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Predictable Buying

by David Brock on March 22nd, 2022

In the roughly 11 years since Aaron Ross published Predictable Revenue, 1000’s of articles have been written about creating predictable revenue, and the selling activities/processed to do this. To be honest, too many of the principles have really been twisted far away from Aaron’s original concepts in the book.

But one thing has struck me as strange, I’ve never heard any conversations about Predictable Buying. Interestingly, we will fail to achieve what we should in creating predictable revenue, unless we increase buyer success in buying. Yet all the data shows the majority of buying journeys fail, some research shows upwards of 60%. Separately, Mort Hansen’s Collaboration, shows a majority of internal collaborative projects fail (whether they involve buying or not).

It seems a key issue, both our customers and we sellers have to address is, “How to we increase customer change management/project success?” If we can do this, we have the opportunity of creating greater predictability in customer change management objectives, and greater predictability in the outcomes of our selling efforts.

To be sure, certain types of buying are far more predictable than others. In consumer goods, we can achieve higher levels of predictability, but that’s primarily through the sheer numbers of people buying and the analytic, AI tools that we can provide–but we still see surprises. In B2B, we can achieve some success in predicting revenue, but only in transactional types of processes. For example, in embedded products, we typically set up supply chain management processes with our customers, collaboratively forecasting demand, looking at schedules, and working together to make sure we fulfill customer expectations. Also, in certain types of highly commoditized offerings, we can achieve higher levels of predictability. For example, one of my clients provides a wide range of cleaning supplies. Another provides office computing technologies, several others provide software platforms used by all employees. These follow the same pattern of embedded products. Once a decision is made, generally there is a contract for a period of time, and purchases are forecast. For example, with each new sales person we hire, we don’t enter into a new buying journey for a CRM system, we buy another license. As we examine these, they are really not change initiatives, but continuation initiatives.

But the problem with predictable buying occurs when we look at complex B2B change initiatives. All of the examples cited above (the B2B examples) were the result of an initial major change initiative. And this is where our customers struggle, and in the majority of times, fail.

So if we are to create more predictable revenue, we have to help our customers be more successful in their change initiatives.

To do this, perhaps, we need to look at what causes customer change initiatives to fail. (We could look at what causes them to succeed, but I suspect we learn more by looking at the failures–since those represent the majority.) While we know each situation is different, we can start to understand common failure modes in change management initiatives.

A starting point is within our own companies (after all, we are someone else’s customer). Learning why and how projects fail gives us a starting point to have different conversations with our customers. “Did we fail to define the change initiative well? Did we fail to have the right people involved? Did we fail to develop a strong project plan with well defined goals, milestones, critical activities? Were we asking ourselves the right questions? Were we learning the things we needed to learn? Did we start trying to figure out what we didn’t know, that we needed to know? Did we gain alignment within the project team? Did we get the right sponsorship within the organization? Did we get distracted to something else? Did our priorities shift? Did we just lose interest? Where did we go to learn? How did we leverage other resources to help us? Did the problem disappear? Did we really understand the consequences of failure? How did we deal with this, later?” A side benefit of doing this, drives increased project success within our own companies–which drives business performance improvement.

Once we start understanding our own failure modes, we learn a lot of things about why customers might fail. Then perhaps we can look at how we conduct loss reviews with customers. Most of the time, when—if—we conduct loss reviews, most of the time, we focus on customers who have made a decision for a solution different from that we were selling. But they’ve completed their buying journey.

There are things we can learn from this, but this only addresses the minority of change initiatives. The majority made no decision, they stopped or abandoned the project. We, and our customers, learn more and gain more by understanding what/why this happened.

These are different conversations than we usually hold with our customers. The “why didn’t you choose me,” conversations aren’t very useful to them, they’ve moved on and our conversation holds them back. But the conversation about, “why did you choose to abandon the project,” is something that can help the customer. It can help them understand how they might be more successful with future projects. If might help them reconsider why they stopped and whether they might restart with a better road map in place.

Even without these conversations, to be honest, too many customers may not want to understand why they failed, we can learn a lot from the data analysis. But we need to change how we analyze these, again looking less at why we weren’t chosen, but rather at what did they do, what didn’t they do that they should have done (based on our experience), how might they have been more successful?

From these conversations and analysis, we can change how we work with and help our customers. If we help them succeed in more change initiatives, we create greater value with them, and we drive huge opportunities for revenue growth.

We are limited in our ability to predict revenue because our customers are limited in their ability to predict their change/buying success. Perhaps, that’s more important for both them and us.

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