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by Frank van Delft & Marc Van de perre
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Frank van Delft & Marc Van de perre
Maximising Customer Value Customer Experience
Customer Insight Management
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www.cim7.nl
Customer Insight Management
Frank van Delft & Marc Van de perre
Maximising Customer Value Customer Experience
Customer Insight Management
&
Introduction
Multi Source Business Information
Customer Insight Creation
The Value-creating potential of Customer Insight: The Concept of Customer Value
The Customer Value Model
Customer Activation based on Customer Insight
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Introduction
What is ‘Customer Insight’
‘Customer Insight’ is considered by some organisations to be just a new name for market research, perhaps enhanced by information from a customer database. But Customer Insight has two forms.Firstly, there are Insights (plural); flashes of inspiration, or penetrating discoveries that can lead to specific opportunities. Market research or customer databases can and often will deliver these. Management intuition can also deliver great insights. However, much more important, and more essential to what organisa-tions need today, is Insight (singular), defined as ‘the ability to clearly and deeply perceive an embedded knowledge about our customers and our markets that helps structure our thinking and decision making’. In a customer-focused organisation, it is something that everyone should have.
Customer Insight involves the classical area of information, like knowing who (potential) customers are, what they do, where they are, what they would like to buy, what media they are exposed to and what media they choose to view, listen, read and interact with.
‘Insight’ also includes psychological areas; what customers think and feel, what their objectives, motives, and tactics are, and how these influence their behaviour, including the influence of external factors (like the economy, society). Customer service, like the experience that the organisation provides to their customers, possibly in comparison with their competitors, is part of the insight.
This also includes their feelings about experiences expressed through complaints, compliments and inquiries for further information. The most important insight is perhaps whether the organisation has delivered on promises made to customers (through product descrip-tions, conversations, branding) and whether it has fulfilled the role that customers expected.
Insight finally includes whether the organisation is gathering and using customer information insight properly, both in the legal and ethical sense.
All this information is not only delivered by market and customer research, but especially by the use of data, the amount and the variety of data that is increasing rapidly, since we have not only customer transaction and customer relation data (CRM), but also web statistics
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and social media data. And all these data are helping us create better customer insight for various marketing decisions but can also be used to develop algorithms for marketing automation and real-time marketing.
Customer Insight is more about collecting data and transforming it into information. The crucial part is to develop and create insight, which is of vital importance for the strategy and tactics that the organisation will implement in order to create Customer Value and Value for the Customer (Customer Experience). This transition from information into Insight is a very challenging creative process.
The Dutch Customer Insight Platform ¹ formulated the definition of Customer Insight as follows:
Customer Insight is a holistic approach to multi source business information for building an actionable understanding of the customers, in order to maximise customer experience and value.
In the next chapter we will explain the definition in more detail.
¹ Customer Insight Platform (CIP)
In 2013 the Customer Insight Platform (CIP) was founded in the Netherlands to
support the development of the profession of Customer Insight Management, in
particular by initiating professional education on post Bachelor level.
The initiators of the CIP are:
• The associations of Marketing (NIMA) and Market research and Customer infor-
mation (MOA). These organisations will be responsible for the certifications of
educational institutes and exams.
• Educational institutes; Beeckestijn, LECTRIC and SRM
• Customer Insight professionals; Frank van Delft (director CIM7 | Initiator CIP)
, Gert Jan Delcliseur (director SearchResult), Mike Hoogveld (partner Holland
Consulting Group), Stan Knoops (Europe CTI Leader & Global CTI Capability
leader and Consumer Technical Insights, Unilever), Robert van Ossenbruggen
(director CustomerCentral).
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The purpose of ‘Customer Insight’
Of course, Customer Insight is no new invention at all. Most likely, the words already appeared in the first marketing books. Originally, Customer Insight was mainly used for the development of (re)new(ed) products or services. Today there are a lot of areas were Customer Insight is used. Let’s have a look at these different areas.
Customer Insight bases for disruptive product innovation
A strong example of the use of Customer Insight in order to create innovation in Customer Value was published by W. Chan Kim and Renée Mauborgne in ‘Blue Ocean Strategy’ in 2005. The process of Creating the Value Curve is identical to the process in Customer Insight Management. The blue ocean strategy is based on getting the relevant and discriminating factors that drive Customer Value, and quantifying these factors for your organisation/brand versus competition as the beginning of a new strategy. The next step is to build a new proposition in which these factors are increased, decreased or eliminated again based on customer information. However, the most important part is creating the new factors that distinguish your proposition from the red ocean: only a sound Customer Insight process can create these new factors. Customer Insight bases for Business Model innovation
In chapter 3 ‘Design’ of ‘Business Model Generation’ written by Alexander Osterwalder, Yves Pigneur and 470 co-creators in 2009, the process was described on how to build a new Business Model based on Customer Insight. The strength of this book is the answer to the question how to build a new Business Model based on Customer Insight. However, the Customer Insight itself is only built on qualitative research, the Empathy map in particular. Use of data did not seem top-of-mind yet in 2009, even with almost 500 co-authors.
Customer Insight bases for continuous product innovation
Today Customer Insight Management provides companies with a continuous stream of information resulting in Customer Insight. For example Netflix, an online content provider, designs new television series on the basis of the viewing history of groups of customers and the attributes or meta-tags of the videos they watched.
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Customer Insight bases for Cross channel excellence
Customer Insight is not only used in the area of product/service innovation. It is also indispensable in integrated channel management. Often the customer journey and experience is the method used to develop great Customer Insight. In chapter 6 of Mike Hoogveld ‘s book ‘The excellent customer journey experience’ (2014) the implementation of Customer Insight in relation to channel decisions is explained in great detail.BCD Travel, a provider of global corporate travel management, segmented their clients and prospects on the bases of predicted Customer Value and Customer Experience and created different strategies for the different segments.
Customer Insight bases for Conversation and Relation Management
Customer Relation Management is one of the areas where the use of Customer Insight is widespread. Because measurement of results of actions is common sense in CRM, the structured search to what Customers really trigger, and how this benefits the Customer Experience and Customer Value, is more practised in this area.As an example, Starbucks, the coffee company, doesn’t use discounts to reward loyal customers, but offers discount to those customers who could become loyal. Loyal customers receive other benefits, but no direct discounts.
Without doubt, Conversation Management benefits from Customer Insight. Steven Van Belleghem explains in his book ‘The Conversation Company’ (2012) how customer experiences, conversations, content and collaboration are connected. KLM built 4 different conversation strategies, based on insight that reach and structural collaboration were the key dimensions to drive conversations. How this framework was built and on which information process, was less clear. If you are looking for further reading on this subject we recommend the book ‘Digital Marketing Analytics’ by Chuck Hemann and Ken Burrary, 2013.
Customer Insight bases for business optimisation
Customer Insight is not only useful for innovations but also for business optimisation. This is already a huge area but according to our expecta-tions this will explode in the near future when predictive analysis will be further developed.
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Some examples in this area: BCD Travel, a provider of global corporate travel management, provides business intelligence programs to their clients so they can analyse all their travel-related spending as easily as they can review a single booking. Amazon, an online retailer, ships items you are likely to buy in the future to a warehouse near you, to minimise delivery time. American Express, a financial services corporation, harvests data from social media in order to reduce customer attrition and the risk of default.
