About Michael Collins

A chartered marketer and Fellow of the Institute of Direct Marketing. An internationally acknowledged award-winning consultant, author and trainer in DM with more than 25 years in database marketing and CRM consultancy. Experience has covered over 120 clients in the UK, continental Europe and North America in both B2B and B2C markets.

Recency, Frequency Value scoring

We are not all the same and neither are our customers! So it should come as no great surprise that customer segmentation is key to ensuring you market successfully to your customers; right message at the right time.

At the heart of direct marketing is data and using this valuable resource to ensure you send appropriate propositions to the right customers segment.

Many organisations think that they need to buy in external lifestyle or business demographics in order to better understand their customers. Whilst there may be benefits from these data resources, the value of in-house data should not be overlooked.

The most common areas around which to segment customers are:

  • Geography: Region, City or Postcode
  • Product: Category or even specific product
  • Response: (which campaigns have they responded to)
  • Source: (how their details were acquired)
  • Gender

These do not provide much insight into what makes a customer different when compared with any other customer in for instance a similar geography. But when combined with a measure of customer performance they become more revealing.

The classic method for measuring customer performance is to score each customer based on:

  • How recent was the last order placed by this customer – Recency 
  • How many times has a customer ordered on average within a predetermined period – Frequency 
  • How much they spend or what margin they contribute when they order –Value 

How to score customers

For each customer run enquiries on your database to identify:

  • Date of first and last order
  • Number of orders
  • Spend value (this can be gross margin, order value, whatever appropriate measure of value of customer expenditure)

From these calculate:

  • RECENCY: Time since last order is current date minus date of last order (Days) 
  • FREQUENCY: Duration of custom: current date minus date of first order (Days) divided by Number of orders 
  •  VALUE: In some cases you may wish to calculate a total value (i.e. sum the value measures) in others an average is adequate, calculated by dividing the total value measure by the number of orders 

These give you absolute values for each customer. The next stage is to categorise these into segments by determining relevant groups of values and then apply a corresponding score to each customer.

Taking each element in turn, the objective is to achieve categories that have broadly speaking similar numbers of customers in each.

The number of categories will vary depending on your business. For example a mail order clothing company:

RECENCY:

  • 1 to 30 days – score 5
  • 31 to 60 days – score 4
  • 61 to 90 days – score 3
  • 91 to 180 days – score 2
  • 181 to 365 days – score 1
  • Greater than 365 days – score 0

FREQUENCY:

  • At least once per month – score 5
  • At least once in three months – score 4
  • At least once in six months – score 3
  • At least once in greater than six months – score 1
  • Once time only purchase – score 0

VALUE: Clearly this depends on the business in question. If Gross margin is not readily identifiable, then the sales value could be used, but consideration should be given to the distribution of margin across the products your organisation sells.

RFV0

Assuming similar ranges of scores are applied for Value then your best customers would be those with scores of 555 (highest worth) down to 000 (lowest worth).

Remember this is not a discrete value of say five hundred and fifty five (highest worth), but an identifier composed of 5 elements: 5-5-5.

This therefore provides an identifier for each customer which can be used as selection criteria for campaigns e.g. You want to communicate with all reasonably recent high spenders who are not the most frequent customers with the objective of increasing the number of purchases they make in a period; so you might select customers with an RFV quotient of 414, 425, 414 and 525.

An on-going scoring process enables you to track purchasing and the effect of campaigns on customer behaviours. So in our example, how many has our campaign converted into 525s and 555s?
RFV1

This comparison will also provide direction for future campaigns to address churn, unprofitable customers and up-selling.

Other attributes can be score in a similar way: how long the customer has been a customer (longevity), number of complaints, cancellations or returns, referrals to friends and relations, abandoned baskets, quotations not taken up, etc., all adding to the behavioural profile of the customers.

Marketing systems can accommodate automated workflows for communications, reporting or alerts when customers significantly change score, to drive relationship marketing strategies.

For more guidance on RFV modelling or help with building your own models contact Michael Collins at mc@dmcounsel.co.uk.

©Michael Collins 2007-2018

 

 

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Considerations for the General Data Protection Regulations (GDPR)

The EU General Data Protection Regulation (GDPR) came into effect at the end of last year and will be enforced from 25th May 2018. This law clearly makes any business that deals with European citizens’ data, fully accountable. This regulation is quite categorical.

