Evolving beyond the basics: How to create leading pricing and promotion strategies through advanced analytics

In this article, we delve into the intricacies of pricing and promotions analytics, explore the questions businesses frequently grapple with, discuss the challenges of finding answers, and illustrate how data science can offer effective solutions.

The stakes are high in pricing and promotion  

In the consumer products and retail sector, inflation, rising costs, and price-sensitive customers have created a challenging landscape. Consumers are not only searching for more affordable brands but also increasingly expect value through loyalty programs and personalised promotions. This shift has led many grocery retailers to adopt technology-enabled strategies like dynamic and personalised pricing. For consumer goods companies focused on growth, net revenue management has become essential. It enables these companies to handle complex decisions around portfolio mix, pricing and promotions. However, implementing these strategies brings unique challenges and businesses are often confronted with a multitude of questions:   

• What should our pricing strategy be?
  Should it be cost-plus, value-based, or competitive pricing? How do we set prices that maximise both market share and profitability?  

• How sensitive are our customers to price changes?
  What is the price elasticity of our products? How do different customer segments react to price changes?  

• Which promotional tactics work best?
  Do discounts, buy-one-get-one offers, or bundling provide the best ROI? How can we measure the effectiveness of these tactics?  

• When should we run promotions?
  Is it better to offer promotions during peak seasons to capitalise on demand or during off-peak periods to stimulate sales?  

• How do pricing and promotions interact?
  How can we ensure promotions don’t cannibalise sales at full price, and how do we balance long-term brand equity with short-term sales boosts?  

The need for a radical shift  

Many retailers continue to operate at a foundational level in pricing and promotions, primarily due to practical factors like legacy systems, organisational inertia, and data quality challenges. While they have the essential elements such as a strategy, some processes and tools, these may not fully meet the demands of today’s competitive market. As a result, their approaches tend to be more reactive, with decisions often based on past performance rather than forward-looking insights.   

Typically, these retailers rely on historical data and manual processes (e.g. the spreadsheet trap) to set prices and plan promotions. This approach comes with several challenges, such as limited visibility into market dynamics and customer behaviour, a lack of integration between pricing and promotional activities, and inconsistencies in analysis across teams. Although data is available for more advanced analysis, it often comes from multiple sources that require a lot of manual manipulation. As a result, many retailers rely on basic methods like market basket analysis and customer segmentation, using simple pricing models and basic promotional strategies to try to achieve some level of consistency.  

In a highly competitive marketplace, retailers need to develop a deeper understanding of the everchanging customer behaviours, market trends, and competitive actions. It is crucial to shift from reactive methods to a proactive, data-driven strategy. Leveraging advanced analytics and innovative technologies can provide the insights needed to overcome these challenges. For instance, using AI to automate pricing solutions can enable real-time dynamic pricing and offer recommendations for product range design, and base price structures that take advantage of competitor gaps and challenge their pricing strategies. Ultimately, by harnessing the right analytics strategy and technology, retailers can develop leading edge promotional strategies that drive growth.   

How can data science and advanced analytics help?  

Data science offers powerful tools and methodologies to help businesses answer key questions on pricing & promotions to help them build and deliver an effective price/promo strategy:  

• Predictive Analytics: Using historical data, predictive models can forecast how different pricing strategies and promotions will impact sales and profitability. Machine learning algorithms can identify patterns and correlations that might not be evident through traditional analysis.  
• Price Optimisation Models:  Advanced algorithms can determine the optimal price points for products by considering factors like demand elasticity, competitive pricing, and cost structures. These models can be tailored to account for different customer segments, helping businesses set prices that maximise both revenue and profitability.  
• A/B Testing:  To understand the effectiveness of different promotional tactics, A/B testing can be employed. By experimenting with various offers across similar customer groups, businesses can identify which promotions drive the most engagement and sales without eroding profitability.  
• Scenario Analysis:  Scenario analysis allows businesses to simulate different pricing and promotion strategies under various market conditions. This helps in preparing for contingencies and making data-driven decisions, even in uncertain environments.  
• Personalisation:  Leveraging customer data, businesses can create personalised promotions that are more likely to resonate with individual customers. This not only increases the effectiveness of promotions but also enhances customer loyalty.  

Combine pricing and promotions to achieve business goals and pioneer success  

To achieve business goals, it’s essential to integrate pricing and promotions into a cohesive strategy rather than treating them as separate entities. When executed effectively, this approach delivers significant benefits.   

M&S and Zara are two pioneering retailers showcasing the power of advanced pricing strategies. M&S’s “first price, right price” model has successfully stabilised food prices without heavy reliance on promotions, resulting in improved operational efficiency, smoother supply chain consistency, and reduced costs. Meanwhile, Zara leverages dynamic pricing, adjusting prices based on demand, seasonality, and popularity, with the help of AI-tools to optimise stock turnover and minimise markdowns. Retailers using similar solutions have seen an average sales increase of 2% to 5%. Both M&S and Zara demonstrate how strategic pricing can drive profitability and enhance operational performance.   

To drive similar results, retailers should consider:   

• Aligning promotions with pricing strategy 
• Optimising timing and frequency using data
• Monitoring and adjusting in real-time
• Ensuing cross-functional collaboration

This data-driven approach prevents brand dilution, maximises impact, and ensures coordinated alignment across departments.

The bottom line   

Pricing and promotions are powerful tools that, when optimised, can drive significant growth and profitability for businesses. However, their complexity requires a data-driven approach to navigate successfully. By leveraging the capabilities of data science, businesses can overcome the challenges inherent in pricing and promotions and align these strategies with their broader business goals.   

We suggest starting small in the four areas below, to build a foundation for more advanced data-driven pricing and promotions:  

• Leverage existing transaction, customer & loyalty data: Start by using available transaction data to identify basic trends, such as popular product bundles and seasonal demand patterns. Incorporating customer and loyalty data allows for deeper insights into the types of customers engaging with certain promotions and helps to deliver more personalised promotions.   
• Measure true promotion incremental impact: Focus on measuring both short-term uplift during promotional periods and understanding long-term impacts that would depend on more factors. It is important to measure promotional impacts pre-activity and post-activity and understand where the volume is coming from, to understand the impact of customers anticipating deals and brand /category cannibalisation.   
• Implement A/B testing: Introduce A/B testing on pricing and promotions to understand customer responses, helping refine strategies and adjust to market preferences with minimal investment.  
• Automate key insights: Use low-cost automation tools to streamline data collection and reporting. This allows retailers to focus on strategic adjustments, gradually scaling analytics capabilities over time.   

In today’s competitive environment, the right combination of pricing and promotions is critical for capturing market share and staying ahead of the competition. We are helping businesses on their journey to transform pricing and promotions effectiveness.

The future is pioneering, are you ready?