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In our previous post, we explored the fundamentals of price elasticity and its critical role in understanding consumer response to pricing changes. Building on that foundation, we now delve into how artificial intelligence (AI) can be effectively applied to price elasticity to enhance revenue growth management (RGM).
In this second part of our series on price elasticity, we’ll cover the business perspectives of using AI to enable more precise and dynamic pricing models. These viewpoints are demonstrated by our successful implementation of an AI tool for a global consumer-packaged goods (CPG) company. Our real-life success story also illustrates how AI can substantially streamline manual processes and reduce the time needed to gain valuable insights, enhancing overall operational efficiency.
Why use AI in revenue growth management strategies
Here are just some of the advantages of using AI in revenue growth management strategies:
Advanced data analysis: AI algorithms can analyze large volumes of historical sales data, customer behavior data, market trends, and other external factors to identify patterns and relationships that influence price elasticity. By introducing machine learning techniques, AI can uncover complex insights and nuances in demand elasticity that might not be apparent through traditional analysis methods.
Dynamic pricing optimization: AI-powered dynamic pricing solutions can continuously monitor market conditions, competitor pricing, and customer behavior to adjust prices in real-time based on changes in demand elasticity.
Personalized pricing strategies: AI enables companies to implement personalized pricing strategies tailored to individual customer preferences, purchase history, and price sensitivity. Models analyze customer data and predict individual price elasticities. AI can recommend customized pricing offers and discounts that maximize revenue while maintaining customer loyalty.
Demand forecasting: AI-driven demand forecasting models use historical sales data, market trends, and other variables to predict future demand elasticity and sales volume under different pricing scenarios.
Optimized promotions: AI algorithms can help optimize promotional strategies by analyzing the effectiveness of different promotions and discounts in driving sales volume and revenue. They can identify the most profitable promotional tactics, duration, and frequency. In turn, companies can allocate promotional budgets more effectively and maximize the ROI of their investments in marketing and promotions.
AI algorithms continuously learn from new data and feedback to refine pricing models and strategies over time. By utilizing machine learning techniques, AI systems can adapt to changing market dynamics and evolving customer preferences.
Applying AI to price elasticity strategies: What data scientists and RGM experts should first do
There are many ways to apply AI in price elasticity strategies, as long as there’s appropriate data and enough information in the data to experiment with.
One important dimension that needs to be considered is the level at which elasticities are calculated. Without granular data, one might be forced to model elasticities, for example, on a monthly level for a given category. This approach is outdated.
Modern techniques, combined with an abundance of data, enable price elasticities to be calculated for individual SKUs for point-of-sale (PoS) segments. Heterogenous price elasticities can go even beyond that and differentiate SKU elasticities depending on some external factors. These could include the number of competitor SKUs in the PoS and competitor PoSs in a given radius (or people density in given radius). Choosing one usually falls on the data scientist building the models as well as the revenue growth management (RGM) experts and should be in line with the companies bigger pricing strategy.
Calculated elasticities can also differ depending on the channel. One SKU can be inelastic in traditional trade but can be very elastic in online trade where consumers have easier ways of comparing the prices against competitors. On the other hand, online trade gives better opportunity to play with dynamic pricing and automatically update the prices as the demand for given SKU changes.
Another source of differentiating pricing decisions is the goal to be achieved for certain SKUs or SKU categories. With data about wholesaler price (e.g., supply chain-related costs, cost of goods), the company can automate the end-to-end price recommendation process to maximize a given KPI, including the following:
- Gross sales value while maintaining a certain level of profit
- Maximum profit while maintaining a certain level of gross sales value
- Maximum market value while maintaining a certain price range
These considerations are why it’s crucial to categorize the assortment into certain pricing strategy categories with regards to the KPI to be achieved. Data analysis can also unveil good candidates for certain pricing strategies (e.g., “margin builders,” “high-low”).
Of course, there is no data-driven decision-making without the internal adoption of these AI-based tools. Creating robust solutions that build trust in AI should be a fundamental step.
Figure 1. A sample visualization of a dashboard showing a more granular analysis of a product’s revenue based on certain price elasticity
Lingaro case study: Enabling a CPG company to address manual work and delayed time to price elasticity insights
Price elasticity is crucial for food and beverage companies in the CPG industry, as it also affects product development, marketing and sales, and supply chain strategies. As the data and AI partner of enterprises and global brands, we played a pivotal role in enabling a global beverage company to utilize price elasticity to its fullest potential for their RGM strategy.