So, despite Brexit, if you handle EU personal data then you must comply. Whilst not dissimilar to the Data Protection Act and the European Electronic Communications Directive, there are new elements and definitions and so there remain some few grey areas surrounding how exactly it might work in practice and the Data Commissioner’s Office is working to constantly update their guidance notes.

Its key elements are the update in definition of personal data (scope also now includes B2B data), clearer requirements on Data Controllers, that each data subject (i.e. person whose data is being collected) must provide ‘explicit consent’ for organisations to store their data, they have a ‘right to be forgotten’ and organisations have an obligation to show where this data is stored. In addition, they must react to a request received from a data subject for access to data and provide a complete record of the data being held within 30 days. Any breaches must be reported within 72 hours. Depending on size they may need to appoint one (or several) Data Protection Officer(s).

A major consideration (and reason to focus on GDPR) are the new cash penalties for non-compliance which can be severe.

A documented strategy that comprises investigating your current data and policies, assessing them, determining how they might be improved and establishing controls to monitor and drive processes is required to minimise the risk for the organisation under the new GDPR.

In addition, all actions taken regarding privacy, minimising data, improving access for subjects, deletions and data rules should be documented along with the reasons why these actions have been taken, to provide a valid audit trail to the decisions, to support your compliance record.

You are urged to act NOW. May 2018 is not so far away, given there are policies to be implemented, risks identified, processes revised, data appraised and improved and staff trained.

For further views on GDPR compliance or if you require assistance with the practicalities associated with it contact Michael Collins on 07958 648014 or email mc@dmcounsel.co.uk

NOTE: This blog posting is for general information purposes only and is not intended to constitute legal or other professional advice and should not be relied on or treated as a substitute for specific advice. Each organisation should take its own decisions and source its own advice on GDPR compliance.

Profile analytics for increased marketing effectiveness

How using data to build member profiles and create relevant customer segments can increase marketing effectiveness

Even though we are seeing some growth in the economy, membership organisations are as ever concerned about withstanding the cutbacks in corporate budgets and pressures on individuals’ discretionary spend. The corporate cutbacks implemented during the recessionary economic climate have become ‘business as usual’ and continue to present the knock-on effects to professional bodies and membership organisations while the reduced individual spend is continuing to restrict personal memberships, meaning even greater competition for share of wallet. It is essential, therefore, for membership organisations to be in a position to address their own business imperatives like pre-empting churn by knowing which members are less likely to renew their subscriptions, determining propositions to help withstand reduced demand for products and services or perhaps strategies for reversing the slowdown in recruitment of new members. Practically all will need direction on how to improve the effectiveness of marketing activity.

The solution lies in leveraging probably one of the most valuable assets of the organisation. By maximising the use of its database an organisation can turn it into a powerful marketing tool. What one knows about members can help drive a better understanding of how they use and value their membership, leading to more relevant, tailored or personalised communications, better management of the relationship and increased loyalty.

The organisation will have membership data. Some organisations will have customer or membership relationship management (CRM or MRM) systems with all data in one place. More likely is the scenario where some may be held in central systems whilst more is held in additional departmental data repositories like standalone databases or as spreadsheets. Whilst we will consider the introduction of MRM or CRM solutions in part two of this paper, irrespective of the technology the immediate objective is to turn all that data into information and that information into knowledge or insight.

The key lies in bringing together into a single view everything you know or could know about your members, prospective members, customers and other constituents, their activities, actions, purchases and behaviour. Through analysis, this will reveal who is active and who is dormant, who will only ever remain a customer for publications and training, who attends events and participates in branch affairs and when and to which campaign they have responded. It can also help determine who your next contingent of advocates may be.

Along with basic personal information, other qualifying data such as gender, age, occupation and professional qualifications are likely to be stored. Ideally the organisation will also have access to details of their behaviour, such as renewal history, event attendance, training, subscriptions and other expenditure and contact history.

There is, however, no substitute for accurate, high quality, robust and reliable data; when aiming to access the elements that go to deliver the profile of a constituent it’s the quality that will determine how much of a grey guide or ‘black & white’ specific answer can be reached.

It is essential that the data is assessed before any analytics are applied. Inaccurate data will skew the results, so it is important that there is at least awareness of the inaccuracies to help in the interpretation of the outcome.

Building profiles of the constituents will reveal how long a member stays loyal and what might be done to pre-empt churn.

