Saturday, 15 December 2007

The Drum - Stuart Watt

Stuart Watt, Head of Sales, The Trading Floor

1. What attracted you to the position?
The pace of growth and the fact that they have such a strong reputation for ‘doing things the right way.’

2. What company did you last work for?
Experian Integrated Marketing.

3. What was your first industry job?
Ten years ago as a corporate account manager for Datel Computing.

4. How did you find your new position?

The Trading Floor had been a client of mine for the last 18 months. We had a great working relationship and I finally decided to join them after a couple of ‘invites’.

5. What would you like as your epitaph?
Winner! Sometimes cheesy.

6. What’s your oddest habit?
I am a fully fledged member of the OCD Club. How about eating cheese sandwiches, every work day for five years? When in the office, always wearing my jacket until noon.

7. Who was your favourite teacher at school and what did they teach?
No idea, I remember going to school and that’s about it.

(The Drum - Dec 2007)

Data Strategy Awards - The Trading Floor runner up

Against some stiff competition from the likes of DLG and the ReaD Group, The Trading Floor were awarded the runner-up prize for Data Provider of the Year at the recent Data Strategy Awards.

"Running a very close second in this category, The Trading Floor brought a revolutionary new data set to market in October 2005 by enabling insurance companies to exchange and trade their dormant enquirer data through a central, independently-managed data pool. The data is pooled, cleansed, modelled and then traded in the full commercial market. For contributing clients, the company has delivered over £1m in data sales revenue in the last 12 months. Since launch, the company has grown substantially and is currently trading over 10 million names per month. At the same time, clients have been able to improve their cost per response significantly."

Friday, 14 December 2007

Data Is Knowledge And Knowledge Is Power

Gathering customer data is one thing. Making it work for you in an effective way to enhance return on investment and further your business is quite another.

At a time when data is the hot topic on everyone's lips, it can often seem that the more information you have, the less you actually know about what matters. Data is knowledge, and knowledge is power - and converting that data into knowledge of customers, and unlocking their spend potential, is a difficult, though crucial, task.

The fractured nature of most client databases, stemming from employing bolt-on "one size fits all" CRM solutions, means pools of valuable data sets are usually isolated and therefore unable to interact to provide the customer intelligence required to drive an effective marketing and communication strategy.

If you want to find out who your most profitable customers are you may find yourself interrogating several systems to add up their spend across each product type. Our database management division has found that generally, the more established the company, the more disparate the data storage facilities. It's a problem worth solving - the more you know about your customers, the better placed you are to maintain their custom, and to sell them additional goods and services.

Key to driving a system rationalisation is getting buy-in across the business - appoint a data steward in each business area, and agree and implement a data governance plan where sponsors are identified and accountable. Rationalising systems to create a single customer view delivers benefits across the three most important challenges marketers face: productivity, customer understanding and financial attribution.

The first piece of the customer intelligence puzzle is assimilating and evaluating your demographic, attitudinal and transactional data sets to build your processes and techniques. By profiling customers and prospects, and identifying the traits of customers that bring greatest value to the business, marketers can target new, high value customers. Equally, by having a full view of every customer's relationship with the business, and the ability to understand the customer life cycle, it is easier to spot and retain lapsed customers quickly, and to build deeper and more loyal business relationships.

Three key steps help to build the knowledge bridge:-
  • Data interrogation - what data you hold on each customer, what information can be deduced from each piece, how it can be harnessed for future sales prospects. Without reliable data, you can do little to build your strategies.
  • Segmentation - identifying groups of similar customers. A variety of clustering and profiling techniques can be adopted to identify customers with similar behaviour patterns and demographic trends.
  • Hypothesis - once the segmentation is complete and you have robust statistical data sets, with a clear model of the customer life cycle, you can start to generate a variety of predictive models for cross and up selling purposes to existing datasets.
"Still too many companies choose to focus on analysing a customer's past behaviour - when what they are really interested in is what the customer will buy in the future"

Because customer segments can be quickly and accurately identified, the time to market for new products is cut. Marketers can concentrate on contacting the right people, at the right time, in the right way, which will eliminate wastage and promotes better customer relations.

