Monday, 31 March 2008

King For A Day - Chris McDonald

Sowerby Bridge-based Chris McDonald, MD of The Trading Floor, won't be Lonesome Tonight with Brazilian supermodel Giselle on his arm. Let's just hope he doesn't get the GI Blues when he leads his nation into battle.

Who would you choose as your Queen, and why? I would choose Giselle Bundchen. Shallow perhaps but I am the King!!!

What one law would you pass to make 'the lives of your citizens better? I would have drivers over a certain age tested every year.

Which form of Medieval torture would you re-introduce, and for whom? The ducking stool. I would start with Kerry McFadden and her husband.

Who would you bring into court to advise you on stately affairs? Jo Brand, Peter Kay and Ben Elton. Between us we could come to a realistic solution to most things!

What would you banish from this Fair Isle? Work shy fools, drugs and middle lane drivers - that would just be day one!!

Your bloodthirsty subjects want a war to boost morale - with which country do you pick a fight? How about the Americans? Only 17 per cent of them would even know where the UK was!

Finally, what monument would you like your people to build in your honour? I would like to have a monument representing England, Scotland, Wales and Ireland coming together as one - obviously in Yorkshire!

(EN Magazine - March 2008)

Sunday, 30 March 2008

The Trading Floor top List Manager league table

... Second-placed The Trading Floor is also a big email data supplier, earning 67 per cent of its income from email in 2007. The company's main source of income is a data pool for the insurance and financial service industries, based on enquirer data.

"People typically get three quotes before they take a policy out," says Zoe Vine, head of data services at The Trading Floor. "So companies have a lot of information on prospects that they don't do anything with for 11 months of the year."

The Trading Floor pools this data from 36 contributor companies spread across the motor, home, pet and travel insurance, mortage and credit card industries. It is then enhanced and sold back out to the wider DM industry, taking 50 per cent of the revenues itself and sharing the other half among the contributing companies, based on a complex set of ' data survivorship' rules relating to who pooled the data and who enhanced it.

(Direct Marketing: The List Report – March 2008)

Saturday, 15 March 2008

Quick Counting - Listknife

James Lawson reviews a simple but very effective fast counting tool.

Time was, all that any database marketer wanted was to have swift access to their data. Running counts and pulling out selections in minutes, no messing about with the IT department - what a dream scenario. Much more may be possible in automated direct channel marketing now, but a surprising number of volume mailers are still in the same position. Listknife might just be the tool they are looking for.

No frills or fancies
Forget about automated multi-wave, multichannel campaigns, triggered communications and clever SOA integration. Listknife is a batch tool in the classic mode, and is all about exploring large datasets quickly.

There's no disputing that it's easy to use. Drag-and-drop Windows functionality plus a selection of wizards make this a code-free experience - no SQL here. And it certainly is fast. The demo system was able to run a complex multivariable count totalling 600,000 records from a 35.5m list in under a second.

The graphical drag-and-drop query builder is the outstanding feature of Listknife

The stand-out feature of the package, and one that must have taken some considerable development, is the graphical query builder. Extremely simple to use, it is one of the more intuitive examples of the breed and something like the AnswerSets tool (last heard of around 2002) that employed interactive Venn diagrams to achieve much the same results.

Users simply choose a variable from the listing down the left-hand side and drop it into the main selection builder ("Count Logic") window to have it included within the selection. You can even drag and drop data straight from the Windows desktop. Clicking on the resulting variable object brings up a dialogue box within which you can refine the details of your count for that variable. For example, if you have included all UK cars in your selection, do you only want Mercedes?

Boolean operators are dragged and dropped in the same way to form boxes within the Logic window (the main window itself effectively acts as an AND function, combining everything within it), and to have that operator act on a variable or another operator, you simply drag the operand inside the operator box. So to exclude all people with CCJs or suppress records from a previous count, you would place the CCJ variable within a NOT box. To build complex multivariable queries, you simply nest blocks of objects to combine AND, OR and NOT operators.

