10 ways to do database marketing badly (and how to avoid them)

There's nothing quite like a summer birthday, is there? ALPSP member DataSalon are celebrating 10 years of helping publishers with the challenges of data quality and customer insight.

We spoke to their Managing Director, Nick Andrews, who shared a little bit of wisdom gleaned from all those years' experience.

"We've learnt a lot over the years about the wonderful world of database marketing, and how things can sometimes go a little wrong if the right tools and processes aren't in place. You'd be amazed at what gets through internal quality checks: some of it embarrassing, some of it downright cringeworthy.

As we reflect on ten years helping publishers avoid making mistakes, here are 10 ways to do database marketing badly (and how to avoid them)...

1. Call your customer "Ms Ass"

Or "Ms Ass Librarian" to be precise. Yes, this really happened. Somehow the job title of "Ass Librarian" ended up in a customer's first/last name fields, leading to a very unfortunate address label. Some basic checking and clean-up could have avoided this particular mistake.

Yes. Really.

2. Get their name (and gender) wrong

Unfortunately, overly vigorous data cleansing can also be a problem in its own right. Our Communications Director Jillian (female) regularly receives post addressed to "Julian" (male), presumably due to a software rule deciding that her real name must be a typo, and unhelpfully "correcting" it. Moral of the story: do clean your data, but try not to make it worse.

3. Try to sell something they've already bought 

With the complex world of package and consortia deals, this probably happens to unfortunate sales staff way more that it should. You send prospects a tempting deal... only to discover they've already bought the product in question. Properly getting to grips with your sales data isn't always easy, but it is the only sure way to avoid this type of embarrassment.

4. Try to sell something they've absolutely no interest in

Another awkward sales scenario: alienating your (potential) customers by trying to sell them products which don't match their interests. The "hey, let's just include everyone!" mailshot is a great way to do this. And the "hey, let's get our data together and do some proper segmentation!" project is a great way to avoid it.

5. Don't respect opt-outs

Ah yes. There is perhaps no greater way to turn a potential customer into an angry ball of rage, than to keep marketing to them after they've opted out. Companies don't do this intentionally of course, but plenty do it by mistake - often when opt-out requests aren't properly consolidated across different customer databases behind the scenes.

6. Don't communicate with opt-INs

Not respecting opt-outs definitely annoys customers, but so does neglecting to communicate with customers who are interested. If John Smith has taken the trouble to tick the box and opt in to your news and offers, you’d better send him some. Asking customers to opt in sets the expectation you'll have something useful and interesting to send their way.

7. Send far too much email

Many people are perfectly happy to receive relevant promotional messages from time to time, but nobody wants to feel bombarded on a daily basis. This can often happen if different departments or divisions are all marketing to the same pool of contacts, without coordinating their efforts to keep it to a reasonable level. A company-wide comms strategy should help solve that.

8. Get your facts wrong

It can make for a really compelling message to merge customer-specific details into your marketing emails, for example: "Your recent high/low usage of product X suggests you're really loving/hating it!!" But of course that's only impressive if the key facts are correct (and it makes a bad impression if they're not). Be sure of the quality and accuracy of your underlying data before trying this type of campaign.

9. Send marketing to the deceased

At its worst this mistake can be very upsetting for relatives of the deceased. There are services like Mortascreen out there to help remove deceased contacts up-front. But even without that level of checking in place, the most important thing is to make absolutely sure that any notice that a contact has died (often sent via email to customer services by a relative) is acted on promptly to ensure no further marketing is sent ever again.

10. Assume everybody has one unique email address

It's easier for databases to assume that one email address equals one person, but in reality many of us will have multiple emails (for home, work, etc.) and some share a single email address ('family_robinson...' etc.) It can be annoying for customers to receive the same message more than once, so it's good practice to get to grips with multiple emails and organize your comms accordingly.


But let's not feel too disheartened - it's true that database marketing can go wrong, but getting it right isn't rocket science. It's just a question of giving proper attention to data quality, establishing some form of single customer view, and ensuring you have a clear company-wide comms strategy. With those pieces in place, database marketing can be hugely effective.

Now, you'll have to excuse me. I have some cake to eat. Happy birthday DataSalon!"

Nick Andrews is MD of DataSalon who celebrate their 10th birthday this summer. Watch this video to find out more about them.

Data, the universe and everything. How data can drive your business

Melinda Kenneway: loves data

Melinda Kenneway, Chair of Data, the universe and everything: How data can drive your business seminar, opened the day by emphasizing how data underpins modern business. She summed up the premise for the day by quoting Jeff Weiner from LinkedIn:

‘Data really powers everything that we do.’ 