Fundamental change
Technology made it feasible for customers to be informed and to interact with businesses and other customers. It became easier, faster, cheaper and occurred at a higher frequency. The same technical de-velopments made it simultaneously feasible for more data to become available on the behaviour of consumers. For customers, the impact of this information explosion is tremendous; from an information shortage to an information overload. The search for what will fit their specific needs best is therefore completely reversed, but it still requires (a lot of) energy. For suppliers of products/services it became more complicated; products/services still need to be produced but much more on demand and in a more flexible fashion. Distribution and communication channels and devices increased rapidly and need to be aligned. Even prices started to be more flexible. In order to survive in this information jungle, customers want to have the information tuned to their needs and desires, by their preferred channels and at the proper time. They are looking for the information they expect but also want to be surprised.
“Customer Insight is not only useful for innovations but also for business optimisation.”
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For companies/organisations this means that they should have strong customer insight all the time. For all strategical and operational decisions, the insight in customers’ needs is the starting point for the development of value propositions, business models and customer relationships, but also for the use of multiple channels and multimedia, e-commerce, social media and mobile devices. This requires a sub-stantially different approach including investment, work processes and organisation. The good part of the story is that all actions are data-driven or data-supported, so that the result of the actions can be measured in comparison with the objectives, and give direction for further actions.
Organisations are aware of the need for Customer Insight
Marketers, Chief Marketing Officers and industry leaders are aware of the need to secure Customer Insight. In two independent studies in fall 2013, one in the Netherlands by the University of Groningen ² and a worldwide study by IBM ³, both indicated clearly that achieving customer insight is the most important challenge for today and the coming years. And both studies indicated that only 18% of the companies feel that they are prepared for the future, since they are collecting customer data systematically and use this for their marketing activities.
In the IBM study, results showed that companies who have a much better understanding of the customer are more likely to be the financial outperformers. Based on the study, IBM categorised the CMOs in 3 categories; the Traditionalists (37%), the Social Strategists, using social media and cross channels (33%) and the Digital Pacesetters (30%). Digital pacesetters use, in addition to the Social Strategists, data and Customer Insight on a structural basis.
² Research by prof. Peter Verhoef and prof. Peter Leeflang i.c.w. European Marketing
Academy and McKinsey,
³ Tijdschrift voor Marketing Oct. 2013, p. 16
IBM CMO suite study, 2013 | 564 CMO’s | (56 countries, 19 branches)
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Why is it so damned difficult to implement structured Customer Insight Management?
• The right data should be collected, verified, stored and made easily accessible. This includes online and offline data, inside-out and outside-in data, qualitative data and quantitative data.
• The variety of data resulted in a variety of systems of storage, a variety of owners and users of the data.
• It also requires different skills and competences to retrieve infor-mation from the various types of data.
• Even companies who share the information from the various sources in a systematic manner do have serious problems in getting customer insight based on this information. It is a well spread misunderstanding that ‘data-driven’ results in more profitable business. Only in a very few cases data alone leads to new insights. It is the combination between information and creativity, fact-based and (management) intuition, that leads to customer insight.
• Creating customer insight is not a one-trick pony; it is a process, a system that should implement in the organisation.
• And also the use of Customer Insight should be integrated in the operations. Whether this is used in a discontinuous environment or in an automated (marketing) system.
• And to make it even more challenging; although many organisa-tions have the specialists on board, they are not educated to work in an area with a wide-spread of specialists and combine data with open-minded creativity.
“Creating customer insight is not a one-trick pony; it is a process, a system that should implement in the organisation.”
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Coming up next in this book on Customer Insight
In the first chapter we described the definition of Customer Insight as - a holistic approach; - to multi source business information; - for building an actionable understanding of the customers; - in order to maximise customer experience and value.
We indicated some areas where Customer Insight is used, we explained the urge for Customer Insight and highlighted several obstacles why it is not so easy to implement structured Customer Insight Management. Now, let us preview the content of the forthcoming chapters.
In chapter 2, we will describe the different sources of business informa-tion. At the end of the chapter it will be easy to understand why only a holistic approach to this information might bring us to an understand-ing of the customers.
Chapter 3 addresses a process to develop Customer Insight. Since creativity plays a major role in the process of getting from information to Customer Insight, it is the part with the least theoretical background. In the dozens of books we have read on this subject, no profound description was made; if mentioned, it seemed to be a ‘black box’ type of activity.
Chapter 4 takes a broad perspective on the concept of Customer Value and Customer Experience.
Chapter 5 will deal with how Customer Value and Customer Experience are linked together in the Customer Value Model. The components of the model will be clarified.
The last chapter (6) addresses how Customer Insight is the driving factor in Customer Activation in order to maximize Customer Value and Customer Experience and how this can be managed.
In exchange for the effort of reading this all, you will get a comprehen-sive understanding of how Customer Insight can be put to work for you and your organisation.
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Multi-source Business Information
If you ask employees who deal with business information what kind of information is needed to get great Customer Insight, based on their answers, you have a fair chance to correctly guess what their job function is.Researchers will answer with the word ‘research’ and give you great examples of how research leads to sharp Customer Insight. Data analysts will say ‘data’, and give you good examples too. CRM staff will refer to reaction and transaction data, DM marketers will refer to campaign data, SEO managers will mention internet data, marketing directors might refer to competitive analysis, benchmarks, trend data, online panels, service desk managers will refer to customer contacts, creative will mention brainstorms and so on.
And all of them are right, but too narrow focussed. The art of getting the best information as bases for Customer Insight creation is to combine all sources of information and extract the business information that counts.
One example of how combined information is more than a single source piece of information, is what happened during a project we worked on for a company in B2B office supplies. The data-analyst showed us the results of segmentation analysis he made over the last 3 years. One of the segments in his customer database, ‘sustainable product buyers’ had grown from 4% to 6% in this 3-year period. Indeed a 50% growth, but still at a low level. He reported this to marketing, and according to his recommendation, they kept focussing on the 3 tradi-tional (much bigger) segments. The marketing manager argued that even though sustainability was a major trend, it seemed logical that in a price-driven B2B market and in a product category that had little impact on the sustainable performance of their clients, this segment was not important enough to spend more energy on. For different reasons, a survey took place with clients and non-clients. In this survey, the ‘sustainability’ segment amongst non-clients was 20%! A year after the company introduced its sustainable product proposition, the segment counted for 25% of its business with above average sales and margin.
“And all of them are right, but too narrow focussed.”
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While there is a wide range of Business information, there is a need to create an overview and to categorise the different sources. Common distinctions are qualitative and quantitative research (based on the method of data collection), inside-out and outside-in information (based on the source of the data from inside the organisation or from the outside world), the qualitative method and quantitative method (based on the method of analysis, according to Smith and Fletcher)⁴.
The grid initiated by Robert van Ossenbruggen is definitely our favourite. He made a distinction between ‘active’ and ‘passive’ in the method of data collection, and ‘structured’ and ‘unstructured’ in the nature of the data.
The ‘active’ way of data collection is when customers actively and consciously participate in the process of data collection (like completing a questionnaire). ‘Passive’ is when at the moment of providing the information consumers are not active and consciously involved. Providing an opt-in and completing an order form with personal information is not regarded as an active information input.
In case of ‘structured’ data, a structure or a grid was already determined before the collection of the data started. Examples are a survey with a questionnaire and a database with transaction data.When there is no defined structure in place in advance, we refer to it as ‘unstructured’. Examples here: focus group discussion, online communi-ties, and social media listening. Of course, when unstructured data are collected, we want to distill structured information from the data.
