Part 1: Making the connection, using data for better supply chain sales performance, by PROS

By Freddie Pierce
By Sebastian Mamro, General Manager, EMEA, PROS Information is power: use it or lose it In the midst of economic uncertainty, companies that manage glo...

By Sebastian Mamro, General Manager, EMEA, PROS

Information is power: use it or lose it

In the midst of economic uncertainty, companies that manage global supply chains continue to face significant pressures. In Europe and throughout the world, these organizations are experiencing consolidation, volatility in raw material costs and margin erosion in highly competitive markets. 

In this article, I’ll go through how many of these companies have done an excellent job managing costs and negotiating deals from the procurement perspective where products are bought into the organisation. Where they’ve often lagged is doing the same for sales performance and product pricing activities, where products are going out onto the market. Whether from lack of resources, focus or simply the ingrained habits of many years, too many organisations fail to make the connection between their approach to procurement and the useful customer data they now collect in their CRM and ERP systems. This information can be combined to improve sales performance and effectiveness, especially in setting prices and negotiating deals.

In today’s supply chain world, information is power. In many cases, those companies that make the most of available information – commonly referred to as  “big data” --have the upper hand in setting prices that keep pace with raw material costs and currency fluctuations.  It also enables a sales force to conduct negotiations from a position of strength based on timely information. When margin levels are razor thin, it is imperative that organisations segment and target their best customers and maximise profit opportunities across the board to unlock the potential of big data to improve sales effectiveness.

Follow the Leader

For those managing hundreds or even thousands of products, the task of determining the value of these products – customers’ willingness to pay – and ensuring profitability has become increasingly difficult. Managing pricing and associated costs for hundreds of thousands of products requires automated systems that can deal with the complexity and variability to help pricing and sales managers make better decisions.

One way to increase margins and sales force effectiveness is analyse the product mix to identify “leader” and “follower” items. For example, many pricing and sales managers instinctively gather similar items into groups. Instead of setting a price for each individual item, the pricing or sales manager identifies and focuses on “Leader items” in order to streamline the pricing effort. By focusing on pricing the leader item, all the remaining price items automatically follow the leader.

This approach helps to identify patterns and differences between individual price points for product groupings and create a way to price those items as a mathematical equation. This can include managing different variations of the same product as well so that these can be grouped in the most efficient way. A good example here is from the chemical sector: products can be priced at different purity levels, but also different products of the same equivalent purity can be combined into groups as well to make pricing management easier.

While this approach reduces the number of pricepoints that must be managed, there is a risk that a broad price level for a group of products could sacrifice margins in exchange for an easier, faster pricing process. Calculating Leader-Follower pricing relationships and automating prices can get complex very quickly, especially with large numbers of products.  

A change in market conditions or dependencies can have a significant impact on leader-follower pricing and margins. A leader item may be selling well at a certain price, for example, while followers are consistently lagging. The formula that expresses the leader-follower relationship may be out of date or have been affected by an item shortage in one region, which distorts the pricing relationship.

One example of this is a large U.K.-based distributor that faced a critical problem: their generic list prices were too high. Sales teams consistently offered substantial discounts on every product they sold as standard. Fearful of losing customers due to the fact that their list prices were high, they were losing margin because they didn’t have knowledge of what their customers were really willing to pay.There was also little consistency in discounting policies, so some customers got higher discount than others irrespective of how much volume they actually represented.

The company implemented a new strategy that segmented their customers into more appropriate groups. By looking at what the customers perceived as valuable and their willingness to pay, the company put new customer-targeted price lists in place so that sales managers didn’t have to discount every product to start. For those products that still needed a discount, the guidance showed a price range similar customers were paying.

Pricing technology can be used to initially set the best pricing and also to automatically adjust pricing of leader items as market and currency conditions evolve. By automating steps in these processes, pricing technologies ensure that decisions about price levels can be made faster, and the results are better for the business.





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