According to Deloitte’s annual Holiday Survey of Consumers, online is king when it comes to the 2019 holiday shopping season. The report found that consumers of all age groups are more likely to shop on Cyber Monday than on Black Friday, the traditional favorite for holiday-related deals and purchases.
The Deloitte survey is the latest research that underscores how digital technology has permanently altered the retail landscape, with e-commerce channels becoming the de facto strategy for success. Of course, no e-commerce strategy would be complete without a robust investment in data analytics. With that in mind, let’s take a look at some key ways in which data science drives e-commerce revenue.
- Market basket analysis: Rather than relying on market research reports to determine which additional products a consumer may be interested in, retailers can draw upon the wealth of purchase history data to find their own product associations to make more tailored, targeted recommendations. This includes determining which products customers are most likely to purchase together, or which product they typically buy next.
- Price optimization: Analyzing data points like seasonality, customer location, supply and demand, and purchase frequency, for example, allows retailers to increase or decrease prices based on these variables. As a result, these brands can operate more competitively and also capitalize on market changes to grow revenue.
- Promotions: Advances in artificial intelligence (AI) and machine learning enable retailers to run promotions at the individual item and customer level. This can be particularly beneficial for big shopping days such as Black Friday, while personalized promotions also increase the likelihood of purchase throughout the year.
- Sentiment analysis: Retailers can analyze social media chatter to improve customer service, optimize their digital channels, generate leads, and identify performance trends, among other benefits.
- Product visualization: Product imagery is critical to e-commerce success, particularly in fashion, jewelry, footwear, and similar industries with a high premium on the visual aesthetic. AI and data science can help retailers identify the optimal combination of product texture, photo quality, light, and numerous other factors that determine how consumers perceive the product.
For more on the strategies above and other ways in which data analytics drives value for retailers, take a look at this Practical eCommerce article.
Of course, there are also some great examples of data science at work in the brick-and-mortar sector. Check out this APEX of Innovation Post for more on data science benefits for traditional retailers.