actors were wearing our shoes in a movie. We were able to monitor users’ comments online, and also measure the conversion rate for our online marketing activities.”

Saturday’s product design teams are also using the IBM solution to monitor customer feedback on color preferences, heel height, comfort level, and other key attributes of their products.

“It’s interesting to see that each region of China has its own preferences,” says Mr. Wen Zhu. “This helps us design products that are appropriate for particular groups of customers, which should help us to maximize sales.”

He concludes: “Before this project started, we were sure that big data analytics would be valuable for our company, but we didn’t know exactly where the value would be. Our objective was to work with IBM to explore our data and learn where the opportunities are. Now we are in a much stronger position to take the next steps on our journey with IBM Predictive Customer Intelligence, and we hope to achieve at least a 0.5 percent lift in sales volumes as a result.”

Saturday Co. Ltd

Saturday Co. Ltd is one of the top three women’s shoe companies in China, with strong capabilities in product development and manufacturing, as well as 13 regional marketing centers and around 1,800 retail outlets across the country. The company is also diversifying its business by entering new markets such as women’s clothing and cosmetic products, and harnessing new technologies to create new sales channels such as online-to-offline retail.
 

Solution components

• Consumer Centric Retailing
• Customer Insights Software Legacy Withdrawn
• IBM Digital Analytics
• Retail: Digital Operations
• WCE Watson Marketing and Commerce

Take the next step

To learn more about IBM Predictive Analytics, please contact your IBM representative or IBM Business Partner, or visit the following website(s): ibm.com/us-en/marketplace/predictive-customer-analytics  

View more client stories or learn more about IBM Watson Customer Engagement