I’ll bet you a doughnut that if you were to ask business people what their biggest challenge to quantifying the value of their solution, most would say “data”. Data: meaning a lack of customer research or competitive intelligence. In fact, we’ve seen managers delay strategically important value initiatives until they spent enough time gathering data. I shudder to think how much profit opportunity their company lost while waiting for their data ship to come in.
Don’t get me wrong, I don’t hold any grudges against data. Early in my career I was a market researcher, so I truly appreciate the importance of market intelligence for strategic decision making. But it’s also true that data-gathering exercises can serve as a safe way to avoid making a tough decision or taking on a tough challenge. And believe me, there are few tougher challenges out there than launching a value initiative, especially inside a company under profit pressure.
Data isn’t the biggest challenge to quantifying value. Rather it’s the ability to imagine yourself in your customer’s shoes. Meaning the ability to visualize all the various ways your product/service offer interacts with your customer experience. This is far deeper than data; it is the true understanding of your customer needs. An understanding that tells a rich value story about:
- How your offer is easier to buy than the alternative solutions
- How your offer is easier to install, start, use, and maintain than the alternative solutions
- Why your organization is more reliable, less risky, and more responsive than cheaper competitors
- How your offer will help your customers win-over, delight, and create loyalty among their customers
Data alone is meaningless without a story to bring it to life. A good product marketer should be able to state multiple value hypotheses along the lines of, “Our solution reduces our customer costs in [functional area] because of our [unique differentiator]” or “Our solution improves our customer’s top-line revenue because…” To put it bluntly, gathering data without first developing a set of value hypotheses is a waste of time and money.
Indeed, it is sad to realize that many product and marketing professionals are incapable of describing a detailed story of how their customers use and benefit from their products. Naturally, they are embarrassed when called on it. Clearly they have not spent much if any time visiting their customers. A few good customer visits can be worth terabytes of data.
Use these customer visits to develop an initial set of value hypotheses. Then test, refine and test again until you have a few solid hypotheses with clear and reasonable value driver logic. The proper use for data is to test hypotheses. Below are different data sources, listed by ease of use
- You/your team’s working knowledge. As a facilitator/participant in many sessions, I’m always amazed by what a small team can pull together in an initial value exercise with just the data at hand. Usually they bring enough working knowledge to identify the most promising value drivers and clearly prioritize which data to be collected and/or verified.
- Sales Channels. Probably the best, relatively untapped data source is already out in the field talking with your customers every day. They have the most current information on what competitors are offering, as well as the operational issues they are facing. All you need to do is ask them and listen.
- Existing research/databases. Data mining existing internal competitive and customer databases, when available, is an excellent, cost-effective way to collect data to quantify key variables in your value driver formulas.
- Third-party research. Analyst firms like IDC in the tech industry, or industry groups like CMAI provide industry-standard benchmarking data for a fee.
- Custom customer research. There are a number of excellent consultants available who can be called on to collect customer and competitive data, as well as to lend a hand in the other steps as well. However, hiring an expert isn’t the only alternative. Conducting just a few in-depth qualitative interviews with customers on your own will generate tons of insight, especially when you test a number of straw-man value hypotheses.
Remember, the goal of collecting value data is not to be statistically valid or scientifically proven, but rather to serve as a reality check on whether customers really accept the logic of your differentiation. Testing the logic is always more important than the data. Data will always be wrong and can be constantly improved. Lack of differential logic is a much more serious problem related to your product strategy.
Put another way, your data assumptions will usually be corrected by the customer. Let them, as long as your logic holds up. Given a choice, which would you rather be right or rich?