The best way of enhancing and updating information held in your CRM, from whatever source, is to pair with an accurate database against which you can match on an ongoing basis. PAR’s One-by-One plug in, for example, enables users of a supported CRM system to input information they have, say from an imported database or even a telephone conversation, and immediately enhance it with all other data from PAR’s master database. Research shows that automatic enhancement and updating can be carried out at less than a tenth of the cost of a sales person asking the questions – and the final results are far more accurate and complete.

The core techniques we believe data owners of B2B databases should be engaging are to cross check their databases with companies who are actually visiting their website, and are thereby qualifying themselves as having some interest in your products and services.

By cross checking and identifying the companies visiting your website your static B2B database becomes a living database; especially if you set up alerts so that the relevant sales and account managers automatically get a notificiation when that company visits your website.

This simple technique should significantly enhance the value of the data that you have and gives you specific calls to action when those companies come visiting your website.

The most effective way to improve basic b2b data is to undertake a process of matching and enhancing. Communication between client and supplier is paramount, and can have a massive impact on the success of matching and enhancement programmes, and the value of the data produced.

Before beginning the task of matching and enhancing, the incoming client data needs to be assessed, to check the key variables (like name, address, postcode and telephone number) are present, and to identify whether the file is company or contact based. It is also essential from a customer perspective to be supplied an ID or URN so that the data can be re-imported into the appropriate software.

Once these and other checks have been carried out, the matching process can begin. Every data provider/data aggregator will use different matching software and different matching algorithms, so it is important to understand what classifies as a match. It is not as simple as just comparing match rates between different suppliers as matching routines will be different and the quality of the match will differ. It is necessary to strike a balance between quality and quantity, and to ensure that the client is aware that this is generally an automated process, so there will be anomalies.

Once matching routines have been agreed, the client data can then be enhanced. It is important here to ensure that the client is aware of what data they will be receiving, and the supplier has covered all angles. For example does the client want data appending or overwriting, do they want codes and descriptions appending, or just descriptions with a separate look up table? Does the client have any specific codes they use for example around business sector or employee size that they want us to use? Asking questions is a vital part of the process, and will ensure the client’s investment in enhanced data is properly realised.

You need to understand what you are trying to achieve with your database.
There are some very good sources of additional information that can be appended to your data, which will no doubt fill some holes, however this should be considered after first reviewing your own data.

And when I say reviewing your own data, I mean all of your customer data, not just that sat in your marketing database. Too often there are sub sets of data spooled off and used away from the main marketing function, (typically the same with web visitors and finance data)

Think of each of these sources as a colour, bringing these colours together gives you a better picture.

Even the most basic stats held on your database can often hold the key to insight and understanding when looked at in a different way. Derived information can be delivered and marketing strategies adjusted accordingly.

It can be a simple as the “duplicates are good” mantra.
Typically you may feel that if your database contains duplicates it’s a bad thing – well it is if they aren’t managed correctly. Duplicates actually give important information to you, information such as why are there duplicates?, where did they come from? These duplicates could be multi purchasers (great customers!), or multi enquirers (tyre-kickers) or the missing link between treating a customer as a prospect and vice-versa (conversions).

A client of ours was looking at headline stats recently. Having discovered that the seniority level of their customers was typically “Manager” level (68%). The majority job sector was “Finance” (45%) – it was easy to assume that the largest chunk of customers were Finance Managers. Further interrogation identified the crossover between Managers and Finance was in fact tiny – accounting for only 3% of the database.

So even if you feel the information you hold on customers is headline – chances are by using some thorough data techniques you can gain further valuable insight without having to spend a fortune.

Additionally each time you have a touch point with the customer/prospect ask a relevant question. Whether it be on a response mechanism, at the call centre or via a tick box on a web site. The only people that really can give you information about your customers are the customers themselves. So ask them!

Incentivisation will always help recipients be more open about what they are telling you.

And finally you can append company attributes to further enhance your customer and prospect knowledge. This should not be treated any differently to buying cold data.