In figure 2.1 you will find the 2×2 grid with examples of types of data sources you can use as the input for your Customer Insight process. Hopefully this will give some overview, and makes clear that market research (the upper 2 boxes) and data analysis (the lower boxes) are more linked than many functionalists of the areas want us to believe.
More important is that it might help you to use the method / source that best fits your needs when looking for insights. Keep in mind that for answers to the famous ‘who, what, why, when and how questions’, some sources provide better results than others.
⁴ DVL Smith and JH Fletcher, the art and science of interpreting market research
evidence, Chapter 1.
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Figure 2.1 – Source & Methodology matrix
(source: Robert van Ossenbruggen & Frank van Delft, 2013)
If we want to know ‘what’ customers are doing, in most cases it is more effective to go to the bottom part of the grid. Asking customers what they have done (or will do) is much less accurate than measuring what they have done in terms of transactions or information request. In this context we are really astonished that NPS is still used as a top KPI in several organisations.
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It is beyond the scope of this book to explain all the methodologies for gathering information. What we notice however is that there are four areas of development:
1. Research that provides answers to why customers behave as they do, what triggers them. Examples here are narrative research, online communities and neurometric research.
2. Research that supports organisations to cope with the complexity of the market and achieve focus. Examples here are persona research, empathy mapping, customer journeys.
3. Of course the area of (big) data analysis, from analysis for ad hoc Customer Insight development to data processed with machine learning algorithms in automated marketing processes. Use of data has already been there for many years (e.g. CRM and DM). By the use of the net for sales and digital communication the amount of transaction interaction data exploded and this will continue in the coming years. The focus will change from data analysis to marketing automation / predictive marketing.
4. In the box ‘passive & unstructured’ we expect many develop-ments using techniques that enable to transform text to structured information. Tools that cope with social media listening & content analysis will be used in a broader perspective including call centre logs and personal customer experiences.
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Dealing with this variety of sources and methodologies of information, it hopefully became clear what we meant by ‘a holistic approach’. But in addition to this we support the idea that a holistic approach also includes what Smith and Fletcher called the ‘mode of analyses’. They encourage quantitative researchers ‘to bring the same level of attacking interpretation to the numbers as qualitatively specialists routinely deliver based on fewer, but deeper, observations’. And the challenge to qualitative research is ‘to take what we define as the “qualitative mode of thinking” out of its rather introspective methodological box’.
Based on our practice we would translate this to; data-analyst should not only deliver the result of the query but also include interpretation of the results (making the hypotheses clear), and qualitative research should not end by the summary of the 20 narrative group discussions, but deliver results that are likely actionable and can be quantified in the next stage.
“Data-analyst should not only deliver the result of the query but also include interpretation of the results.”
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In the previous chapter we discussed how data from different sources could contribute to Customer Insight. We provided an overview of a range of data options and explained how to get information out of the data. But information is not the same as Insight. Figure 3.1 shows the difference between information and Insight. Getting information out of data is basically a systematic process of ordering data (in various ways) or use some mathematical procedures to find correlations. Although this could become somewhat complicated, it is basically a rational process. To get Insight from the data there is also a process of creativity involved.
If we elaborate on the metaphor of the figure below, do you see other pictures (Insights) in the data-spots?
Just take 3 minutes and try it before you read further!
Figure 3.1 – Difference between data, information and insights (source: Frank van Delft, 2014)
Customer Insight Creation
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During several workshops, we invited the participants to do this exercise. Before looking at any results, we asked participants what type of ‘Insights’ they were looking for; 4 out of 5 mentioned ‘an animal’ at first, but most of them didn’t really exploit this direction. Within the limited time only 1 out of 3 came up with an attempt of a drawing; most trials stuck because they limited themselves unnecessari-ly, sometimes with the boundaries of the figure. Even more participants connected the dots, using them as points of return with sharp edges. A few use only straight lines. These two restrictions were quite remarkable since in the example of the cat these limits were not used at all.
The fundamental question is “how can we achieve better results?”If we reframed the question to ‘do you see other animals in the data-spots’, would more participants come up with new figures? In general, a restriction or a more focussed question generated more ideas than an open question. And if we showed more examples produced by the participants of the previous workshop together with the cat, showing a variety of figures using different techniques, the participants came up with many more new figures.
This all seemed quite obvious but it is exactly what happens every day in organisations when people working with data and information were asked to come up with great Insights. Very few new Insights will show up. The major point we want to make here is that great Customer Insight is based on excellent information and creative thinking.
But how can we get there? It will not show up just by blending these components. During our practices with Customer Insight Management we gradually developed a process of 7 steps (see figure 3.2).
Customer Insight process | 7 steps
1. Define objectives and Customer target groups.As we have seen in the exercise with the ‘Insight Cat’, focus on the objective we want to achieve generates more results than a very broad scope. We all (should) know the SMART requirements of proper goals, so we don’t need to spend time on this. Even though we know all this, in reality we behave like real people and forget these basics. When we worked with an energy company, the objective for the customer department was ‘to become the best performing energy company, by providing the best personal service, the lowest energy invoices and at the same time remain the largest producer of renewable energy.’
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Figure 3.2 - The Customer Insight development process (source: Frank van Delft, www.cim7.nl , 2013)
The business intelligence team and the marketing team were asked to come up with great Customer Insight on which activities could be developed that contribute to the realisation of this ambition. After 3 months, a lot of data, research and information passed the desks of the employees, resulting in a long list of interesting points but no clear Customer Insight.After the decision was made to focus only on the ‘best personal service to the customers’ the process became focussed and in 6 weeks excellent Customer Insight was developed.The same is valid for customer target groups. Are we talking to new customers, loyal customers, non-loyal customers, churners?
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Or are we dealing with segments within our target group, personas? For example in the hotel business: do we define a customer as a guest who has visited us once, this year, within the last 5 years? It doesn’t matter what we decide, as long as it is clear who we want to talk with.
2. Define your information needsIn the previous chapter, we looked at the different information sources and methodologies that are available. To define what data you need heavily depends of course on the objectives and the target group. Assume your organisation has the availability of the following customer data: value-based data, loyalty-based data, behavioural data, demo-graphic data and attitudinal data. If we want to attract new customers, it is clear that we have to look at the attitudinal and demographic data. To improve your churn rates, it is more logical to look in behavioural and value-based data, and to enlarge customer loyalty, the behavioural and loyalty-based data will bring more information.
Aside from the type of information, it is good to realise that you need to switch from ‘what and how’ information to the ‘why’, and from structured to unstructured and vice versa. First formulate the question(s) – literally, write them down! – and from there make a mind map of where you can most likely find the information. And make this an iterative process; if you have the first results; do you have a clear answer to your questions? If so, what is your next question? If not, what else can you think of to find these answers?
It is most important that all steps in the process (except for the objective setting) are not delivered by one person or one department / business unit / specialist group / function, but by all issue-related function areas. Share the request for information with others and you will be surprised to see the rich variety of data sources.
3. Insight analysesYes, today big data is a trendy topic, but we are convinced that data will become even more important than it is already now. The reason is that data not only can deliver us information even on a continuous base, but it also measures the results of our efforts to increase Customer Value and Customer Experience.Dealing with data, however, is for many people not really their most favourite part of the job, and whether they are right or wrong, it will most likely be for the wrong reason. Data analysis is not as complicated as many people think, it is only a lot of work.
