The relevance of database marketing in the digital marketing age

Still striving to deliver the right message to the right person at the right time

As someone who has over 30 years’ experience of data-driven marketing I don’t see any great dividing line between the age of traditional direct marketing and the digital marketing age in terms of strategy. What has occurred is the introduction of new methods of analysis and communication which facilitate concepts that previously were difficult to achieve.

Techniques like Artificial Intelligence, Machine Learning and Predictive Analytics support concepts that have been around for all the time I have been in database or data-driven marketing, addressing the need to direct audience selection as well as personalization of the creative treatment, the expected outcomes and channels of response, leading to the construction of customer journeys and profitable relationships.

The adage ‘right message, right person, right time’ formulated back in the 1980s is as true a strategic objective today as it was back then.

The New Strategic Cycle

Traditionally, a campaign would be implemented using traditional communication media and this campaign would generate a response. The marketer had full control to the greater extent, of who would receive the offer and hence the response could be viewed as controlled as the respondents were in the main a function of the audience.

That response would be analysed, and the outcome would be used to fine tune the strategy and drive the next round of campaign communication (see Fig 1).

 

Fig 1 Traditional Strategic Cycle

Whereas previously the marketer could anticipate with some degree of control when a response would be generated, the digital world has reduced this element of control. A marketer can no longer engineer when a contact may wish to encounter their message, proposition or brand, or, indeed, come across it. Hence, there is a need to establish valid methods for acquiring data from customers and website visitors.

The digital marketing age has delivered not only ease of data collection and analysis in order to drive the commercial relationships and measure the outcomes, but an absolute need for interpretation of the data that finds its way into the database not only from campaign responses but additionally uncontrolled responses, enquiries and tracking from website visits, to establish its value to the strategy (see Fig 2) and provide information that will drive automated communication strategies through a set of strategic rules and a broadened selection of communication media, in real time.

Fig 2: The Digital Strategic Cycle

These new concepts also have implication on clustering and segmentation. Combinations of demographic and behavioural criteria have traditionally defined clusters. Once defined, these have been used to drive product development and marketing activity.

Clustering

Cluster sets change their content and their direction; individuals join and leave as new information is learned about them and as the importance of the business rules inherent in the data relationships is recognised.

This means that the clusters must be recognised as volatile and allowed to be dynamic.

Their dynamism must be tracked, and the changes identified in order to keep the marketing strategy and communications schedule on course.

Marketers can find that markets for their products are dwindling, readership for their publications is flagging and response to their once attractive offers reduced. This may not be because the product is any lower quality, or the price has leapt; it can be that the customer profile once associated with that product or market has changed shape and has moved out of the target zone for that marketer. Has the customer turned 31 and so is no longer eligible for a Club 18-30 vacation? Has a child reached an age where its parents would no longer be interested in nappies?

These changes and new viable targets must be recognised to effect and maintain product and communications strategies. Early warning of changes in customer profiles can be provided from constant data feeds with triggers identified to drive personalization of proposition, communication channel and delivery.

Personalization

Personalization is not a new thing. Forty years ago, the large direct marketers like Readers’ Digest and Damart were thrilling their customers and prospects by incorporating their names, their street or their town in the text of the multi-page letters they sent.

This basic personalization continues today, with marketing communications incorporating the same text variables. However, these days no-one is particularly excited by the fact that direct marketers can reproduce their name on the page or the screen, irrespective of how large the font size is.

Others go one stage further to annoy the recipients of their marketing collateral, by making either guesses or misguided assumptions about the recipients’ interests.

To personalise relevantly, you need to understand the context of your products and how this will fit with the context of your customers, unlike this Amazon blunder exposed by a customer last year on Twitter:

So, data-driven personalization should be a business priority. Research over the last three or four years has determined that conversion rates can be increased as such relevant marketing messages make it more likely that people will engage; the efficiency of marketing spend can be improved; and customer lifetime value will be increased.

Considering the context of your products and services must be matched to the context of your customers – their actions, transactions and engagement and combinations of how long they have been a customer, complaints, referrals or advocacy and such classic measures as Recency Frequency and Value score.

You must also match to their personal profile or persona. Rather than targeting just based on demographics, consider how people behave and what this tells you about what might engage them.