Tabular profiling gives list managers what they need to know

It sounds tortured but is actually very simple in practice as the screenshots on this page should hopefully show. Lots of nested statements might get a little messy but probably less so than any other graphic selection tool.

Clicking on the "i" button on the top row then runs the count, which can easily be saved as either data or the query itself. A useful small display in the bottom corner updates the count as you then make changes to it. Other nice tweaks include flagging individual records as members of the same household during the database build. This means you can immediately get a count of the number of households within an individual-level selection.

With a selection live, clicking on the Profile tab at the top of the main window opens up a tabular profile report on the attributes within the current count: the percentage that any variable makes up of universe volume, the percentage of selection volume, and the index and z-score built from those two figures.

In same way, you can explore profiles of variables within the whole database by simply dropping them onto the Profile window and it's also possible to compare two profiles side-by-side. There's a simple wizard for this but it's barely necessary.

The third and final function of the main window besides profiling and selection building is a cross-tab function that can handle two or even three variables. Like the rest of the package, this is simple but elegantly implemented and very fast. Again, you simply drag and drop the variables you want onto the window, while the cross-tab values can be dragged straight into Excel.

Two- or three-dimensional crosstabs are the work of seconds.

There's a good data browser that can handle up to a million records on its own. Showing its list management roots, the package also offers decent tools for splitting out lists, for example, into males and females or to create test cells or 1 in N selections. Another good idea is to be able to flag a record as part of a test cell on the database.

Reporting is minimal. Clicking the Audit function on the bottom window gives you a tabulated report of the current count process, but further charting and manipulation would have to be done within Excel. For this, you can simply drag and drop selections into Excel with a single click rather than having to set up an export. Further connectivity employs ODBC to access databases, though no native connectors for the likes of Oracle appear to be available. Text files for import and export are another option.

Another notable feature of Listknife lies in how it is built around the use of URNs rather than holding customer names and addresses. In this age of corporate concern over data security, it means that losing a laptop full of customer data doesn't necessarily put your company on the front page of next morning's papers.

It also gives a way for the package to easily match lists without having to get into full-blown name and address processing. For example, if you have the URNs of a list of respondents from a campaign, you can easily import them and match them to the original target list by using URNs.

When it comes to production, one restriction could be its lack of remote access capability. Though the vendors say that remote access is possible, there are speed implications and no current customers use it in this way. Listknife does have an up-to-date .NET platform so there should be a way around this, but it's an isolated batch tool as it stands, and as such, a tad lacking in today's connected world.

Dedicated functionality makes splits and control cells easy to set up.

Like almost all fast counting tools, Listknife has a proprietary, highly-indexed database structure and requires a pre-load import stage to convert data into this format. Both of these may well deter a company's IT department, but it's par for the course in this area. A standalone utility is used for the import, with five million records claimed to take an hour's processing. No "trickle feed" updating is possible; the whole database has to be rebuilt to incorporate any changes.


Focused on lists

For end user marketers who run a few big monthly campaigns around a monthly database update cycle, Listknife would do most jobs perfectly well. Where one person has only, say, half their time allocated to handle mailings, they could easily jump in front of Listknife and sort out that month's selections. The main competition comes from other straightforward fast-counting tools like Minotaur or a low-cost standard application like SQL Server running on fast hardware. Many tools today can do much more, often as part of a wider suite that takes in stats, web-published reports and campaign management functionality. Something like FastStats Discoverer is a much more capable package.

But if running lots of counts and generating lists is where it's at, then Listknife could well be for you. The name is something of an indicator: it's perfect for list owners and managers. In fact, consumer data specialist The Trading Floor liked the tool so much, it bought the company.

Costs and specification
Listknife pricing is by annual fee with a nominal set-up charge. The fee tends to be from £14k to £20k depending on the number of users and database size/complexity. Listknife only runs on Microsoft operating systems. www.thetradingfloor.co.uk

(Database Marketing: Software Review – March 2008)

Fill The Pool

Once you've decided to build a prospect pool, how should you select the data to put in it? James Lawson investigates.

