The primary aim for predictive analytics is to leverage Big Data to make better business decisions. Predictive analytics cannot guarantee an outcome – but it can help minimize risk and reduce uncertainty. Predictive marketing tools can also be hugely beneficial to the customer experience, making them highly desirable in the ecommerce sector.
How else is the world of retail using them? Here are just a few examples:
While traditional analytics can tell you what happened and why, predictive analytics can inform you as to what a shopper’s next actions might be. By tracking browsing patterns, purchase history and other forms of engagement, predictive analytics can make recommendations about relevant products based on their shopping and viewing behavior.
A study from Invesp reports 45% of online shoppers are more likely to shop on a site that offers personalized recommendations. And, not only do these recommendations have a higher chance of converting, serving shoppers with a tailored website experience is great for engagement and turning first time customers into repeat buyers.
A successful marketing campaign draws on fact-based insight about your audience and their preferences. Predictive algorithms collate data from other sources (such as demographics, market size, response rates and geography) and past campaigns to assess the potential success of your next campaign without wasting a penny of your marketing budget.
Anything that is able to make the shopping experience smoother and more user-friendly is well received by customers. It may only seem like a small thing that can easily be taken for granted, but a site search is one of the primary ways a customer interacts with a retail site. Using continuous analysis of customer history, behavior and preferences, a predictive search function can anticipate what a shopper is looking for by typing just a few letters. When an increasing number of visitors are shopping using mobile devices, making purchases more effortless can help drive sales.
No matter how advanced technology becomes, the laws of supply and demand are unlikely to change any time soon. Predictive pricing analytics looks at historical product pricing, customer interest, competitor pricing, inventory and margin targets to set optimal prices in real-time that deliver maximum profits. In Amazon’s marketplace, for example, sellers who use algorithmic pricing benefit from better visibility, sales and customer feedback.
According to retail intelligence company Upstream Commerce, an automated predictive and dynamic pricing tool is able to deliver up to an additional 20% net profit gain to catalogues that had been partially managed.
Being overstocked and out of stock has forever been a problem for retailers but predictive analytics allows for smarter inventory management. Sophisticated solutions can take into account existing promotions, markdowns and allocation between multiple stores to deliver accurate forecasts about demand, and allow retailers to allocate the right products to the right place and allocate funds to the most desirable products with the greatest potential for profit. According to statistics from the Predictive Intelligence Benchmark Report, after 36 months, predictive intelligence influenced 34.71% of brands' total orders.
With the ability for more effective marketing, happier and more engaged customers, higher profits and more controlled outgoings it’s easy to see why predictive analytics have become a critical component to an ecommerce retailer.
From the importance of good data as a foundation for your strategies, through to building your own predictive model, A Guide to Predictive Analytics look at how a range of applications can both reinforce customer loyalty and increase the probability of purchase. This informative eBook covers: