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Retail prices are easier to find than ever for customers – and more complex to deliver than ever for retailers. Every day, tens of millions of shoppers around the world go online to get reliable price comparisons in seconds. This phenomenon, the “Amazon effect,” will only get stronger.
Setting the first price in a retail setting is like making the first move in a three-dimensional chess game in a hailstorm: it’s only the beginning, conditions are shifting by the hour, and each move changes competitors’ responses. Multiply that complexity by thousands of SKUs per store, hundreds of stores, multiple channels from digital and catalogs to bricks & mortar, disjointed promotional and markdown decisions, and shoppers whose preferences and behaviors vary by region and demographics, and the game becomes far too complex for even the most experienced human to master.
With the right data, tools and talent in place, enormously valuable insights can emerge. As they explore broad issues with advanced analytics, leading supermarket retailers are also drilling down to learn much more about their most profitable customer segments, for example, with pricing, promotions and targeted marketing to appeal specifically to them. The analytics can then calculate the overall financial impact of these activities across the pricing spectrum: regular, promotional and markdown.
How can grocers unite both art and science – including their analytics, people and processes – to improve lifecycle pricing?
Align Software with Human Decision Making
Professionals of all kinds, from pilots to architects, now rely on software to handle work previously performed manually. I believe software should enable a “manage by exception” approach to pricing. Advanced analytics solutions should include “guardrails,” wherein managers can set prices without approval if the prices fit within the prescribed guardrail. When a manager’s experience and judgment tell them they should set a price outside a guardrail, they need to seek approval only for that change.
With this consistent, straightforward approach, managers don’t need to rethink prices in every situation. Further, the ability to focus only on those prices requiring further scrutiny enables pricers to more effectively leverage analytics to determine the right action to take.
In addition, having tools and analytics that are purpose fit for understanding a product’s lifecycle enables a more robust view of the right action to take across the pricing spectrum. For example, a new item in the store may be hot in the market with competitors aggressively driving price down. If a pricing manager has access to strong analytics, he or she may understand their customers will respond better to a higher EDLP price with hot promotions. Additionally, seasonal or limited-time products need to be managed effectively with robust clearance analytics to ensure the product can be cleared through as profitably as possible.