Predictive algorithms have been available for a very long time. Initially, analytics were run within the night batch process, with results distributed only to a happy few recipients.

Those analytics were not very well received, especially in an industry like Wholesale Distribution, as you needed a data scientist extract the information. The high cost of data scientists exceeded budgets, in an already low-margin, Business-to-Business (B2B) distribution marketplace.

Two things have fundamentally changed that make Predictive Analytics very attractive for Wholesale Distribution:

  1. The availability of real-time technology databases, such as SAP HANA for immediate results versus relying on lengthy night batch processes.
  2. Now, the predictive model can be easily utilized, without the expense of a costly data scientist. The model can be developed during programming, so Distributors can implement these analytics with any additional headcount, bringing tremendous value to their business

Wholesale Distributors work with large product volumes in their catalogs, many times in the millions; and serve hundreds of thousands of customers; with each customer requiring individual invoices that incorporate unique, negotiated pricing.

The greatest benefit to Wholesale Distributors is that they don’t need to build Big Data models, as they already have analytics embedded within their business processes.

Because Wholesale Distributors need to invoice every customer and record every transaction, Big Data models are extremely valuable to daily operations. By comparison, the Retail industry has to build loyalty programs in order to reach the same level of customer detail and they rely on customers registering for those loyalty programs, so may only have partial access to data.

Leveraging Big Data is a springboard for Digital Transformation and early adopters are succeeding through:

  • Based on recent sales order and profiles, predictive analytics enables a better understanding between customers and sales representative.  The data allows them to detect risk for churn at an early stage and also provides insight to new product categories to be included in upcoming orders.
  • Efficiency. When visiting a customer or talking via phone, sales representatives need to be efficient with their time and cannot present thousands of product in their catalogs. The average sales visit is 20 minutes in Wholesale Distribution, so a representative may only have time to introduce 3-5 new products. Predictive analytics provides the sales rep with the 3-5 products that the customer is most apt to purchase.
  • Customer service centers, where operators have customers on the phone, have solved their problem and may also take an order. Customer service representatives, now have the ability to recommend the best options to customers based on their original needs and profile.
  • Special promotions, which are being used more frequently in wholesale distribution. When introducing new categories, sales reps can leverage the optimal customer profiles to introduce new products and penetrate the market.
  • Post-sales. Customer behavior can indicate a higher risk of payment failure as well. For example, in a wholesale construction situation, an unprecedented large order may indicate a project or company going bankrupt in the near future.
  • Supplier Data.  Distributors can leverage Big Data to analyze products sold and who those products were sold to and give a predictive flavor.  This is a strong service for suppliers, who typically only have data on quantities sold, but can now take advantage of precision marketing and customer segmentation to further grow their business with a valid product strategy. This new service to suppliers value even more the position and the proximity with their local customers.

In summary, Wholesale Distributors, also known as the “middle man” still have a lot to offer. They are seeing the value of Big Data and Predictive Analytics to create new sources of revenue and increase their margins to foster profitable growth.

FM

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