Pricing is the fastest and most effective way for a company to realize its maximum profit potential. One industrial component manufacturer we worked with, increased operating profit by nearly 30% with a mere 2.5% improvement in prices in the SMB segments. An advanced material manufacturer boosted operating profits by 35% by carefully managing price levels up a modest 3%. We have worked with a variety of businesses, including those in maritime logistics, energy, insurance and telecom and have seen them achieve comparable results. Yet, many companies in the B2B arena are still not leveraging the potential of data and analytical horsepower to optimize their pricing performance. These companies still price based on tribal knowledge, raise and slash prices across the board, discount non-strategic deals excessively and shy away from investigating and addressing their sources of margin leakage. They leave millions on the table every day.
Pricing initiatives are rarely ever low-key; usually they get the attention of board members and discussions always start at the highest and most strategic level. Most companies we come across have a good handle on the strategic product positioning and tactical customer segmentation dimensions of pricing. These sound board-level strategies & tactics on pricing, however, they generally are lost in the process of being translated to everyday operational deal guidance for sales account managers.
Sales account managers need to translate high-level pricing directives into price quotations for customers of varying sizes buying hundreds of products in varying quantities in regions with different competitive dynamics, day in and day out. This in additional to travelling, calling, networking and building relationships with customers spread far and wide.
How do you translate pricing strategy and growth directives into day to day execution?
Like most other things these days – the answer lies in the data. And some good ol’ fashioned statistics. Most companies are sitting on large reservoirs of transactional data waiting to be tapped. This data contains years of customer buying & negotiation behaviours, the impact of economic ups and downs, the success and failure of products and much more.
Running some basic statistical clustering and basic regression techniques can already start giving cues on how to where to begin.
In the case of pricing – it is possible to cluster customers, products & deals into small, identical sub-groups with similar pricing behaviour – based on past buying patterns & price aggressiveness of customers, product lifecycle, attributes and pricing power, competitive price dynamics in regions etc. Insights from the past can be used to frame future price guidance in the form of customized price recommendation to sales at levels more detailed/nuanced than product groups, region or customer segments.
Below is a simple yet elegant example of how one specialty chemical company has chosen to enable sales account managers into making better pricing decisions and optimizing revenue on each deal.
By providing key orientation points, such as last quote price, peer prices on similar deals, a price recommendation – sales account managers are able to aim at a price that isn’t merely the floor price or the last price. Data on these different dimensions enable sales account managers to make better informed pricing decisions by relying less on intuition and guess-work but rather on hard facts.
What this visual clearly tells is that the CURRENT PRICE for this deal is far below the benchmark for this HOMOGENEOUS CLUSTER OF PEERS.
Reducing the gap between the cluster benchmark and the current deal will help recover money left on the table and lift margins.