Wholesale distribution business is generating a great amount of data which is including at least thousands of products, quantities, pricing, customers, and inventory, just name it. This simple fact makes them really suitable for data analytics modeling in order to solve specific business problems or to gain efficiency within already existing processes. In this blog post, we are highlighting just 5 of the potential benefits that a wholesale business is capable of extracting from modern days data analytics.
With a4wholesaler this is happening fast, easy and in a time-saving manner.
Identifying and projecting customer profitability
Clustering existing customers in terms of profitability is a crucial operation for wholesalers. By knowing which clients are profit makers and which destroy profitability you`ll be able to focus on the right side of the business. Once this is clear, you can spend your efforts on initiatives encouraging profit making customers to order more as well as to fix the issues with those who lag in terms of profit contribution.
Even more, predictive analytics makes possible to forecast particular clients ordering. On top of this, you can be alerted when some of your customers are going to get big enough that they might turn from a client into a competitor.
Modeling business scenarios
Let’s consider a situation when two of your clients are in M&A procedure. You are serving some of the local retail operations to both of your clients. The merger between them will change the way they do business and inevitably the way you do business with them.
A wholesaler with predictive analytics in service will be able to forecast risk factors, potential restructuring in distribution networks, and generated economy of scale in terms of purchasing. Modeling such data is shaping potential business scenarios and their impact on a particular wholesale business.
Improved marketing campaigns
When a sales representative visits a client he spends about 20 minutes for such meeting. During that time he/she is capable of introducing two or three new products to this particular customer. If the newly added products are above 20, a decision for that very customer should be made. Predictive analytics is prone to human bias and is capable of short listing the right products to highlight for a particular client. This is resulting in improved products launches and is increasing customer satisfaction. Combining with the skills and experience to a great sales rep, this might be a profit rocket.
Anticipating future demand
Combining weather forecast with historical sales data is among the most powerful data analytics features for wholesalers, that’s why it is available in a4wholesaler solution, offered by A4E. The outcome of such data mix is enabling wholesaler with information for potential demand in foreseeable future.
If you know that in the following next 30 days there will be unusually cold weather, you can contact clients with items like hot chocolate in their product portfolio. Just let them know that you expect higher demand due to expected cold weather and ask if they want to order more. Keep in mind that all this work can be automated.
Predictive analytics is capable of an immediate impact on a business like wholesale distribution. It can extract value and benefits through Big Data in order to create new sources of revenue and increase their margins. Because of the nature of their business, wholesale distributors need to invoice every customer and this create a lot of data points which can be modeled in different use case scenarios.
Price is just one of them.