Demographics are largely static and may not always influence how or why people buy, but psychographic and behavioural personas can give insight into who does what, and why based on aspirations, attitudes, self-view, price sensitivity, journey stage, satisfaction and sentiment; create strategies that target each behaviour-based profile.

Try to introduce personalization in real time – driving dynamics in the customer experience. It has been suggested that not personalising in real-time is not personalising. We didn’t have the channels for communication or the technology for driving real time dynamics back in the 1980s, but such tools are available today.

Personalization should enable immediate reaction – like face to face, and so you need personalization technology that can understand, react to, and optimise customer journeys in real-time by applying data analytics to deliver the right message or experience to the right person at the right time – that adage that we worked to 30 years ago!

Dynamic content presented to customers can be achieved using machine learning that decides what the best content for each customer is, based on such parameters as purchase history, preferences, persona and browsing and buying behaviour, along with the customer lifecycle.

But don’t just consider what to personalise, but how to personalise it; use the data-driven personalization to drive the creative presentation in copy, images, format, offer and the response channel.

Make personalization an integral part of the experience but don’t go out of your way to push it in the face of your customers. Don’t do something just because you can, like the meaningless incorporation of the customers’ names and towns discussed earlier in this article.

Similarly, don’t flaunt to customers how they are tracked or the data you hold – I have seen this just unnerve them and so it has a detrimental effect on the experience. It has been suggested that your tactics should go unnoticed and create an effortless experience.

The best personalization is that which enhances the customer experience without them querying how or why, but also demonstrates an understanding of the customer and reflects the truth about them.

Good, reliable data is the fundamental ingredient

Poor data is the single most quoted reason for failure of such communication strategies. Ask yourself:

Do I have a data strategy?

Can I rely on my data?

Have I undertaken a gap analysis?

Am I maximising the touchpoints?

If the answer to any of these is ‘no’ then this is your start point.

So, in summary…

  1. Basic personalization tactics are no longer enough to engage members
  2. Using personalization intelligently is the best way to predict and shape behaviour
  3. Following the elements of data-driven personalization helps you develop your strategy
    • Embrace predictive analytics
    • Use data to drive dynamics in segmentation, offer, creative and channel
    • Establish measures that will feed the intelligence behind your personalization
    • Ensure your goal is based on improving customer experience and you’ll see increased engagement, retention, commercial success
  4. Ensure everyone has a single view of the truth through sound data management and governance

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

Personalising the customer experience

Personalisation is not a new thing. Forty years ago, the large direct marketers like Readers’ Digest and Damart were thrilling their customers and prospects by incorporating their names, their street or their town in the text of the multi-page letters they sent. This basic personalisation continues today, with marketing communications incorporating the same text variables.

However, these days no-one is particularly excited by the fact that direct marketers can reproduce their name on the page or the screen, irrespective of how large the font size is. Others go one stage further to annoy the recipients of their marketing collateral, by making either guesses or misguided assumptions about the recipients’ interests. Here are a couple of emails I received last year:

Both managed to get my name right but equally both were writing to me about issues in which I had no interest. Irrelevance is the antonym for personalisation and will contribute to the failure to engage. Using personal information isn’t enough to motivate action; 50% are likely to engage when they receive an interesting proposition and a study by Pure360 in 2018 found that only 8% react to marketing that addresses them by their first name and 7% with a personal themed message (e.g. birthday, work anniversary, new job, etc). What is Fundamental is relevance and intelligent use of personalisation.

The origins of this more intelligent or data driven personalisation go back further than one may think. Back in the late 1980’s there were two marketing tools that tended to prevail if a company had problems. One was a loyalty card and the second a customer magazine. If the company had real problems, then often they used both!

