10 Ways Retail Predictive Analytics Can Change Your Business

10 Ways Retail Predictive Analytics Can Change Your Business

The predictive analysis gives a clear insight into the already available data. It informs the responsible individuals about the probable outcome and events, thus preparing a timely action plan beforehand. Several predictive analytics retail examples outline their benefits for a business. Retailers can increase sales, identify better-selling products, optimize the existing supply chain, and become more efficient. Let us know about the ten ways it can improve your business:

  1. Utilizing the customer response
  2. Companies now can get reviews and feedbacks about their products through multiple online and offline channels, like websites, apps, e-commerce, social media, and actual markets. The responses generate a considerable amount of corresponding data for every client, including their preference of products, brands, stores, and other facets.

    Retailers can build predictive data models to connect various data points that can help them make data-driven decisions, resulting in increased revenue, campaign conversions, well-performing products, channels needing a boost, and identifying at-risk customers. The leaders can make informed recommendations about the products with data about their buying patterns and purchase preferences.

  3. Improving customer service 
  4. With the advent of e-commerce, customers are making online purchases more. In 2020, the number reached 4 trillion worldwide. Using predictive analytics in the retail industry, the retailers can extract data from the customer through various sources: POS, online channels, sensors, cameras, etc.; retailers can use the data to map their in-store and online behavior and know the impact on sales.

    Retailers can predict the customers’ needs through their purchases and strategize to create efficient promotions, increase impulse purchases, reduce front and back-end costs, and personalize in-store services.

  5. Refining inventory management
  6. Knowing about the products and their availability is a must for retail management. If there is little balance between the demand and supply, a high-selling product can lose its market just because it is unavailable. In addition, it provides inaccurate data for analysis and wrong information about demand. The retailers can now use predictive analysis to pinpoint the in-demand, slow-moving, and without any order.

    They can make a product available to a place where it’s in demand. They can optimize revenue, satisfy client needs, reducing inventory costs, preventing sell losses, streamlining the supply chain, and reduce cost by predicting the when and where of stocking a specific product. 

  7. Better customer profiling
  8. Marketing companies can use retail predictive analytics to divide customers into various groups by analyzing their past behavior and purchases, which can help create targeted marketing strategies. Marketers can use the data from these strategies to design the next campaign to run. Analytics can also calculate a customer’s lifetime value from their online and offline purchases, enabling the company to take corrective actions at the non-functioning areas by providing offers to maintain customer presence and loyalty. 

  9. Elevating Business Promotions
  10. Trade promotions are a valuable part of retail marketing as per the data available from several predictive analytics use cases in retail. Therefore, it can also be optimized and analyzed meticulously like the online data. BI tools can analyze organized and raw data generating from customer touch points and create essential insights. Retailers can successfully foresee the most rewarding trade promotions in terms of ROI and implement them more. 

  11. Optimize store expansion projects
  12. Expanding the stores to another location is a sure sign of business growth. Predictive analysis can help optimize the effort and cost of the expansion. BI tools can provide valuable insights on customer preferences, audience reach, prospective sales, and other data to zero upon the most effective location and devise the most effective marketing strategies. 

  13. Develop optimal pricing
  14. Customers backing up on a potential transaction because of a non-attainable price is just an unsuccessful sale. This event could be corrected using the predictive analysis tools. BI platforms can depict the optimal payable fee for each customer and identify the correct one to increase the sale. Now, BI tools with real-time ML capabilities can use retail predictive analytics to impact the influence of reasonable pricing and the sufficient regularity of price-based promotions. The tools consider various data points like real-time sales data, register levels, weather estimation, product movement, purchase history, and many more to determine the best price.

  15. Providing personalized recommendations
  16. Customers will prefer the retail institution more where their product or choice or the next best thing doesn’t take long to find. BI tools with predictive analysis will give an accurate personalized recommendation to upsell certain products for respective customers from their previous buying habits, incentives, and preferences. The platforms utilize the available socio-economic and interactive data sources to suggest personalized products or suitable substitutes for the clients and even predict future purchases, giving the business stock up on the product.

  17. Predicting revenue generation
  18. Retailers can now use predictive analysis to depict the revenue-generating from the organizational level. The BI tools analyze past, most current, and real-time data about buying patterns, market developments, and socio-economic conditions of the clients to predict the optimum organizational revenue under the given conditions. Retailers can also use it to perform what-if analysis to adjust the independent variables and predict the effects, such as the ideal employee-wise or day/hour-specific revenue.

  19. Enrich marketing campaign targeting
  20. Marketing campaigns are essential for new or under-performing products. The retailers can use data available from various sources and analyze them so that the results can be used to design the appropriate campaigns, both online and offline. Marketers can utilize digital tools to help marketers recognize new openings, optimize directing, lessen expenses, decide on the preeminent channels and time to market, and increase the ROI. 

    Predictive analysis allows for more directed marketing campaigns to further targeted customer divisions. Retailers can create a customized offer for specific customers and make a preference-based campaign for a particular group of people.

Choosing the tool

One thing you should keep in mind that the predictions will be different for every business. It is not assured that performing predictive analytics will boost your company overnight. The retailers may have to tweak the strategy so that it fits with the ultimate business goals. 



Wersel Marketing Team