Working with a prospect database is increas­ingly acknowledged as the most cost-effec­tive way for serious direct marketers to acquire new customers. Farming a semi-permanent file rather than hunting and gathering lists every time you want to send a campaign out of the door takes marketing up the evolutionary chain, and learning can be fed back to a central point for future application too.

So the argument is over and a prospect pool is on the horizon. But what are you going to put in it?

What's in the pot?
The initial attraction of a prospect database for many is the opportunity to save money when buying data by negotiating terms in advance. There no doubt that buying all the data you need for the year up-front is a lot cheaper than ad hoc list purchasing. However, many companies tend to maximise the size of their discount by doing a deal with a single data provider and so sticking to a single source. Does this lead to a lower performing pool?

Opinions tend to be split on the route to take, with some advocating simply selecting a tranche of lifestyle data or geodemographically-coded Electoral Roll data that matches your customer profile, while others would take a more traditional route in bringing together tens of lists and updating them quarterly thereafter while testing new sources. The middle way is to use some sort of base file and pull in valuable extra sources if they will really make a difference.

"You need to use a base file with other information added to it;' says Richard Higginbotham, head of mar­keting at CDMS. "On top of that, you can layer your response data and other elements of prospect tracking like how many times a contact has been mailed."

Multisourcing a file may make the task of building a pool more complex but has a number of benefits. For a start, even if all you are doing is adopting worst practice and carpet bombing the Electoral Roll- the approach used by US credit card companies over the past decade to build their UK market share - maxi­mum coverage will come from multisourcing different versions of the Electoral Roll. If the aim is to whittle down a UK base file to match your needs, having the biggest possible footprint to start with has to be a good idea.

In a study by The Customer Partnership in 2005, Julian Berry and Paul Rowson found only a 75 per cent overlap between two competing replacement Electoral Rolls. So the consequence of going with just one Electoral Roll supplier is either a 25 per cent loss of mailable volume, or having to mail parts of the file that are much lower down your propensity models' gains charts, with a consequent drop in response rates. As the opt-out level has risen by nearly ten per cent since that study, the figures would be even more compelling now.

You need to be able to process quickly and accurately - Rob Salmon, managing director, meta-morphix

Using multiple fIles should also give more variables to use for predictive modelling. Berry and Rowson found that the four separate sources they used were all involved in each of the five propensity models they built. "We could not have scrapped any of them with­out seriously weakening our models;' says Julian Berry, managing director of The Customer Partnership.

Multisourcing will also help reliability by providing multiple instances of the same individual or house­hold, and so multiple examples of critical variables like date of birth or insurance renewal date. Based on the reliability of each source, the data processor can choose to pick one piece of data from a record, merge other fields or simply ignore it.

"We currently take 38 different feeds to build our own pool and we have a hierarchy of data survivorship rules which govern which source takes precedence;' explains Zoe Vine, head of Data Services at The Trading Floor. "Multiverification helps increase confidence in many areas, particularly residence:'

Set against these benefits is the extra work and therefore cost involved in the initial build, then the ongoing maintenance cost plus the liaison work with each supplier: the more sources a file has, the more complex it will be to refresh. Each data supplier will supply updates at different intervals, and each one will have a different idea of what "update" means, so you could be processing the same data over and over again.

Building a model may also be slightly problematic if, despite a lengthy search, variables are not populated or consistent across the whole data set. However, there are ways around this.

"You may have to build separate models for individ­uals that appear on one source but not on others;' says Berry. "But usually have 80 to 85 per of them across your main sources so it's not as had as it sounds."

Test your files
How to choose the size and composition of the base file, and which lists to layer on top is the heart of the challenge. Tim Beadle, managing director of Marketing Improvement, says that paring down the size of the "addressable target universe" should be the first step.

"Take a brutal and pragmatic view;' he says. "If you are selling Jags, then you don't want high-rise council block tenants. Far too many people try to make their prospect database as large as possible which carries a massive cost."

Just as in classic profiling and targeting, understanding which are the key attributes of your own customer base will help inform which other files might help improve prospecting performance - though restricting a pool solely to the profile of your existing customers could limit growth.

