Predictive Analytics Improve Business Outcomes for Retailers

If data is the new business currency, then predictive analytics are the means in which organizations can take control of that currency to maximize its benefits.

For years, the retail industry based business strategies on historical and seasonal trends to help inform their current strategy. But with the explosion of big data and the abundance of customer touch points, it’s clear now that those strategies were only partial informed. Pulling data from the various sources important to a retailer allow for predictive analytics solutions that provide insights into future endeavors while preserving resources. For the remainder of this article, we’ll focus on how predictive analytics can improve a retail organization in the areas of pricing, recommendations, returns and engagement.

Dynamic Pricing Capabilities

Thanks to predictive analytics, retail organizations have managed to evolve from fixed pricing models to dynamic pricing. Dynamic pricing is a key factor for customer satisfaction because it reflects the appropriate price based on current market trends and demand. Adopting this approach allows for informed pricing models for which retailers will most likely sell a product or service, cut prices, or make small pushes.

Predictive analytics use a variety of data such as inventory levels, competitor prices, purchase history, and even weather to come up with the optimal price to bolster sales revenue. As a simple use case, take for example the ride hailing apps Uber and Lyft. The cost of a ride is the result of multiple variables such as traffic, weather, demand, etc. By utilizing dynamic prices, powered by predictive analytics, customers know they are paying a fair price and this, in turn, increases loyalty.

Smart System of Recommendations

With the exponential growth of e-commerce and online shopping, it would behoove retail organizations to incorporate and improve upon recommendation systems that are currently in place. Smart systems of recommendation enhance upselling capabilities as well as establish brand loyalty. Predictive analytics use cumulative data from various sources to analyze browsing history, the current season, recently added items, and other data to generate accurate recommendations for a more personalized shopping experience. To take it a step further, in the case of an out-of-stock item, these predictive analytic systems can provide suitable substitutions to help satisfy the customer’s needs. For e-commerce giant Amazon, recommendations account for a substantial portion of its sales1.

Return Propensity

According to recent research, 30% of all online purchases are returned by the customer. And 92% of consumers say they will buy again if the return process is easy2. Therefore, it is imperative that retailers get this area of the supply chain right. Utilizing predictive analytics for the return process helps the business lower inventory costs, avoid markdowns, and reduce waste for sustainability.

By pulling together data such as consumer behaviors, seasonality, current trends, product condition and competitor pricing, the returned item(s) can by routed to optimal locations that have ideal demand for resale. Modern return analysis can also have a positive influence with differential returns policy, sales & marketing strategies, return control measures as well as refinement of loyalty program.

Improved Consumer Engagement

Analysis of past behaviors and purchases allow retailers to parse customer activities and segment them into specific groups. By having this ability, retailers can now focus marketing efforts on the specific tendencies of those segmented groups and provide the ideal, targeted shopping incentives to the customer.

Predictive analytics also lend themselves to understanding the complete consumer journey on both online and offline channels. Specifically, they offer forecasting capabilities that can predict a customer’s lifetime value and highlight the ones at risk. This insight allows retailers to take the appropriate corrective actions to reduce churn and provide effective offers to sustain those relationships. Thus solving an on-going challenge for retailers by turning a one-time customer into a brand advocate.

In conclusion, customers are not only interested in finding the right products and services, but also in buying them from a brand that they can trust. If retailers can anticipate customer needs regarding pricing, recommendations, returns and engagement, customer loyalty will naturally follow. At Data Ideology, we have worked with many retail clients on data management initiatives to harness predictive analytics capabilities in all areas of the business. Schedule a free discovery session and let our experts help your organization reach that next level of improved business outcomes.

Written by Toby George

Co-Founder & Chief Executive Officer at Data Ideology

Toby George is the CEO and Co-Founder of Data Ideology with over 16 years of experience in developing and executing data management strategies, Business Intelligence methodologies, and complex analytic solutions.


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