How to Grow With Predictive Analytics in Retail

Retailers can use predictive analytics to better manage their inventory, create superior demand forecast models, optimize customer retention, increase sales, and expand operations.

But perhaps the biggest business benefit of predictive analytics is that it can be easily adopted by retailers that are already collecting large amounts of data. Through the use of machine learning models, predictive analytics can extract highly valuable insights from the information that companies already collect on customer purchases, procurement volume, sales trends, supply chain operations, inventory status, and more.

To be fair, most retailers are getting insights from this kind of data. However, the standard analytics typically used to process this information is backward looking: meant to study the past with the goal of improving operations for the future. Predictive analytics differs from this approach by using current data to project future outcomes for right-now decisions.

How is AI Used for Retail Predictions?

Predictive analytics is currently used in a number of different retail areas, including inventory management, assortment planning, and promotions.

Properly managed inventory facilitates a retailer’s cash flow by minimizing the amount of money locked up in physical goods. A well-managed inventory also provides customers with a better experience, as they can buy the products they came for and are more likely to receive orders on time. Conversely, a poorly managed inventory can lead to low profit margins, low customer satisfaction scores, and lower overall brand value.

Predictive analytics for retail can support superior inventory management for both the short term and the long term. For the short term, predictive analytics can help retailers optimize their inventory levels, avoiding overstock or stockout situations. In the long term, predictive analytics can reduce risks related to inventory management, such as inventory running low due to natural disasters or disease outbreaks. Predictive analytics can also indicate optimal changes to inventory based on shifting customer sentiment toward products, raw materials, places of origin, brands, regional politics, and other similar factors.

Predictive analytics can also help with assortment planning, which involves placing products where they are most likely to be bought. Assortment planning ranges from determining when products are placed on store shelves to the way web pages are laid out on a retail website. Choosing how to display the right mix of products and product quantities is a massive challenge, and predictive analytics is well suited to processing all of the relevant data. Customer surveys, sales histories, customer demographics, store location, market trends, shopping patterns, supply chain factors, and more can all be used to project the best possible product assortment.

Customer relationship management strategies also stand to benefit from predictive analytics, particularly strategies related to customer retention. Using predictive analytics, retailers can better target loyal customers with rewards and offer targeted promotions to retain disengaged customers. Machine learning technology can be used to refine these proven tactics that reduce customer churn.

How Predictive Analytics Can Transform Your Retail Business

While empowering retail business functions like inventory management and customer engagement sounds great, it’s also important to understand that predictive analytics can produce real results and transform a retail business into a more successful enterprise through established use cases.

1. Better inventory management. As described above, one major benefit of the technology is reduced stockouts and overstocks. Poor inventory management can also lead to higher operational costs connected to storage and increased logistical demands. Procurement teams can use predictive demand models to make adjustments and ensure that inventories are kept at optimal levels.

2. Lower supply chain risk. Predictive analytics can also help retailers avoid supply chain disruptions by supporting better logistics demand forecasting — which projects the demand for logistics services. Most retailers rely on a highly complex supply chain that is prone to disruptions. Logistics demand forecasting can help to minimize these disruptions and maintain stock levels by anticipating demand within the supply chain.

Unfortunately, developing accurate logistics demand forecasting models is a difficult undertaking that is often confounded by the number of shippers and suppliers in an organization’s logistics network. The logistical capacity of each one of these shippers and suppliers is influenced by a number of factors like regional traffic and weather conditions.

Clearly, powerful data tools are necessary for accurate logistics demand forecasting, and predictive analytics is up to the task. Predictive models are capable of analyzing regional weather, traffic patterns, shipping lanes, flight data, and other factors to anticipate demands on an organization’s supply chain. This predictive capacity allows an organization to shift strategies with predictive inventory management, marketing campaigns, shipping, and other business functions in order to meet customers’ demands and expectations.

3. Reduce customer churn. Many retail companies are seeing the value in offering subscriptions to the customers, then predictive analytics is able to analyze user activity to predict churn before it happens. In their interactions with the company, subscribers often have telltale signs regarding their potential to cancel their subscription. While historical analysis can be used to project churn, predictive analytics allows a retailer to identify and prevent churn once the initial signs have been revealed. This helps the retailer retain valuable customers, optimizing lifetime value.

4. Optimize lifetime value. Predictive analytics can also be used to predict ways in which retailers can earn more revenue from existing customers. While this can be done by identifying upsell and cross-sell opportunities, predictive analytics can also be used to keep customers loyal.

5. Better marketing. From online tracking cookies to customer loyalty programs, retailers are awash in customer data. Conventional data analysis can provide some marketing-related insights, but predictive analytics for retail is capable of supporting more targeted marketing efforts.

By organizing all of the data that an organization can collect on its customers, predictive models allow marketing departments to better target specific segments of consumers, develop targeted promotions, identify upsell and cross-sell opportunities, and retain valuable customers before they churn.

Retailers can also perform better targeted marketing on e-commerce platforms. Through predictive modeling, e-retailers can optimize cross-selling and upselling opportunities through data and automation.

6. Targeted expansion. Comprehensive planning is necessary when a brick-and-mortar retailer is considering opening a new location, and predictive analytics can allow a retailer to make planned expansions more likely to succeed. A predictive model can use geographic data related to past sales, demand, commercial property values, and other factors to predict future sales and inform an expansion strategy.

These are just a handful of use cases for predictive analytics in the retail industry, and the number of use cases is likely to expand. As retailers gain access to more and more data, predictive analytics will help them further boost profit margins through increased sales and greater operational efficiency.

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