Industry
Retail
Omnichannel CX and Intelligent Innovation – The Future of Retail
Highlights
- Technologies such as cloud and microservices architecture and best-in-class solutions can ensure effective retail pricing strategies for an enterprise.
- As pricing strategies need demand visibility, advanced feature engineering techniques can improve demand forecasting by tapping into newer data sources and external data signals.
- The right pricing technology is critical for retailers to ensure they are not only relevant but also competitive.
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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, macro events such as Brexit and COVID-19 stress 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 (see Figure 1).
Figure1
- 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.
Enhance systems and technology
Leveraging technology and deploying or building best-in-class solutions that use retail analytics can lay the foundation for effectively managing complex retail pricing strategies throughout the organization.
Solutions that leverage cloud and microservices architecture should be implemented. By leveraging cloud in a platform-as-a-service (PaaS) or software-as- a-service (SaaS) mode, run costs can be effectively managed for demand peaks. Using a microservices architecture ensures that the IT investment is easy to integrate and deploy while providing the necessary scalability and reusability. This also allows for continuous improvement and continuous delivery (CI/CD), thereby enabling quick upgrades in tune with business needs.
Leverage AI, automation
Replace disparate, legacy-based IT systems and tools with software that provides advanced retail analytics and insights using algorithmic techniques for forecasting and determining optimal prices and pricing strategies.
Leading edge or advanced approaches that utilize artificial intelligence and automation will unify price, promotions, and markdowns, ensuring optimal decisions across all the three decision points rather than just within these silos. Optimization solutions provide balanced automation that frees up time for value-added activities such as experimenting with different pricing strategies.
Advanced feature engineering techniques will improve demand forecasting by tapping into potential new data sources. By utilizing the abundance of external data signals such as weather, social media, and local events, coupled with the developments in ML techniques, it is now possible to accurately forecast demand with advanced feature engineering. Even basic historical sales data can help improve the accuracy of ML predictions by creating data metrics such as day of week, week number, and quarter.
Unlock pricing strategies
Deploying AI-based technology is a game-changer for dynamic pricing strategy enablement.
The right technology and system (or platform) will lead to increased agility in pricing decisions, efficiencies through automation, drive more accurate, customer-focused prices, and capture better value through improved retail analytics. It opens the door to potentially more nuanced pricing strategies such as enhanced differentiated pricing, dynamic pricing, and unified approaches that have an impact on customer loyalty.
Limited by manual processes and simple one-dimensional pricing architecture, many retail organizations may “differentiate” via set zone prices with simple rules and a simple calculation, often applied as a blanket strategy across the whole zone. This “one-dimensional” approach fails to maximize pricing outcomes and, consequently, there may be inconsistent variations in target margins.
An example of this enhanced differentiation would be the ability to price within a single location based on channel or similar attribute–this would allow pricing to be set for an in-store item as well as online (see Figure 2). In fact, with the prevalence of alternative fulfilment mechanisms, the number of pricing options will be capable of expanding to satisfy the retailers’ omnichannel strategies, as shown in Figure 2.
Figure2
Data-driven analytics enable more sophisticated customer, demographic, and geographic pricing levers. It also provides the ability to flex dynamically, based on attributes such as location type, channel, and even customer behavior. ‘Segments’ could be created by clustering a combination of such factors and reviewed periodically.
An AI-enabled pricing platform can drive not only greater pricing differentiation, but also pricing dynamism and unified pricing approaches that all yield large benefits for the retailer. Such capabilities allow retailers to respond to competitive price changes automatically and rapidly, while also giving flexibility to execute short-term, short-turnaround promotions, and integrate markdown decision-making.
This unification allows retailers to maximize decisions across traditional silos, rather than within such silos, and unlock greater commercial value in retail merchandizing.
Empower process change
Fundamentally, retailers want end-users to be productive, engaged, and fully adopt the technologies and strategies employed.
Correct implementation will improve the capabilities for execution of pricing strategies, reduce the time taken for process execution and has the potential to provide overall full-time equivalent (FTE) saving due to the increased efficiencies and process/task rationalization. The right organizational design and change management plan is critical to the successful adoption of a leading pricing software or platform.
Understanding who is responsible for managing and executing pricing, having clear roles and responsibilities laid out, along with a plan that manages any change and communication is vital. It is also important to ensure pricing strategies are clear and communicated well throughout the organization. These organizational change tenets should be an integral part of any programs implemented for retail pricing strategies.
A game changer
Leaders in the retail pricing space are leveraging technology and AI-ML to execute new pricing strategies, remove spreadsheet-based pricing practices, and drive more automated, flexible, smart pricing systems and processes.
This is game changing—it enables greater refinement of strategy and efficiency through automation and, importantly, helps drive the commercial outcomes retailers and, in particular, grocers need to survive and thrive. At the same time, pricing leaders are also keeping their end-consumer or customer at the forefront of their decisioning by leveraging customer data, and customer-first pricing and retail merchandising practices that drive price perception and customer loyalty.
Effective pricing is one of the most potent levers to attract the digitally empowered consumers whose behavior is rapidly evolving and changing. The right pricing technology or platform is, therefore, critical for retailers to ensure that they are not only relevant but also competitive.
Solutions combining complex data analytics with artificial intelligence-machine learning (AI-ML)-led pricing intelligence can enable retailers to dynamically set and maintain pricing, respond quickly and efficiently to market trends, understand changing customer demand patterns and price newly sourced items.