It will facilitate the building of predictive models and identify the communities – the segments within the membership base, for one-to-one marketing.

Whilst most people see the benefit of segmentation, the challenge is often how the segments should be defined. One may segment, for example, geographically, by demographics, by professional qualifications or by behaviour. I suggest a combination of all of them.

Equipped with the kind of data mentioned above one can start revealing important measures. Start with trying to understand the level of engagement by assessing which of your services and products the member has used or encountered. Then evaluate how many have availed themselves of the various combinations on offer. The table below shows an example of this.

member_engagement_table

This is the same concept that we have come to recognise when buying consumer products on-line, often referred to as ‘The Amazon Effect’ – the helpful hints that tell us that people who have bought the book we are looking at have also bought these other titles, green socks and a set of gardening implements. It is known as ‘market basket analysis’ and is a totally valid model across any combination of goods or services.

The next most interesting insight is usually revealed when the organisation management considers the outcome of the analysis. This is where they apply tacit knowledge – the experiences and understanding that exists within the minds of the people who run the organisation. Using this to interpret the outcome of these analyses is the tipping point between information and true knowledge.

Gaining insight into members, customers and other contacts will help a membership organisation understand who the ‘best’ members are, what opportunities exist, how to hold on to them and find more like them. Insight also provides the other side of the coin: who the ‘worst’ are and what they are costing in lost opportunity so that they and others like them may be avoided in the future.

To provide a complete profile one must include a view of the attitudes and aspirations that drive their membership decisions – the psychographic dimension.

This can be achieved through fusing the outcome of targeted market research survey data, revealing the hearts and minds of the members, with the analysis of member and customer involvement acquired through tracking behaviour across all of the points of contact. The enhanced profiles created can be used to determine how best to manage the relationship, pre-empt churn or to determine if they are merely approaching a major milestone in their membership such as a membership level that requires examination or CPD audit and which has traditionally been a jump-off point for members like them.

Having insight into what motivates their behaviour can be used to generate a relevant communication, dynamically drive the content for a digitally produced newsletter or even direct that member to the right web page or telephone agent as part of a strategy to retain their membership or upgrade into a new level of membership.

So, we have established the importance of bringing together all the data about members and customers into a single view for analysis to drive a better managed relationship, resulting in increased loyalty and reduced churn.

What is determined about current members can also be applied to prospecting. By applying the profile knowledge to new applicants, the organisation can be better prepared for what kind of member the new applicant is likely to be and so drive a nurturing programme or member journey that is personalised.

Now entering the broader realm of Customer Relationship Management (CRM) or Member Relationship Management (MRM), we encounter a concept that relies on a culture that puts the constituent at the heart of all processes, communications and policies.

Activating advocacy through analytics

In previous articles I considered how a business can maintain and even grow revenue levels through the analysis and application of data to drive targeted marketing, help pre-empt churn and develop relationship pathways. I suggested that this can also help determine who the next contingent of advocates might be and it’s this concept of advocacy that I wish to expand upon in this article.

Organisations have the ability to build and nurture advocacy tactics from within their own customer data and considering overall relationships with the organisation will assist in creating a relationship pathway that creates advocates. The object is moving the individual along the traditional ‘loyalty staircase’ from ‘Suspect’ (unqualified lead), to ‘Prospect’ (qualified opportunity), to ‘Customer’ (converted), to ‘Client’ (an engaged customer that has a low likelihood of churn), to the ultimate rank of ‘Advocate’.

But also recognising and pre-empting the potential churn points (when they may show themselves to be a ‘Rejecter’). Customers may become dormant, prospects can become turned off. There must be safety net to catch those that want to be saved and the strategy must make allowance for their rejoining perhaps at a different level.

From a database marketing or CRM point of view there are two types of advocacy – active advocacy and passive advocacy. Both can be managed through the database or a customer relationship management (CRM) system.

Active advocacy describes a scenario where a satisfied or engaged customer will recommend the organisation, its services or products to their peers and wider acquaintances. This will be by word of mouth, through member-get-member initiatives, peer-to-peer referrals, viral marketing or social networking. In this case the customer actively, consciously makes the referral.

Passive advocacy concerns using customer insight to facilitate the ‘cloning’ of new customers and the planning of pathways for existing ones. By analysing the behaviour, the profiles and history of customers to create segmentation, others who meet similar segmentation criteria can be deemed to appreciate similar status and services and so behave in a similar manner, thereby establishing the pathway that they should follow. The customers who are being used to create the templates for others are not intentionally advocating.