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The process of data analysis can be divided into three phases; data preparation, the data analysis itself and the presentation of the results. • Data preparation.
In most cases, the data is spread over several, sometimes tens or even hundreds of databases. This is not a problem as such, but in almost all cases data were not defined in the same manner; different user IDs, different storage of names, numbers, blanks in strings, spelling differences, missing cases, duplicated data, missing variables, outliers and many more inconveniences. In most cases, preparation takes 40% to 50% of the effort of the analysis process.
• Data analysis itself. Running the analysis is less abracadabra than it seems. Basically there are only a limited number of processes in data analysis. According to Smith and Fletcher 5 there are ten levels: o Understanding the structure and pattern of raw data o Ordering the raw data o Cross-tabulation to examine sub-group variations o Measures of location (mean, mode, median) o Measures of variation (standard variation, variance) o Measures of significance and difference (confidence intervals) o Measures of correlation/association o Visual mapping o Prioritisation of needs (conjoint analyses) o Segmentation (classification) Today we also include predictive modeling, estimation and sequence detection to the basic techniques in marketing analytics. On most of these levels, you do not really use mathematics; it is more a grouping exercise (with some calculations) to make the data more meaningful. 5 DVL Smith and JH Fletcher, the art and science of interpreting market research evidence,
Chapter 9.
“Share the request of information with others and you will be sur-prised to see the rich variety of data resources.”
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Data analysis is a very lively process; on one side you are working towards reducing the data and focussing on the goals, on the other side you look for unorthodox data analysis modes and serendipity. The analysis part takes about 20% to 25% of the effort of the total analysis process.
• Presentation of the results. Just making an analysis is like writing a book for yourself; no one will receive the advantage. The art of presenting results from analysis is crucial. Use of visualisation will help ensure that the information will arrive in people’s minds even if they are less analytical oriented. You would be surprised by how much informa-tion you can get across with 2×2 matrices. But we also need to pay attention to the action ability of the infor-mation in relation to the decision; what is the weight, the power and the direction of the evidence? Communication of the findings also takes a significant part of the effort of the analysis 6.
4. Interpretation and synthesisIn this 7 step process we make a distinction between step 4 (Interpre-tation & Synthesis) and 5 (Customer Insight Creation). In practice, these two steps are in most cases more integrated. If the members of the Customer Insight team present their findings, the creation process im-mediately starts off. And there is no reason to stop this process as long as the Customer Insight Manager keeps track on the process.It is a delicate balance between using and understanding all available information and synthesising it to the essentials and the free flow of creation insights on this.Sometimes teams stuck to the information, questioning all presented research results, do not make any syntheses or conclusions. Of course that can be caused by insufficient or inadequate insight analysis, but more often it is caused by unclear objectives, the composition of the team or an unqualified Customer Insight Manager.For the same reasons, project workshops can result in open brainstorm sessions based on only a limited part of the available information with no clear output.
6 DVL Smith and JH Fletcher, the art and science of interpreting market research evidence,
Chapter 13.
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Essential in this step is that the members are well prepared to present their key findings in an impactful way to the team while the team is constantly looking for cross-border connections. In the next step we will have a closer look at the workshops themselves and how we cope with it in practice.
Figure 3.2 suggests that the 7 step process is a straightforward, longi-tudinal process. In reality, steps 2 up until 5 will be run through several times. Quite often it will appear that in step 4 we must conclude that crucial information is still missing or that we discovered, as a result of the process, that we have to look for information we never thought of, or that we need several workshops with different team members. That is all part of the Customer Insight Management process.
5. Customer Insight creationAs members of an advertising agency know, creativity is not only a matter of inspiration. It is also a structured process and hard work, and therefore more than a black box. During our practices in this phase, we developed a working approach which we named Cyberworks®.
The members of the Cyberworks® (6 to 8 preferably) are specialists from different areas. They are also a mixture of more analytic and intuitive persons (see figure 3.3) and will be most likely a mixture of primary and secondary thinkers.
Figure 3.3 – Intuition of Analysis; drives for decision making
(source: Harvard Business Review, by Shvetank Shah, Andrew Horne and Jaime Capellá, 2012)
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At least half of the participants need to be involved in the previous step 4 of the process, bringing in some new members will increase the creativity.
The Cyberworks members will receive a brief about 24 hours before the meeting covering the objectives and the main findings. This document needs to be short (max 2 pages).
Cyberworks® starts with a short repetition of the brief, immediately followed by the generating phase in which generating ideas – lots of ideas – is the primary task. No discussion of ideas however, they need to be understood by everyone in the workshop.The second phase of the meeting is the co-creation phase. The initial thoughts will be clustered, discussed, elaborated and some will grow into more profound ideas. These initial insights will be elaborated in more detail and explicitly linked with the results of the analyses. This last action can also be transferred to the next meeting. We experienced that it is better to have more different, brief Cyberworks® (around 60 to 75 minutes) instead of half-day sessions.
Depending on the objectives and the complexity of the challenge, it takes more workshop sessions to create a potential actionable Customer Insight. In most cases steps 2 to 5 are done several times. In case Customer Insight Management is a structured process in the organisation, potential insights go to step 6 while information will be further explored until the objectives are met.
6. Validation and Customer Co-creationWe have now completed 5 steps of the 7-step process. Related to the definition of Customer Insight we have now succeeded in building a potential actionable understanding of the customers based on a holistic approach using multi-source business information. The question now is whether we can build propositions, channel and content strategies, sales and relation strategies based on the Customer Insight. Is it an actionable Insight in order to maximise customer experience and value?
In the next chapters we will discuss the concept of Customer Value and Customer Experiences, and how to maximise both.In the 7-step approach, we are now at the stage were we validate the Insight on its action ability. Can we develop propositions/strategies that generate Customer Value and Experiences? And if so, can we offer this to an acceptable price and under acceptable conditions?
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A great advantage of the Customer Insight approach is that the process started with data. Compared with ideas that came up with flashes of inspiration or penetrating discoveries, it is now much easier to validate the Insight. But it must be done anyhow.
Direct involvement of Customers in the development process will always remain a difficult question. We all know the famous examples; Steve Jobs firmly neglected all direct customer involvement in the de-velopment process. His view was that customers are not able to see the benefits of really new developments. The first field experiments with the Nokia cell phones supported his vision. Initially when people in a small village on the countryside got cell phones, they placed them next to their cable phones. If the cell phone started ringing, they knew exactly where to find it. On the other hand, when Philips developed the cd-i (a combination of a video recorder, an audio system, a game computer and an education tool), it was maybe the biggest invention of the 20th century. But the system was very complicated to use and content for the system was not developed. A great technical innovation, but no (positive) Customer Experience and therefore no Customer Value. Maybe they should have involved customers earlier in the process.
Generally speaking, it will be more difficult to involve the customer in major strategical innovations, and easier in tactical and operational innovations. But involvement of customers is always needed. In this stage of the process you need to think of how, when and where you will co-create with the customer. Involvement of customers may prevent you from a disaster and in most cases it will sharpen your Customer Insight.
“Compared with ideas that came up with flashes of inspiration, it is now much easier to validate the Insight. But it must be done anyhow.”
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7. Actionable InsightAn actionable Customer Insight is not a proposition or a strategy yet, but a strong insight will make it much easier to develop the proposition of the strategy.Working with an organisation in the leisure market, we developed the insight that once families with small children visited a specific amusement park they became regular guests. Reducing the thresholds for the first visit with young children is vital in this process. A special rate card, special sanitary facilities and a selective communication effort were not quite difficult to put into practice. Creating an actionable Customer Insight that leads to an increase of the Customer Value and Customer Experience, is not a one-off exercise or project. It should be a continuous activity in the organisation.
