How to prepare your marketing data

15 February, 2019

Your data is only as good as what you do with it. Preparing your data properly is essential to getting the best out of it. Here’s our five step essential guide to preparing your marketing data for maximum effect:

1. Get everyone on board

‘Data analysis’ is a scary term, and people are dubious of phrases like ‘data-driven marketing’. The words evoke memories of impossible maths exams and mind-numbing spreadsheets – not to mention Cambridge Analytica and the GDPR. This semantic fear is one of the major reasons why data and the people who work with them are often found in silos. Content, comms, and other marketing people shy away from these scary semantics – out of sight, out of mind.

Honestly, though, there is really nothing to be afraid of about data analytics. It’s basically a very dry term for getting to know your customers through their demographic information, their preferences, and their behaviour patterns. Given that Mailkit can do most of the data-sorting work for you, even the most innumerate among your team can see the data figures and their associated insights at a glance. And it’s important that they do so, because otherwise you risk a disconnect between what the customers are telling you and how your team is responding.

So, get everyone on board the data train, and make sure that they’re engaging with it!

2. Work out what data you need

Once you’ve got everyone engaged, you need to agree what kinds of data are relevant to what you’re trying to achieve. This is why (as mentioned above) it’s so important to pull everyone into the data aspect of things, as different people on your team will have different insights into how your data will benefit their aspect of the campaign.

No data is useless, but some data is more useful than others. For example, if you’re planning to give your customers a shout-out on their birthdays, or targeting marketing according to age demographics, you’ll need to prioritise birth date data. Your lead scoring and predictive lead scoring strategies are useful here, as they’ll give you an immediate idea of what kinds of data you should be underlining, and which kinds of data you can put on the back burner for now. Mailkit’s campaign management and list management features make prioritizing data-types a doddle.

3. Segment

Segmentation usually follows on pretty easily from working out what kind of data you need. During the data diving, you’ve almost certainly come up with some outline profiles of the kind of customers you’d like to target. Now, it’s just a case of solidifying those outlines, and segmenting your data into distinct customer profiles. You can use these segments to target different kinds of content at the people who’ll appreciate it most.

Mailkit’s list management feature makes segmentation and segment-targeting easy. Give it a try!

4. Personalise

We all know that it pays to personalise. Use your data to learn about your customers, and use that knowledge to personalise your emails to them (without being creepy!) If you’ve analysed your data properly, you should be able to do things like address your customers by name, reach out to them on their birthdays, show them geographically relevant content and so on. Personalising not only helps you to build a relationship with your customers, it also ensures that you’re showing them content which is relevant to them. Relevance and personal relationships are crucial to modern marketing.

5. Keep it clean

No, we don’t mean censoring (although, that might be a good idea as well...)! Disorganised data, and data hitting programmes in inaccessible formats is a bigger problem than most people realise – even those who are working directly with that data. If you’re not regularly cleansing the junk from your databases, if your databases aren’t neatly sorted, and if data is running through your programmes in formats that the machines can’t actually read – well, you’re not only making life a lot more complicated for yourself, you’re also potentially missing out on a lot of valuable and interesting data. Oh, and you might also be breaking the law. If you don’t know what you’ve got in your databanks, how will you know whether or not it’s GDPR compliant?

In short, make sure to keep your databanks uncluttered, and put measures in place to ensure that all data coming in is readable. 

Mailkit – for all your data sorting needs

Mailkit is one of the best data handling tools around. As well as helping you to create great-looking, deliverable emails, our features also enable you to sort and analyse your data with ease. Check out our solutions to see how we can help you.