How a Leading Brewery Discovered Revenue Boost Opportunity Worth $250M by Optimizing Prices

  1. Problem statement
  2. Why
  3. What
  4. How
  5. Case Study
  1. Problem Statement

    A leading brewery wanted to understand the impact of price change on product sales to develop a pricing strategy across SKUs and maximize the top line. The pricing team faced the uphill task of designing a unified pricing strategy across 200+ SKUs manufactured worldwide, without causing a negative impact on sales.

    To understand how this problem statement was solved, we’ll first take a look at how beverages manufacturers would approach a price optimization problem, and the various market factors that collectively impact prices.

  2. Market Scoping: Why is Price Optimization Necessary in the Beverages Industry?

    Price sensitivity is of keen interest to sales managers in the beverages market, given how the smallest price variations can change consumer perception from ‘a real bargain’ to ‘too expensive’, upending sales and market shares alike. Price optimization exercises may become essential while extending existing market share, defending from new product entries, or breaking into a new market altogether. Price modelling is of essence to product managers as well, as it can guide new product development roadmaps, go-to market strategies, assessing CLTV, ROI, market share, among others.

    Typically, prices are determined factoring in multiple aspects, so it hits the sweet spot where neither sales nor revenue figures are sabotaged; it takes an understanding of what would appeal to customers as the ideal price (presuming most customers have an elementary understanding of a product’s price), while tuning into optimal quality, quantity, features, competitor pricing models etc., at said price point, to nudge purchase action, without compromising on business costs. Then again, pricing cannot focus on business costs alone, but also capitalize on market movements, product demand, among other factors. It is not uncommon for businesses in the beverages industry, more so new entrants in saturated marketplaces, to align prices within the range of competitor prices, with minor price discrepancies. Of course, there are other factors like product affinity, availability of substitutes, purchase frequency etc., that govern price elasticity and in turn buyer decisions.

    How do Price Fluctuations Occur in the Beverages Industry?

    The beverages industry particularly notices alcoholic drinks in high-price categories, to be inelastic for the most part - underpinned by loyal patrons and understandably low purchase volumes given the hefty price tag. However, a dynamic pricing strategy that accounts for varying market factors usually comes into play with relatively inexpensive beverages that are prone to a higher price elasticity, as in the case of energy drinks, fruit juice etc. A price-conscious customer, who would predominantly lean towards inexpensive beverages, assuming he/she already has an affinity towards the product, would also find it easier to switch brands in the event the price of the desired product increases. The price elasticity of beverages even varies significantly across markets, emerging economies and developed countries; for instance it’s seen that alcoholic drinks are particularly prone to greater swings in consumption volume owing to price elasticity in developed countries when compared to developing countries. It's evident that multiple factors are at work in governing how pricing affects purchase decisions. Let’s delve into what price optimization entails in the beverages industry.

    Why Do Beverage Businesses Need Analytics to Optimize Prices?

    The saturated disposition of the beverages market renders traditional pricing and revenue management strategies unproductive. Without data, it is difficult to gauge purchase drivers – whether it is a shift in consumer purchase pattern or promotional offers, price changes in competitor products, or more. With multiple market factors at play and strong competition at its heels, beverage manufacturers are leaning on advanced analytics to strategize pricing and in turn manage revenues. Price Optimization AI applications help in growing short and long-term sales prospects with insights on the most optimal pricing, thereby helping beverage businesses to boost revenue and manage sales more effectively, in an otherwise highly competitive market.

  3. Essential Functions of a Price Optimization Application 

    We look at a data-driven Price Optimization tool essentially as a custom AI application, which is contextualized to the specifics of the beverage manufacturer, such as data, processes, etc., and accompanying market dynamics. Our beverage price analytics experts leverage Co.dx, our proprietary platform, which contains codified knowledge and modularized algorithms from past learnings in the F&B market, to deliver an application that offers a well-rounded view of price movements and pricing recommendations to maximize margins

    Let’s take a look at some essential capabilities of Price Optimization applications

    Price Optimization

    A smart price optimization application offers insights to optimize prices across consumer personas and markets by predicting actual incidence rates for a given price point. Design thinking frameworks further help in making a beeline from customer personas to packaging preferences, price sensitivity and more, to devise a holistic pricing strategy.

    SKU dynamics

    Given how product categories are susceptible to multiple market factors that cause prices to oscillate, it’s essential to assess impact from price changes in SKU volumes, volume shift to other SKUs, launching new SKUs in the market, and so on. A smart view of SKU dynamics allows sales teams to maximize volume/ marginal contribution.