Because Customer Insight can be used for several actions, the form of how the ‘Actionable Insight’ will be delivered is different.• In case of a new proposition this could be a strategy map, • In case of cross-channel activation this could be the channel
coverage map, the omni-channel funnel (including KPI’s). For further reading on this topic we recommend ‘Cross Channel Excellence’ 7.
• In case of customer service optimisation it could result in a defined next best action.
• In case of differentiated communication it could lead to a clear segmentation.
In most cases, the Customer Insight process needs to be automated. Developing algorithms for a continuous refreshment of the analysis is a crucial part of the Customer Insight process in order to deliver an Actionable Insight.
In all cases, data will support the Insight. This means that we not only have a baseline measurement, but we also defined the metrics that can be used for measuring the result of the actions. Customer Insight creates the standards for accountability.
7 Cross Channel Excellence’, Mike Hoogveld,2012, Chapter 7
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The Value-Creating Potential of Customer Insight: The Concept of Customer Value
In the previous chapters we have explained that Customer Insight goes beyond the mere collection of data and transforming these data into information that is relevant to the organisation. The ultimate goal of customer insight is to offer an organisation the ability to create increased and sustainable value, both for itself and for its customers in particular. As such we can consider Customer Insight as an asset just like e.g. production equipment, patents, trademarks, specialized skills, etc. In this chapter we will elaborate more on the concept of customer value and how customer insight will increase its value-creat-ing potential.
The concept of customer value
Value is a concept that is used very often in organisations, and probably much more often in marketing. How often do we hear that we must generate value and that we have to do everything we can not to lose it? Value is often discussed as if it were a physical good, but at the same time it is also a controversial concept, with little agreement on the actual meaning of the word.
Looking at value from the organisation’s perspective
In practice, Customer Value comes in two flavours: the – usually financial – value of the customer as seen through the lens of the organ-isation, and the value for the customer as seen through the lens of the customer.
From the perspective of the organisation we could describe value as satisfying the needs of the customer at the highest possible yield for as long as possible (also referred to as the customer’s lifetime value). How to best satisfy the customer’s needs, is up to the management’s vision, the market and customer knowledge, the norms and values, and of course the possibilities at the organisation’s disposal. At first sight, it seems more than logical that an organisation, with this in mind, is interested in optimizing the income and especially a relationship with the customer that lasts as long as possible.
Looking through the lens of the customer
From the customer’s perspective, value can be defined as the difference between the benefits the customer receives when choosing a certain
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offering and the counter-offerings he must deliver in order to attain it. Note that this is a very general description of the concept of value for the customer. This definition allows for the counter-offering to not be monetary in nature, but something completely different that can contribute to the increase of the value for the customer. As a result, value proves to be very context dependent and this will determine how the individual involved perceives value, and which choices he will eventually make to optimise it. Since context constantly changes, it’s more than likely that the value perception will continually adapt.
Figure 4.1 - The two meanings of customer value (source: Marc Van de perre and Ton
Kuijlen, Ken uw Klant, klantwaardemanagement als motor van uw marketing, Lannoo-
Campus 2010)
The value balance
Since both the organisation and the customer want to generate as much value as possible, you can imagine that both parties will have conflicting goals most of the time. A customer, for instance, will want to pay as little as possible for as much services in return as possible,
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while the supplier, on the other hand, will want to keep the price as high as possible and the cost to serve as low as possible. The figure below clarifies this from both points of view. There are two important dimensions: the price the customer is willing to pay, and the total cost for the supplier to fulfill the customer’s needs. These two points of view will eventually have to reach a modus vivendi: a situation that is acceptable to both the buyer and the seller, and which results in a deal that both parties feel content with. If one of either party decides that the counter-offer is too big for the value received, then it’s very likely the transaction won’t proceed.
The modus vivendi that appears between buyer and seller is called the value-balance. In fig. 4.2 we see this value-balance represented as a diagonal line. This line shows the situation where both seller and buyer benefit from the transaction. A buyer will be willing to pay a high price if he gets high value in return. That means the costs for fulfilling this customer’s needs will be high. The same reasoning applies to products and services that in the eyes of the buyer have low value. The price he will pay for that is low, which means a win-win situation can only be es-tablished when the supplier keeps the cost for fulfilling the customer’s needs low
Figure 4.2 - The value-balance (source: adapted from Marc Van de perre and Ton Kuijlen, Ken uw Klant, klantwaardemanagement als motor van uw marketing, Lannoo-Campus 2010)
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The value-balance applied to the concept of customer value
The reasoning we used to establish that there is a value-balance can now be generalized to the concept of Customer Value. In fig. 4.3 we see what this means, namely that a win-win situation appears when the value for the customer is in balance with the value from the customer. Deviations from this value-balance result in an unstable or unmanage-able situation. The situation is unmanageable for the supplier when the customer continually receives a value that is not in proportion to the contribution he must deliver. In other words: the supplier spends a portion of his means for which he receives nothing in return, and it’s clear this is a situation that can’t last too long.
Figure 4.3 - Balancing customer value and customer experience (source: adapted from Marc Van de perre and Ton Kuijlen, Ken uw Klant, klantwaardemanagement als motor van uw marketing, LannooCampus 2010)
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The condition is also unstable in the opposite situation when the value the customer receives is lower than what he gives in return. The customer then constantly pays too much for what he gets, which upon realisation will result at least in a discussion to rectify the situation. In the worst case, the customer doesn’t engage in conversation and drops out of the agreement.There is more, however. Aside from the appearance of a value-balance, there’s also the effort made by both the customer and the supplier to maximize the value they receive. Value maximisation is one of the goals that form the basis for customer value management, and we will shortly take a look at what exactly this entails.
Maximising Customer Value
How can we make Customer Value – that is the Value from the Customer – as high as possible? This is, in the end, in many cases one of the goals of a business, namely maximising profits in the long term. To answer this question, we need to determine which factors influence customer value. To do this, let’s return to the definition we gave earlier where we chose profit as the company’s goal: ‘Customer Value is the totality of contributions made by a customer to the fulfillment of an organisation’s goals during the entire relationship of the customer and that organisation.’
With this definition in mind, we can enhance Customer Value in two ways:
1. By relying on factors that increase profits;
2. By doing business longer with customers with whom we can seal more (profitable) deals.
Originally, businesses focused more on the first possibility. It was only in the nineties, however, with the growing popularity of relationship marketing, that people started to be convinced that customer value can and will be influenced by paying attention to the relationship with the customer 8.
8 See for example The Loyalty Effect (1996) by Frederick Reihheld, who was the first to offer
us an insightful and structured explanation of the effect on customer value when making
customers more loyal, and therefore increase the duration of the relationship.
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The most important factors that influence the increment of total profits are:
• Acquisition costs, which naturally, we try to keep as low as possible. We can achieve this through a more targeted approach and a solid pre-evaluation of potential customers.
• Turnover growth through cross and up-selling. • Saving on costs, achieved by having better knowledge of
customers resulting in more efficient and targeted actions. • References. Satisfied customers tend to share this with their
relations. These recommendations are often an important source of income.
