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Step 2: Continuous Innovation, The Basics

· 12 Steps

In Step 1 of this series we discussed the possibility of creating Flow Circles to allow you to discuss key issues like what is blocking change. In this article I am going to concentrate on refreshing your customer segmentation. Why bother?

The importance of customer segmentation renewal

You might have expected us now to launch into a peon to Agile or Lean and how they give you adaptive goodness straight off the bat. But we're not going to do that.

We'll concentrate on customers instead and not because we are customer engagement eggheads. We like the art and science of figuring out what customers really want and what would bring more goodness into their lives.

It goes without saying that there is a philosophy behind this.

Many companies sell a lot and sell badly (sorry to say, sadly means immorally). We have to move beyond inappropriate selling if we are to build defensible customer loyalty.

We've seen many instances of inappropriate selling recently and these have been very big issues (think finance (10 years of dishonestly repackaged credit), insurance (credit insurance mis-selling), cars (selling cars with dishonest environmental estimates), houses (wasting a generation of consumers bu upsetting aggressively)).

It is bad for enterprises as well as for customers to upsell and cross-sell inappropriately. Ask any of those banks that are still clearing down fines for bad behaviour.

Focusing instead on what brings success to customers is not only better business but it also far better, fairer and more interesting for employees.

They don't have to be the conduits for immoral selling, bad faith and bad arguments; and they get the chance, in FLOW, to engage in more authentic relationships that translate into continuous innovation where value can be tested by customers.

The honey-soaked land of a technical term: new economies of scope

There is a technical change behind all this diddlee-do with customers, too. We now have access to a totally different set of economies of scope. 

Well, what's one of them?

To understand, think conventional management. For decades executives have been told to stick to a core competency and avoid adjacencies.

An adjacency would be, say, a concrete manufacturer being naturally close to the housing market, and its ebbs and flows, deciding to selling lawn mowers. Great idea but it nearly sunk Blue Circle, a major European concrete-maker whose adjacencies were ill-judged. So too Kodak which moved in to chemicals outside its knowledge base. Businesses have been wary of adjacent businesses for a good reason, in the old, old days of limited computing power and poor market information.

In those darn old conservative days, businesses had a narrow idea of what an economy of scope could mean (explained below). Today major platforms use economies of scope as a natural part of dominating broad-based markets. They sell millions of products!!!

However, an economy of scope used to be something really challenging like, OK I sell toothpaste, would I not try selling toothbrushes too, and maybe floss? Near-adjacencies with identical distribution channels.

Even those tightly knit adjacencies used to be thought of as racy. Toothpaste is what you know!

`since 2007 and the rise of Amazon and Alibaba and platform businesses in general we now know that companies can handle millions of products and still stay focused on success. In other words economies of scope came of age.

The disruptive danger of poor segmentation

Because of that, knowing your customer customer segmentation is now more important than ever. Pour quoi?

Because economies of scope function through long-tail markets. That is to say, we are not aiming to sell a toothbrush to someone who buys our toothpaste. We now aim to sell relatively obscure Tibetan salt to the same person who opts for a New Zealand shower gel and our Aloe Vera toothpaste. And what's more they might be good for some deviate music that gives eternal kudos to David Bowie.

Those are the market segments we want. And we want them now. And fast.

Recently global card issuer, Mastercard acknowledged they now look to microtrends - something that might evolve in the market and die over a three day period - to create a micro-market or micro-advantage. Companies want micro-segments and micro-trends. And they need to deliver something of value to these fruit-fly markets ultra-fast.

Most companies ignore these developments eve though companies that neglect their customer segmentation are palpably and demonstrably vulnerable to voracious competitors.

We live in an economy where it is possible for startups, or any other competitor, to spot or create an unmet need, or poorly served customers, and then move in with a new offer, typically one based around a platform and a low-cost of customer acquisition.

Here's an example from the finance industry.

Currency conversion companies, like TransferWise and CurrencyFair, as well as many companies in working capital provision, spotted that banks have very weak customer segmentation.

Many retail banks think of their segmentation as: customers, premium account holders and high net worth individuals. 3 segments.

TransferWise spotted that many bank customers are heavy users of foreign currencies and they've made hay in that market. The banks were not simply being ignorant of their customers needs. They failed to spot that the need for simple and cheaper currency conversation had become a fast growing market.

Over the past decade smaller companies had grown their "exotic currency" needs as they traded into non-core currency areas (outside the Euro, dollar and sterling zones). Of course with different commerce platforms they were also trading heavily into those core zones.

These smaller companies had wandered into the area of global trade and began to encounter other needs. They were growing and that meant a need for working capital provision. They were probably encountering longer payment periods too, adding to that need for more working capital.

Google and Alibaba, as well as Amazon, know that banks do not do good working capital provision to small businesses. Tech platforms are now moving into this market. They will come to dominate it because they have data on a small firm's trading and receivables. Using data they can reduce the cost of risky lending and they can get a better sense of how much working capital is needed. 

In Alibaba's case, they can also help promote companies who are going through a sticky patch. By the way, which company poses a threat to the market for home improvement lending. Yes, Airbnb.

