This is the first of a series of blogs in which we will discuss the digital economy’s impact on the wholesale distribution industry. This piece focuses on the need for predictive analytics to keep pace with the changing dynamics of the digital economy.
For this series, it is important for us to define the “digital economy.” Economist, Thomas Mesenbourg, describes the digital economy as the economic landscape based on utilization of computing technologies, with primary components being infrastructure, e-business processes, and e-commerce.
Evolution of Wholesalers
As the digital economy expands, wholesalers must evolve from product distributors to information driven and customer focused companies. This can be accomplished by leveraging in house data to predict what will happen instead of reacting to what has happened. By utilizing technology to go beyond traditional data analysis, wholesalers can use predictive analytics to:
- forecast future customer needs
- discover trends to foresee future business scenarios
- predict changes in customer segments and the impact to the organization
- develop future pricing strategies
- improve upcoming marketing campaigns
- project customer profitability
- develop strategies to maintain customers
- evaluate their exposure and risk profile
- identify potential new customers, markets and segments
Predictive analytics offers vision into potential changes and is a key means by which wholesalers can increase profits in the ever-evolving landscape of the digital economy.
Putting Predictive Analytics to Use
The importance of predictive analytics is continuing to increase in the wholesale industry. With the recent upswing of M&A, wholesale distributors need to consider exposure to top clients. For example, a wholesaler supplies products to a segment which is about to undergo a key M&A transaction of Company A and Company B, two national convenience and drug store companies. A wholesaler with predictive analytics processes and technology in place will be ahead of the curve by being able to forecast risk factors, customer profitability, and potential restructuring of customer tiers and distribution networks resulting from the merger. By looking at historical sales data combined with other data such as earning reports and unstructured sources of data such as analyst reports, from predictive analytics wholesaler distributors can determine exposure as a result of scenarios arising from the likelihood of a merger. For example, historical sales data from Company A in a region can be analyzed to forecast seasonal ordering patterns for Company B before the merger occurs. Predictive analytics can also examine what could happen if another customer, such as Company C, gets acquired. This can help the wholesaler evaluate how to structure future contracts and take action if another merger or acquisition occurs. Similar analysis can be done upstream with potential M&A from key vendors.
A robust tool that wholesalers can utilize in today’s digital economy is SAP HANA integrated with BW. This powerful in-memory technology integrates ERP data with predictive analytics and can transform how information is interpreted. Automated analytics can drive efficiency and enable firms to be proactive and customer-focused, instead of being reactive and operationally focused. HANA has capabilities supporting customer engagement, analysis of customer profitability, optimization of segmentation processes and realization of new revenues through real-time predictive analytics applications. By leveraging the use of predictive analytics, wholesalers can gain the upper hand over competitors in today’s digital economy.
In the next post, we will cover the significance of internet of things (IoT) and its ability to enhance operations across a wholesale distributors business, leading to several competitive advantages from increased supply chain transparency to improved customer service.