For some time, big data has been on the mind of business executives because of its power to transform processes with machine-like efficiency. As businesses start relying more on data, it has trickled down to the SME as Instagram launched business tools and Google opened the Data Studio.
The next logical progression is machine learning and predictive analysis. Predictive analytics is set to profoundly impact e-commerce in the coming years. It uses machine learning principles and algorithms to make predictions that influence business decisions.
When we talk about predictive analytics in e-commerce, Amazon often comes to mind. A robust personalized recommendation engine, adaptive pricing, and forecast analysis are all features that cemented Amazon as the major leader in e-commerce. Taking a page out of Amazon’s playbook, here are three ways retailers will use predictive analysis to deliver value throughout the online shopper experience:
The Power of Suggestion
Data-savvy consumers in this on-demand era know that their data is an asset that can be leveraged to deliver personalized experiences. Retailers are sitting on a deluge of historical customer data: viewed items, purchase history, and customer data. When you blend that with traditional demographics, trend forecasting, and product performance, you get a 360 degree overview of the customer.
Measuring customer intent is one of the most powerful advantages companies can have over their competitor. When companies are equipped to calculate the customer’s propensity to buy or not buy, cart size value, and/or be a repeat buyer, they are able to precisely target promotions to the customer at the right time.
Eric Siegel gives this example of how establishing patterns for repeat buyers:
“This often comes in the form of business rules, such as the following fictional example:
If the new customer comes to the Web site off organic search results,
and buys more than $150 on the first transaction,
and is male,
and has an email address that ends with .net,
then this customer is three times as likely to be a returning customer.”
Retailers have a unique opportunity to minimize uncertainty and be proactive in retaining customers.
This goes hand in hand with forecast analysis. A common problem that e-commerce retailers face is taking advantage of the data in real time rather than letting it sit in a data silo. Inventory management combined with forecast analysis allows for price adjustment to be responsive in real time. Not only do customers get the best deals, but companies save on inventory costs.
The Internet of Things will only richen data sources. Wearables and connected consumer electronics will add a whole new dimension of actionable insights while beacon technology will continue to blend online and offline experiences. However, data isn’t going to quick-fix solution, even for retailers who adopt it early. Rather, Predictive analytics can help teams with strong analytical capabilities to work faster, develop stronger hypotheses, and make better decisions.