Good customer segmentation and good data pose a long term threat to many bank services.

The benefits of good customer segmentation

Keeping segmentation under review is essential:

  • It helps you target innovation
  • It alerts you to the range of potential micro markets and micro trends
  • It is a buffer against disruption
  • It allows you to think of specific customer success factors in long tail niches.By that we mean you can think how you can make customers more successful if what they do
  • It allows you to develop economies of scope, i.e. by offering multiple products into many niches - and we are in the age of scale, scope and speed.

So how does this fit with Flow?

It is absolutely central.

Continuous innovation vs product innovation

In today's business environment there is a lot written about startups and MVPs or minimum viable products, a way to build and test new products with customers. However, most firms are not necessarily building new products from time to time. That certainly used to the the case. Firms would have innovation funnels. 

New ideas entered the funnel say two to three years before going live and would pass through a series of stage gates or tests of their fitness for the market.

Very often companies would set up stage gate committees or review panels to decide which projects went further down the funnel and which were to be thrown out.

The difference with today is that we are now relentlessly changing what we are doing. It's not about the product or the MVP.

Think about that for a while. We still need to generate new product ideas but simultaneously we are:

a. changing how we deliver products, features, services for example by adopting new software paradigms like micro services

b. constantly introducing new features, functions and services

c. delivering to a wider range of customer needs and therefore multiplying the innovation requirement

d. bringing on board massively new technologies like Cloud, Big Data, AI/ML, IoT and blockchain

In this environment we have to draw on new analogies for how innovation defines our behaviour. This is no longer about a funnel. It is about a huge pipeline of novel activity. Inside that pipeline, the novel activity has to flow to a live-state all the time, sometimes multiple times per day.

Flow is the new context for this change. We put a lot of emphasis on targeting work at customer value delivery in Flow. The reason is, why else would you work? Value is a really good discipline for guiding work and making decisions. It guides work from customer segmentation down to delivery.

The diagrams below illustrate the path that work now takes (for "Projects" think of "collections of work units").

Continuous innovation and continuous value

It is arduous to go into work to learn something new each day. Ever harder to accept that we have to change the ways that we work at such a fundamental level.

Nonetheless, it is imperative that we learn new ways of work (what Fin calls the WOW factor) and there's an additional career benefit if you can be part of the group leading a simple and effective journey to a digital future. In our view the digital future is all about process model innovation as defined by Flow.

Digital companies keep processes loose and aways open to reinterpretation and reinvention. In the past, processes were the pillars around which organisations functioned. They were the fixed points, the rules, the guidelines.

Companies like Netflix now argue that process is only necessary if your staff are not smart and bold enough to take on the job of constant invention.

The value of that idea, process is not an anchor or a pillar, is that we need a shifting consensus on the right way to work. it is a particularly valuable insight when you also see your customer segmentation in a new light.

A company like Netflix is learning more about its customers all the time, through data. Therefore its segmentation is not a fixed point either.

That is such an exciting idea. It means there are constantly choices about what to serve up and how to add to the customer's sense of success in life.

If you admit you have a complex customer segmentation then you can set about innovating in new ways.

Another way to put this is, as we said earlier, "economies of scope". In the old days economies of scope consisted of small cross-sell and up-sell opportunities. Today scope is a competitive weapon light years removed from the old idea of a core competency and a narrowly define scope of activity.

Flow is a system to continuously co-create the right way to get the right work done in the age of scope economies. And it begins either with reviewing the Executive Portfolio or with renewing customer segmentation by creating a Customer Wall, Customer Insights Wall, or Customer Innovation Wall.

The only way to innovate effectively is to target the multiple segments that gather around your products and services or those that might transform your markets. The Customer Segmentation process is core to that.

If you are starting a Flow Circle you might want to begin a session by asking the question: How do we currently segment our customer base? How much do we rely on assumptions to do this? Have we tested our segmentation with any data?

The use of social data

A useful exercise before beginning that conversation is to use social media tools to help identify the range of interests of your keenest fans.

I use Michael Hussey's StatSocial for this but you could also use Sprinklr, Crimson Hexagon and any number of others that might give you a structured overview of customer interests.

And by the way we think of customer segmentation as a dynamic task - it is a Wall up there in front of you and you can constantly help evolve it.

What do these analytical tools show?

Customer segmentation of financially excluded people as a customer base

Let me give you an example. By way of background I have used these to create quite complex customer segmentations that reveal a new range of innovation possibilities. You can go at this problem through a more intuitive route but I prefer to use data.

The example: In Africa many NGOs task themselves to improve financial inclusion for the poor.

Some brief reflection with people on one of my trips to Kenya and we began breaking down this idea of "the poor" into many segments, some of which had no use for financial services as we usually understand them.

Pastoralists on the Tanzanian border give their market-day money to local shops who then get to use it as working capital while the pastoralist goes back to the hills.

Other tribes, who were domestic herders, were bound by local tradition to give their products away to family. It was an obligation of owning property (cows).

Others only got work because they would accept payment in cash.

