Retail Predictive Analytics Improve Business Outcomes
If data is the new business currency, predictive analytics enable organizations to maximize its benefits.
For years, the retail industry based business decisions on historical data and seasonal trends to help inform their current strategy. But with the explosion of big data and the abundance of touch points within the consumer journey, it's clear now that those strategies were only partial informed. Pulling data from the various sources important for retail predictive analytics to provide data-driven insights into future endeavors while preserving resources.
We'll focus on how predictive analysis can improve a retail organization in the areas of pricing, product recommendations, returns, and customer engagement. By enhancing these important areas, you can improve the customer experience as well as your bottom line.
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, calculated pushes to gain a competitive advantage.
Predictive analytics use a variety of information such as inventory levels, competitor prices, sales data, client preferences, and even weather to come up with the optimal price to bolster 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 brand loyalty.
Smart System of Recommendations
Given e-commerce's exponential growth, retail should enhance current recommendation systems. 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. This generates accurate recommendations for a more personalized shopping experience that include custom offers.
In the case of an out-of-stock item, predictive models can provide valuable insights for suitable substitutions to help satisfy the customer’s needs. For an e-commerce giant Amazon, recommendations account for a substantial portion of its sales.
According to recent research, 30% of all online sales are returned by the customer. And 92% of consumers say they will buy again if the return process is easy. Therefore, it is imperative that retailers get this area of the supply chain right. Utilizing predictive analysis for the return process helps the business lower inventory costs, avoid markdowns, and reduce waste for sustainability.
By analyzing consumer behavior, trends, product condition, and pricing competitiveness, returned items can be sent to ideal resale locations. Modern return analysis can also improve operational efficiency by having a positive influence with:
- Differential returns policies
- Return control measures
- Sales & marketing campaigns
- Refinement of loyalty program
Improved Consumer Engagement
Analysis of past customer behavior 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, supported by artificial intelligence, 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 customer 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.
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. This will overall improve your customer service.
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 retail 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.