4.3% boost in margin for a leading coffee chain enabled by PriceSmarts price optimization engine powered by advanced machine learning algorithms
Pricing has always been, and will continue to be the core capability of retailers. Getting their pricing right can help retailers streamline critical processes, improve margins and drive volumes.
A retail brand’s price position is crucial to the way it is perceived by consumers. A well-carved out pricing strategy thus leads to a well-performing organization.
Having stated the importance of the right pricing strategy, it is worth mentioning that a majority of retailers flounder with the brands, product categories, items, and SKUs that can undergo pricing changes. Managing in-store and online operations, delivering a personalized shopping experience and mounting competitive pressure are some of the most prominent challenges for retailers, all while crafting the best pricing strategy for their products and offerings.
In this dynamic market scenario, price optimization has emerged as an essential tool for retail success and profit. Price optimization is the strategy that enables retailers to determine the optimal price at which products should be sold to attain optimal sales levels and the maximum gross margin levels.
Clients are a speciality coffee roaster and retailer. They operate over 200 retail locations in 11 states and sells coffee in over 14,000 grocery stores across the United States. The client was facing multiple challenges in its pricing strategy.
- The current pricing strategy was having Inconsistent pricing differential between different sized drinks.
- Clients existing pricing tiers based on store location did not a factor for demographics and location.
- Clients price position was also varying with its competition with many instances of underpricing.
The retailer partnered with Impact Analytics to leverage its advanced analytics capabilities to revamp its pricing strategy and achieve agility, responsiveness, speed and accuracy through a newly defined price optimization solution.
Step 1: Store segmentation As the first step to addressing the client’s challenge, Impact Analytics segmented the retailers stores into various clusters, using competition, demographics, past sales and other variables as key parameters for segmentation. This enabled the client to identify highly sensitive clusters of stores having a high presence of competition that made price fluctuation a difficult step to implement. At these highly sensitive store clusters, any steep price increase could lead to a loss of customers.
Step 2: Item classification As the next step, Impact Analytics classified items or clusters of items on the basis of their price elasticity, to gauge the effect of price fluctuation of items on the demand for those items.
Step 3: Price recommendations Based on the price elasticity coefficient and competitor pricing for relevant products in the same area, Impact Analytics made price recommendations in terms of percentage changes.
Impact Analytics PriceSmart application enabled the client to glean the following benefits from its implementation
- Price elasticity & competition intensity based pricing revisions
- Location demographics and elasticity based shifting of stores to higher priced tiers
- Test & learn based price increments tested for optimum increases without hampering demand
5-6% average price increase across key products
~4.3% Margin impact
PriceSmart, IA’s pricing optimization analytics product helped a leading coffee chain boost margins by 4.3%