• Price premiums. In most businesses, we notice that older customers very often pay higher prices than new customers. Think, for instance, of the many introduction offers from mobile carriers, and newspaper and magazine subscriptions (often to the frus-tration of existing customers who believe their loyalty should be rewarded).
• Price premiums, however, are often the result of the relatively higher price insensitivity of existing customers. These customers have a certain relationship with the supplier and are familiar with their products and services. They are therefore better equipped to estimate the value, and as a result they are more price sensitive than the new customers who are only just considering the acquisi-tion of their first product or service.
Reichheld researched all these factors and showed that each started to play a bigger role as the relationship progressed. In conclusion, if we want to optimise Customer Value, we don’t only have to consider the factors that directly influence profits, but we also have to know and understand the life cycle of the customers. This Customer Insight allows us to better quantify and take into account the indirectly influential factors. We will elaborate more on this in the next chapter.
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Case: assessing business targets based on Customer Value
A technological company has developed a product line that has been successfully brought into the market for several years. The product line is renewed and expanded, and therefore a business development strategy was created for both new and existing markets. One raises the question, however, whether the turnover goals are realistic, and how great the efforts with regards to new business will eventually be. For the current year, the turnover is predicted to be €400,000, but for the following years, turnovers of €1,400,000, €2,200,000, €3,500,000, €5,500,000, and 8,800,000 are planned, each time with a margin of 50%. In other words, a considerable growth has been assumed, and the question is whether a business development manager would feel comfortable with this plan.
As usual, the solution for this type of questions may be found in the customer and accountancy databases. When having a closer look at the data, a few interesting observations pop up. First, it appears that there’s a strong correlation between turnover and the realised margin. Second, after doing some simple calculations, we find that the total expected turnover for the running year is 2,323,627 euro, that the average customer retention is 79%, and that purchases tend to be made regularly, although with varying purchase amounts. Based on existing customer data, it’s now not difficult to make a simple calculation of Customer Value using the base formula CLV = m x r/(1 +d – r), where m stands for margin, r for retention and d for the disconto. Assuming a disconto of 10% and the provisioned margin of 50%, we end up with a total customer value of €5,921,500 for all 420 customers in the portfolio. In other words, the existing customers will achieve this margin together for the complete duration of the customer relationship. Intuitively, we feel that the goals for the first few years will not pose any difficulties, and may even be realised with the existing customer portfolio. But until when exactly? And how big a gap will we need to bridge? Put differently, what is the margin that has to be realised through newly acquired customers?
To determine this, we have to convert the predefined goals into actual value (NPV), so that the base of comparison with customer value, which is of course also actualised, is the same.
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For the provided margins, this gives us the following actualised amounts in million euros:
YEAR 1 = 0.2 x (1 + 0.1) = 0.22YEAR 2 = 0.22 + 0.7 x (1 + 0.1)² = 1.067YEAR 3 = 1.067 + 1.1 x (1 + 0.1)³ = 2.531YEAR 4 = 2.531 + 1.75 x (1 + 0.1)⁴ = 5.093YEAR 5 = 5.093 + 2.75 x (1 + 0.1)5 = 9.522YEAR 6 = 9.522 + 4.4 x (1 + 1.01)6 = 17.317
We can now immediately observe that the current customer portfolio generates sufficient customer value to meet the goals of year 4. From year 5 onwards, however, there’s a difference in actualised value of 9,522 – 5,921 = 3,601 million euros, and in year 6 this is already 11,396 million euros!
The business development manager now has an idea of the efforts he will have to make. He also has a few strategic choices. Firstly, he can choose to increase the margins, for instance by altering the price or by selling more to each customer (up and cross-selling). A second option is to influence retention. This generally has a bigger effect than trying to increase the margins. It allows you to, for instance, demonstrate that increasing the average retention to 85% with the top 50 customers can make you realise the goals of year 5 with the current customer portfolio, which gives the new business development from year 1 plenty of breathing room to realise the difference (8 million euros).
Actualised margin goals (in Mio)
Turnover goals (in Mio)
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The Customer Value Model
It should be clear by now why influencing Customer Value can be a complicated process. Of all the possibilities a business has at its disposal – advertising, changing prices, retention programs, working with low cost channels, etc. – it’s difficult to decide which will result in the best return of investment. A number of researchers have delivered groundbreaking contributions in this area, which lead to some interes- ting insights. Probably the most important conclusion is that Customer Value must be seen as a combination of value components or equities. The opinions regarding the exact content and meaning are quite divided. People generally do, however, agree on the fact that working with value components makes the calculation and management of customer value simpler and more insightful.
The components of Customer Value
Our experience teaches us that there are three value components that play an important part in the appearance of Customer Value: the proposition value, the brand value, and the relational value (fig. 5.1). The first component, the proposition value, concerns the objective, rational features of products and services. These are qualities that make a direct interpretation and evaluation of the customer possible. Due to the fact that a specific product or service has a certain price, we have a given goal that is unlikely to be misinterpreted. But price can also be subjective if it’s considered an indication of quality. Quality itself, in turn, is another component that can, for instance, very easily be defined based on standards – like for instance the ISO standard – and as a result is difficult to be interpreted rationally.
Figure 5.1 - The base components of customer value (source: adapted from Marc Van de perre and Ton Kuijlen, Ken uw Klant, klantwaardemanagement als motor van uw marketing, LannooCampus 2010)
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Not all features of a product or service can be labeled rational, though, and as a result they allow for a certain subjective evaluation. This subjective value, usually based on image and emotion, is what we call brand value. The investments made by businesses in order to support their brands and the company image are enormous, and not rarely these are the sum of the intrinsic values of products and services. The purpose is for these investments to influence the perception of customers, and in their eyes create a positive attitude and added value as a result. For each customer, that interpretation is very different and subjective. Take card brands, for example. Why is one customer devoted to BMW and the other to Mercedes? The proposition value of both is essentially the same, but the subjective value is very different.
The proposition value and the brand value seem to be good approaches for explaining the rational and subjective value components of customer value. But what about the portion of customer value that is the direct result of the customer’s life cycle? We can explain this by means of the relational value component. The relational value shows to what extent the business is capable of keeping its customers. Relational value is thus the basis for a time dimension in the entire customer value concept. Without relational value, there is no bond with customers, which means that purchases made by customers occur almost completely independently from each other. In practice, this means that a customer will make his decisions purely based on rational motives and his attitude towards the brand.
How should we interpret relational value? Simply put, we could say that relational value is the extra added value that we can generate by retaining customers for a longer time, and as a result increase the profits from new or repeated purchases. Of the three value components, relational value is probably the hardest to quantify. Indeed, the longer retention of customers, or loyalisation, is dependent on the creation of a relationship with the customer, but how do you define a relationship, and more difficult still, how do you quantify it? Before answering these questions, we must first put on some new goggles and look at the value components through the customer’s eyes.
“Of the three value components, relational value is probably the hardest to quantify.”
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The components of Customer Experience
Each customer, aside from a few exceptions, cares little about the manner in which companies try to optimise customer value. What counts is the value and added value he receives when he chooses a specific supplier. But just like customer value, the value for the customer can’t be determined purely rationally. Here we can distinguish between three components as well, which together form the total value perception for customers: the rational value, the emotional value, and the experience value (Fig. 5.2).
