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3 Reasons Predictive Analytics Should Be Part of Your Integrated Retail Management Strategy

Using predictive analytics for retail management

Once considered complex and costly, predictive analytics — using data, machine learning and statistics to predict future trends — is now more affordable and should play a role in your integrated retail management strategy.

An article on the Practical Ecommerce website highlights three ways predictive analytics can elevate your business operations.

  1. Find that sweet spot: Recommending the right product at the right price to the customer remains a challenge for many retailers. But predictive analytics increases the relevancy of the product recommendation by analyzing a customer’s purchase history, browsing pattern and how well other products are doing on the site.

    For example, mobile app store Shopify offers an app that uses predictive analytics technology to make relevant product recommendations and also create a “unique predictive model for each online retailer based on its product type, customer base and sales forecast,” the article notes.

  2. The price is right: Predictive analytics can enhance your pricing strategy by predicting the ideal price that will close the sale, maximizing your revenue and profits. Online retailer Amazon uses this technology, offering different prices for the same dress shirt depending on the color.
  3. Give them what they want: For your supply chain to run smoothly, it takes an integrated retail management solution to forecast, fulfill, deliver and accept product returns. Predictive analytics can help to tie your supply chain operations together, consequently improving inventory management, optimizing warehouse space, replenishing high-selling items and more.

Predictive analytics should be one of the factors in your decision-making, but it’s not strong enough that it can be a standalone decision point. It’s best to start with your own repository of data that your business is generating at a granular level. Then you can use industry-wide predictive measures as modifiers for your decision-making process. While there is some benefit to using predictive analytics on its own, it’s far more effective to use it as an influencer to your own data. 

The key is to know your customers. Otherwise, what are you going to do with the data? For example, say predictive analytics shows a trend that relates to males between the ages of 25 and 40. You need to know what percentage of your customers falls into that demographic to best take advantage of that information.

One other word of caution: Before implementing predictive analytics as part of your integrated retail management strategy, the predictive models should be tested thoroughly and the program should be managed by a person. It’s great to have systems make recommendations and do the heavy math so you have relevant information for decision-making. But it’s a bad idea to allow computer systems to make decisions for you without any type of human intervention.

Source: Practical Ecommerce, July 2013