Consumer packaged goods (CPG) firms have increased prices multiple times to cover rising wholesale costs, which have soared 35% since 2019. However, retailers and consumers are resisting additional increases. Consumers are willing to switch brands, channels, and products based on their budgets, consumption occasions, and purchasing intent.
As a result, IT, data, and analytics leaders need to evolve revenue growth management capabilities to develop better strategies. One key strategy is to develop cross-price elasticity (CPE) capabilities to better navigate this new normal.
Improving Pricing Strategy and Execution
CPG firms can adjust both strategic (everyday value) prices and tactical (promoted) prices across all the channels they sell. However, to develop best-in-class pricing strategies, CPGs need to develop a broad set of data and analytics capabilities. They include:
- Building a data foundation: CPG teams want to create data assets to drive product innovation, distribution, and pricing strategies. They can do so by enriching existing data with alternative datasets, building a harmonized commercial data lake, and creating foundational analytics. These core analytics include developing granular product and customer segments, consumer behavior on various occasions, baseline prices, and elasticities.
- Enable predictive analytics: With robust data science techniques, CPGs can measure CPE and demand transfers more accurately. Pricing analysts can model and understand the effect of various price and promotion changes before implementing them by channel and pack to reflect the target state of consumer need.
- Creating prescriptive analytics: Analytics tools provide pricing optimization and recommendations, enabling CPG teams to set new prices that consider customer price zones and product levels. They can also connect these analytics with demand forecasts to project sales volumes over different timeframes, implementing the right supply plans during promotions.
With a solid data foundation and predictive and prescriptive analytics, CPG firms can now model, test, and execute a wide variety of scenarios that were previously difficult or impossible to analyze.
CPGs can leverage thousands of machine learning (ML) models for store clusters (price zones), product segments (price per volume, or PPGs), baseline forecasts, their own & competitor price elasticities and halo effects. Teams can use this information to measure the transfer of demand from one brand to another due to price changes, promotions, or elasticities by different fulfillment channels for the same retailer. They can measure potential impacts at a price zone and product segment level, determining how much sales loss will be temporary or long-term. They can also simulate other price points before accepting or adjusting the models’ recommended prices.
With granular price zones, CPGs can now develop and easily implement different pricing strategies that historically have been standard at a retailer or banner level. They also can drive ROI and lift from tactical pricing strategies like consumer promotions. After implementing new prices, CPGs can leverage advanced artificial intelligence (AI) techniques to evaluate results and plan future promotions. AI models identify which promotions to run, change, and eliminate, as well as what the promotion vehicles should be.
Advanced Pricing Capabilities Help CPGs Navigate the New Normal
New CPE capabilities can help CPG firms:
- Navigate shrinkflation: CPGs can use analytics to determine which products are good candidates for reducing size and volume while maintaining sticker prices. By so doing, they can continue to capture sales from price-sensitive consumers who otherwise might defect to other brands.
- Understand channel shifts: CPGs can use analytics to understand how demand is shifting across online and offline channels due to trends such as hybrid work and consumer financial pressures.
- Price new products: CPG teams want to determine the best price for new products to ensure they are successful. They can use CPE to examine scenarios such as line or brand extensions, change to Price Pack Architectures (PPAs) or pricing new products in any adjacent categories.
Identifying 1200 Pricing Opportunities With >90% Accuracy Using Granular CPEs
In a macro situation where consumers are constantly looking for value and CPGs are stretched on margins and remain profitable, granular CPEs can help unlock specific opportunities without impacting market share. Like the work we did for one of our large Food & Beverage customer in North America in their Frozen Foods category, across 3 large retailers including 120 SKUs across 104 Retailer Markets accounting for ~2900 top contributing SKU Market combinations. We identified 1200 opportunities to increase price without impacting sales at an accuracy level of 90-95%.
Topic Tags
Next Topic
Unlocking the Power of Transfer Learning and Data Augmentation in Deep Learning
Next Topic