The real segmentation in this financial market was actually very diverse and using the terms "poor" or "financially excluded" actually masked a lot of opportunity for different types of services or no service.

Poor customer segmentation in autos

Another example: In the auto industry, segmentation is usually by income group or family setting - saloon cars for executives, SUVs for families, starter cars for young drivers; sportier cars for petrol heads.

The interesting element of the western car industry is that mass market auto-manufacturers make their money on parts rather than on the car. They are in the Wilkinson Sword business model.

Executives tend to forget this. They have bought into the idea that their future lies in big data and the market of one - the single view of the customer mantra.

Most auto-companies have launched massive data projects that will take years to come to some kind of fruition. Will they ever yield the customer preferences data that consistently allows car makers to offer the right product with the right terms and conditions to make the right kind of sale?

Possibly, but it is a long way off. Maybe when all cars are autonomous and are funnelling new kinds of data to the car maker then the perfect sell will be in the cross-hairs but even then I doubt it.

Meantime big companies like big systems and car makers are sold on the idea that they can access data that has the same power as that enjoyed by companies such as Netflix. There's a clue of course why Netflix will make better use of big data. 

Netflix has to continuously monitor and assess what content interests people. They can do this by interaction and observing consumption patterns. But a car-is-a-car-is-a-car. There's not a fantastic amount to learn about what people think of them.

The single view of the customer mantra also overlooks the fact that in the Wilkinson Sword model, at least for large purchases, the decisive factor at play is getting people to return regularly for a dose of the Kool Aid - in the case of cars that means servicing.

It is very difficult to see what big data will give a company in the way of relationship building insights that will stop customers drifting off to much cheaper servicing alternative at the local mechanics.

Getting into the Flow with customer social data

In Flow we are committed to small steps rather than big big projects. The returning customer problem can be best approached by understanding customers better and that can be done very quickly through social data.

Analysing the interests of mass market car owners through social media data, they appear to be more diverse than product line-ups suggest. In fact reviewing the interests of mass market car owners you see things like:

1. Big switching potential as many also follow BMW and Mercedes

2. A love of poetry (Paulo Coelho features large)

3. Heroes such as Jeremy Clarkson and Bill Gates

4. Cycling

5. Motorbikes

6. Vintage cars

7. Wine

8. That only 30% of women follow mainstream car makers even though they make up over 50% of buyers and 80% of decision makers

9. Millennials are under-represented as are all ethnic groups

10. They have a disproportionate interest in e-commerce compared to the population at large but are less likely to follow money-saving sites

11. Have a very high level of interest in rentals

12. In parts and services, are disposed towards international news and travel

13. Are more likely to be interested than radio than the average online social media user

14. Are more prone to gamification

Two examples will help illustrate how these kinds of data (and there is much more of it) can help build segmentation strategies.

First there is a large segment of the car-buying fan-base that is also part of the bicycle fan-base. Second, the proportion of female fans in the after-sales fan-base is much larger than in the purchasing fan-base.

Among all the other data available just focus on these two. What do they suggest?

1. After Sales, where your profits are, is a primary concern of female drivers. But how many distributors prioritise female customers at garages? There is a host of ways to do that to improve loyalty and to build margins for exceptional service.

2. Cycling. What's the fastest growing form or urban transport globally? Bikes.And that many of the customers actually want also to ride bikes, so what could you do with all that distribution real-estate or with your business model to serve these customers better? Cycling is the fastest growing form of urban transport globally!

Looked at this way, what we are trying to do is move from:

How can I cross-sell and up-sell or entice customers back?


How can I create success for my customers? How can I help them achieve goals that are explicit of implicit, and of course profit by providing that value?

We need to get into the habit of seeing "better" in terms of customer success factors. Better is not more up-sell - up-selling cars is great for distributors but it can poisons a car-makers credit portfolio. Up-selling sounds great. but it can be disastrous. Instead we need to align with customer success. It is worth reading the work of people like Lincoln Murphy in the SaaS environment on this important topic.

But back to big data and customer segmentation. We think that a good antidote to big projects is to ask what can be done next week or the week after to improve customer satisfaction? Flow is all about the next action.

There is no need for a big data project to answer questions like: Are women adequately catered for in distributor garages, especially those on the edge of town? A small research project would help you find out.

Long before a big data project got under way, auto-makers could be improving the experience for a disproportionately important segment in the After-Sales market if they were to ask what they can do to help bring more success to female After-Sales customers in their experience of car servicing.

Equally, without spending a fortune, a company can start to figure out how to serve drivers who also love bikes in new ways.

These are just two small examples of customer segmentation insights. in FLOW we seek out customer value all the time. When we break work down and when we prioritise time-use, value for customers drives all decisions.


Customer Insight Walls and Customer Segmentation Walls can force the issue of customer success factors to become a daily concern - as they should be. They help shape up people's thinking around value. We'll go into this idea further in Step 6 where we will more closely relate segmentation to value and to work breakdown. More later.

Every company will find that their fan-base wants something more from them. Good customer segmentation can help start the flow of innovation that will deliver it.

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