Price attracts customers

Retailers are profoundly impacted by the dizzying speed at which retail is evolving and technology and customer behavior are changing.

As the retail environment gets more complex and consumers become more tech-savvy, retailers need to focus on their pricing strategies to stay competitive and manage consumer price perception to ensure customer loyalty. That’s because at the end of the pricing process, there is a customer in a store (or online), for whom pricing is the critical factor to determine where they shop, what they shop, how they shop, and their perception of the retailer. This customer perception must be at the forefront of retailers’ strategy and technology choices. Added to this, more recent challenges such as Brexit and COVID-19 have stressed the need for flexible, robust technologies and processes to help retailers cope with market shocks and changes in customer behavior.

Solutions combining complex data analytics with artificial intelligence-machine learning (AI-ML)-led pricing intelligence can enable retailers with both setting and maintaining dynamic pricing, respond quickly and efficiently to market trends, understand changing customer demand patterns and price newly sourced items. Embedding this intelligence within the pricing process enables greater resilience for retailers, and ultimately drives greater commercial outcomes.

A framework for implementing best-in-class capabilities to drive the key aspects of retail pricing strategies will help overcome some of the common industry challenges faced by enterprises.

Pricing technology challenges retailers face

There has been a proliferation of retail price optimization software over the past decade, as retail operations increasingly incorporate data, analytics, and pricing intelligence for decision-making.

The adoption of such approaches in retail merchandizing, however, has been beset with some problems (Figure 1).

Problem scenario 1: Large capability gaps still exist within organizations due to either the lack of, or the sheer number of, complex systems and processes required to make a pricing software effective. These gaps adversely impact the ability to drive more sophisticated and dynamic pricing strategies.

Legacy merchandising technology landscapes are, therefore, often not aligned to delivering modern pricing needs.

Problem scenario 2: The above problems force users to manage a vast array of spreadsheets, resort to off-system activities, and use manual processes to derive the retail price of an item or a set of items in retail merchandizing. This greatly limits the ability to manage complex customer and product differentiation. Often, this also offers little or no analytics on the impact of these price changes, therefore, necessitating the repetition of these complex manual processes. It also hinders the ability to learn from and develop best practices. This lack of joined-up technology and process stops retailers from operating efficiently, and critically stops retailers developing a virtuous cycle of continual pricing learning and evolution (both operationally and strategically).

Problem scenario 3: In addition to the challenges posed by application landscapes and business processes, there is often a distrust of technology, particularly in a business where users have been crippled by the lack of, or excess, processes and technology. This problem almost always is a byproduct of the first two problems. A look at these issues in detail will underline the need to leverage data science, AI-ML, as well as technology programs to deliver the benefits of improved pricing strategies to the business. 

Fix the problems

Any attempt to resolve just one problem in isolation does not provide the desired results.

As shown in figure 1, the problems can be a cycle of linked events that contributes to the issues retailers face with dynamic pricing. These are the practical steps we must implement holistically to mitigate all the discussed problems as they can:

• Enhance systems and technology
• Leverage analytics, AI and automation
• Unlock pricing strategies
• Empower business process change

In a nutshell, the overall aim should be to improve systems and technologies by leveraging analytics, AI-ML, and automation to develop clear and robust pricing strategies that are understood and adopted by business users following clear governance in line with corporate strategies and targetable benefits.