Diary of a Marketing Insight Guy

Commencing an engagement earlier in the summer with a company for which I had previously worked, I was issued with an Exchange account for internal communications whilst on-site. Not surprisingly, my external email address was the same as it had been when I was employed there, since it adopted a standard format comprising my first and surname together with the company’s domain. What did surprise me though, eighteen months after leaving the company, was the steady stream of emails I began to receive from lists to which I had been subscribed before I left.

Now perhaps I should have diligently ensured, before moving on, that I had unsubscribed from these lists or informed their senders of my change of address. The reality though is that this is often harder than it seems, between keeping track of the lists to which you have subscribed and knowing how to advise your new details. It’s usually not the highest priority when moving on either.

These emails sent to my old address would certainly have been bouncing back to the originator for quite some time. The failure, or conscious decision, by these senders not to process these bounces and use them as an opportunity to update their databases is astonishing. Across the entirety of their databases and subscriber lists, given the rate of decay of business data, these senders must experience significant volumes of email delivery failures.

Just as with spam, it’s tempting to dismiss such considerations on the grounds that the cost of continuing to send to dead addresses is minimal, the effort of doing something about it substantial and the overall impact negligible. This is not the case however, and persisting in sending to bounced addresses can lead to deliverability issues and represents a missed opportunity for database management.

Repeatedly sending to non-existent addresses and incurring the bounce back messages this generates gets noticed and can lead to being placed on spam offender lists. This could cause all email to be blocked by spam filters with obvious dire consequences for campaign effectiveness. You may not even know that here is a problem, except for the rather disappointing response rates.

Failing to update marketing databases with bounced addresses also means that the opportunity to track the fact that the record itself may be invalid is also lost. If other activity is being driven from the database, such as DM, then significant cost can be incurred sending to contacts who are no longer there. Acting on email bounces also offers the opportunity to proactively update the database. If an individual represented a high value contact (someone in a senior position or a frequent purchaser), perhaps it’s worth a call to establish where they’ve moved in order to re-establish contact or identify a replacement?

I’m not complaining that I’m receiving some of these emails again, and it may even be to some of the senders’ benefit in the end. But the likelihood of this situation arising is tiny and the potential negative impact significant. There’s no excuse for bad practice.

I wanted to briefly mention a great new resource for anyone involved in online data collection, brought to us by international data quality and addressing guru, Graham Rhind. “Better data quality from your web form” is a free download ebook in pdf format that is designed to help achieve effective international name and address Internet data collection. In the spirit of full disclosure I should mention that Graham asked me to take a look at the book before he published it and as such I can say it’s an invaluable source of information.

Exhibiting Graham’s customary thorough and comprehensive coverage of the topic, the book includes guidance on name and address capture, use of pick-lists and other form elements, usability and data validation. Longe-standing readers of my blog will know that web forms are something of a hot topic for me and I hope this book will help curb some of the worst examples of bad practice out there!

The book is available for download from Graham’s site, and whilst you’re there you should take a look at the wealth of additional information he makes available.

Writing in his “Optimize Your Data Quality” blog recently, Jan-Erik Ingvaldsen of data quality solution developer Omikron referenced an article on destinationCRM.com about a piece of research that’s a must have for anyone building their data quality business case.

In their recent research study “The Impact of Bad Data on Demand Creation”, sales and marketing advisory firm SiriusDecisions assert that following best practices in data quality led directly to a 66 percent increase in revenue. Whilst I’ve outlined some generic business case drivers in the past (see “Building a data quality business case“), this is the kind of quantitative study that can really grab C-level attention when you’re trying to justify investment in data quality. The research outlines how addressing quality issues early on in the data life-cycle has an almost exponential benefit in cost efficiency and highlights the importance of collaboration in driving quality improvements.

“It is something that your organization simply can’t afford not to do,” says SiriusDecisions’ senior director of research, Jonathan Block. No argument here!

I mentioned recently that I had signed up to the International Association for Information and Data Quality (IAIDQ), who run webinars from time to time as part of the membership package. One such session took place yesterday, in the form of an “Ask-the-Expert” presentation by Danette McGilvray of US based information quality consultants Granite Falls Consulting. Danette outlined 12 Dimensions of Data Quality, including considerations such as integrity, accuracy and coverage. Although all crucial to attaining high quality data, I particularly liked no 12 “Transactability”, defined as “A measure of the degree to which data will produce the desired business transaction or outcome.”

In some regards this is a “softer” dimension than more quantitative ones like validity, but is at the same time what all data quality should really be about. DQ shouldn’t be seen as something that’s just good to do, but something with an ultimate goal, namely allowing us to do business the way we need to.