Yet still too many companies choose to focus on analysing a customer's past behaviour - when what they are really interested in is what the customer will buy in the future. The gap in knowledge between knowing how someone has previously behaved, and predicting how they will behave is massive. The data your company has may be transactional, it may be clean, but it won't necessarily give you the key buying triggers and variables required to drive predictive models. Customers always act within a context, and this constantly changes as new products emerge, events happen, people move. You know some of what they did, but rarely why they did it.

It's likely that in order to examine context and behaviour you will need to supplement existing data sets by matching to third party transactional, demographic and lifestyle data sets such as The Trading Floor's insurance/finance pool.

Identifying trigger data through your previous data interrogation and segmentation work, you will select variables that provide the most value to your data set - anything from date of birth, to occupation, to what newspaper they read on a Tuesday, through to flags indicating a recent house move, their car insurance renewal month and additional contact information.

Complementing your database with additional information and producing qualitative and quantitative segmentation is the cornerstone to driving a customer intelligence strategy, and really understanding why your most valuable customers are buying from you, and how to find more of them.

Pin down your what, why and your when, and you'll not only cement a customer relationship for life, you'll have the biggest competitive advantage. 'Examine. Extrapolate. Enrich' should be the 2008 mantra for the customer-savvy marketer.

(Precision Marketing - 2008: The Year Ahead, Dec 2007)

Monday, 10 December 2007

Peter is Trading Places

Leading database management and prospect pool specialists, The Trading Floor, has appointed Peter Mayne as Head of Agencies. Following an intensive period of growth, the newly created role will focus on optimising opportunities within the agency sector.

Peter has over ten years experience in the data sales industry and has previously worked at LBM, IPT, Prospect Swetenhams and Mokrynski International. He has worked in both list broking and data sales roles and will use his extensive experience to build The Trading Floor's presence in the South of England whilst maintaining their strong hold in the North.

Peter adds: "The Trading Floor is an exciting young company that is making a big impact in the data industry. I'm delighted to be joining the highly reputable team and I look forward to being a part of the next stage of the company's rapid expansion."

Based in St Albans in Hertfordshire, Peter will be reporting to Stuart Watt, Head of Sales at The Trading Floor. Stuart comments: "Peter brings with him a great deal of knowledge to our business through his many years of experience within the data industry. Specialising in the agencies market, Peter's skills will be an asset to The Trading Floor and our ongoing expansion programme."

Thursday, 6 December 2007

Merging And Matching

The story of UK consumer data supply has traditionally been one of innovation. Whether building and maintaining niche lists, introducing complex stats to build modelled Electoral Roll-based files or offering third-party-supplied names with transactional data attached, this market rarely stands still.

Head to head?
The latest entrants to consumer data sourcing have continued to seek out new sources. The Trading Floor has made insurance application data from the web available for marketing use, while companies like Transactis or Abacus with its Publishing File have brought data that would traditionally be restricted to closed co-ops out to a wider market.

The availability of transactional information on real buying behaviour plus proprietary UK-wide merger databases are the two biggest innovations of the last few years. Now these files are maturing and, with the grater volume and selectivity, are ready to take on lifestyle.

“The pools created by people like Alchemy and Transactis are emerging as alternatives to lifestyle databases,” says Michael Smith, senior account manager at Prospect Swetenhams. “You get plenty of selectability plus the transactional data shows what people really buy. With credit data and many charities contributing, there’s some really interesting stuff on there. It’s used across the board and works well in most sectors.”

Many UK marketers still stay loyal to one data supplier, often choosing to simply licence a large chunk of lifestyle data. Matching the variables to customers, running profiles and then constructing ongoing campaigns is much more straightforward when you only have to deal with one type of data. So are these pools or “open co-ops” really a valid alternative to lifestyle data?

“The majority of proprietary pools still do not remotely have the depth or the selectability or lifestyle,” says Richard Webster, group communications director at DLG. “The source of contributed transactional data also tends to be anonymised so it’s hard to test one source against another. With lifestyle, you can test on 400 separate variables. And there’s also sponsored client questions that simply aren’t possible with a third-party co-op or pool.”