Figure 5.2 - The base components of customer experience (value for the customer) (source: adapted from Marc Van de perre and Ton Kuijlen, Ken uw Klant, klantwaarde-management als motor van uw marketing, LannooCampus 2010)
The meaning of rational value is fairly analogous to the proposition value we defined earlier as one of the customer value components. A customer will always evaluate a product or service based on a number of objective, cognitive, or arguable qualities. Most of the times these are factual features that don’t allow any doubt or differences of opinion, such as price, quality, proposed delivery time, quantity, etc. In many cases, these factual features will have a big effect on the customer’s potential interest in the product or service. Price buyers are a good example of a category of customers who mainly pay attention to the rational value of a product or service. Another example is the release of requests for proposal (RFP), also called tenders, by government organisations and large businesses.
In these RFPs, the criteria, which a supplier must satisfy to apply, are accurately detailed. These criteria are valued very rationally, often using rating scales, after which eventually the most favourable supplier is selected.
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Of course rational features aren’t the only deciding factor. Even price buyers will take previous experiences into account – either their own or those of others – to make their decisions. Bad experiences with suppliers, their products, or services result almost always in the decision to choose a different supplier for the next purchase. We explicitly state almost, because there are situations where even bad experiences with a supplier still result in loyal behaviour from customers. If this sounds hard to believe, then consider certain forms of public transportation – for instance trains – where customers are often not satisfied with the provided service, but remain loyal nonetheless. This means that alternate forms of transportation have a lower value perception, for example due to price, bad connections or itineraries, and the duration of the trip. The opposite is also possible: very good experiences with a supplier and its products don’t necessarily mean unconditional loyalty from the customers. It’s often observed that businesses with satisfied and very satisfied customers still have very high churn rates. The reason is that they succeed in delivering high quality and getting high customer satisfaction, but not enough to transcend the level of the competition.
The aforementioned shows that experience is an essential component of the customer’s value perception. The growing attention spent on experience management serves to point out the importance that many companies attach to the understanding, and especially the improve-ment, of the customer’s experience.
Finally, there’s the third value component, the emotional value. It’s the component that offers an explanation why Disney, Nike, Starbucks, Apple, Harley Davidson, Ikea, Amazon and many other brands received world fame. Would you rather have a few examples from your immediate surroundings? Albert Heijn, KLM, Côte d’Or and Heineken are certainly no strangers and bring up certain images and emotions, just like Efteling, Plopsaland and Antwerp do in their own specific way.
“Very good experiences with a supplier and its products don’t necessarily mean unconditional loyalty from the customers.”
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Emotion is for customers – whether they are consumers or members of the decision-making unit in a company – a fact we can’t ignore. No matter how rational we are or want to be, emotions will always influence our decision making process before a purchase. As neurologist Donald Calne said so wonderfully: ‘The essential difference between emotion and reason is that emotion leads to action while reason leads to conclusions.’ The emotional value of a customer is a measure of attachment to a certain vision, life standard, ethic, and image. Companies try to respond to this through their brand or company communication, the manner in which they deal with their employees (and somehow communicate this externally), and their corporate governance.
Figure 5.3 - The customer value model (source: adapted from Marc Van de perre and
Ton Kuijlen, Ken uw Klant, klantwaardemanagement als motor van uw marketing,
LannooCampus 2010)
“No matter how rational we are or want to be, emotions wil always influence our decision making process before a purchase.”
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The value-balance that appears between customer and supplier, and the Customer Value components we discussed earlier, result in the customer value model of fig. 5.3. This model shows that the balance between Customer Value and Customer Experience can be seen as a balance between their respective components. In practice, this means the difference between the proposition value and the rational value needs to be as small as possible, and the same is true for the brand value and the emotional value, and the relational value and the experience value. Note that the difference between these components doesn’t have to be equal to zero, but still as small as possible. In the next chapter, we will take a look at how we can put this model into practice. The essence of Customer Value management is to aim for maximising both the value from the customer and the value for the customer, and at the same time making sure that this happens by optimally satisfying the customer’s needs with an optimal utilisation of means.
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Customer Activation Based on Customer Insight
The essence of Customer Value management is to aim for maximising both the (financial) value of the customer and the value for the customer (the customer value perception), and at the same time making sure that this happens by optimally satisfying the customer’s needs with an optimal utilisation of means. However, before we said that creating the ideal balance between customer value and customer value perception is probably unrealisable. Indeed, it would mean that for each individual customer, and for each potential customer, we would have to adapt the combined components of the value pro- position, the brand value and the relational value to their rational, emotional and experiential counterparts.
Figure 6.1 - The 7C customer activation model (source: Interface Marketing, 2014)
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The 7C Customer Activation Model
In practice, this is obviously not feasible, so we need a more structured and aggregated approach to managing customers and prospects with very similar characteristics. This approach, which we’ll call the customer activation model, can be summarized as shown in figure 6.1.
It doesn’t really matter where to start in this cycle but it is important to know that a clear customer vision must exist before any customer
activation action can be undertaken. A customer vision describes how your company can uniquely help create a better world for people, where they can achieve more in one way or another, by using your products or your services. It’s important that this customer vision is developed from inside the business and that it should not come from an advertising agency. But let’s get back to the model. To make things easier most organisa-tions will kick-off in the step they feel most comfortable with or where urgent actions are required. Our experiences learn that it is best to start with developing the necessary customer insight. Let’s have a short look at each of the steps in the cycle.
Customer Insight
We may assume now that you already have a good idea of what is meant by Customer Insight and what specific information is needed to support the next steps in the customer activation cycle. In particular, it concerns a.m.:
• the Customer Value (from the organisation’s perspective); how it is calculated (customer value model) and, if anyhow possible, how and based on which parameters it can be predicted
• the Value for the Customer, and more specifically the customer’s drivers of value and their eventual relationships
“It is important to know that a clear customer vision must exist before any customer activation action can be undertaken.”
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• the customer’s needs and wants and how they may influence the customer’s buying behaviour
• the customer life cycle• identification of the customer’s touch points and how value is
exchanged with them• how and why people buy products and services like yours, and how
you could possibly influence this process• differentiation of customers by customer segmentation• why customers are loyal to your products and services, or why and
when they churn While all of this insight may seem obvious, actually creating it can be quite complex and, apart from the necessary qualitative data, requires advanced analysis techniques and methods like e.g. clustering, classifi-cation, estimation, predictive modelling and affinity grouping, to name but a few.
Customer Value segmentation
Segmentation of customers is often used in Customer Insight develop-ment. But is also a key step of the customer activation cycle by profiling and segmenting customers along different dimensions. Through customer segmentation it is possible to find groups of customers who will fall into defined categories by virtue of matching customer attributes against recognised profiles.
The result is that a workable, customer-centric segmentation usually has three dimensions, which proof to be essential for successful and efficient customer activation.
1. A Customer Value segmentation, which categorises the value of the customer into a number of segments which are relevant for the organisation. In practice, this may e.g. be an ABC-categorisation with most valuable customers, middle-tier customers and loss- generating customers. But this may evenly be a more elaborated segmentation based on customer value in combination with demo-graphic and behavioural characteristics, like e.g. an RFM-like model (RFM: recency, frequency and monetary value of purchase).
2. A Customer Experience segmentation which categorises the value for the customer into a number of so-called personas that describe the specific needs, wants and behaviour, and how and in how much a customer perceives value with respect to the offered solutions.
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3. A Customer Lifecycle segmentation that identifies the different and relevant steps in a customer’s relationship, characterised by a common evolution of needs and wants with respect to products and services being offered in your market. Take the financial market as an example where the first step would be to open a bank account, then opening a savings account could be the next step, followed by a mortgage, specific insurance products, investment products, etc. Identifying this lifecycle (or even different lifecycles) makes it possible, in combination with the other dimensions of segmentation, to address your customers or possible customers with the right offering at the right time and in the best way.