Also at the Data Management and Information Quality Conference (see previous post) I signed up with the snappily titled International Association for Information and Data Quality (IAIDQ), a worldwide organisation devoted to the pursuit and promotion of data quality. Professing that “All those impacted by poor data and information quality, and those who just want to learn more, are welcome”, I can recommend membership to anyone with an interest in this area. Registration is not expensive (I even received a free mug, although I can’t vouch for whether the offer is still available!) and provides access to a wealth of information and resources, including occasional webinars and online tutorials.

Fine oratory (such as that of a certain Barack Obama) often deploys what’s known as the “tricolon” or rule of threes – three words or phrases that create a pleasing cadence and drive a message home. Whilst the oratory of information quality guru Larry English at the recent Data Management and Information Quality Conference may not quite have been of President-elect standard (though still pretty good!), here are two sets of three rules relating to data quality that I picked up – a double tricolon, if you will!

3 Steps to better data

1. Understand – perform interactive analysis (profiling) to establish what you’re got and where any issues lie.
2. Improve – apply change to both underlying data and processes to enhance data quality and address the issues identified in step 1.
3. Protect and control – on an ongoing, business as usual basis ensure that issues are identified and improvements made.

Handle information as you would any asset

1. Acquire – data, like other assets, must always come from somewhere, be it an external source such as a list broker or in-house data capture. Consider what these sources are and how they’re selected.
2. Manage – assets such as plant, equipment and stock all need managing (especially if you’re stock is perishable, like data), so take care of it in the same way.
3. Dispose – at the end of its lifecycle, an asset is disposed as it no longer has value or performs its task. Data decays and looses value, so plan for it’s disposal.

I’ve written a lot about integrating sales and marketing databases (posts too numerous to provide links – search on “integration” in the sidebar), but so far I haven’t mentioned the third source in the marketing data ecosystem – order processing systems. Order processing systems are where the sales orders that leads and opportunities (hopefully!) eventually turn into are captured, invoices created and ultimately customer status converted. It may also be known as an enterprise resource planning (ERP) system, and also handle financials, human resources and other functions (possibly even CRM).

The reason these systems are important within a marketing operations context is because they are generally the system of record regarding whether an organisation is a customer or not, and what their purchase history is if so. Although the sales and marketing systems should have a view of completed opportunities and closed deals, there is inevitably a disconnect from what was supposed to have been sold and what was actually booked. Put starkly, once the deal is clinched, Sales’ enthusiasm for making sure it is accurately reflected in the SFA system wanes considerably; commissions are likely to be calculated based on what the order processing system says.

Care needs to be given to designing order processing links though. Here are some considerations:

• Is the feed uni or bi-directional? In other words does the marketing database just receive updates of customer status and possibly purchase history. Such feeds are often one-way, as the owner of the order system will jealously guard their data integrity – not unreasonably, as it represents the “real” customer database for the company. However, if there is no feedback mechanism, then it may not be possible to correct issues with the data, such as missing address elements, inconsistent country values or duplicates.
• How does the order system handle accounts and organisations. As a result of the different imperatives of ordering systems (delivery, invoicing, credit accounts), data is frequently held in a way that is inconsistent with that of the marketing database. If different departments of the same organisation, for instance, have made separate purchases, the order system may create separate records which will be perceived by the marketing database as duplicates. Take care in removing these duplicates from the marketing database however; not only might they simply turn up again with the next order system update, but you will loose the account number reference in the marketing database which might be a crucial external reference.
• What purchase history data is available? If the feed is at “account” level (which may not be the same as unique organisations) it may include most recent order, invoice or contract date. That might be enough to derive a “customer” status, such as having ordered within a specified time frame or are within a maintenance contract, but may not include any information on what was ordered. On the other hand, you might be faced with a feed of every order or invoice, which is considerably more challenging to integrate.

Unlike the third rail of an electric railway, which you shouldn’t ever touch in order to avoid electric shock, the order processing systems is generally avoided even though they’re a crucial source of marketing data. Which isn’t to say you won’t get a shock if you try and integrate it!

I’ve written before (see Using reference sources in data quality maintenance) about the benefits of matching marketing data, particularly organisations, to external reference data with regards to data quality improvement. We’ve just signed an agreement with Dun and Bradstreet to match a core subset of our database with their global database and enhance it with key attributes, such as industry, size and enterprise relationship. The plan is to refresh the matched data on a monthly basis so that we always have the most up to date view.