At that time, the UK motor industry had been using direct marketing for years and had tended to make that mistake demonstrated above of guessing about the customers’ intentions regarding buying a new car. The assumptions were that as the customer was approaching a significant anniversary since the purchase of the car (say 24 or 36 months), they would be in the market for replacing it with one of similar specification. Wrong. They had won the football pools or the premium bonds (this was before the days of the National Lottery) and so wanted a Rolls or a Ferrari or else the customer had married a divorcee with five children and a Mini was no longer appropriate. They did not know. One company, Austin Rover, recognised the need to maintain a relationship with their customers in that long period between buying a car and replacing it; after all, if someone has bought a car it is unlikely they will need another one next month. To personalise relevantly, you need to understand the context of your products and how this fits with the context of your customers, unlike this Amazon blunder exposed by a customer on Twitter:

Of the two prevailing marketing initiatives mentioned earlier Austin-Rover chose the customer magazine, but a customer magazine with a difference. This one was personalised to engage the reader. Not only did they get the customer’s name and address to show through the cover (to drive it through the mail)

but the reader could select the content that wanted to read about in forthcoming issues – subjects like travel, food, sports, etc. so that it became a personalised lifestyle magazine for the customer. There was a quid pro quo in that the reader was asked to complete a short form indicating when they were likely to replace their car and what kind of car was most likely to be their choice. This generated over 50,000 qualification updates to the database in each issue and meant that the customer would not receive any car marketing until they approached that ‘window of opportunity’ that they had indicated on the survey and would only receive information about an appropriate model and the content selection drove the type of incentive offered for test drives or purchase. This was in 1988, over thirty years ago and I am delighted to have been part of the team that conceived and delivered this strategy for the intelligent use of personalisation, winning awards in both the UK and USA for what award judges termed as ‘direct marketing as it should be’.

So, such intelligent use of personalisation should be a business priority. Research over the last three or four years has determined that you can increase conversion rates as such relevant marketing messages make it more likely that people will engage, it can increase the efficiency of marketing spend and will help improve customer lifetime value.

Considering the context of your products and services must be matched to the context of your customers – their actions, transactions and engagement and combinations of how long they have been a customer, complaints, referrals or advocacy and RFV score. You must also match to their personal profile or persona. Rather than targeting just based on demographics, consider how people behave and what this tells you about what might engage them. Demographics are largely static and may not always influence how or why people buy, but psychographic and behavioural personas can give insight into who does what, and why based on aspirations, attitudes, self-view, price sensitivity, journey stage, satisfaction and sentiment; create strategies that target each behaviour-based profile.

Try to introduce personalisation in real time – driving dynamics in the customer experience. It has been suggested that not personalising in real-time is not personalising. We didn’t have the channels for communication or the technology for driving real time dynamics when we did the Austin-Rover magazine, but such tools are available today. Personalisation should enable immediate reaction – like face to face, and so you need personalisation technology that can understand, react to, and optimise customer journeys in real-time by applying data analytics to deliver the right message or experience to the right person at the right time – an adage that we worked to 30 years ago!

Dynamic content presented to customers can be achieved using machine learning that decides what the best content for each customer is, based on such parameters as purchase history, preferences, persona and browsing and buying behaviour along with the customer lifecycle. But don’t just consider what to personalise, but how to personalise it; use the data-driven personalisation to drive the creative presentation in copy, images, format, offer and the response channel. Make personalisation an integral part of the experience but don’t go out of your way to push it in the face of your customers.

Don’t do something just because you can, like the meaningless incorporation of the customers’ names and towns discussed at the beginning of this article. Similarly, don’t flaunt to customers how they are tracked or the data you hold – I have seen this just unnerve them and so it has a detrimental effect on the experience. It has been suggested that your tactics should go unnoticed and create an effortless experience.

The best personalisation is that which enhances the customer experience without them querying how or why, but also demonstrates an understanding of the customer and reflects the truth about them; poor data is the single most quoted reason for failure of such communication strategies. Ask yourself:

  • Do I have a data strategy?
  • Can I rely on my data?
  • Have I undertaken a gap analysis?
  • Am I maximising the touchpoints?

If the answer to any of these is ‘no’ then this is your start point.

So, in summary

  1. Basic personalisation tactics are no longer enough to engage customers
  2. Using personalisation intelligently is the best way to predict and shape behaviour
  3. Following the elements of data-driven personalisation help you develop your strategy
    • Embrace predictive analytics
    • Use data to drive dynamics in segmentation, offer, creative and channel
    • Establish measures that will feed the intelligence behind your personalisation
  4. Ensure your goal is based on improving customer experience and you’ll see increased engagement, retention, commercial success
  5. Ensure everyone has a single view of the truth through sound data management and governance

©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

 

 

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’!

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