Today’s hyper-competitive environment is a zero-sum game in which one retailer must lose market share for another to gain. Winning this game requires a retailer to focus every action in every area of the organization on earning shopper loyalty – because the continued downward spiral of price matching is a strategy in which every player loses.
Yes, pricing is top of mind for shoppers and retailers alike- and competitive pricing is critical. However, pricing competitively is not about being the cheapest on every product: it’s about driving loyalty by having what they want, when they want it, where they want it, at the price they want it – balancing the need to provide shoppers with value and the need to provide shareholders with value.
Dynamic Pricing Becomes ‘Business as Usual’
One trend that has escalated dramatically over the past few years is in the number of price changes being executed. Amazon has successfully led the charge by executing more than 2.5 million price changes every day and raising the bar on the need to be responsive to shopper and competitor behaviour.
By making dynamic pricing ‘business as usual’, Amazon has also conditioned shoppers to expect prices to change. This presents a significant opportunity, which is the ability to vary prices across different channels and locations based on shopper price sensitivity and competitive environments. However, the opportunity presents itself with a strong caveat – retailers must surgically and strategically select the right products to drive their price image and not practice across the board price matching polices.
Balancing shopper and shareholder value at the speed and scale required to maintain competitive stance requires all the components of optimization: data + science + strategy + rules + constraints. These disparate inputs must be systematically operationalized into a process that enables retailers to rapidly analyze alternatives, prioritize options, make decisions and take action.
Using science to understand shopper price change response, as well as understand which competitors impact shopper demand, permits the implementation of tiered competitive positioning strategies that incorporate the most appropriate competitive data at the most appropriate frequency.
Many times, very different shoppers are engaging retailers across their various channels – requiring variation in key value item lists (i.e. price image drivers) and strategy selection at a very granular level because products are playing different roles in different channels.
Many times retailers identify far more price image drivers than necessary. Science takes the emotion out of KVI decisions and finds the balance between driving profitability and loyalty by choosing the most effective items for competitive strategies and allowing the balance to drive margin.
Dynamic pricing is here to stay, as is volatility in shopper and competitor behaviour. These shifts can be viewed as a threat – or as an opportunity for retailers willing to embrace the new optimization technologies and mine the insights in data streams that are continuously emerging.
Demand-Based Science and Predictive Analytics for Strategic Competitive Positioning
A purely competitive price matching strategy on all products is a lose-lose proposition for both retailers and shoppers due to the unsustainable business model leading to an environment where choice is limited to only the largest players who have cost and volume advantage. Of course, price is only one component of a broader strategy that must be developed to create differentiation and drive loyalty – but it is an extremely important lever in that strategy.
Using demand-based science and predictive analytics to incorporate all of the factors that impact shopper demand enables retailers to be responsive at the speed, scale and frequency required in today’s rapidly changing retail environment. Understanding which products are the right ones to invest in and which ones have margin flexibility across different channels and locations makes it possible to compete effectively, work within constraints and achieve profitability, loyalty and strategic objectives.