The application of CRM in addressing customer diversity and inclusion

When I saw Roy Gluckman (Diversity & Inclusions Specialist at Cohesion Collective) present on Equality, Diversity and Inclusion (EDI) at the Association of Association Executives congresses in Manchester in December 2017 it inspired me to consider how an organisation’s customer relationship strategy can encompass these important elements. And when I heard him again at the AAE World Congress in Antwerp in March 2018 it became clear that the techniques used in CRM, in data analytics and in data-driven processes can be directly applied to managing an organisation’s approach to EDI.

Gluckman says that “our thoughts, beliefs and opinions make up who we are and are central to our identities”. Hence “truth is just a perspective” – but it is our perspective and we carry on with our lives as though it is the only view that anyone can have, that it is “singular, universal and correct!”.

One of the main objectives for CRM that I encounter with the organisations I consult for is the need for all the data they hold about a customer to be available and reliable so that anyone in the organisation has a 360˚ single view of the truth.

In fact, in a survey run in preparation for the AAE World Congress in March 2018 showed that almost 55% of organisations said that their contacts will not receive the same answer to the same query however they communicate with the organisation and 61% did not have a single view of the contact. Hence no way to handle the contact in a way that relates to their expectation and their perspective of their relationship.

We all recognise that to turn the information into knowledge there must be a level of interpretation in the light of the user’s tacit knowledge – their personal experiences, local or topical facts and attitudes – that puts it into the context and setting on which to base the relationship management.

But, using the personal interpretation to build knowledge furthers the influence of that singular perspective that we believe is correct. However, the customer with whom we are trying to build a relationship may have a totally different set of attitudes, aspirations and views, especially regarding their relationship with the organisation, their view of the organisation and the relationship they envisage as existing between them.

There are three constituent parts to a CRM strategy: the Operational element concerned with process management, delivery and collection of information at touchpoints and strategic communications; the Interactive element concerned with tactical communications and social media to drive the relationship; and the Analytics element that aims to turn the information gained through the first into knowledge that can be used to drive the second. Only by analysing the operational and transactional data acquired through business processes and interpreting them with the benefit of psychographic data can a clearer view of the truth be achieved.

A great example comes from one of my clients in the events industry. As a major exhibition organiser, they knew who pre-registered for an event and if they attended or not. What they had not done was to fuse the data collected by the exhibitors through swiping a badge or using an electronic ID device to identify which exhibition booths the individuals visited.

When this was done, we identified an interesting contingent who had pre-registered and subsequently attended over several years but had visited virtually no booths in the exhibition. When their business profiles were examined it showed that they were all very small one- or two-man businesses and when a sample of these were contacted it transpired that they used the event as a market place to meet and network with other small businesses in the aisles, rest areas and café.

The organiser’s image of the show was the key forum for that sector, attracting the major names, whereas these visitors felt excluded as they were not able to do ‘big business’ but saw it just as the facilitator for their networking activity. So, the organiser was advised to establish a ‘small business forum’ in an empty part of the exhibition hall the following year, to consciously include these small businesses by especially inviting them to use it and benefited commercially with a lucrative sponsorship deal to support it.

This concept of data fusion is very important in adding the psychographic dimension to the customer’s profile so that one can gain a view of the attitudes and aspirations that drive their purchase decisions.

This will enable an organisation to pre-empt churn, identify opportunities for cross-selling as well as up-selling what they buy, understand why specific propositions are successful with certain types of customer, reveal preferences and increase the effectiveness of prospecting.

This is because the organisation will understand how to be inclusive in its messaging and in managing the relationships, leveraging the knowledge it has as to the beliefs and opinions of the individual customers to tailor proposition and communication.

This can be achieved through the fusing of the ongoing analysis of customer involvement derived across all the points of contact with the organisation along with targeted market research survey data to determine the true differentiators – to establish the individuals’ perspective through their viewpoints and beliefs.

The outcome can then be used to create communication strategies to ensure that no opportunity is lost, and that the customer is always confident that the organisation knows them, and they can continue to feel part of the group.

Consider the scenario where the information that is acquired through tracking responses and behaviours is matched to the researched view of attitudes and aspirations to create complete customer profiles.

These can be used to determine how best to move the relationship forward as well as flag up any 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 which has traditionally been a jump-off point for customers like them?

