Price Optimization

A fortune 100 retailer gains incremental $19 million in top-line margins with Impact Analytics’ price optimization solution

Retailer streamlines in-store and online operations and improves margins.

Big Picture

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 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.

Challenge

The client is a fortune 100 retailer, selling more than 5000 products across 12 categories, with some of them being sold exclusively online. The client was tasked with a challenging situation of getting their pricing right. Without a robust pricing strategy in place, the retailer lacked the agility to respond to the e-commerce explosion and decelerating margins.

Solution

The retailer partnered with Impact Analytics to leverage its advanced analytics capabilities to revamp their 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 co-efficient and competitor pricing for relevant products in the same area, Impact Analytics made price recommendations in terms of percentage changes.

Approach

The salient features of Impact Analytics’ unique approach for addressing the retailer’s challenging situation can be summarized below:

  • Feedback from store associates was given its due importance as a supplement to automated data.
  • A comprehensive pilot on 10% of the total stores and eight product categories was performed to observe and record the impact of the recommended solution.
  • Various secondary and peripheral effects such as cannibalization and halo effect were given a due consideration before implementing the recommended solution.

Outcome

The client accrued the following benefits of Impact Analytics’ unique, data-driven and structured solution to their monumental pricing challenge:

  • An increase in the price of inelastic products (products whose demand is not affected by price fluctuations) added an incremental $14 million to the top-line.
  • A decrease in price of elastic products (products whose demand increases as price drops) yielded $5 million worth of top-line improvement.

Impact Analytics’ price optimization solution enabled the client to gain $19 million as incremental top-line margins per year.