    Geo-specific Predictions

    With multiple product categories speckled across geographies, it becomes essential for sales managers to understand volume outlook based on different product-location combinations. The application will have to factor in hyper-local market variables to get a comprehensive and more importantly realistic views of buyer persona and behavior.

    Simulation & Opportunity Mapping

    A buyer behavior simulation tool helps decision-makers to visualize impact and answer various questions associated with pricing. A smart pricing model also unearths insights on opportunity cases to boost revenue in existing product categories, responding to changes in competitor pricing, and other market movements.

  4. Workings of a Price Optimization Application

    Consumer price sensitivity is measured across categories and geographies using conjoint analysis. The indirect pricing technique helps in understanding how consumers perceive or value different facets/attributes of a product, which in turn influences their purchase decision. The proceeding results can then be used to chart out pricing models for individual markets.

    Surveying customers

    Conjoint analysis, a market research technique, is helpful in discerning consumer decision, by weighing in both statistical and real-life aspects. Typical survey methods are used for conjoint analysis, presenting different products from owned and competitor businesses across price points to sample shoppers, to gauge purchase affinity in each category. Sometimes, integrated VR platforms may be used as an option to conduct surveys for conjoint analysis, where simulated shopping setups have consumers choosing products on different screens.

    Market segmentation variables

    It is equally important to tune into market dynamics in different geographies. Conjoint surveys can be extended to sample customers in various geographies to understand price-location combinations conducive for sales. Here again, mining data on segmentation variables that are telling of customer persona such as economic indices, demographic factors, behavioral economics etc., across market variables like product features, packaging, advertising etc., helps in making realistic projection of sales volumes in different markets.

    Purchase decision hierarchy model

    The survey results are modelled to gain a keen view of consumer preferences and price sensitivity, and accordingly optimize prices & distribution to maximize volume and marginal contributions. Purchase decision hierarchy model is developed using focus group data, by quantifying the utility of product attributes through multinomial discrete choice models.

    Product-location price analysis

    The pricing model is developed for different product-location combinations, visualized in an easy-to-useapplication. Socialization of results, duplicating pricing models, etc., can help in scaling tool adoption across geographies at speed.

    Now that we have a grasp of what goes into the workings of a price optimization application, let’s now take a look at how the aforementioned brewing giant utilized a Price Optimization Contextual AI Application across geographies to discover revenue opportunities worth hundreds of millions.


A leading brewery wanted to develop a unified pricing strategy across 200+ SKUs manufactured worldwide, without causing a negative impact on product sales


TheMathCompany utilized conjoint analysis survey and discrete choice modeling techniques to determine optimal pricing and distribution of SKUs across geographies for the beverages and brewing manufacturer.


  • A conjoint survey was designed & rolled out to sample shoppers, who were presented with different price points on SKUs across the category.
  • Discrete Choice Modeling was used to simulate customer choice and understand the impact of price changes; a VR platform was leveraged to simulate real experiences, in the context of advertising, pricing, packaging, features, promotion, and other variables to derive the importance of each marketing variable
  • A database was created from the survey results. Additionally, unconventional data sources were explored to capture information for economic, demographic indicators (IMF, Worldbank, Survey reports etc.) to forecast volume outlook for countries.
  • Impact assessment was carried out to understand effects of price increase on SKU volumes, movement of volumes to other SKUs in the market. Optimal price & distribution architecture was developed across SKUs to maximize the volume / marginal contribution
  • Design thinking frameworks helped to create outcomes and roadmap (packaging preferences, price sensitivity etc.) for devising a holistic pricing strategy
  • With the use of our proprietary analytics engine, Co.dx, our experts created an ML workflow with automated code modules, reducing traditional build time by nearly 60%. The nuances of the business were taken into careful consideration during the creation to customize the solution. The analytics workflow was configured with the data from all sources.  
  • Through Co.dx, a simulation application was built to enable decision-makers to visualize the impact and answer various questions on pricing
  • The automated application was scaled across 25+ countries with zero duplication of effort 

Discovered 16% opportunity cases for price optimization and a potential improvement of $250M in revenue

Computed pricing for 2500+ product-location combinations


An advanced price optimization analytics application that offers a holistic view of beverage prices across geographies in a fiercely competitive market, can allow sales teams to manage prices and revenues effectively, from bottle sales to bottom line.

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