Unbundling value to align packages with customer segments

By Steven Forth

In much of our work, we are asked to unbundle an offer so that it can be repackaged as a set of offers that will command a higher price. Even when this is not specifically part of the mandate, it often becomes a precursor to our work on value-based market segmentation and pricing. This opens the question though, ‘Why do offers need to be unbundled?’ and ‘How does one go about unbundling an offer?’

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Before digging in, it is worth asking why offers even need to be unbundled.

Often it seems simpler to just bundle what customers are used to buying, and the company knows how to sell and deliver. Unbundling can open a can of worms and lead to uncomfortable questions about what functionality is being used and why. Nevertheless, unbundling and then repackaging can be the key to successful pricing innovation. It is difficult to price before one has done some research into how different parts of an offer contribute to value. Remember, the best pricing model is one in which the value metrics (the unit of consumption by which users get value) are tracked by pricing metrics (the unit of consumption that is priced).

The first reason unbundling can be necessary lies in product history. Most successful products began as a simple idea that filled a clear need. “How do I know the skills of the people in my company?” “How can I track prospects through a pipeline and generate reports?” “How can I distribute load between multiple servers?’ The idea starts simple. As it goes to market, the provider comes to realize that other things are needed in order to have a ‘whole product solution.’ This term, popularized by Geoffrey Moore in his classic book Crossing the Chasm, refers to all of the different functions, integrations and services needed to get value from a product. The agile idea of a ‘minimal viable product’ derives from this and is the minimum offer that can create value.

Over time, the whole product expands. New features are added. New services are wrapped around the offer. When a product is successful, and even creates a new category, a whole ecosystem evolves around it. In many cases, features and services are simply layered on top of the original idea. Sometimes they are layered on so thickly that the original vision gets lost. In other cases, the evolution of the ecosystem creates new niches and opportunities that are not well articulated at first. A patchwork of features and services sprout up to try to fill each of these niches. Unbundling is a way to see how all of the different parts fit together and how different combinations are creating value.

There are many ways to unbundle a solution.

One approach is to unbundle on a feature by feature basis.

The software engineers and user experience (UX) team generally have a pretty clear idea of what features the offer has.

Another way to unbundle is to separate out features, data and services.

Data, in particular, is playing a bigger and bigger role in creating value, and there is generally more than one type of valuable data locked in any application. When an application has grown into an ecosystem, the number of different types of data can be quite staggering, far longer than the list of features.

One of the key questions in unbundling is deciding how granular one should get. If the analysis is too coarse, ways in which value is being created can be missed. An example is talking about the ‘value of the data’ without being explicit about which data and how it is used. One can go too far the other way as well. Staying with data as an example; in most cases, one does not normally need to drill down to each metadata field (metadata is data that is used to describe other data).

At Ibbaka, we use a value lens to guide our approach to unbundling.

Basically, before we try to unbundle an application, service or solution, we work to understand value from the buyer and user perspective. We use our standard taxonomy of economic and emotional value drivers to do this. In some cases, we expand this to include social or community value drivers.

The value drivers then becomes the filter we use to unbundle. We are trying to find the different collections of functionality and data that enable each of the value drivers. One way to do this is to build a benefit ladder, but then extend it beyond the benefit to value. See a simple example below.

Benefit ladders are a commonly used marketing tool that helps us think our way from features up through functions to benefits and on to value. Below is a frequently cited example of the Starbucks benefit ladder developed by BCG.

The important thing to note here is that there are many paths up the benefit ladder. The same feature and attribute can support several different functions. The raw GPS data has multiple uses. The same functions can have many different benefits. One way to use benefit ladders is to see if there are features that do not support functions or functions that do not link to benefits.

Take these ladders and layer on top the economic and emotional value drivers. It is the value drivers, and the paths back down from benefit to function to feature that you are trying to get to when unbundling an offer for pricing purposes.

This does not mean that each of these value clusters should be its own offer. That generally leads to too many offers and market (and pricing) confusion.

The other part of this work is to match the offers to the value-based customer segments. For Ibbaka, a good segment is one that gets value in the same way and that buys in the same way. Once one has a value-based clustering of features and functions, and a value based clustering of potential customers (the market segmentation), it is usually pretty clear how to combine them into offers. Because this has all been done on the basis of value (emotional and economic) it is easier to discover the value drivers that will inform pricing.

All of this is easier said than done. A mature application can include thousands of features wrapped in services powered by data. Data analytics often needs to be used to find the clusters of feature-function-benefit-value. The same is true for the market segments. At Ibbaka, we are pioneering techniques from social network analysis to find the meaningful and actionable clusters in data. We started off doing this for market segmentation, and we are now extending it to the unbundling part of our work.

Reach out to us if you would like to explore how your applications and services can be unbundled then put back together in ways that create compelling value for specific market segments.