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

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.

Where does the provision of marketing information belong?

There has been much debate about whether the business or IT should lead information provision. IT is unlikely to be in a position to fully appreciate how the business could take advantage of the data because they are not involved in the daily process of sales and marketing of their company’s products and services. But equally, the business not really wanting IT to be involved in analysis and reporting, do not fully comprehend the nature and nuances of the data that is available to them, after all it was IT that designed the systems in the first place!

Without a thorough understanding of the data it is highly likely that irrespective of who performs the analyses the wrong conclusions will be reached.  Despite some product vendors exploiting this divide of opinion to their own advantage, it has been proven beyond doubt that the greatest successes come from those organisations that have created a culture where business and IT truly work together with complementary objectives and common business goals.

This is by no means an easy recipe for success with possibly years of divided working practices and politics to overcome but with strong leadership and active project sponsorship at the highest-level of an organisation success will be inevitable.

Data Fusion delivers the “Why” Chromosome In Customer Management

When considering the uses of a database for marketing, the issues go far beyond the realms of purely direct marketing. There is a role for the database in supporting most elements of the marketing mix. Advertising can benefit from the profiling of the database to determine the tone of voice of copy, the image of the characters in TV commercials or press ad photography and the media selected to carry the ad. Similarly, market research can be segmented and directed to profiled individuals so that more robust samples can be constructed. The database can assist in product development, the management and motivation of a sales force or channel of distribution and in sales promotion.

However, as we have seen in this column in previous issues of Review the integration of data into data warehouses or data marts for marketing analysis means that the elements of the marketing mix can contribute as much to the database as the database benefits those elements. Knowing who has bought what product, when and in response to which campaign, through what communication channel and with which payment method is very useful. We can understand the “Who”, “What”, “When” and “How” that were behind the customer action. What if we were able to add the “Why” factor – the psychographical element that establishes true differential between customers. Why does a customer remain loyal? Why does the customer value the relationship with the company? Why does the customer maintain a dialogue with a preferred supplier?

The same techniques that have brought about the expansion of data warehousing have facilitated the use of data fusion, the process by which marketing research data can be introduced into the marketing database and matched to customers and prospects so that the complete view can be established.

Whether the marketing research is bespoke to the marketer or is syndicated or publicly available, the ability to match it in to the customers either by direct personal identifier or by segment or cluster, behavioural type or demographics can permit the extrapolation of the research factors right across the marketing database.

This means that response by a representative panel of customers or prospects to questions regarding key purchase considerations, performance indicators or customer expectation levels can be levied right across the database to help derive more intense customer profiles and establish the key differentiators between clusters previously considered similar.

Knowing a customer’s expectations in terms of quality of product and service, preferred channels and extent of communication and preferred promotional activity can enable marketers to tune services and propositions to meet perceived customer needs. Knowing how the company matches up to these expectations provides an important new dimension to customer relationship management which, if acted upon, will result in customers that feel valued and are likely spend more and be increasingly loyal.

Whilst scoring techniques such as compiling recency, frequency and value quotients give an important basis on which to determine segmentation, it does not necessarily demonstrate loyalty. Customers will find their own level of relationship with the company and cannot be forced to assume a closer one.

There is nothing to say that a customer at the left end of the spectrum is any more loyal than the customer at the right end. The fact is they may be both equally loyal but view the relationship differently, both equally satisfied. The customer who expects everything, however, may end up being less profitable than the other extreme, since the level of communication may will be greater for perhaps an equal (or even lesser) amount of sales activity. Hence the need to understand the customer attitudes and aspirations to establish the performance indicators that mean the company can gauge if they are getting it right.

The actual data fusion process requires an understanding of the data that will contribute to the analysis universe. This will normally start with the company’s house database of customers and prospects. The data will need appraisal and a high level audit to ensure that it is fit-for-purpose and to highlight any issues concerning data quality or integrity that may affect the creation of the survey panels and eventually the outcome of the analysis.

An initial round of data analysis to establish customer segmentation and profiles for the research would then need to be undertaken. Often this will entail constructing purchase value or product profiles and the introduction of external qualification data such as demographics or lifestyle information, to help establish the segments for research. Then it is just a matter of running extracts of the data that meet the profile requirements for the research panels; remember, the research will normally be far more meaningful if applied to segmented panels rather than solely right across the database. These extracts can then be used to create representative samples, output as:

–         Call lists for telephone research

–         Mailing lists for postal research

–         E-mail lists for electronic questionnaires

In each case the segment reference is maintained against the customer record and the response process designed to make the loading of the responses as easy as possible.

Having carried out the research, the raw response data can then be matched back to the database, with response outcome extrapolated throughout. Once complete, the database can then be analysed using the analysis tools I have discussed in this column previously, to follow train-of –thought exploration and data mining discovery techniques to identify the latent trends, opportunities and threats.

 This technique can address some of the key business imperatives uppermost in the minds of marketers:

 –        The need to know more about their customers

–        How to identify and protect against churn

–        Direction for business growth

–        Areas where customer service needs improvement

–        Pinpointing the opportunities for cross sell and up sell

 The company can be in a stronger position to increase sales to current customers, improving customer retention and loyalty and to find new customers that match the best customers already on file. Similarly, by profiling prospective customers, the research findings can be applied from the very inception of the relationship, thereby increasing the effectiveness of the prospect conversion process. In both cases, the company can see increase in turnover, a reduction in prospecting costs and maximisation of the efficiency of the marketing budget, meaning more to bottom line profit.

The combination of research and database analysis will also provide the basis for development of new products and identification of new markets and can be a key factor behind maintaining and increase market share. It will provide a competitive edge that not only helps prevent market share erosion but also assist in gaining rapid share of new markets.

 Last, but by no means least, the ability to carry out the data fusion process and regularly refresh it provides a powerful measurement tool to assess the effectiveness of marketing initiatives.