Our client has a diverse portfolio of products in different packaging, such as glass, cans, and polyethylene terephthalate (PET). Their company has a strong brand presence globally, producing a wide range of beverages catering to various consumer preferences and lifestyles. Each of their brand targets different market segments and consumer demographics. They also operate in various geographic regions, each with their own unique market dynamics, competitions, and consumer preferences.
With all these distinct variables, the CPG company struggled to act on their RGM strategy. Manual workflows for data integration led to long delays in getting relevant and time-sensitive insights. Performing price analysis was not easily accessible to and understandable for business users, so their information on price elasticity and most favorable options for a price change are limited. Price elasticity modeling lacked consistency and are not readily available. The significant delays and inaction on price optimization left their teams with the inability to confidently answer questions on their approach and strategy.
Figure 2. A sample visualizations of an AI-powered dashboard that could automatically plot the price elasticity of each product
Lingaro solution: AI-enhanced tool for price elasticity strategy
The solution was more than just an enterprise tool for supporting decision-making on pricing. Apart from designing the tool’s sophistication to suit their business needs the most, Lingaro’s data science and AI and commercial analytics practices also had to consider data limitations.
The Lingaro team had three core considerations:
- Identifying all potential use cases
- Precisely defining the solution’s end users
- Designing the solution’s architecture to be flexible and scalable
With these essentials, we tapped into the expertise of solution architects, data engineers, data scientists, AI and ML engineers, business/RGM experts, and UI/UX designers. Our RGM experts worked hand in hand with data specialists to ensure that the solution speaks the same language as the company’s business end users, which helps build trust in AI and drive the solution’s adoption.
Applying AI in price elasticity is all about enhancing the accuracy of data-driven decision-making. This requires business users to have the insights presented to them in an easy-to-digest manner. The Lingaro team developed a business intelligence (BI) tool that transformed their decision-making process into a user journey through descriptive, predictive, and prescriptive analytics. The user-friendly dashboard/front end was supported by advanced analytics models that provide:
- Access to price elasticity estimates with certain pricing strategies recommended for each one
- Interactive what-if scenario simulation to enable users to understand the impact of certain pricing decisions on KPIs such as sales volume, gross and net sales value, and gross profit
The whole solution was implemented in a way that enables quick scaling for other markets, which had already happened for a few of them. The Lingaro team not only supported the data science and AI behind their price elasticity strategy, but also from the business perspective. The team onboarded the end users through user acceptance testing, explaining the solution’s functionalities step by step and showing the possible use cases they can draw on. The team also used these sessions to get additional feedback for the solution’s future enhancements.
The automation of price elasticity calculations significantly lessened their team’s manual work. Additionally, the data visualizations that the dashboard provided gave the end users quicker time to derive insights and see the various correlations of their data. The solution is now the CPG company’s go-to source for pricing strategies in four global markets, with plans to expand the solution’s functionalities and roll it out to more markets.
Preparing price elasticity data, IT environment, and people for AI
Incorporating AI in price elasticity analysis might require changes to existing workflows and decision-making frameworks within the company. Employees also require training and support to adapt to new tools and methodologies, and resistance to change from internal stakeholders can hinder adoption and implementation efforts. Before creating any tool for RGM, companies need to establish the process for using the tool, and the framework for supporting end users. Without a proper plan, adoption of the tool will be low.
In our case study, Lingaro’s commercial analytics practice provided learning assets and sessions to ensure user awareness and adoption. These included demos on:
- Finding the optimal price for their products.
- Understanding competitor pricing.
- Identifying trends that could influence their pricing strategy.
- Mitigating risks and adverse impact of price changes.
- Assessing the effect on the brand after changing the product’s price.
It’s also crucial to define certain goals for building a price elasticity solution and defining data limitations. CPG companies have many data constraints (not enough data or not enough granularity, for example) that could hinder the accuracy of price elasticity calculation. All these limitations need to be known beforehand and addressed with specific workarounds and explained to business users. Involving the end users is an essential element for adoption and building credibility for the tool.
From a business perspective, it’s also key to track the decisions recommended and implemented by the solution. That way, business can also calculate the incremental profit and ROI from using their tool. And as AI advances, companies can further harness sophisticated algorithms to tailor more precise pricing models. This enables not only more effective responses to market shifts but also a deeper understanding of customer price sensitivity, ensuring that businesses remain agile and proactive in their revenue growth management.
If you’re exploring how an interactive, AI-powered price elasticity tool could help improve your business’s RGM strategy, reach out to us and we’ll schedule a real-life demo of how the AI solution can optimize pricing for your products.
