Unlocking the Value of Win-Loss Data in Pricing

It wasn’t all that long ago that I used to work tirelessly to convince executives that “more data” was necessary in order to improve pricing strategies.  But in today’s world of Big Data and Gen AI, I now find myself having to underscore just ‘more’ data, but relevant, impactful data. 

Pricing strategies often rely heavily on data from wins—and for good reason. A historical win indicates that price did not present a barrier, making this data a reliable foundation for shaping future pricing decisions.

Companies often want to discuss ways in which they can enhance their pricing strategies, and one of the concepts that comes up often is leveraging win-loss data from quotes to fine-tune prices.

The idea often revolves around identifying categories or groups of products with declining conversion rates. Companies often believe that adjusting prices in response can improve their win-loss rates, increase conversions, and ultimately boost their bottom line. But as promising as this sounds. If only it could be so simple…

The Complexity

While win-loss data can certainly inform pricing decisions, assuming that a trend in conversion rates is solely due to price can be misleading. Often, what's missing in win-loss data is an understanding of the reasons behind the loss. In many businesses, there are multiple reasons for not converting a quote, and price may only be one of them.

Companies typically lack visibility into why a loss occurred. This is especially true in B2B environments with high transaction volumes, where hundreds or thousands of quotes are issued daily, and it is not possible to know the reason for every quote that doesn’t convert.

You Know What They Say About Assuming!

Assuming price is the sole reason for not converting a quote can lead to unnecessary margin erosion. What if the real issue is something else? Freight, packaging, payment terms, and delivery requirements can all impact the decision, not just the product price. If we automatically adjust prices based on conversion rates, we are leaving money on the table. Without a true understanding of loss reasons, we risk putting ourselves at a disadvantage by making price changes when the real issue lies elsewhere. 

The Challenge of Multiple Factors

Even when price is a factor, it's rarely the only one. Other elements of a quote including freight, packaging, payment terms, delivery requirements, or the availability of other products may also be a factor. If a customer chooses not to buy, it might be due to a combination of factors. In such cases, simply lowering prices is not likely to improve conversions and will erode margins.

Quantity Quoting: A Nuanced Challenge

In some businesses, quoting isn't a straightforward, one-to-one process. Customers might request quotes for multiple quantities of the same product to determine the most cost-effective purchase volume. This "quantity quoting" can result in only one line item converting, while the rest appear as losses. However, these "losses" aren't necessarily indicative of pricing issues but rather reflect a customer's decision-making process. 

These customers are simply seeking the most cost-effective quantity within their budget and inventory limits. Misinterpreting this data can lead to skewed conversion rates and false alarms about the need for price adjustments.

The Risk of Data Overload

In our data-driven world, more data isn't always better, especially if it's not well understood or properly leveraged. Win-loss data should prompt a closer look at pricing strategies but isn't an automatic signal to change prices. Monitoring trends over time and understanding the context behind them is crucial before making price adjustments.

What Else Might the Data be Telling You?

Changes in conversion rates can often signal broader market trends or shifts in competitive behavior. It’s not always about competitors dropping their prices; they might have rolled out a new loyalty program, introduced a bundled offer, or launched a competing product that meets certain needs more directly. In such cases, a price change might not be the best response. Instead, companies should consider how to differentiate their offerings in ways that address these market dynamics while reinforcing their unique value.

One approach is to refine sales messaging to better address customer objections and highlight the product's unique benefits. If losses reveal miscommunication or misunderstandings around product value, sharpening the sales message can go a long way in aligning customer expectations. Additionally, analyzing win-loss data across customer segments—such as industry, size, or geography—can reveal which groups are more likely to convert. This enables the team to focus efforts where they’ll be most impactful, personalizing messages and resources for these higher-potential segments.

Win-loss data can also reveal what drives value for different segments and uncover which product features, capabilities, or bundles may be lacking. Instead of defaulting to price adjustments, companies can use these insights to enhance their product or introduce strategic bundles that meet specific needs, strengthening the offering in ways that resonate with customers. This refined approach allows for more meaningful, data-driven adjustments that improve competitiveness without sacrificing margin.

Conclusion

Win-loss data may be considered when developing a pricing strategy, but it requires careful interpretation. By understanding its limitations and the broader context, companies can make informed decisions that enhance their competitiveness without falling into the trap of unnecessary price wars.

Ready to leverage win-loss data? Go beyond the numbers—understand what drives wins and losses to refine your approach and enhance your value without sacrificing margins.

Contact us today if you're looking to elevate your pricing strategy with data.

Matthew Knaggs is a Senior Business Value Lead at Zilliant, where he works with customers and prospects to demonstrate the ROI and business impact of implementing Zilliant solutions.