Are you applying best practices in your pricing?

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Design your business for agile innovation


Ibbaka's focus is very much on the pricing of innovations. We help our customers understand the value of what they are building and then to segment the market and choose target segments based on that value. Once we have done that, we can develop a pricing model and the supporting data models that will connect price to value and deliver long-term customer value.

As a result, we get to see how many different companies organize for innovation. At some companies, corporate structures work against sustained innovation. Innovation at these firms is intermittent and seen as something separate from normal business activities. These companies often struggle to bring their best new ideas to market. There are other companies that have redesigned their entire business model around delivering sustained innovation. Let's look at one pattern we are seeing.

This pattern is generally seen at companies that combine professional services with a software platform. The professional services are delivered through the platform and the revenue model is a combination of services, platform subscriptions and in some cases data subscriptions. This bundle of services, platform and data is likely to emerge as one of the most successful in the emerging AI/data driven economy. It is different from the currently popular 'pureplay SaaS' pattern that has been popular with investors (and therefore start-ups) over the past decade.

Here is a quick sketch of what these companies look like. They often lead with services, but the services are part of a platform. The combination of services and platform is much more scalable and profitable than a pure subscription or pure services model and is generally needed to be customer centric (see Peter Fader's work on customer centricity). The services and platform alone are not enough. AI and prediction engines are becoming a standard part of business and AI takes data. The three data circles are drawn to indicate that most but not all data will be connected to the platform and that data is most likely to provide insights when data from two or more sources are combined.

Blending services, platform and data

What sort of organizational design will work for this business model? One critical issue is that these platform-based models can be heavy and slow to adapt to changing customer needs. Product managers need to be very careful about how they prioritize and roll out new functions and the overall market needs can be different from the needs of a specific client. Services though, have to be tailored to the specific opportunity. There is a tension between the product driven business model and the services model that becomes a barrier to customer centricity. This tension has to be addressed by new organizational designs.

Here is one design we have seen working. The company leads with some form of solution sales strategy (or when the offer is truly innovative with the Challenger sale). Professional services and customer success teams work hand in hand from the beginning. This is different from what we see at many companies, where their professional services is engaged at the beginning and then hand off the client to customer success. The critical innovations though, are the use of Ninja teams and the Data Analytics and Visualization teams. More on these below. Having at least a small R&D team, that is doing exploratory and foundational work is also critical to sustained innovation. Ideas from customer services, ideas and code from the Ninja teams, Data Analytics and Visualization, and R&D all feed into the platform. The Ninja and Data Analytics and Visualization teams are generally on fast cycles where 2-6 weeks seem to work best. The platform moves more slowly managing a 6-12 month roadmap, and R&D needs to be able to make longer term investments.

Organize for innovation

Let's look a bit closer at the Ninja and Data Analytics and Visualization teams. 

The Ninja team is a small agile group that includes business consultants, coders and UI people as needed. They solve customer problems that need a software solution and often draw on platform APIs (including some that may not yet be publicly available). Sprints are short, usually a week, but I have seen some teams do two sprints a week. They are more likely to use design sprints than a full blown design thinking process. Project length is usually 4-6 weeks and when there is more work to be done it is broken into smaller deliverables. Over time, the best and most general applications from the Ninja teams get rolled into the platform.

The Data Analytics and Visualization Team uses data from the platform, clients and from external sources to provide insights and visualizations. These are then used by the Ninja teams and the professional services teams in their own deliverables. As with the Ninja teams, the best ideas get rolled back into the platform.

Over time, as companies grow, they will want to have more than one growth platform. This may come from any of these teams - the platform/product team, a Ninja team, or Data Analytics and Visualization. Most likely, it will come from some mashup of all three with ideas and code from R&D as well.

What does all this have to do with pricing? Think of the Ninja teams as ways to explore new ways to create differentiated value. To do this effectively, they need to understand the basics of value-based pricing and be trained to gather data about alternatives, emotional value drivers and economic value drivers. The Data Analytics and Visualization team also needs to understand value-based pricing, and know how to collect the right data and then search for patterns that suggest value-based market segments (Ibbaka's internal software platform is focused on this).

The agile teams need to understand and be accountable for understanding the emotional and economic value and they should be equipped to gather the most relevant data. This will be of value when determining what goes into the platform, how it creates value and how it should be priced.

Pricing, in the form of value understanding and data capture, should be part of the front end of your innovation process.

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