The complexities of pricing have been a hot topic with distributors lately, and for good reason.
First, determining prices for hundreds of thousands of products across geographies can be quite complicated. Second, distributors can struggle with pricing inconsistencies as final pricing decisions are often left to individual salespeople. And finally, today’s dynamics of increasing price transparency, intense competitive pressure and inevitable organizational demands for better margins amplify this complexity.
Overwhelmed, pricing managers seek solutions that can enable systematic pricing. Here is an account of a pricing manager who successfully systematized the distributor’s pricing process and now supports pricing decisions with confidence and certainty at scale.
Razor-thin margins, and pressure to get pricing right
This large distributor, like many others, was operating on razor-thin margins. They were juggling a secular industry decline, poor margin sales contracts and rising operational costs. Additionally, their sales teams often attributed loss of sales to being more expensive than the competition.
That’s not uncommon. But it resulted in mounting pressure on the pricing manager to objectively understand their price position.
The pricing manager began to gather competitive pricing insights manually. However, there were several challenges. The volume of products kept growing. There was incomplete data online. And often, the pricing manager was unable to access pricing data, as it was hidden behind log-ins and, usually, handshakes. By the time the pricing manager gathered, synthesized and presented pricing insights from multiple competitors, it was often too late for meaningful action. Furthermore, there were multiple errors. Price variance was high due to miscalculated Unit of Measures, or UOMs. Inaccuracies in matching private label products were also observed.
The pricing manager went with a technology-enabled, systematic approach designed to deliver timely and accurate pricing insights at scale. The hypothesis, “We are priced higher than the competition” was put to a test. And the analysis proved the opposite – they were cheaper than the competition and could raise prices.
Attempts to reconcile these findings were made as teams had divergent points of view. In a culture where decisions were primarily driven by gut, relying on data-driven insights seemed unusual. The pricing manager had to drive a gradual transition to a data-driven culture.
Success in phase I—i.e., competitive price monitoring—led to the distributor implementing the other phases of a systematic, data-driven pricing approach as well. Below are brief descriptions of the three phases in an end-to-end data-driven pricing approach.
(Phase I) Competitive Price Monitoring:
During this phase, insights are derived by systematically tracking competitor prices. It helps gain an objective understanding of price positions and enables gathering of insightful competitive intelligence at scale. This, in turn, helps pricing and sales teams collaborate better and confidently determine prices.
(Phase II) Rule-Based Pricing:
This is the practice of assigning prices to products based on pre-defined rules. Price recommendations can automatically be fed into internal systems, thus institutionalizing price changes. Teams have the choice to accept or override a price recommendation. Rule-based pricing enables automated, timely pricing decisions at scale and lowering pricing errors.
(Phase III) Price Optimization:
Success in the first two phases results in distributors gradually evolving to this third phase. This phase leverages advanced data-driven algorithms that tailor prices based on strategic inputs, customer behaviour and competitive information. It results in offering the right price that customers are willing to pay, is competitive and maximizes profits or revenue.
By implementing the three phases of this systematic pricing approach, the large distributor experienced in some categories a 3% to 4% increase in margins. The systematic approach also ironed out some crucial complexities—confidence in pricing at scale, consistency within the organization and competitiveness in the market.
But, of course, they are ironed out only until new complexities arise.
Mihir Kittur is co-founder and chief commercial officer of Ugam, a provider of data and analytics products and services.Favorite