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 to extract the information. The high cost of data scientists exceeded budgets in an already low-margin, B2B distribution marketplace.
Two things have fundamentally changed that make predictive analytics very attractive for wholesale distribution:
- The availability of real-time technology databases makes results immediate instead of relying on lengthy night batch processes
- The predictive model can be easily utilized without the expense of a data scientist. The model can be developed during programming, making it easy for distributors to implement analytics and bringing tremendous value to their business
Wholesale distributors work with large product volumes in their catalogs, often in the millions, and serve hundreds of thousands of customers that each require individual invoices with 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 – and rely on customers registering for them – in order to reach the same level of customer detail, and therefore may only have partial access to data.
Leveraging Big Data is a springboard for digital transformation, and here are some ways early adopters are already succeeding.
- Predictive analytics based on recent sales orders and profiles enhances understanding between customers and sales representatives. The data allows reps to detect risk for churn at an early stage and also provides insight into new product categories to be included in upcoming orders.
- Sales calls can be more productive. The average sales visit is 20 minutes, so a wholesale distribution sales rep may only have time to introduce three to five of the thousands of products in their catalog. Predictive analytics provides the sales rep with the three to five products that the customer is most apt to purchase.
- After solving a customer’s problem or answering a question, customer service center operators can recommend relevant products to the customer, based on their needs and profile, then take a sales order.
- Special promotions are being used more frequently in wholesale distribution. When introducing new categories, sales reps can leverage data about the optimal customer profiles to introduce new products and penetrate the market.
- Certain post-sales customer behaviors can indicate a higher risk of payment failure. For example, in a wholesale construction situation, an unprecedented large order may indicate a project or company is going bankrupt in the near future.
- Distributors can leverage Big Data to analyze which customers purchased certain products to predict future sales. This is a strong service for suppliers, who typically only have data on quantities sold, and enables them to take advantage of precision marketing and customer segmentation to improve product strategy and grow their business.
Wholesale distributors, commonly known as the “middle man,” are using Big Data and predictive analytics to create new sources of revenue and increase their margins to foster profitable growth.
New ways have emerged for data to add value to products, become the product, or even become the business. Learn more about Data – The Hidden Treasure Inside Your Business.