Why A Hindsight-Analytics Approach To Pricing Fails

While there may be some value in knowing where you’ve been, backward-looking analytics can’t provide value when it comes to making better pricing decisions in the future.

Id 1371 Prices

B2B companies often choose the path of least resistance when embarking on a pricing project. That path usually begins with analytics — report-centric, hindsight analytics, to be more specific. While there may be some value in knowing where you’ve been, backward-looking analytics can’t provide value when it comes to making better pricing decisions in the future.

When it comes to pricing, reports and hindsight analytics often show where pricing mistakes were made, such as where you discounted significantly from the list or matrix price. However, they don’t tell you how to set and dynamically update prices, negotiate going forward, and more importantly, how customers will respond to those prices. This often causes companies either to leave money on the table or lose the sale as a result of being too aggressive.

To set prices effectively, or forecast price response in the market, you need a predictive model, not a view into the past. Zilliant recently revealed five reasons why the hindsight-analytics approach to pricing fails:

  • Backward-looking. It’s not easy to execute on the findings from analytics. You may learn from analytics that a particular sales rep made a bad decision on a given price last quarter, but how do you prevent him or her from making similar mistakes next week when faced with slightly different selling circumstances? It’s evident that backward-looking analytics may help companies identify a wrong price in the past, but it can’t help them capture the right price in the future, which causes sales rep to continue making the same mistakes.
  • Front-loaded results. When your analytics report identifies a bad price in your matrix, you get a one-time margin bump once the program has been addressed. However, after fixing a handful of obvious pricing issues, you still can’t realize sustainable price improvement in the next quarter and beyond. Over time, the incremental margin gain for one-off, analytics-driven “fixes” gets smaller and smaller.
  • Inability to guide your financial future. When you set out to fix a broken price, what’s the right price to fix it with? If your new price is too high or too low, what will happen to revenue and margin? Hindsight analytics can’t tell you that, causing companies to make pricing decisions in the dark without understanding the impact of those decisions on their P&L.  
  • Overly general segmentation. When planning to put a corrected price into the market for your entire business, you need the proper price segmentation that will identify thousands of distinct price segments and surgically set the right price for each selling circumstance. When companies mistakenly use one price for all segments or set prices for segments that are too broad, they are setting themselves up for a poor outcome that will drive away some major deals and leave money on the table for others.
  • Too many people resources required. Even if you build a great analytics system, it will likely require hundreds of pricing analysts to perform a thorough analysis on each and every customer and product in your book of business on a weekly, or even monthly, basis. It’s unrealistic to set the proper price when a company has several thousand SKUs and 20 plus regions and customer types, which will lead you back to setting one price for all segments.

Ultimately, the time- and labor-intensive effort needed to produce reports based on backward looking analytics typically proves overwhelming when it comes to boosting revenue. The reality is that the hindsight-analytics approach to pricing crumbles for companies that face massive complexity, so you inadvertently relegate millions of decisions to your sales reps and force them to guess about the best prices to hit your objectives. On the other hand, the predictive analytics approach can quantify the true factors that affect price outcomes, predict customer buying response to different prices and enable companies to predict and control how their pricing strategies will impact their P&L.

Barrett Thomson is the general manager of Zilliant’s pricing excellence solutions. With over 20 years of experience, Barrett has helped businesses with complex pricing environments achieve rapid and effective margin improvements.

 

 

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