The desirability of extra files will obviously vary for each sector: car insurance renewal dates will be top of the list if you are marketing car insurance, while indi­cators of a propensity to go on foreign holidays will attract travel operators. Co-op data showing multi­buyers is a likely purchase for a mail order company and mortgage providers may be attracted to some of the warm lead-to-purchase data aggregated by the likes of The Trading Floor.

Some marketers may have their heart set on using a certain set of lists that have worked for them in the past. Alan Thorpe, commercial and operations director at G2 Data Dynamics, gives the example of a financial services company that wanted to employ lists of read­ers of financial magazines to drive its contact pro­gramme during the ISA season.

"But they could only get a couple of hundred thou­sand names from those' he says. "By imputing variables from the lists across a larger EuroDirect base file, they got the volume for roll-out along with the accuracy they needed:'

Far too many people try to make their prospect database as large as possible - Tim Beadle, managing director, Marketing Improvement.

However, it's possible to go too far in adding response-built lists, and Beadle advises caution. "Sure they responded, but do you know why?" he asks. "Or why they purchased? It's ok if the list is precisely right but the overwhelming majority of lists don't have the information on them to help you make that decision."

He states that it can often be best to stick to a simple base file along with a few highly predictive variables like age, gender, location and income. Targeting can then be based on those in combination with geodemo­graphics.

"Using the most comprehensive sources to get the right footprint and selecting rigidly on tight geodemo­graphic values works very well;' Beadle argues. "Where people live is the most highly predictive variable out there:'

So when it comes to deciding which selection of sources will work best in a pool, the only really accurate way to do it is, as ever, to test in advance. No dis­count is worthwhile if the data involved doesn't work.

"You need to get your short list of potential data suppliers to match up their data to your model development data sets, and then let the statisticians tell you which data items you need, and how much you need any particular piece of data;' advises Berry. "No-one can tell you which are the right variables until you model and test, but very few people do that. If the data item is very expensive and the least important variable in your propensity model, you could decide to drop it and save the money."

No-one can tell you which are the right variables until you model and test - Julian Berry, managing director, The Customer Partnership.

But having to think two or three years ahead and set up a testing programme can be rather daunting when the priority is to get the pool built. No wonder so many companies listen to the sales pitch and sign up with a single data owner.

"It is very hard to test the pool components with different timings and creative," says Vine. "It's a different thing to classic DM list testing. But if you're going to licence up front for three years then you simply will have to do it:'

One option that holds out the promise of cost and time savings is to employ pre-merged pools. Why reinvent the wheel? This encompasses products like CACI's Ocean and Alchemy's AMS pool and also includes Ai Data Intelligence's pooling proposition.

"We have a large merged database as standard;” says Ai's managing director Jon Cano-Lopez. "We can build client-specific views based on which supplier data they want to buy."

The company has recently added credit data to its merged file and also offers Pipeline, a web-based pay-as-you-go list sales service that pulls records from the same central database.

"This might not necessarily be the whole answer but it has to be better than a single source;' comments Berry.

In all cases, it's well worth checking that the company building your pool will be able to source all the data you might need from the major owners. In some cases, it has been known for data owners to refuse to supply some service providers and not just because they threaten their own prospect pooling business. For example, one major data owner has previously refused to supply its file to a third-party provider because the terms of the proposed contract meant that the provider would have had unlimited use of the variables when transformed into scored values within a pool.

Hosting a large prospect file with a specialist supplier rather than holding it in-house is one of the classic decisions in direct marketing. Marketers have always used bureau services to run the complex merge/purge routines needed for list building, and the initial and ongoing data processing required for a permanent pool is no different.

We take 38 different feeds to build our own pool - Zoe Vine. head of Data Services, The Trading Floor.

Unless your technical staff, systems and applications are up to the job, then forget going in-house. There's also the time and effort involved in maintaining the reference files required to update and suppress the prospect data that mitigate against doing much of this work yourself.

