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

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

 

 

Knowing customers wherever they touch your business

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

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.

Can you risk leaving travel customers to make up their own minds?

A recent survey by Directline Holidays concludes that  Word of Mouth is most trusted recommendation when it comes to booking the right trip.

Travel companies have the ability to build and nurture such tactics from within their own customer data. Considering the customer’s overall relationship with the company will assist in creating a customer journey that delivers advocates who will either perform actively or passively for the brand.

Active advocacy is where the satisfied customer will recommend the firm to their relations, friends and wider acquaintances; sometimes a reward sweetens the process. Passive advocacy is where using what you know about your best customers enables you to ‘clone’ new customers who are likely to appreciate similar destinations and levels of service and so behave in a similar manner.

All of this can achieved by collecting and maintaining the right data about customers, engaging them in a relationship and progressing them up the “loyalty staircase” to advocacy.

The survey also stated that “almost a quarter of those surveyed said they were not influenced by anything” (source: e-tid 20/8/12). Can you risk your customers being left to their own devices? Make sure they are influenced by their peers, by relevant propositions and a customer relationship that delivers real benefits.

Travel companies are lucky….

Travel businesses are luckier than many other sectors. They have data on their customers and their customers’ enquiries and purchases and so can derive tremendous benefits from analysing these vast amounts of data, uncovering hidden nuggets of opportunity among their data, once they know how to go through it systematically and leverage the effect on profitability. Analytics software is useful, but some concept of what should be analysed is also of value.

We will often create a RFV model for tour operators or travel agents, combining Recency (of last booking), Frequency (regularity of bookings) and Value (either total spend or average booking 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 RFVCIA where you add on the instances for example of Complaints, Introductions to friends or relations and Abandoned on-line baskets committed by the customer.

This score then becomes a comparator 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.

This leads us no to the second most frequent model we create: the market basket model. Knowing what destinations or types of travel different customers prefer is fundamental in programme planning, product development and in driving cross-selling or up-selling opportunities. Being able to compose and analyse the combination of travel products that customers buy will enable the travel marketer not only to determine how best to develop the business with those customers, but, by extrapolating this information, estimate the buying potential amongst the remainder of the customer base.

The often-cited (and some say apocryphal) “beer and nappies” story is an illustration of what can be achieved. Those that don’t know the story can read it at www.dmcounsel.co.uk.

This type of analysis is certainly not the exclusive domain of supermarkets or Amazon.com (we’ve all encountered their ‘people who have read this book have also bought this other book’).

If you can identify a customer as having purchased a product then market basket analysis such as was applied here can deliver cross-sale opportunities by making the right proposition in communications, positioning complementary products together on the shop floor or on the website or catalogue page or driving the pitch made by a telephone sales agent.

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.

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.

E-communication underlines the need for greater control of the Campaign Management cycle

The growth of the internet has implications on both sides of the data-driven communications equation. On the one hand, it provides an exciting and convenient medium for delivering a message to customers and prospective customers. On the other, it presents a channel for data collection that makes possible concepts that were only hypothetical in the past.

The beginning of the 1990s saw the introduction of data to drive variations in customer magazines and newsletters across a variety of media channels, bringing lifestyle “versionalisation” and reader personalization.

However, in all of these cases the systems that drove them relied upon pre-selection and qualification of the data. The influence of the internet imposes new complexities to the concept of the strategic cycle.

In a traditional, tactical direct marketing scenario, you can start with planning a campaign, which will generate a response, which, in turn can then be analysed (even a zero response can be analysed!). This analysis of the performance of the communication can then be used to fine-tune the communication strategy for the future, and so the process continues. One needs to imagine this in 3-D, more of a spiral than a circle, because, if all turns out well, the initiative will be moving the enterprise forward while the strategic cycle turns.

The key element here is control. The introduction of the campaign to the marketplace is controlled: The marketer knows when this has been done; and the expectation of response is controlled, because the execution of a campaign will generate response over which the marketer has control in terms of channel and timing.

The internet has imposed new, reduced timescales between those points in time when the relationship is measured and tested or where opportunities for collecting, verifying or qualifying information are encountered. Previously, the direct marketer could establish when a mailing or phone call was executed and within some degree of control anticipate when a response would be generated. Access to the web has reduced this element of control. A marketer can no longer engineer when a contact may encounter his message, proposition or brand. One now has to consider the implications of both broader data source opportunities and communication media. With the web and the attendant swing toward a demand-led marketing environment, with its greater customer expectation of availability, the response is no longer controlled, and your strategy must account for that factor.

For years marketers strove for one-to-one relationships relying on the traditional tactical spiral model discussed above, and its success has been commendable within the context of the available techniques. New concepts, tools and expertise now help deliver an outcome that is closer to the vision. The development of the web has coincided with an increased awareness of the availability of data within organizations and the appearance of new, intuitive data analysis tools, employing techniques that provide for the derivation of information and the definition and interpretation of patterns within timeframes conducive to achieving the required dynamics.

As companies seek to integrate their suppliers, their customers and their marketing partners in complex relationship structures, new quantities of data are becoming accessible for exchange and sharing, and the value of the data as a corporate asset is increasing.

The income of inquiries, web site hits, new data, business intelligence and market data can have a real-time effect on the way the strategy proceeds, and so the strategic cycle becomes more complex. The marketer must now be prepared for uncontrolled response arriving from customers and prospective customers drawn to or discovering the company’s web site. Similarly, as new information is acquired internally into the data warehouse from, say, the accounts system or externally from suppliers of market information and competitive intelligence, the data has to be analysed and its value to the strategy interpreted and used to automate variations in the strategy. This ensures relevance of message, continuity of relationship and maximal effectiveness of any marketing communication, getting ever closer to the “holy grail” of real one-to-one marketing.

The strategic cycle, therefore, takes on new elements, to account for the uncontrolled data entering the process and the need for aligning its value to the strategy and translating it into rapid response in terms of proposition and delivery of a communication.

The sheer wealth of information available now flowing from the web, the ability to identify and acquire it and the tools now available to manage it in all its varying formats and structures all mean that disparate data, wherever it may reside, can be made available to add qualification, enhancement or verification to a marketer’s database. The natural corollary to that is a keener degree of customer profiling and targeting, a greater level of personalisation of message, proposition and presentation, resulting in improved marketing effectiveness and efficiency.