This analysis goes beyond reporting. One major difference between data analysis and reporting is that reporting provides a snapshot, a level of performance at a given point in time. It does not reveal why that level of performance or under-performance was attained.

This requires a train-of-thought process, an investigation to identify the reasons behind the results and it is the desire to find ways to improve performance and seize new business opportunities that calls for analysis rather than just reporting. It is this creative use of information and the knowledge of data that will lead to successfully exploitation of the organisation’s valuable data assets.

The challenge that faces organisations of all sizes is how to access and use reliable, high quality data to maximise the opportunity for acquiring information and create knowledge for commercial advantage. All of this can be achieved by collecting and maintaining the right data about customers, engaging them in a relationship and fusing the outcome of research or social media against the relationship database to enrich the segmentation and selection, thereby progressing them to advocacy.

The key is to understand how the customers wish to manage their relationship with the company.

I call it the ‘spectrum of engagement’ which runs from the apparently active customer who says ‘send me every communication, proposition and offer that there is’ at one end to the customer who appears distant or dormant at the other who says ‘I know where you are when I need something; don’t bother me’. The key concept to grasp here is that your most loyal member and potential advocate could exist at either extreme and, in fact, anywhere in between. On the one hand, the active and the other the aloof or one could say there are committed customers and those that participate on an ad hoc basis. You need to know where each constituent is on the scale and it is the organisation’s role to determine the position and use that knowledge to drive the relationship, the regularity and content of communications and the propositions offered.

So, how do you understand your customers? How can you identify ‘advocates’?

Active advocacy can be identified amongst committed customers, whose attitude and behaviour reflects satisfaction or who have been satisfied customers. Advocates will also hide amongst bloggers or complimentary contributors to the organisation’s chat-rooms and forums or social media whose engagement score or profile supports their sentiments.

Finally, the obvious ones will be those who have referred in the past or proven committed customers who have registered testimonials that can be used in marketing activity. These are the one whom the CRM should be inviting to participate at a higher level and should be included as the first wave for any viral marketing activity.

Identifying the passive advocates is where the analytical and data fusion techniques can be applied. Information from research surveys, from other external sources and from social media fused with the analytics from the data held on the organisation’s database to contribute the psychographic profile – their attitudes and aspirations and thus reveal the individual’s sentiment towards the organisation.

The fusion technique follows a fairly straightforward path. Firstly bring all the data into one database, making sure all those little departmental spreadsheets and odd copies of Access or Act are included! Then review the data and identify any requirement for enhancement or enrichment, perhaps adding some demographic or lifestyle information that may have an effect on behaviour or purchase decisions.

Next, mine the data and identify the segments or communities that go to make up the constituent base and define the differences in behaviour between them. This will facilitate what questions to pose in the research surveys or issues to seek from social media which I refer to as the ‘golden questions’; these will be the key differentiators between groups of customers.

Then execute the survey activity – often several variations of the survey are required to target the golden questions to right segments.

Research data can be both discrete and general. If research is discrete then it can be matched back to the subject, providing specific qualification criteria for those who respond. Information can then be extrapolated across other similar subjects to provide selection criteria, expanded profiles or segmentation.

If the research data is general, knowing which segment or community was subjected to the survey permits extrapolation in the same way.

The passive advocates will potentially be those with a positive outcome from the data fusion process. Their profile should be used to create a template for the relationship with other customers that look similar.

I envisage a three-element scenario at each step in the pathway: the state, the condition and then the action. In order for the step in the pathway to be identified a state must coincide with a condition; for example the constituent’s state may be that they are a new customer (say transacted for the first time within the last 3 months) and the condition is that they have made a subsequent transaction. The action may be a personal invitation from the MD to joint their loyalty programme.

Imagine it like a railway with the train being the customer. The track it is currently on is the state; the station it has arrived at is the condition; which way the points are set after the station directs the action, i.e. which track does the train take now.

These double triggers enable a greater granularity in the plotting of the customer on the pathway and the relevant action to be undertaken.

These three elements can then be interpreted as process or workflow and modeled in the CRM and by introducing the attitudes, aspirations and sentiments and extrapolating them across segments the organisation can refine one-to-one communications to move the constituent along the appropriate pathway.