Customer strategy
Having defined your customer segmentation, it is time to define or update the customer strategy. Or better, the customer strategies (plural), since the above described customer segmentation will require developing different sub-strategies for each target segment with its specific objectives. Theoretically, the number of customer value segments can become very high and therefore it is necessary to select only those segments that will deliver a positive contribution to your organisation’s objectives. After that, you should assign a priority to each of these segments and define the most optimal strategy that should be applied to them.
In general, there are three basic customer strategies to increase customer value: acquire new customers, a growth strategy and a strategy to retain customers as long as possible (a so-called retention strategy). An example of a simplified, graphical representation of an acquisition strategy is shown in figure 6.2.
Customer solutions
This step (and the next) is where the fun begins for those who are more inclined to the creative part of business. Now that you can target your customers with a cruise missile like precision, it is time to think as a problem solver and as a solution provider. Defining the right offerings for each customer value segment is partly defined by the previous analytics. But there’s still an important remaining part where creativity and innovation are necessary to provide customer solutions that will stand out from the competition. But bear in mind: the analytics and the customer insight must exist if your creative and innovative process is to be any good!
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Figure 6.2 - A simplified graphical representation of an acquisition strategy (source: adapted from Marc Van de perre and Ton Kuijlen, Ken uw Klant, klantwaardemanage-ment als motor van uw marketing, 2014)
Customer Value delivery
Finally, the last step - before starting all over again - is probably also one of the most complex steps to execute. It’s about getting the right offering to the right person on the right time and using the best possible way (i.e. channel) to do that. In other words, this is about identifying and executing the “Next Best Action” to bring value to your customers.
Why is this step so complex? Not only because of the infinite number of possibilities in which this step can be executed, but definitely because of the interdependence and the interference between the value delivery channels. Advertising on TV will possibly have an impact on the number of your website visitors or on the sales in your POS. If you send a pro-motional mail, a certain part will be forwarded to the receiver’s friends or colleagues. Maybe, the proposition in that mail will be posted on social media and read by someone who calls your contact centre and orders a product! The big challenge here is to track and trace all these interactions and figure out the customer journey. If it’s possible to do it, it will be very difficult, but the benefits will be correspondingly big. This explains why there’s so much interest in omni-channel marketing, attribution modelling and big data.
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Customer & Market Intelligence
Although we said above that Customer Value delivery is the last step of the customer activation cycle, there’s still one “C” in our 7C-model that we haven’t mentioned yet. In fact, Customer & Market Intelligence is the central part of our model and, leaving it out would have the same effect as flying a plane without any instruments: your customer activation process will be as good as your own perception and your gut feeling. That may be nice when the weather is fine, but we would feel very uncomfortable when a storm comes near!Customer & Market Intelligence is linked to all previous steps we discussed. Its objective is to collect all the necessary data in each step and to transform these data into knowledge. And results of actions will be measured. Based on that information new /additional/adapted Customer Insight will be created to direct new actions.
Customer Insight Management maximising Customer Value and Customer Experience
In the simplified 7C Customer Activation Model, Customer Insight is one of the steps in the process to create Customer Value & Experience.In real life Customer Insight is used in every ‘translation’ from informa-tion into action. Depending on the phase in the process this will have a different impact.
If segmentation and strategy don’t deliver the expected results, chances are that your strategy must be revised, but it is also possible that the underlying customer insight for designing the segmentation and the strategy is insufficient or maybe even wrong and will have to be adapted.
If the Solutions and/or the Customer Value scores are below expecta-tions we have to refine, modify and sharpen the Customer Insight and adapt the Solutions accordingly. In this case we most likely need to change the model we developed for this part of the Customer Insight process, change the input and rerun the model.
And if individual Customers behave differently (based on data collected through the system whether the source is man or machine), outcomes of the model change accordingly. Since the algorithms of the model are based on Customer Insight even in this case Customer Insight has an impact on the actions. This explains why Customer Insight is used in quite different areas, as we described in the first chapter (see figure 6.3 for an overview).
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Figure 6.3 - Customer Insight for maximising Customer value & experience | CIM7, Frank van Delft, 2014
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In chapter 2 and 3 we clarified how Customer Insight is based on multiple sources of information and we transfer information into pointed Customer Insight. Remember the picture with the number of apparently random dots and the image of a cat?
The concept and the components of Customer Value and Customer Experiences were portrayed in chapter 4 and 5. And finally we discussed how value and experience can be activated based on Customer Insight.
In theory it is quite easy, but research (chapter 1) indicated that in only 18% of all companies there is some form of structured Customer Insight Management. We listed several obstacles we encountered in organisations, so there is a tough job to be done. You have to master the ever-growing set of analytical techniques and methods. You will need the necessary technology like IT infrastructure, process measurement devices (think about sensors, cameras, cash registers, RFID, etc.). You need to co-create with colleagues across the organisation, specialists, customers. And you need the competence in your organisation. That might be an issue since expectations are that in the Benelux we need about 30,000 Customer Insight Managers in the coming years.
However there are 2 good reasons to invest in Customer Insight Management:
• The entire process of Customer Insight Management is based on measurement; the results of actions can be compared with objectives and in between; pre-eminent accountability.
• Today, a great Customer Insight is vital for organisations: “The ability to learn faster from your customers than your competitors is the only sustainable advantage”.
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Marc Van de perre
Marc Van de perre is managing partner of Interface Marketing, a Belgian marketing agency that advises and assists organisations in building, developing and understanding customer insight, and the practical application thereof in customer value management. He is an experienced marketer with a focus on business development and marketing accountability (ROMI). He is a specialist in marketing analytics, including data mining, predictive modelling and the latest techniques and technology for big data. He uses this know-how in applied fields such as segmentation, database marketing, marketing automation and CRM. He has extensive international experience in large and medium-sized enterprises in both business and consumer markets. In March 2015 he joined CIM7 as a partner.
Frank van Delft
Frank van Delft is managing partner of CIM7 in the Netherlands. CIM7 supports organisations in getting the best customer insights, if appro-priate on a permanent base (Customer Insight Management), in order to better serve customers and as a result, increase customer value. He is an experienced commercial manager and specialist in implementing digital marketing processes, combining data analytics, research and marketing, including customer- and business intelligence, marketing automation, marketing analytics and customer insight creation. With applications in the fields of innovation, channel optimisation, CRM and customer communication. Educated as a quantitative economist, hotello and computer scientist and experienced in marketing, commu-nications and data business he is at home in the world of Customer Insight Management. He started CIM7 in 2013.
Authors
Colophon Concept and text Frank van Delft Marc Van de perre www.cim7.nl
Photography and infographics Delfts Design www.delftsdesign.com Graphic Design Sophie van Bragt www.sophievanbragt.com Print Tripiti, Rotterdam Printed on FSC-Certified Paper
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CIM7 Customer Insight ManagementCopyright © 2015 CIM7 B.V.
All rights reserved. No part of this publication may be reproduced, dis-tributed, or transmitted in any form or by any means, including photo-copying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law.
Frank van Delft & Marc Van de perrewww.cim7.nl [email protected]
Amsterdam / Antwerp. January 2015
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“The ability to learn faster from your customers than your competitors is the only sustainable advantage.”