The data we’re enhancing consists of customers from the last few years together with Sales’ key prospects. By developing a better understanding of these organisations, we can not only target them more effectively, for instance by undertaking industry selections, but also better understand the interrelationships between organisations. We may have received a lead or have an existing relationship with a subsidiary that could be leveraged into the parent organisation, for instance. Our marketing activity can become more advanced, in terms of targeting and segmentation, as a result of this intelligence.

De-duplication is also a key benefit, as I’ve said before, as D&B are able to match using previous names and alternative trade styles together with other sophisticated techniques, that highlight duplicates that were otherwise not evident. Again, this can bring together otherwise hidden relationships and opportunities.

The drawback with D&B is that they’re quite expensive, and matching/enhancing hundreds of thousands of records is prohibitive. Although we’re enhancing our core data, some of the benefits I’ve outlined are lost when working with a subset; we don’t know if the records we’ve chosen are duplicated or have a relationship with others in the database. I’m hoping to discuss with D&B the idea of matching our entire database (an inexpensive activity at a few cents a record) and then enhance only those in which we’re interested, specifically those related to our core dataset. This isn’t a standard service D&B offer, and it can be a challenge to have them move outside their usual modus operandi, but hopefully they can be persuaded! I’ll let you know how I get on.

I’ve written previously about the importance of address management (see International address management) in maintaining data quality, and I mentioned that we planned to implement a new set of web enquiry forms with an address auto-completion feature (see Using web visits to build contact profiles). Well, I’m pleased to say the forms are online and working very nicely, improving not only the quality of address capture but also the user experience as well. Reducing the keystrokes required to complete a form, I believe, leaves more goodwill with the enquirer to answer a few more profile building questions.

The easiest way to see how the forms work is to try them for yourself, so take a look at the UK form and try filling it in. Once you’ve completed the postal code, the system looks up the address in the background, and as you start typing the first few characters of the street address, it presents options as to what the address should be. Once you type enough for a definitive selection, the address is completed (or you can pick from the list). In the UK, many business postal codes are sufficiently specific that the address is completed without typing any further, except perhaps for a street number.

The forms work across nearly all of our local EMEA sites and are localised for each one. In fact, on the UK form linked above, if you change the country and language options, the address field labels change to match. Unfortunately we’re not quite slick enough to change the entire form, but if you link via the relevant local site the page is fully localised, with the address elements driven by the addressing solution.

The address look-up solution is powered by UK specialists Postcode Anywhere who support the system via a simple AJAX based web service. The service is charged on a per click basis and is remarkably inexpensive, with credit packs covering several thousand look-ups available for just a few hundred pounds. Due to technical resource constraints, the forms themselves are actually hosted by my old friends at CRM Technologies but we’ve tried to make the overall experience as seamless as possible.

A number of potential enhancements have already presented themselves, in particular the ability to perform an organisation look-up on the fly and pre-populate profile fields such as revenue, number of employees and industry, based on Dun & Bradstreet data. This will even include DUNS number, adding to the reliability with which we can match web data capture back into the marketing database. I hope to update on progress soon!

Offering general advice on putting together a business case for a data quality initiative is challenging, because the business benefits and therefore payback are so dependent on specific circumstances. Here, however, are some key areas around which I’m constructing our justification, which I’ve tried to make sufficiently generic to be of wider use.

• Website, Internet and miscellaneous data capture – our process relies on extensive manual effort for transposing/re-keying data, with very limited validation and standardisation etc, particularly for international data. Ironically, this has some benefits as there is a human element involved in matching incoming contact to existing data, but it’s hugely time consuming. If there’s any manual effort involved in your process, it’s an obvious source of efficiency gains and savings, not to mention quality improvement.
• Address quality and duplication – based on various initial data quality assessments (such as outlined in Data health check previously), there is a 10% undeliverable and 3 % duplication rate among contacts in our database. Based on even a single direct mail execution per year, the waste in terms of undelivered and duplicated mail pieces is significant.
• Campaign execution - list preparation effort (identification, selection, cleaning) can be greatly increased due to poor data issues, whilst limited targeting and segmentation may still only possible. According to a recent Aberdeen Group study (“Customer Data Quality: Roadmap for Growth and Profitability”, June 2007), “89% of Best-in-Class firms reported positive performance in the time necessary in preparing customer data” on improving their data quality.
• Legal and best practice compliancy – the ability to reliably match new and existing data is crucial to recognising and observing privacy and other communication preferences. The reputational impact of not respecting contact preferences together with legal compliancy failure (especially in Europe) creates exposure to the risk of litigation or prosecution with potentially substantial penalties.
• Lead quality and qualification - time savings and effectiveness benefits through more complete and informative leads (such as full contact details and organisation profile).
• Time savings for general query resolution - reporting anomalies, data queries etc.