The process can be extended by invoking a process of ‘cloning’ for new customer acquisition. Cloning, in this case, refers to the application of the appropriate relationship template to prospective members using the best members and most profitable customers to deliver high quality new customers – note: database marketers were practicing cloning long before we heard about ‘Dolly the Sheep’!

©Copyright Michael Collins 1998-2014. All rights reserved

Knowing customers wherever they touch your business

We have seen businesses who have been later entrants into internet and even mobile shopping losing ground as they lose customers who view such retailers as out-of-touch with their requirements. HMV was a great example where it seems their lack of investment in online in preference for diversification left a great brand anachronistic and irrelevant for its customer base and struggling for market share.
Today’s consumer wants a relationship with a brand that he or she values, trusts and wants to participate with through any channel. In return they want to be recognised as the same customer whether they engage on-line, in-store or on a mobile device.
Contact through any of these are valuable touchpoints where the relationship can be tested and data acquired, validated or augmented.
We had one client in fashion retail who were looking to stop publishing their printed catalogue since the number of orders being placed by phone or order form was declining. Analysis of how many of their best customers wished to manage the relationship across all channels was fascinating; typically they would wait eagerly for the latest catalogue and then having selected the items that attracted them they would go on-line to browse these products in more detail. Having made the decision, they would then visit their local high street branch and try the product and buy in-store. Taking away the catalogue would have had severe implications on their store sales.
The key is to recognise the customer however they present themselves to the business but ideally without the need for a loyalty card to encumber the experience as it replaces the enjoyment of shopping with a mundane device. In the work I did in the UK theme park sector in the 1990s and 2000s we shunned a ‘back-to-earth’ effect of data capture, relying alternatively on an in-context method of acquiring data which became part of the fun day out.
Once this can be achieved in retail (and I have some ideas of how to do it) the relationship can be integrated and rather than just today’s special offer being emailed or sent by SMS message, the individual can enjoy a much more tailored experience.
If any retailers would like to discuss this methodology do get in touch at mc@dmcounsel.co.uk.

Have you got ‘big data’ or do you just need to apply a ‘big data’ strategy?

I remember, in the 1980’s, being issued with my first desktop PC. It had a hard drive that held a total of 40mb. It was luxury. I could hold all the data I needed about clients along with documents and spreadsheets. Nowadays we won’t even look at a USB stick unless it can hold gigabytes of data.

Similarly, our clients’ databases then containing their several thousand customers with histories of their purchases were the epitome of ‘big data’, needing at minimum a mid-range computer.

What changed the view of ‘big data’ has been the explosion of digital information chiefly through the development of the world wide web and the ability of the industry to both meet the storage volume requirements and reduce the cost. Back in 2007 Information Week stated that “for the first time, the amount of digital information generated will surpass the storage capacity.” Since then, it has been estimated that 90% of all data in use today has been accumulated within just the last two years.

The term ‘big data’ implies volume and it relates not just to the amount of data being received but also the interaction between those data elements to derive new values and so create more data. However, consideration of ‘big data’ goes beyond just volume. As far back as 2001 Gartner, the technology research resource, identified that volume had to be considered along with ‘Variety’ and ‘Velocity’. We are seeing that today, with variety comprising more and more unstructured data for example in notes,  blogs and social media postings or photographs, videos and audio with all their attendant metadata (who took it, where, when, size, etc). Velocity relates to the speed of the flow of information.

Consider these examples of ‘big data’ in relation to your own organisations. Ebay receives 100 terabytes of new data every day, YouTube receives 48 hours of uploaded video every minute and the Cern Hadron Collider sensors deliver data 40 million times per second. That is big.

However, the underlying concept of how big you consider your data to be must be how you use it to derive knowledge and insight, identify trends and deliver nuggets of commercial opportunity. What may be considered ‘big data’ will depend on your data management proficiency and the capabilities of the technology you have; if it’s too big for you, then it is ‘big data’!

Unless you are really dealing with many terabytes or even the unimaginably large petabytes of data, don’t be too concerned about the technology and complexities of ‘big data’ if you are able to manage the data you have and the data you need satisfactorily. Consider rather how you can best use what you have effectively and efficiently and ask yourself are you collecting all the data you need to meet your objectives.

By understanding how to derive additional values you will turn it into information; by interpreting that information with your own tacit knowledge of your members, your industry, your markets then you will turn the information into knowledge.