If we’re not selling content, what are we selling? Data is also becoming a product or service itself now. An era of big data can help us understand and deliver a much better experience for customers In 2012, 90% of all the data that existed in our entire history has been created in the previous 2 years. We need to get the basics of data right, but also have a strategy.

It’s knowing what you need to know rather than trying to find out about everything. Have clear objectives and stick to them. Don’t forget to consider privacy and legal issues about data. Deliver useful services that build on what the market thinks is acceptable. And if you don’t like data now? You should get out of marketing.

Colin Meddings from DataSalon focused on why you should care about cleaning up your data. To start with it can embarrass or annoy your customers when it is wrong and it’s open to error when users don’t put in info correctly.

Colin Meddings: not vague about data

Continuing with quotes from the great and the good, he cited an observation from 2013’s UKSG conference by Liam Earney from JISC:

‘Publishers can’t even tell a library what they subscribe to.’ 

We’re too vague about absolute fundamental data. The basic process of publishing involves: submit, review, copy edit, publish, purchase, read, cite, licence. All of these aspects generate data. The volume of data is huge and all these areas are where errors or mistakes can happen and where data can conflict.

Reflect on your efficiency with dealing in data. Do you struggle with data wrangling where you have to continually review and refresh datasets? See data as an asset in your business. It is worth investing in. You will get a return. Don’t forget that there’s a legal requirement around data to ensure it is accurate and fit for purpose. He recommended the DQM Data Governance Maturity Model. Be aware. Become reactive. Turn this into proactive. Then make it a managed process. And finally, it becomes optimal. 

Meddings outlined the seven deadly sins of data quality:

1. Missing data 
2. ‘Siloed’ data 
3. Invalid data 
4. Out of date info (people move around) 
5. Inconsistencies 
6. Right information, wrong field 
7. Duplicate and conflicting data. 

Put some effort and resource into sorting data. Task to someone specifically, create champions, set targets. It’s great if you can employ a team, but if you can’t, get senior management buy-in and work with those who have aptitude/passion for data. Start with one problem, don’t try to fix all at once.

Do a data audit. It can be manual. People who work with data everyday will know where the problems are. Sometimes an automated audit is better – good at finding information about your data for identifying empty fields, etc.

Colin Meddings: Why data quality matters.

Colin Meddings is the Client Director at DataSalon. Colin will be one of the speakers at the forthcoming ALPSP seminar Data, the universe and everything taking place in January.

Here, in a guest post, he reflects on why good quality customer and internal data is important for scholarly publishers.


'Only four types of organisations need to worry about data quality: Those that care about their customers; Those that care about profit and loss; Those that care about their employees; and Those that care about their futures.' – Thomas C. Redman (2006)

Over recent years publishers have had to overcome many hurdles in the digital world, such as making content available online, managing complex consortia deals, creating new packages of content and tracking usage statistics. The result of all this digital activity is vast amounts of data. However, the pace of change can often distract from the careful governance of this data, leading to gaps, inconsistencies and inaccuracies.

But why does the quality of all this data matter so much? Good data is your most valuable asset, and bad data can seriously harm your business and credibility…

What have you missed? 
At a management level, poor data quality equates directly to poor visibility of key trends in the growth or decline of certain products or markets. At the contact level, you may miss out on valuable sales opportunities if email address fields aren’t filled out correctly or customer names are wrong. Having good data will help deliver better customer service and enhance your reputation, and it means you can make better selections for targeted prospecting, cross-selling and up-selling.

When things go wrong.
Bad data can lead to ‘accidents’ and wrong decisions or actions which can affect customer confidence. You’ve spent time building up a valuable customer list – so it’s important not to waste this by sending campaigns to the wrong people, or with messages which don’t match their interests, or to out-of-date or deceased contacts. Data quality issues can also cost you money directly – for example if invoices or renewal notices are sent to the wrong recipient, or at the wrong time.

Making confident decisions. 
Data quality matters most of all because it enables your staff and management team to really trust the accuracy of the reports and analysis they’re given. Without that confidence, apparent trends or new opportunities will always leave you wondering whether they really present a true picture. But with a complete and accurate view of your customers and prospects, comes the confidence to make well informed business decisions and commit fully to your strategic planning.

So, data quality is a very important foundation for a publisher’s entire business planning process and customer contact strategy. Good data quality will allow your business and its reputation to grow and flourish.

Data quality is just one of the topics in the forthcoming ALPSP seminar Data, the universe and everything. Other areas covered will include the use of institutional and personal identifiers in the scholarly publishing supply chain, publisher metadata, data relating to open access publishing and some case studies from publishers who have tackled data issues.

This post originally appeared on DataSalon’s own blog From the Armchair.