Always
a) Ensure your data is clean before this process to ensure best match rate
b) Ensure 3rd party data is recently updated
c) Ask the source of data and how it has been compiled - it is credible?

Important notes:
Its very easy to want to add lots of additional data just because its available.
Don’t forget that additional variables that can be added from 3rd party sources will need updating as they decay with the rest of your database – needing further budget, internal resource and management

As we all know, not all B2B files are created equally. Before buying, marketers should ask for anonymous samples using postcode level IDs and then seek to understand the depth of data on each record at an aggregate level. This can be accomplished by ingesting the data into a data visualization tool and looking at density of variables in the sample records. It should then be possible to select the best data provider for that particular campaign.

“Ultimately the goal of analysis is to maximise the return on investment of each marketing campaign. This is achieved by delivering the right message to the right person at the right time. But what is the right message? Who is the right person? And when is the best time to market? A holistic picture of the customer is required to address these questions, this, coupled with comprehensive prospect data and modelling techniques may extrapolate what is known about a customer to each prospect.

Marketing must begin with a clear and well thought out business plan. Aiming to increase turnover is short sighted, other areas should be considered:

· Do I want the more profitable customers, why recruit customers who have no loyalty

· Do I want to minimise risk of bad debt, I could offer different payment options and terms

· How can I make distribution more effective and efficient, email delivery may be cheap, but data is expensive, so why waste it on people who do not respond

Having determined the business requirements then the process of acquiring the appropriate data attributes to meet the requirements can begin. There are many different sources of attributes, some may be sourced from your own internal systems i.e. average order value, months traded, average days to pay and trading method. Data suppliers have other attributes i.e. SIC, employees, credit risk, turnover and profit these can be used as attributes for modelling customer data and applying to prospect data. Other data can be collected via telephone research and customer questionnaires i.e. channel preference and contract renewal dates.

Having gathered the key firmographics there may be an exercise of pulling all of the data together into a single customer view, hanging each attribute from a single unique contact or workplace. The single customer view and it’s firmographics must then be profiled and segmented using such techniques as Chaid – which will auto generate segments of significant attributes. The segments may be analysed against the business plan to select the “Best Customers”, these segments may then be extrapolated across a good quality prospect base and an appropriate marketing strategy applied.”

Traditionally, B2B marketers have sourced their data from large volume compiled data sources that arguably offer quantity over quality. More recently a broad range of proven and innovative data sourcing options have become available encompassing recency, transactional and industry information. Buying data that is modelled using these variables can add an extra dimension, with real value, to the traditional selection criteria of SIC, job title and number of employees.

To gain an edge and differentiate your company from competitors, you need to move beyond relying on compiled data sources to find new customers. Successful marketers rely on a combination of data sources including compiled, vertical lists and transactional data which they integrate with their analytical efforts. The result: Intelligent targeting built upon a data solution of enriched content that improves acquisition performance cost-effectively.

When looking to enhance your B2B database, there are certainly many considerations – beginning with the differing ways customers and prospects need to be approached. Iain Lovatt from Blue Sheep is absolutely right when he talks about marketing to the right person with the right offer at the right time. To this I would add that, as you probably already possess some degree of customer insight – ie. who they are, what and how they buy – then recency, frequency and/or value data can inform both your sales strategy and the selection criteria you apply to any prospect data you’re looking to purchase.

How best to do this? Well, ensuring that your data is clean, format-standardized and de-duped are essential B2B ‘good housekeeping’ first steps. Are all address fields in the right place? Do all the records have a company name, postcode etc? With these matters attended to you can then progress to applying Preference Service and suppression products to your datasets. This will not only increase match rates for enhancement against a reputable live universe or prospect pool but also – and perhaps most importantly! - save you money. Mainly because you’ll only be appending new data to records which have a high probability of soliciting a response.