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

Equally as important is to be able to recognise good, profitable customers who, by their profile and viewpoint may never become your top customers, but who seek a relationship where they don’t feel discriminated against and can feel comfortable buying on an ad hoc basis whilst avoiding being bombarded with sales propositions. Consider my concept of the ‘spectrum of engagement’ which states that customers’ profiles fall along a line between two points – at the one extreme are those customers who want to receive every proposition available and, at the other, those who will only make contact when they need something. The most loyal customers can be at either extreme or anywhere in-between. It is this aspect of diversity that needs to be addressed and knowing where each customer is on the spectrum is the responsibility of the organisation to determine this position and to use the knowledge gained to drive the relationship as well as the regularity and content of communications and direct which propositions are offered. In this way each customer will feel included and that their relationship is on an equal footing with other customers.

Armed with this knowledge the organisation will be able to ensure that its marketing strategy addresses any such issues to drive the relationships most effectively thus offsetting the potential for decline.

©Michael Collins 2019

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

 

 

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.

Scoring your customers

Companies must continue communicating with their customers through a consistent strategy that is driven by customer insight. Rating current customers is not solely based on how much they spend but has as its basis the classic RFM (or RFV in the UK) model that creates a score based on recency, frequency and monetary value. My recommenation is not to stop there but to add more elements such as Returns, Complaints, number of enquiries, length of time they have been a customer and create a contact plan that reacts to the dynamics of these scores.

Often we will identify cohorts of customers that represent bad business. The solution can either be to review the business process for how you do business with them, e.g. relegating them to an ‘exclusive’ on-line relationship where the cost of managing them is reduced rather than taking up the time of a salesman or telephone agent; alternatively the bullet might have to be bitten and you resign the account.

However, building segmentations or communities of valuable, profitable customers by profile and comparing their behaviour will also drive the communication; but don’t just use it to determine when to make contact. Customer insight should also drive the ‘next best proposition’ for each customer so that the sales person can be proactive in establishing opportunity. What you know about your customers can also be used to drive new customer acquisition by comparing prospects’ profiles with your customers and determining the best proposition.

Customers rate companies with whom they deal by the quality of the communications and this means relevance, personalisation and timeliness.

The extended 3-letter acronym to drive retail direct sales

Analytics software is useful, but some concept of what should be analysed is also of value. Retailers will often create a RFV model, combining Recency (of last purchase), Frequency (regularity of purchase) and Value (either total spend or average order value, for example) to create a score for each customer.

We have found that adding additional elements expands the score from the 3-letter acronym to a multi-letter acronym such as RFVRCA where you add on the instances of Returns, Complaints and Abandoned on-line baskets committed by the customer.

This score then becomes a comparitor between customers, a selection criterion for campaign or proposition and changes in the individual’s score over time become a valid measure of success of the relationship.

Applying demographic, lifestyle and psychographic profiling provides an even more granular segmentation and the ability to apply the concept to prospects, with the benefit of making relevant offers to convert them to customers.

Such models can be created without investment in analytical software and can be applied in rules-based workflow and business process, with the dynamics regularly reviewed and the model(s) enhanced.

Turbocharge automotive marketing

The key to building insight about automotive customers and prospects lies in bringing together everything you know about them. This can reveal the ‘who’, ‘what’, ‘when’ and ‘how’ but what if you were able to add the ‘why’ factor?

 To achieve this you must also include attitudes and aspirations that drive their purchase decisions.

 Consider the scenario where information acquired through tracking response and behaviour is matched to the researched view of attitudes and aspirations to create profiles. The outcome can then be used to create communication strategies to ensure no opportunity is lost and the customer or prospect is always directed towards the best next action, driving relevant, targeted communications.

 A good example of this occurred when consulting for an automotive manufacturer. A list of potential customers for a new mid-range car was tested and ostensibly, the profiles absolutely matched the positioning of the model to be sold. However, research revealed their preference for a pre-owned luxury car with personalised plates to disguise the age of the car, as they could not afford their ideal car new. So a new mid-range car proposition would have been totally irrelevant and failed.

 Such insight can also be used to determine how best to move customer relationships forward or flag danger signs. Does recent action (or inaction) indicate possible churn? Has an enquiry been exceptionally different to all those before? Are they approaching a milestone in their relationship with the company?

 Similarly with prospects – insight can help in directing their passage through the ‘prospect funnel’ and reduce the number that are normally haemorrhaged on the way through.