— Marketing

Untidy data is inefficient data – and that wastes money

Summary

Spring is in the air.

Yes, the turn of the seasons may lack the same significance as other annual events (EOFY or Christmas), but it’s a fantastic opportunity for marketers to revisit their customer data ahead of the impending end-of-year business rush, and any major summer campaigns or initiatives on the calendar.

So, what steps can you take to spring clean your organisation’s data?

1. Check for contact errors

 

First, run a search to check for duplicate contacts hiding in the CRM. Customers will likely find it spammy to receive marketing emails more than once. 

  • Key tip: Don’t delete contacts haphazardly. First check that duplicate entries aren’t updated versions of an old entry (as opposed to mere copies). Sales data such as purchase history can also be used to check for which contact entry is the most active.

Once duplicates have been removed, you can then move on to inactive contacts and junk emails. If a contact doesn’t engage with marketing materials, consider purging it from the list. An email verification campaign will help sift through entries and pinpoint fake details and customers who simply don’t want to be on your mailing list any longer. 

  • Key tip: A simple reactivation email campaign can be a powerful thing before beginning big marketing activity. By simply asking users to “update your details to learn about exciting new developments”, you get assurance that the database you’re left with is engaged, so you can better benchmark your results. 
You can throw away unengaged contacts, or try to re-engage them in future.

2. Ensure all your segments are still accurate

 

Segmentation is a powerful tool, enabling a marketer to directly pinpoint key demographics, personality traits, purchase histories and so on in order to maximise the effectiveness of campaigns.

But much like everything else in the data world, segments should not be a set-and-forget strategy. There must be a process in place to review all customer segmentation (and associated information, such as customer personas) on a regular basis to ensure that they remain up to date with the evolving business. Of course, now is the perfect time to review your segments.

  • Key tip: A data spring clean is also an opportunity to review marketing goals and ensure that all automation and data-based campaigning is aligned to the business objectives.

3. Tidy up your remaining information

 

By following the above steps, the only data left should be unique and relevant. However, that doesn’t mean the spring clean is over.

A lack of data uniformity can cause inefficiencies in the marketing process. Indeed, when data isn’t uniform, it becomes harder to use – if one contact is listed as being from “AKL” but another from “North Shore”, you can’t include them as easily in a mailer sent out to “Auckland” customers despite both contacts residing in the city.

Uniform data is more efficient to use.

Marketers would be wise to go through their remaining contact lists and check for uniformity. Dates, phone numbers, locations, postcodes – whatever data is applicable, it should exist within a set format so that it’s easier to automate and there’s less risk of forgetting key contacts because of something as simple as, say, a misspelled word.

4. Run a hygiene campaign

 

Thankfully, you do not necessarily have to go through this process alone.

Hygiene campaigns use email and/or social media to invite users to update their email preferences, check their contact details, add missing information, or get them to self-select a segment. While this process does not mean the other three steps we’ve discussed are unnecessary (for example, misspellings may still occur) it can save a considerable amount of time when used in conjunction with the other steps – and could be a fun way to have customers engage with their own data, for example by offering an incentive.

  • Key tip: It may not hurt to back up all contacts at this stage, given the time and effort that has now gone in to improving the quality of the database.

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