China’s online retail market is the world’s largest, and e-commerce now accounts for more than 13 percent of the country’s total retail sales of consumer goods. In fact, the penetration of e-commerce has begun reaching saturation levels: in top-tier cities, roughly 90 percent of Internet users and 70 to 80 percent of consumers as a whole are shopping online (exhibit). As Chinese consumers enjoy the options and transparency available online, they also are becoming increasingly choosy—often visiting four to five sites before reaching a purchase decision, according to our research.
The implication is that grabbing e-commerce share will depend increasingly on coaxing customers to shop more frequently, to make larger purchases, and to buy from a broader array of online-shopping categories. E-commerce players have a fighting chance to do all this because they are sitting on an enormous volume of information. The growth of data on digital shoppers, product SKUs, price changes, promotional performance, and purchase habits has been exponential. This tidal wave of data is a strategic asset for e-commerce players, and leaders have begun employing advanced analytics to up their game in at least three ways.
First, leading players are building models aimed at boosting retention rates and spending per customer. For example, e-commerce businesses can be far more effective in identifying “value leakage” points, such as losing too many customers after their first purchase or experiencing a deterioration in shopping frequency by customers with unhappy experiences. Insight comes from using data to scrutinize the way value migrates across the customer life cycle, which includes the conversion of new customers, first-time repeat-purchase behavior, and purchases that involve trading up or category expansion. In addition, the use of data to segment and identify customer cohorts at different life stages (such as young professionals, new mothers, or new-house decorators) helps leading e-commerce players target their offerings to stimulate consumption.
Second, leading players are starting to adopt analytics-backed pricing and promotional approaches. Some e-tailers are even using machine learning to get a product lines and on the effectiveness of promotional activities. A 1 percent price change on a key value item, such as a popular smartphone model, might greatly affect sales volumes. On the other hand, Chinese consumers (like many others) often are less sensitive to price differences for long-tail or unique items.
Third, advanced-analytics systems are facilitating quick-cycle test-and-learn approaches. In some cutting-edge applications we have seen, e-commerce players have set up weekly test cycles to adjust prices (both upward and downward) or to launch targeted promotions on a select set of SKUs. Early results suggest a potential for 10 percent profit increases. A/B-testing validation and rapid-response cycles bring the “analytics back room” much closer to business teams striving to keep up with China’s increasingly competitive e-commerce marketplace.