Traditionally, there has been skew on both lifestyle and transactional data. The former because surveys have historically been more attractive to older women, the latter because they are composed of mail order buyers who also tend to be older women: great if you are targeting though the post, otherwise perhaps not so desirable. There’s also a question mark over lifestyle’s reliability though this can easily be addressing by testing.

“You do get a really skewed sample of the population on lifestyle,” says Zoe Vine, head of data services at The Trading Floor. “They tend to be female and over 40, or students. Also, people filling in insurance applications tend to tell the truth.”

“Because of multi-channel collection, the female skew simply isn’t true anymore,” argues Webster. “We take a third from the net and gather the majority of the rest over the phone.”

In fact, with multisourcing and merging now accepted as the best way to get optimal prospect coverage and depth, it’s becoming apparent that lifestyle and transactional data are far more effective working in harmony than posing as alternatives to reach other, whether the merging work is done by the data end user or offered as a proprietary service.

The success of this approach was borne out in the classic 2005 study by consultancy Tank where a multi-sourced file built from two Electoral Rolls (ER) and a third source outperformed individual Rolls on both coverage and predicted response. Specifically, coverage was up 25 per cent with the merged file and 6.63 per cent of its top decile were predicted to respond, the highest figure of all files tested.

Chasing transactional
Collecting data via transactional means in some form and adding it to their base file is becoming very desirable for many data owners. It allows targeting based on actual behaviour rather than what has been claimed in a survey and the continuing customer relationship also serves to periodically validate the data. It’s also a lot cheaper to collect and collate than traditional or even online surveys.

“It’s becoming less and less viable to manufacture data with surveys,” says Chris Morris, managing director of Transactis.

With much of the UK’s data becoming ubiquitous due to multiple licensing deals, merging and modelling work is becoming more of a differentiator in campaign performance, and being able to offer transactional data is very desirable. But it’s not all that easy to get hold of – there are only so many high-volume UK mailers. As in the suppression market, stringing deals to source these files from home shopping or travel companies is conducted in great secrecy in case the competition get wind of it and try and corner that feed for themselves.

For example, Transactis doesn’t pass on transactional indicators to third parties, partly due to the fact that much of the company’s data is only permissioned for analysis and enhancement rather than for list usage. “We keep these for ourselves,” says Morris.

DLG has already moved some way toward this third-party approach by signing up Friends Reunited as a contributor. Though this is not transactional data, customer details are continuously reverified as registered users enter the site.

We’re coming from the other side,” says Webster. “We’re looking to draw other data onto lifestyle as part of our consumer data hub. We’d like to p0artner or acquire a significant owner of transactional data.”

The drive to build multisourced data products is far from new. Companies like Experian have long since used a mixture of their own and external data to build geodemographic segmentations while other suppliers happily employ data from third parties to fill in the gaps in their national coverage. For example, Transactis supplies AcXiom with data to help fill the gaps in the ER base file it used for its modelled Infobase Lifestyle Universe product.

For its part, EuroDirect’s Data Exchange has to be the granddaddy of the open consumer prospect pools and the company could fairly claim to have been well ahead of the game when it launched it almost a decade ago. Claimed to be the UK’s largest prospect pool, it is built from a mixture of every type of consumer data on the market: compiled response lists, credit, lifestyle, contributed transactional information, public register and more.

“We have transactional data that is typically sourced from mail order,” says EuroDirect’s database solutions director Tim Pottinger. He says that the transactional data is more typically used as an indicator rather than being used at a more detailed level to drive offers.

“We use that information at individual level to model out to the rest of the population,” he says. “If someone is mail-order responsive then we’ll use that as an indicator of channel preference. It’s our intentional to gather more transactional data and put it into Data Exchange at a lower level with greater details.”

Beyond the data owners, data-independent companies have been building merged UK files from license third-party data for some years now, with products like CACI’s Ocean and Alchemy’s AMS pool bringing many of the benefits of multisourcing to straightforward “per-thousand” rentals. But to realise the most from multisourcing, some form of data set bespoked to your own requirements is the way to go, bringing together the most relevant files that may not be available in “pre-merged” form.

