Whilst it's very easy to get excited by the latest software technologies that promise to revolutionise our marketing worlds, a simple truth remains: Unless you focus on the basics of your marketing data, no end of automation or predictive analysis is going to deliver the improved results you have promised.
The bad news is that your data is probably in something of a mess. The good news is that most of your competitors data is in a similarly poor state. It simply hasn't been a focus area for marketers until relatively recently - but now, as our industry re-learns that marketing is all about serving customers, we are starting to revisit what we can learn from the signals that our clients and prospects are already providing. And they do this every time they engage with us, talk about us, buy from us, ignore us or even complain about us.
Last week I participated in Marketing's B2B Marketing Transformation conference in Hatton Garden, London, along with some excellent speakers from Google, Dell and SimplyBusiness to name three.
I outlined a 5 step approach to getting your arms around your marketing data:
1. Capture the Right Stuff
It starts with capturing the data that will help you serve your clients better. Are you gathering insight about their interests, the people who influence them, the places they look for insight?And if you are currently looking to identify new strategic investment areas for next year, you had better ensure that the data that you are capturing will help support that strategy. It is also important to have a good dialogue with the sales function so that you can minimise the amount of useful data that is being recorded on individual laptops rather than being shared for the common good. The same applies to external list purchases - you should never buy in a new list without figuring out how you are going to integrate it with the rest of the data that you have.
2. Organise it to be Useful
In order to be usable for analyis or outbound marketing activity, you will need to structure the data so that you minimise the amount of unnecessary variation. Creating a data dictionary is a good place to start to see the scale of the problem. This is simply a table of all the available fields along with all the potential values for each field. Then you can start to introduce standardised fields for key areas such as job roles, territory assignments, country etc. For example, you may want to group Marketing director, VP Marketing, CMO, and Head of Marketing all under a single job role of "Marketing Leader".Remember - it's an order of magnitude less expensive to capture data correctly at the entry point than to resolve and correct later. Examine your forms and other capture points - ensure that you use dropdown menus to minimise data capture errors/variance and use mandatory fields if there are specific fields that you will need later on for targeting and analysis. All this would be second nature to a systems person - but most marketers today do not have any background in systems.
Increasingly people are starting to look at Social Signon systems as way to ensure that (normally correct) information from LinkedIn etc is shared, without the need to present further forms. After all, 88% of us admit to having deliberately submitted false information into a contact form - I guess the other 12% just don't admit it!
3. Fill in the Gaps
The client is not the only source of information - and we all have a loathing of long contact forms. We can supplement the data we've gathered with information from 3rd party sources such as D&B, Experian or Jigsaw.Furthermore, integrating with data from social streams is an emerging area of marketing data enrichment. And don't ignore any opportunities to augment your data with useful insights from your customer service systems or from your channel partners.
4. Get Creative
In my corporate career, I was frustrated that the dialogue between the marketing program teams and the data analysts was only ever along the lines of "Give me a list of all the IT Directors in the Finance Industry". Too late and too dull.Instead of basing our outbound campaigns purely on demographic and firmographic data, you will get much greater ROI if you combine that with behavioural insight - have they declared specific interests via some form of a preference centre; or have they already displayed an interest in the particular topic by their engagement in a previous activity? The big learning I took away from those experiences was that it's never to early to involve the data analysts in the campaign planning process - you will almost certainly make your campaigns more effective and less wasteful.
5. Keep it Clean!
In an ideal world you would be able to go to a dashboard that gives you the key metrics around the health of your marketing data whenever you choose to view it. However in my experience most people don't have this luxury yet (although it is a feature of several marketing automation platforms). Therefore some element of a database audit is required. Sound scary? It really isn't. It starts with looking at the completeness of key data fields (ie is there data in them or not?) and then looking at the validity of that data (ie is the data any good or are the name fields full of "Micky Mouse"?)For many, an audit might be the first place to start on your marketing database journey of discovery - at least it will inform where you have the greatest challenges.
And how often should you be auditing your data? Ideally it should be a constant part of your management system, but at the very least it should be done whenever there is a significant change in the business - eg a change in strategy, an acquisition of another company, a change in the pattern of marketing results, or a significant spike in opt-outs.
Love your Data in 2013
The bottom line is that we are all trying to get closer to our customers. This means that we need to looking more closely at the data that we have about them, and developing our skills to turn that into analysis and insight and ultimately revenue.The full presentation from the conference is below. What's your data resolution for 2013?
