Considering the importance of the quantified value proposition (QVP), the actual process of developing it could be one of the most valuable initiatives your company undertakes this year.
Since a QVP is, by definition, individualized to the customer for which it’s designed, there will be some variation in how the details are developed on a case-by-case basis. But the following principles should help you understand the fundamentals involved in developing a QVP so you can head down the right path in creating your own.
You must have a value proposition before you can quantify it.
This seems insultingly simple, but the fact is that businesses don’t actually have a defined value proposition in place. They’re not really sure how their products, services, or solution sets provide value for their customer. Or, they have a good idea where the value is, but they haven’t documented that value proposition or shared it with the sales and marketing teams to communicate it effectively.
So, putting the effort into identifying and documenting your value proposition is necessarily the first step in developing a QVP.
Step One: Identify and prioritize value drivers
Once you have identified your value proposition, you can begin applying that to the individual customer you’re analyzing. The process of developing a QVP is 90% research and analysis that results in the powerful remaining 10%: communicating the end result to the customer.
The first step of that research and analysis stage involves identifying what really drives value for the customer. A value driver is something the customer needs in order to reduce cost, enhance revenue, or provide an emotional benefit. In some cases, the customer may not even realize they have this need, but the fact that you can tie it directly to value is what convinces them that they do.
Some common value drivers include:
- Salary or compensation savings (reduces costs)
- Gains in market share (enhances revenue)
- Peace of mind (emotional benefit)
- Risk avoidance (reduces costs)
- Gains in productivity (enhances revenue)
- Improved reputation (emotional benefit)
It may take some creativity and digging to unearth what really drives value for each customer you work with, but after all the value drivers are identified, it’s time to prioritize. For example, if you identify 10 different value drivers but three of them account for 90% of the value the customer is concerned with, it’s probably not worth spending the time and effort fully researching the other seven drivers, since the customer won’t be as interested.
Step Two: Identify how your solution relates to those drivers
Once you have a prioritized list of value drivers you know your customer cares about, you need to go back to your documented value proposition and begin correlating the two.
This phase requires having an equally deep understanding of your own product’s benefits, the benefits offered by the next best alternative product (or the version currently being used by the customer), and the customer’s current situation.
When you understand all three of these well, you’re in an excellent position to determine how your solution addresses the customer’s value drivers more effectively than the competitive solution(s), and to present these in the best way to appeal to the customer in their current situation.
Step Three: Build a value algorithm
Here’s where you begin adding quantification to the mix.
Essentially, a value algorithm is a written formula that highlights the quantifiable aspects of your customer’s business, processes, and current situation, that have a direct bearing on the value drivers you identified and prioritized in Step One.
Based on the number of different drivers you could identify as important and the number of product benefits you were able to map to those drivers, you may be able to create several different value algorithms. This is fine, as long as all of them are based on quantifiable facts and not conjecture or assumptions.
To illustrate: If the you’ve identified the reduction of unexpected downtime as an important value driver for the customer, and you’ve mapped that to a specific feature of your widget that improves the customer’s ability to foresee maintenance issues before they become emergencies, your value algorithm may be, “incremental number of down days reduced, multiplied by the average downtime cost per day.”
This step directly correlates the value you’re offering the customer to numbers that can be readily analyzed and calculated in real world dollars and cents.
Step Four: Estimate the dollar value for each algorithm
It is risky to claim in concrete terms that your product would put a specific dollar amount in the customer’s pocket by the end of the year, as if it were a guarantee. Although testing and analysis can get you very close to guaranteed results, no one can predict the future flawlessly.
In Step Four, you need to take real figures provided by the customer (if possible) or reasonable estimates based on published output or other available information, and combine them with the expected effect your solution will have on their situation.
For example, using the same algorithm noted above, let’s assume previous experience involving other customers with similar usage has told you that your widget reduces downtime by 15%. Your current customer estimates the cost of downtime at $21,000 per day and they average 12 days lost to downtime per year.
Using the algorithm you created, you can clearly explain that installing your widget in their production line will reduce their number of downtime days to 10 instead of 12, saving them $42,000 in the process. Now, rather than simply saying your product can help your customer reduce downtime, you have a specific dollar figure that they can directly compare to the price of your product to determine its real-world value to them.
That is a quantified value proposition.
There’s an important fifth step in the process that we don’t want to overlook, but we’re going to cover it in another post later this month. Stay tuned to learn how to validate the QVP with customers in order to fine tune your assumptions and make the results even more reflective of reality using data and analytics along with customer interviews.
In the meantime, please offer your own ideas or questions in the comments. We’d love to hear from you.