The key is to have a robust data strategy that enables you to define the data sources and satisfy yourself that you are acquiring the right data from each touchpoint. Put in place processes for evaluating the data’s importance and therefore what role it has in achieving your objectives and a capability to implement analytics to deliver insight.

Data –whether ‘big’ or not – is only worth collecting if you are going to do something beneficial with it. I remember seeing an old photograph of a US military mess hall with a large sign over the trays of food saying: “Take all you want, but eat all you take”. It’s the same with data.

 

Michael Collins

©Michael Collins 2014

Know your members, grow your business

Whilst reduced discretionary spend continues to increase share of wallet and restrict personal subscriptions and cost cutting means less corporate money to pay for staff memberships there is an approach that can help maintain and even increase revenue levels.

It is essential to be in a position to address your own business imperatives like pre-empting churn by knowing which members are less likely to renew their subscriptions, determining propositions to help withstand cutbacks for your products and services or perhaps strategies for reversing the slowdown in recruitment of new members. Practically all will need direction on how to improve the effectiveness of marketing activity.

The solution lies in leveraging probably one of the most valuable assets of the organisation. By maximising the use of your database you will turn it into a powerful marketing tool. What you know about your members can help drive a better understanding of how they use and value their membership, leading to more relevant communications, better management of the relationship and increased loyalty.

Your organisation will have membership data. Some organisations will have membership relationship management (MRM) systems with all data in one place, but more likely is the scenario where some may be held in central systems whilst more is held in additional departmental data repositories like standalone databases or as spreadsheets. The object is to turn all that data into information and that information into knowledge or insight.

Using your database goes far beyond just direct marketing. The key lies in bringing together everything you know or could know about your members, prospective members, customers and other constituents, their activities, actions, purchases and behaviour. A single view can reveal who is active and who is dormant, who will only ever remain a customer for publications and training, who attends events and participates in branch affairs and when and to which campaign they have responded. It can also help determine who your next contingent of advocates may be.

Along with basic identifiers like name, address, telephone number, Email address and date of birth you are likely to hold qualifying data such as gender, occupation and professional qualifications. Ideally you will also have access to details of their behaviour, such as renewal history, event attendance, training and contact history.

This will give you access to the ‘who’ did ‘what’, ‘when’ and ‘how’ that go to deliver the profile of a constituent. There is no substitute for accurate data – analysis tools can’t compensate. Meaningful results can be achieved without sophisticated modelling but with high quality, robust and reliable data; it’s the quality that will determine how much of a grey guide or ‘black & white’ specific analysis can be reached.

It is essential that you assess your data before trying to develop any analytics. Inaccurate data will skew the results. If you cannot improve on the quality in the timeframe, then at least being aware of the inaccuracies will help in the interpretation of the outcome.

Building these profiles of your constituents will enable you to determine how long someone stays loyal and what might be done to pre-empt churn. It will facilitate the application of scoring and lifetime value techniques to help build predictive models and identify the communities within your membership base for targeted marketing.

This identification of communities can be viewed as segmentation“….the division into homogenous clusters which may be identified and marketed to with a specifically targeted communication strategy”. It is potentially more costly since it requires greater levels of management yet it promotes greater effectiveness through improved relationships.

Most people see the benefit of segmentation but the question is how should you segment them? By geography, by demographics, by professional qualifications or by behaviour? I would suggest it is a combination of all of them. The other dimensions to consider are concerned with how the constituent wishes to conduct their relationship with the organisation. I call it the ‘spectrum of engagement’ which runs from the apparently active member who says ‘send me every communication, proposition and offer that there is’ at one end to the member who appears distant or dormant at the other who says ‘I know where you are when I need something; don’t bother me’. The key concept to grasp here is that your most loyal member and potential advocate could exist at either extreme and, in fact, anywhere in between.

You need to know what is happening within your constituent base. Equipped with the kind of data mentioned above you can start revealing important measures. I normally start with trying to understand what touchpoints each constituent has had with the organisation. Taking each of the services or product areas your organisation offers count how many constituents have availed themselves of the combinations on offer. Table 1 shows an example of this.