"You need to be able to process quickly and accurately," says Rob Salmon, managing director of meta-morphix. "We have 240m records to match to and make sure that are up-to-date. There's a proliferation of reference data and there's a huge physical overhead to manage and update it. Compared to that, a ten-million record database is quite small."

Retain control
In filling up the pool, the decision is more about whether you should you buy your data from and host your database with the same provider or use an independent broker along with a hosted specialist. Another wrinkle on this is where the hosting company not only brokers data but is also a data owner itself. Can a data owner really be independent?

"You can be independent and a data owner," insists Experian's Colin Grieves. "Our base data sets are where we start." He declines to comment on whether he is under any internal pressure to sell his company's own data as part of a hosted prospect pool.

"The large data owners will have an axe to grind," states Salmon. "They will definitely be under pressure to use their own data."

Or maybe you're happy with the data suppliers but not the service you get from your hosting provider or bureau - this can present real dilemmas. The advice here is to make sure that your data pool is portable rather than in some format known only to your incumbent supplier.

You'll have more power over your bureau and should get a better service as a result. If a prospect pool is indeed a long-term commitment, taking the time to pick the right range of suppliers will pay dividends in future - just like the data.

(Database Marketing: Prospect Data Management - March 2008)

Adapt Or Die

In an industry traditionally addicted to volume, is tighter targeting good or bad news? Robert McLuhan looks at how consumer data suppliers are catering for smaller, precise campaigns and a growing demand for richer, lead-to-purchase data.

For years, direct marketers have been besieged by public concerns about unwanted mailings, intrusive home calls and misuse of data - and have mostly escaped unscathed. Now they have found themselves in the firing line over fears about global warming and the environmental damage caused by waste. With volumes in bulk consumer mailings already on the wane, how does this new world order affect the outlook for suppliers and users of consumer data?

Sitting duck
Together with newspapers and magazines, direct mail is estimated to account for 14 per cent of household waste. When dumped in landfills, paper creates methane, a gas that is 23 times more polluting than carbon dioxide. All this makes direct marketers an easy target for politicians: in January the environment minister Joan Ruddock warned that a mandatory opt-in for commercial mail is "still on the table".

Marketers can justly retort that the days of blanket mailings are over. These days much more effort goes into profiling to ensure that mailings are properly targeted and that waste is avoided by using more deceased/goneaway suppression amongst other initiatives. As for environmental concerns, by 2006 the industry had boosted the amount of mail going to recycling to 30 per cent, as agreed between DEFRA and the DMA, and is on track to meet its commitments of 55 per cent by 2009 and 70 per cent by 2013.

But the agreement also commits the industry to reducing volumes through a mixture of better targeting and hygiene. If that starts to happen, then presumably companies will need to source less data, and that in turn could impact on the data industry. With bulk prices arguably already at rock bottom at around £80-90 per thousand records for the most generic data, smaller volumes could make it hard for many suppliers to generate the revenues they need to stay in business.

For the moment, suppliers claim that mailing volumes have yet to fall significantly. "If anything, volumes have been going up, a sign that direct mail really works," argues Richard Webster, commercial director at DLG.

"Companies are not going to hold back on a successful medium as long as it continues to bring good returns, and if they can demonstrate that it is not leading to waste;' he says.

However some agree that a decline in the quantity of commercial mail is a logical outcome of current trends. "Over time, the industry will be selling less data overall," predicts Peter Lupa, sales director for data at Acxiom.

We are at a watershed - Annette Holmes, managing director, Prospect Swetenhams.

What is clear is that the consumer data market is undergoing a seismic shift. Annette Holmes, managing director of Prospect Swetenhams, points to the need to reduce overall volumes but argues that more spend will be needed to support better targeting.

"We are at a watershed, as suppliers and owners start to wake up to the need to control waste and change their business model;' she says. She then identifies the nub of the problem for suppliers: reducing volumes cannot lead directly to less revenue, because if it did the whole industry could collapse.

