In the retail industry, pricing is tantamount to survival. If prices are too low, you’re leaving money on the table. If they’re too high, buyers walk away empty-handed. Adopting a successful price optimization strategy has become more important than ever to profitability — and even viability.
But even companies that adopt price optimization strategies sometimes underutilize these investments, or have trouble achieving adoption throughout their organizations. As with any investment, you don’t get the return on investment (ROI) unless you use the solutions you’ve invested in, which means meaningful, rapid adoption. Here are four things you can do to avoid the pitfalls of sluggish adoption and poor outcomes.
1. Keep it simple
A dirty secret of the price optimization space is that many purchases of pricing tools become “shelfware,” i.e., unused software that is doing little more than collecting dust. There are a few reasons for this. One is that after some initial adoption, the pricing models may grow stale and irrelevant without costly professional service engagements to keep them current as market conditions, shopper behavior and items change. Self-learning models leverage modern science and technology to avoid that risk (see No. 3). Another challenge is that a difficult user interface can inhibit adoption. If they plan to have strong adoption, retailers need an interface that is intuitive, easy to use and relevant for business users, category managers and analysts. A simple, effective design and user interface are essential.
2. Examine data performance and architecture
Retailers need access to all their relevant data and insights, from pricing history details to fast-breaking market and competitive changes, so they can simulate, optimize and reprice with the agility needed to take advantage of changing marketplace conditions. As the pace of retailing continues to accelerate, demanding more price changes more frequently, this can be the difference-maker that ensures having the right price for every product all of the time. For some retailers, this may even require intra-day pricing. Retailers should demand high-performance, agile data architecture that can enable them to swiftly optimize price decisions.
3. Predict future shopper behavior
Every retailer struggles with constant and unpredictable changes in market trends, shopper preferences and the competitive landscape. In response, demand models need to be “self-learning,” which means they not only evolve in real time with changing shopper and market behavior, but they also anticipate changes. Moreover, today’s companies need differentiated pricing strategies that support their customers’ category roles, intents and business objectives, and which change over time.
4. Provide transparency into the how and why of pricing decisions
Price optimization can deliver a powerful impact, but not if it is locked away in a black box shrine that no one can enter, much less understand. Users need to see and understand the how and why behind the pricing recommendations so they can always confidently answer the question: “Why did the price change?” When price recommendations come out of a black box without clear explanations of the assumptions or settings that factored into those recommendations, analysts may feel uncomfortable defending the recommendations to management, or not implement the price change due to lack of confidence.
It’s not just clarity regarding how prices are determined that is important. It’s equally critical to have visibility into how different pricing scenarios would play out in the real world, ideally with real-time iterations of what-if scenario planning so you can rapidly simulate a wide range of business strategies and/or rules to compare and contrast outcomes — before you commit to a pricing change.
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