Count of current members

Bookings (courses/ events)

Attended event / delegate

Made enquiry

Took exam

Book shop sales

6381

Y

Y

Y

Y

Y

2066

Y

Y

 

Y

Y

1066

Y

Y

Y

 

Y

4061

Y

Y

Y

 

 

2989

Y

Y

Y

Y

 

2075

Y

Y

Y

 

 

1133

Y

Y

Y

 

Y

7340

 

 

Y

Y

Y

6836

Y

 

Y

 

 

1407

 

 

Y

Y

Y

606

Y

 

Y

Y

 

6375

 

 

Y

 

Y

3132

 

 

Y

Y

 

1904

 

 

Y

 

 

1068

 

 

Y

 

Y

13680

 

Y

Y

 

 

3349

 

 

Y

 

 

200

 

 

 

 

 

Table 1

This is the same concept that we have come to recognize when buying consumer products on-line; the helpful hints that tell us that people who have bought the book we are looking at have also bought these other titles, green socks and a set of gardening implements. It is known as ‘market basket analysis’ and is a totally valid model across any combination of goods or services.

Here we can see how many members have been active and which dormant. It also reveals some issues for further investigation like the 6,000+ members who took an exam without buying any course books or the 7,000+ who make bookings but never attend i.e. do they book on behalf of others?

The next most interesting insight is usually a view on churn (see Table 2). By matching the year of defection against the recruitment year trends can be identified. It appears, realistically, that churn increases the longer the membership period. However, what happened at the end of 2009 to make the number increase and carry this trend on into 2010 with 186 members not even completing their first year? This is where the penultimate data element comes in: tacit knowledge – the experiences and understanding that exists within the minds of the people who run the organisation. Using this to interpret the outcome of these analyses is the tipping point between information and true knowledge.

Years as a member

Set up Year

0

1

2

3

4

5

6

Total

2005

3

25

22

26

19

34

48

177

2006

31

52

40

35

231

51

440

2007

30

70

60

239

289

688

2008

33

85

242

461

821

2009

19

86

204

309

2010

186

219

405

2011

17

17

Number of defecting constituents

Table 2

Gaining insight into your members, customers and other contacts will help you understand who the best are, what opportunities exist, how to hold on to them and find more like them. Insight also provides the other side of the coin: who the worst are and what they are costing you in lost opportunity so that you avoid them and others like them in the future.

If you don’t have the insight into your members, how do you hope to manage the relationship?

What we have considered so far helps us understand the “Who”, “What”, “When” and “How” that were behind the members’ actions. The final data element addresses the ‘why’ factor – the additional dimension that establishes true differential between passive passenger and active member? This is the difference between reporting on the data and analysing the data, to reveal why a member remains loyal and leverages his affinity to the organisation in his working and social life.

To achieve this you must include a view of the attitudes and aspirations that drive their membership decisions. Known as adding the psychographic profile, such insight into members will:

  • Help identify and pre-empt their failure to renew membership;
  • Show opportunities for cross- selling other services and up-selling on what they buy;
  • Help you understand why specific propositions are successful with certain types of member or customer;
  • Reveal preferences, such as which types of contact will never become active members or participate at local branch level; and
  • Increase the effectiveness of prospecting.

You can achieve this through fusing the analysis of member and customer involvement derived across all of the points of contact with your organisation with targeted market research survey data to determine the true differentiators. The outcome can then be used to create communication strategies to ensure no opportunity is lost and the member is always directed towards the best next action, driving relevant, targeted communications.

Consider the scenario where information acquired through tracking response and behaviour is matched to the researched view of attitudes and aspirations to create member profiles. These can be used to determine how best to move the relationship forward or flag up potential danger signs. Does their most recent action (or inaction) indicate possible churn? Has their most recent behaviour been exceptionally different to all that that came before? Are they approaching a major milestone in their relationship with the organisation such as a threshold of membership level that requires examination or CPD audit and which has traditionally been a jump-off point for members like them?

Having insight into what motivates their behaviour can be used to generate a relevant communication or even direct that member to the right web page or telephone agent as part of a strategy to retain their membership or upgrade into a new level of membership.

Remember the ‘spectrum of engagement’. On the one hand, the active and the other the aloof or one could say there are committed members and those that participate on an adhoc basis. You need to know where each constituent is on the scale and it is the organisation’s role to determine the position and use that knowledge to drive the relationship, the regularity and content of communications and the propositions offered.

What you can determine about current members can also be applied to prospecting activities in a similar way, to help meet acquisition goals. By applying the profile knowledge to new applicants, the organisation can be better prepared for what kind of member the new applicant is likely to be.

Armed with this knowledge you can ensure your marketing strategy addresses these issues to drive the relationships.