In Holmes's view, in future, it will not just be the lists themselves that will be driving the market so much as the in-depth analysis, profiling and modelling required to identify targets. It is precisely because clients are now looking for greater return on investment from their data that they have a greater appreciation of the need for quality. Instead of flogging large quantities of shallow data for poor returns, suppliers will increasingly be supported by the demand for the extra services that they can provide.

A sense of what can now be achieved with this sort of data/analytics packaged approach is provided by a recent campaign by Eurodirect on behalf of the DVLA, persuading drivers to pay annual road tax online or by telephone instead of going to the post office. Eurodirect first profiled and segmented online customers to help identify prospects who would most likely convert to online registration. From this pool, it used GIS software to identify drivers who lived further than 30 minutes away from their nearest post office.

It then filtered prospects against tax disc renewal dates in order to slice up the audience into monthly mailing flies. This preparatory work paid huge dividends, with a response rate of over 36 per cent, a far cry from the one or two per cent that satisfied many companies as little as five years ago.

John Regan, CEO of Ai, says: "Focusing much more closely on targets makes campaigns more efficient, with fewer non-responders. It doesn't mean that the numbers of customers that companies are acquiring via direct mail will reduce. So clients accept there is value to be had from those services, and that keeps the spend up."

There is a growing feeling that the costs and pricing model has had its day. Certainly there is a shift away from commoditised data purchase. Regan adds: "The pricing model should be based on the recognition that profitability comes from efficiency, not from driving the price of data through the floor."

If pricing is based solely on short-term economy, he points out the data will soon cease to exist: it will become old, because owners can't afford to refresh it, or it will be poorly maintained. "We have to stand firm on the value of our proposition," he says. "It consists not just of so many records, but also the result of the analysis we carry out before we even think about sourcing."

Pooling for prospecting
As a sign of changes in the market, Regan says consumer data suppliers now often walk away from a prospective deal because they can't make money from it. This is hurting those brokers who specialise in sourcing data at the cheapest price, as they can no longer fulfil their unrealistic promises. And companies making tenders are starting to realise that firms offering rock-bottom prices can't follow through.

All this heralds a shake-out, with the "cheap and cheerful" merchants most likely going to the wall. But there will be a positive knock-on effect, Regan suggests: consolidation among data suppliers will result in the pooling of data sets, and this will help bring about higher quality.

Clients will be able to choose from more than one record of a particular consumer, as they did when they went to multiple sources for their data. But these days, instead of picking the cheapest for the sake of economy, they are opting to pay for two or even more versions of the same record, in order to have as much relevant data as possible.

"It can be valuable to know that someone has appeared on more than one database;' Regan says. "But also the information will be slightly different and that is useful for targeting as well. So we will see hybrid data pools where data sources combine together, which doesn't happen much at the moment."

Webster too is enthusiastic about the benefits of pooling information. Data is becoming harder to source in the conventional ways: consumers are much more protective of their information, and certain methodologies such as printed surveys, the bedrock of lifestyle marketing ten years ago, have become much less responsive than they were. Instead his company is relying more and more on tightly linked networks of data contributors.

"There is as much data as you could possibly want out there, but it is not necessarily in the right place," he says. "So one has to be cleverer in how one collects and distributes data. Our goal is to try to establish a rapport with our clients, looking at their prospect databases, and helping them utilise their information, while they use ours in a multifaceted way, via a whole network of companies and suppliers."

One has to be cleverer in how one collects and distributes data - Richard Webster, commercial director, DLG.

Another way in which suppliers can help generate better responses is by speeding up the delivery of data. "The more recent it is, the better the conversion:' says Lupa. "Online we can do that in real time, and with post the gap is little more than a day. So clients can act on the consumer information far quicker, which makes the offer more timely and relevant."

With the growth in popularity of rich data and sophisticated targeting techniques comes a greater interaction between buyers and sellers. "In the past, data suppliers were kept at arms length, but these days they are developing closer relationships with their clients," Holmes says. "It's about sharing the objectives and also the responsibility if the responses are not there,"

Could that mean companies paying for the leads that a list generates rather than for the campaign data? Unlikely, since a sale depends as much on the creative and the offer, over which data suppliers have no control. Lupa says he does not know of any attempt to implement "pay-per-response" that is working.