For the largest mailers, building their own pool in this way has long been the most controllable and efficient way to run prospecting. Over time, more and more marketers have started to take this route, attracted by the results it offers and also by the savings to be made by efficient long-term usage. Pay-as-you-go deals, introduced by Experian and now taken up by a number of other suppliers, have helped ease the upfront costs, but financing a bespoke database build is never cheap.

Accessible pools
Now more suppliers are coming up with a lower-cost route into merged prospect pools by offering their clients access to their master pre-merged database and then adding bespoke data on top rather than starting each project from the ground-up each time. Ai was one of the first to go down this route.

“We have a large merged database as standard,” says managing director Jon Cano-Lopez. “We can build client-specific views based on which supplier data they want to buy. There’s not much transactional data in there as standard, but we will add anything a client wants us to add that is available.”

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

“This gives access to a multi-sourced pool for smaller users,” says Cano-Lopez, who notes that each client’s view of th4e data in Pipeline has to be set up in Advance depending on how many sources they want access to.

More data owners are now starting to do something very similar to the data-independent Ai, building out from their base data sets by adding in third-party data from other owners and offering them as a richer base for a bespoke pool (and spinning out subsets of the data as list rental sources too). Here, multi-sourcing improves selectability, reduces skew in the original data and crucially makes much large volumes available to sell to their clients.

Transactis has used CACI’s Ocean as the base file for its consumer data from its earliest days and adds in Equifax credit data for pre-scoring prospects, plus Council Tax Band information to help with household and neighbourhood- level targeting.

It labels its Vision service as an “off-the-shelf solution that includes every key data component used in today’s targeting arena”.

“Prospect pools aren’t just a solution for the big mailers anymore,” says Morris. “Prohibitive de-duplication costs from bureaux and poor access to their data are holding companies back. The pre-built element of our solution lowers the cost and we’d add any data set the client wants to mix.”

The Trading Floor has hung its data on a third-party data skeleton since day one (CACI’s Ocean again). With around 8.5 million of its own records, it is now adding more data to the mix to improve volume and details. “We can have fantastic information for one transaction, but might need more behavioural or life stage detail,” says Vine. “We partnered to bolster these aspects. Now we’re adding EuroDirect’s Cameo Financial overlays and merge in more insurance date renewals from external sources.”

With its pre-merged file as a base, the company has recently launched a database management division offering bespoke pool building and online access to hosted prospect databases. However, simply gaining volumes is not what it’s about.

“Anyone can build a UK reference file,” says Vine. “We’re more interested in accuracy. It’s not so much about volumes, it’s the value of the records that are in there.”

The increasing number of merged files and pooling services can make it very hard to tell the difference between the competing suppliers. More and more databases are on offer with most records being the same data license from the same original owners. And where you are dealing with the data owner, there will inevitably be pressure on sales staff to sell their own data rather than that of a third party simply because of the grater margins.

As to how any pool is built and run, any supplier mentioned here should be happy to do it all for you including hosting, or to work with your incumbent data processor if they are doing some or more of the work. With the newest data pools offering cost reductions over bespoke builds due to their pre-merging work, how the lowest “total cost of ownership” matching up with performance during testing looks like the equation to solve. More and more vendors are offering packaged deals rather than volume-based pricing, and the cost-per-acquisition route to pricing we mentioned by all commentators as an option.

Merging the most
So rather than the latest data sources taking over from lifestyle as top dog, the direction is the other way: merging lifestyle data with everything else and seeking out ways to tap transactional data sources that haven’t already been ring-fenced by competing suppliers. A series of “super-merged” UK universes have emerged that, when combined with some specialist files, have performance comparable to the best bespoke prospect pool.

We can see these rich pools becoming the base for a new range of products and services. For example, EuroDirect has long since used Data Exchange as a base for its classifications and now has an alerting service based on new incoming data. If an individual’s income or address changes, they are flagged up for potential contact.

“There’s no getting away from testing in direct marketing, but the combination of transactional, Electoral Roll, credit and lifestyle data has to be the way to go,” says Morris. “Now it’s coming down to two key sources: lifestyle and transactional.

(Database Marketing - Dec 2007)