Value discovery is one of the most important tasks a company can undertake. This is more so now than ever because of the way consumer tastes are changing so rapidly. Here’s how and why Ecosystem Thinking, rather than Lean Startup, should help you raise your game.
Something has changed dramatically in the world of value discovery. That we use this term in place of older, more familiar ones, such as R&D or product development, tells its own story.
Products and R&D are very solid concepts. You spend money, you get your product. But value is nebulous, captive to the eye of the beholder. Companies can spend billions on products and services and create very little of it. Ask Uber who are $12bn into their journey designed to create value in ride hailing.
In recent years, though, the emphasis in innovation circles has shifted away from the product (with all its tried and trusted tools of product development) to the value we create for customers (which customers, how much value, when will we know if it was a success?).
Some good companies as well as VCs have been left behind.
When more is less
There are at least two reasons for the shift. The first is because we are in a digital world. Now the cost of creating new products can be extraordinarily low (a developer, a laptop). And the cost factor has given rise to new methodologies like Lean Startup and concepts like Fail Fast, Fail Cheap.
As enterprises adopt these techniques, they push more projects into corporate innovation pipelines. More on the impact of that later.
The second reason relates to software development and delivery methods. It is now possible, often necessary, to chunk software into smaller and smaller units of work and push these into a live test environment with users relatively quickly.
Both of these approaches are creating problems. They reinforce the view that more is better. And both also reinforce a challenging proposition: enterprises can be experimental laboratories. Are you starting to get the picture? More ideas of dubious and yet-to-be tested value find their way into your workflow!
Perhaps enterprises can convert this negative into a positive but to do so means stitching together a value discovery process with very good value management and delivery. In other words, to be such an enterprise you need to have an end-to-end value capability. Unfortunately, most companies lack precisely that.
The problem with value
The origins of more is better lie in the promise of techniques like Lean Startup. Going back to that idea of experimentation, the new orthodoxy is that product development will become more scientific, if ideas are better tested.
This is a valuable idea because for the most part companies are very weak on one important aspect of innovation: customer insight. Too many companies use personas to represent customers rather than actively seeking and interpreting customer data for product development purposes. Lean at least takes us beyond the persona.
A theory to lean on
Here’s a brief summary of the playbook. Startups, the Lean theory says, can start with a good idea but they have little data of whether or not the idea has value to customers.
By creating a minimal viable product (MVP) they can test their ideas out and, a big bonus, they begin to gather that data or proof points. Over time, they can iterate the product in response to customer feedback and strengthen the conviction that this is indeed a valuable product. Or alternatively they can pivot to a different idea or just leave the game.
In contrast, the old ways were pretty cumbersome as well as being both complex and naive.
They were complex in the sense that companies employed people with vision to look five to seven years out. They would take a view on the products and configurations that the market might look for in five years’ time and if those views withstood scrutiny they would begin forward-looking R&D and the early patenting work that goes with it.
They were naive in that 18 months away from a prospective launch they slipped into the product development and marketing cycle with an under-tested assumption that they would sell.
The good, the bad, and the balance
Both techniques – old and new – have strengths and weaknesses. In given circumstances you have to work even as far as a decade out – each generation of mobile network technology falls into that category. But you also know that you will need a range of applications, say, for 5G if it is going to get real traction, and those applications might only surface months after launch. As an example, Samsung took 10 years to operationalise the OLED screens that it now dominates.
Value is always about balancing the risk of investment decisions, on the one hand, with understanding how to strike a chord with customers, on the other.
Iterating the way there with Lean Startup techniques is fine for startups who, at the outset, are one-product heroes. But it is problematic for companies that have a giant matrix of products, features, services and engagement styles.
What a to-do
Here’s the problem. Lean, or fail fast, usually leads to too many projects clogging up the work flow. That would be great if we knew they were projects of value, but in reality workflows are drowning in work of dubious and even low value.
Software teams almost everywhere face far too big a backlog of work because the ability to spin an idea up and make it a piece of work is so easy. Developers have to accede to demands that they switch between projects to show progress in as many of them as possible. This erodes productivity through what is known as context switching.
In fact, some measures of flow efficiency (a metric for exactly this kind of problem) rate companies below 10 per cent. That means they are wasting 90 per cent of their software resources. You might find this shocking but it is not uncommon!
The efficiency paradox also means that, however good some of those original ideas might be, they are not getting iterated because of the backlog. And those that are still carry a high risk of reducing productivity.
That’s why I said earlier that techniques that promise an experimental enterprise need an end-to-end capability in value discovery and management. Here’s why.
Doing better value discovery
I mentioned above that companies tend to make use of personas rather than using good data for innovation. Personas are part of the fail fast, fail cheap mentality. There is no reason to structure value discovery in a way that provides some guarantees of value. Just spin up a persona and give it a go.
Improving this situation is more complex than just reducing the number of projects or, to follow the current vogue, calling a project a product. It requires companies to structure change into their processes…
At the start, it means that companies have to do value discovery better. They need to retain the benefits of experimentation or test-and-learn while improving their ability to identify value much further upstream.
The answer to much of it is emerging in the ecosystem world. If you look at a company like Alibaba, it uses an extensive catalogue of content to probe the market for signals about what is needed or wanted. These can be the 4,000 or so video stars that stream product videos all day long, data from its content platform Youku, data from corner shops that indicate what people are buying on an hourly basis, or intelligent search that probes people’s reactions to autofill suggestions.
Data of this kind is also collated by Amazon from its ecosystem. It’s all about market signals. Netflix is another accomplished company in the field. After all, every time you watch a programme you are telling the streaming platform something about your tastes. These companies are all using data to discover value trends.
All in good taste
Yet here is something that might surprise you. Netflix has a market segmentation that consists of 1300 taste communities. Yes, it has an apparent free-form approach to massive data sets. But these are parsed through a highly structured and detailed view of the market. Amazon and Alibaba are similar. They live by segmentation.
The point about these new segmentations is that they allow companies to infer with some degree of accuracy that, for example, if you belong to taste community 1, with a secondary presence in taste communities 2 and 3, you are likely to be interested in films that community 3 has avidly consumed. Data, in other words, is best used with structure provided by segmentation.
In training sessions about value discovery, my colleague and I get asked one question fairly regularly – do you really need detailed segmentations of markets? And the reason they ask that is they are accustomed to the lazy world of personas and cheap failures (so cheap it prevents good work getting done).
The reality is, the more detailed your segmentations, the more easy it is to ask questions like: what does this segment really want? What will make these customers more successful? Are there product refinements that would bring me closer to their interests? Can we devise new propositions for some of them? Or do we need to invest in adjacencies?
And it also does something else. It means that product development has a head start on the journey towards value. By identifying discreet, granular needs you know better how to design for their success, you know how you might communicate with segments, and you know who to go to for feedback.
You set value management off on exactly the right foot and you reduce the congestion in the corporate innovation funnel. The advantages are so profound that, guess what. Companies forging a new path for the global economy live by this form of value management.