"It sounds tempting but the supplier can't sell the proposition at the point of sale," he says. "We can deliver consumers who are about to renew their policy to an insurance client, for instance, but to actually convert that is the job of the offer itself."

Companies might still prefer to invest in small numbers of certain prospects rather than taking potluck with a large volume of cheap data. "It's a reasonable way to go if the industry is serious about targeting better and causing less waste," says Holmes.

"If you can't buy 100,000 names and only pay for those who convert, it makes sense to buy 30,000 that have a high likelihood of converting. That way you will still save loads."

Of course there is a cost attached: once a list has been winnowed down to likely responders, investment is needed to verify the details. This will normally be done by cross-referencing through different transactional information sources. Business-to-business companies will often use the telephone, but this can also be worth doing for high value consumer items too, such as cars, jewellery or luxury holidays. "Once you have narrowed the list down, it becomes cost effective to invest more," Homes says.

This approach is still quite new but it is starting to gain critical mass, she adds. "It has a logic you can't argue with. It's a question of finding companies that are prepared to test it and then stick with it. If they do they should find the return on investment improving over a period of time." Of course it is up to suppliers to convince clients that this is the way forward.

"There will always be the innovators and people who follow the crowd," she says. "We choose clients who are ready to take that on board and work with us in partnership, and fortunately there are plenty of those around right now."

Volume versus value
Quality of targeting is not the only way to respond to environmental concerns. Another is to shift from postal to email marketing, and this is definitely a major trend, says Zoe Vine, head of data services at the Trading Floor. And it will become even bigger when the acquisition rates increase, she adds; these are as yet relatively low, because marketers have not yet acquired the same level of expertise that they have in mail. But it does not imply a falling-off in demand for data.

Focusing much more closely on targets makes campaigns more efficient - John Regan, eEG, Ai.

"Marketers are still looking to get more value from their existing customer base, appending variables to take them into different markets or to give them different contact methods for different customers, only by email instead of post:' she says.

Any decline in demand will hit large aggregated data sets, while niche lists will continue to bring good revenues, Vine predicts. This is one reason why her company prefers to specialise in particular sectors, mainly insurance and financial services, as well as some travel and charities. It also aims to keep response rates up by limiting the number of contacts for its data.

For instance, insurance lists only go to four different users, which makes them far more productive than if they went out to twenty, as happens all too often. Any revenues that are lost by this voluntary constraint are more than made up for by the premium the supplier can charge for data that is more responsive and niche to buyers' needs.

Any decline in demand will hit large aggregated data sets - Zoe Vine, head of data services, The Trading Floor.

There's no doubt that direct marketers' skills have been improving by leaps and bounds, and that new approaches enable them to avoid much of the waste that so upsets the public. But will this be enough to stop the government imposing legal constraints?

Some feel it won't matter how much mail gets pumped out as long as it is recycled afterwards. The real driver here is the return on investment. "If response increases as a result of better targeting, companies would possibly use us more, and volumes would stay up," says Webster.

But he concedes that perceptions are everything, and direct marketers will not necessarily be given credit for tightening up their act. "Politicians are driven by their own agenda. It's very easy to use us as a whipping boy to demonstrate to voters that they are taking action," he says.

Regan shares that worry, concerned about what steps the government might take in the future to meet fears about global warming. Given the emergence of carbon tax and trading, it would be a logical step to tax other resources such as paper, he suggests. Even if the politicians don't intervene, pressure on suppliers could come from clients responding to public concern. For some time, their contracts include a clause committing suppliers to ethical employment policies, and they are now also requiring similar undertakings to avoid undue waste.

As things stand, this is little more than lip service. Regan says. "What is undue waste? If a company wants to send out a million records we will supply them." But that may not continue indefinitely, and suppliers, especially those who supply the bigger banks, could find themselves having to sign up to some quite specific targets. That may be some way in the future, but one thing that suppliers can be sure of: the pressure is unlikely to let up.

(Database Marketing: Consumer Data - March 2008)