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Data Smart: Considering Your Data Gathering Strategy

by Cheetah Digital, on 18 March, 2021

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Data is cheap and plentiful, so it’s easy to get caught up in the Veruca Salt, “I want it all and I want it right now!” mentality. Before amassing an enviable data hoard that would make the Ghenghis Khan of data jealous, there are several considerations, including privacy, data value, ability to action, and ability to facilitate the customer value exchange. Let’s walk through each of these a bit more.

Data privacy is at the forefront of consumer minds

The world has changed dramatically in the past few years when it comes to data privacy. From the Cambridge Analytica scandal, GDPR in the EU, CCPA, and potentially CRPA in California, we continue to operate in a swiftly changing world. Gone are the days of companies being held lightly accountable for misdeeds and missteps with their data. 

The question now becomes, can you comply and keep the data you are gathering safe? Are there data elements you may accidentally capture, thrusting you into a potential HIPAA challenge? 

It is no longer just about gobbling up all the data you can, but determining what you will do with that data and how you will protect it, as well. Consider, for example, how your data was obtained; was it through unreliable and shady third-party data sources? Or straight from customer keyboards, in the form of zero-party data? Are you inadvertently gathering the data of minors that could potentially open you up to more risk? How does all this impact your data strategy? 

As you walk through these questions, your priorities and further risk tolerance will emerge, as you determine what kind of data you need and how you intend to protect it.

Data value: How do you define it?

The word value is so subjective these days and we all throw it around without ever really quantifying it, but how do you know if something is valuable without assigning a metric to it?  

When building your data strategy, consider how easy or difficult your data is to obtain and subsequently leverage. Do you have a monetary value assigned to each data element in your database or is that not realistic? Do you know what high-value data looks like compared to low-value data and do you even have a method to differentiate one versus the other? There may be a lot of heavy lifting to get a customer’s anniversary date, but if there’s no increase in lift when you campaign against it, is the juice worth the squeeze?  

It’s important to ask yourself the true value of data to your business, before committing to collecting it all. Perhaps it is easy to collect and easy to action, but the risk it brings by storing and protecting it may be too high in the event of a breach and it’s important to consider these issues, as well.  

Ability to take action on your data & the customer value exchange

When you think about your ability to action on your data, it’s important to put it into the context of the customer value exchange. For example, if you collect birth dates and know you will see a 15% lift on these marketing campaigns, it likely makes a lot of sense to collect it.  

Birthday campaigns are pretty common and popular among consumers, making the value exchange high. In this case, it’s worth the time and effort to collect birthday months or other limited information, where you and your customers both have something to gain in the transaction. As a bonus, this is an evergreen campaign you can keep breathing life into year after year, thereby maintaining your customer relationship. Looking at your data in the context of recurring versus one-time value for you and your customer is also another helpful way to inform your overall strategy.   

There are myriad factors to consider when creating or re-evaluating your data strategy, but thinking ahead about what type of data you want for your campaigns can help you supercharge your data strategy. 

Check out Cheetah Digital's Ebook here, to find out more about creating personalised, relevant experience in the Digital Age. Go, right now. You won't be disappointed, promise.

Topics:Data

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