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Win-Loss Analysis, a key tool within your revenue management toolbox

Gathering market and competitive data to optimize pricing models is not always straightforward, especially in B2B. Opposed to the consumer market, B2B prices are less transparent, require effort to gather and are quickly outdated. However, most B2B companies have internal data that allows them to get a good proxy of relevant market dynamics, in an automated and real-time setup. This post will explain how conducting an analysis on your won and lost deals can improve pricing insights and guide your revenue management team to make better decisions.


To perform a win-loss analysis, data on all historical quotes is needed, both for the lost and won deals. The transactional data in these quotes (such as quoted price, volume, date, product type, customer, etc.) contains very valuable information. Some companies enrich this data even further by asking their sales teams to log competitors, competitive prices, reason for win/loss, etc. For this post, we assume that only the transactional data is available.

Basic insights

Plotting your lost and won quotes in a scatter plot as shown in figure 1 already illustrates the power of a win-loss analysis. It helps to identify where negotiations should occur, where salespeople may have been overly ambitious, and where some successful deals might have been priced too low. The linear trend line in this visualization shows where deals are won on average. As expected, we typically observe a negative trend, indicating that the higher the volume, the lower the price per unit is.

Figure 1

By regularly making this scatter plot for your relevant products and pricing segments you will observe changes in the market and competitive positions. Those insights should be combined with your value-based pricing strategy in order to balance the desired and feasible price position.

Another way to monitor evolutions are win-loss ratios. There are different definitions and ways of calculation, but we prefer the simple win ratio as it clearly expresses the effort it takes before a quote is converted into a win.  

If for example the win ratio is equal to 33%, this means that on average, a salesperson in the company needs to negotiate 3 quotes before one deal is won. It is clear that when this ratio decreases, the market becomes more difficult, e.g. competitors price more aggressively or demand is slowing down, and more effort is required from the sales team to win deals.

Price optimization

A win-loss analysis also allows you to predict and optimize prices. Basically, there are 3 key parameters: volume, net price and probability to win. The latter enables us to turn the win-loss data into an optimization model.  

Every quote has a specific volume and price. Based on previous quotes, it is possible to predict the likelihood of turning that quote into a contract, being the probability to win. If for example the probability to win of a quote is equal to 20%, we can expect that for that volume and price, 1 in 5 quotes will be converted into a contract.

Leveraging the historical win-loss data, we can simulate the probability to win for each price and volume combination. As such, it is possible to calculate the optimal price for a specific volume, being the price where the expected revenue for that volume is the highest. Figure 2 shows how we typically represent the results of such an analysis. For a volume equal to 62kg (in this case the average volume of all quotes), the blue curve gives the probability to win and the red line shows the expected revenue. Based on this graph, we can conclude that the optimal price for a volume of 62kg is equal to €66. In that case, the expected revenue is almost equal to €2200.

Figure 2

Figure 2 gives the optimal price for the average volume of 62kg. When repeating this calculation for all possible volumes, we can plot the optimal price point versus the volume (see figure 3). Here, we observe that the optimal quote price decreases if the quote volume increases. This is in line with the trend line that was observed in the scatter plot visualization.

Figure 3

The above explained insights can easily be automated, allowing real-time insights on how the market and your sales team is performing. We recommend to use those insights always in combination with other pricing techniques like peer pricing or value-based pricing, since the art of pricing will always be about balancing the value we believe we deserve versus the market evolutions.


About the authors

Dries Debbaut is partner at Chronion.

Emiel Raveschot is consultant at Chronion.

Do you want to know more about Win-Loss Analysis, contact us.


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