My B2B data mantra has always been to make the most of what you already have and only then supplement it. Otherwise you run the risk of just appending data for the sake of appending data and wasting money. Big isn’t necessarily better, after all. As many marketers transition from a volume - to value based direct marketing model in response to the current recession, getting down and dirty with your data by identifying which client/prospect segment(s) potentially represent your most lucrative targets is a tactic which is bound to increase ROI and keep your cash flow conscious CEO happy.

A little time and effort attending to your data management basics can indeed go a heck of a long way.

There is no doubt that databases with just headline stats about your prospects and customers allow you to do little more than send out untargeted campaigns that ultimately damage your brand and could result in a negative return on investment.

With this is mind there are a number of techniques marketers can use to improve their data which will ensure your message gets to the right people at the right time. A first stage should be to decide what information you need to hold about customers and prospects – which is usually a business activity code (SIC) and a view of company size (number of employees or turnover). This provides a basis for some simple targeting and selection.

A quick and relatively easy option is to match your data against an appropriate third party database which has the additional information you need. However this relies on finding a supplier with a database that is accurate and is broadly similar in profile to that of your own data. You may also be able to find suppliers that can provide some very specific information that would be even more useful in selection, targeting and analyses – for instance Harte Hanks CiTDB database contains over 100 fields of selectable information about the installed IT and telephony equipment in larger corporations all around the World.

Whilst file matching is cost effective it’s unlikely to be able to enhance all of the data and therefore other complementary techniques will be needed to fill in the gaps.

Another option marketers might want to consider is using telephone research - targeted to priority sectors – as well as ongoing desk research. Clearly this will give you a much better depth of knowledge but has relatively higher costs involved.

Its worth remembering that businesses can also enhance their data via every communication sent out. Ultimately collecting and maintaining data should be considered as strategic – allowing you to engage with your current customers more effectively and address the potential needs of your prospects. Every touch point should be coordinated and used to collect information that is required to put customers and prospects at the heart of all activity.

The true solution is therefore strategic, where the elements are various and complementary. But whatever the constraints, businesses must consider the risk of not taking the time to do this properly.

The first would be to create an online preference management centre allowing users to maintain their own contact information. By asking recipients to maintain fields on who they are, together with when and how they wish to be contacted, companies can minimise list fatigue and maximise response rates by ensuring relevant and timely content reaches each individual. This will also help reduce opt-out rates as recipients can choose to select specific content of interest as well as having the option to opt-out of all communications.

Another way of ensuring data is kept up-to-date and valid is by targeting inactive users with a re-opt-in campaign. Every database has a percentage of inactive users that are uninterested or do not wish to receive your communications, but do not tell you so. By targeting this segment with an email campaign that reminds an inactive audience of the company’s offering, you will establish who the best recipients are on your mailing list. Additionally by providing clear opt-in/out options, customers who had been inactive can be revived and those truly interested in your offering but are not ready to engage can be identified. Where recipients do re-opt-in, they can be directed to their personalised preference management centre to update their choices. Those that do not respond can be flagged as unsubscribed and set aside for future direct mail or calling campaigns.

B2B data decays a lot faster than B2C, particularly in the current economic climate. So before looking externally, the first new parameter to capture in your data is Last Update date. This will give an initial confidence level on the likely data accuracy, then online tools can provide a quick sanity check on the overall data quality. Suppression files are then available to further improve the accuracy. Subsequently, consider validating additional older records that have a value. All of this supplies a more accurate base to start promoting from.

Adding financial measures, such as transactional value or financial contribution, is the next steps. These will exist in your organisation, but may need to be collated from a number of sources. Then you have general business demographics, such as a size measure and industry type classification, and you can go further with parameters such as performance trend or financial health indicators.

With this information available the first question is, where is cash or profit currently coming from, and do you have the processes or activities in place to retain this? The second is, where do you think your best new customers will come from? The steps above will give you these answers, but the starting points is always with accurate base data.

"By cross checking and identifying the companies visiting your website your static B2B database becomes a living database; especially if you set up alerts so that the relevant sales and account managers automatically get a notificiation when that company visits your website"

Yeah right - what a load of tosh. Does this guy know anything?