You Ask, I Answer: Balancing Privacy and Marketing Effectiveness?

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You Ask, I Answer: Balancing Privacy and Marketing Effectiveness?

Dennis and Kim ask, “Is it possible to be an ethical marketer and still use data to inform the ways we interact with our customers? Or are we crossing a line that puts us in opposition to our audience?”

In short, yes, it’s possible. A few principles come to mind:
– Be clear and honest what you do with the data
– Minimum viable data – collect only what you need
– Specificity isn’t helpful to AI – the more specific a piece of data, the less useful it is for modeling
– Encrypt and encode – protecting privacy and making data useful for machines
– Delete what you don’t need and capture just the models (and watch out for model drift)
– Most organizations will need a DPO – integrate them

You Ask, I Answer: Balancing Privacy and Marketing Effectiveness?

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

In today’s episode, Dennis asks, Is it possible to be an ethical marketer and still use data to inform the ways we interact with our customers? Are we crossing a line? That puts us in opposition to our audience? Great question.

It is absolutely possible.

And the reason is possible is because we can do a tremendous amount with the data we have.

For example, very simple example, Google Analytics for bids, personally identifying information for being inserted into the application, you are expressly not allowed to collect it.

In Google Analytics, you’re not allowed to store it in Google Analytics.

And if you violate the terms of service, your Google Analytics account consumer only be cancelled.

So things like names, email addresses, all those things, those are things that cannot be stored in in Google Analytics at all.

And yet, it’s the most popular the most well used, probably the most common web analytics, application and marketing.

Almost every small website, most large websites use Google Analytics, even organizations that have another analytics solution, often run Google Analytics as a backup, alongside just a quality check and have a plan B.

So with that, as our example, it is absolutely possible to have good customer data, good data about our audiences, and respect privacy.

At the same time, there’s six major principles that come to mind that we need to do at bringing into our marketing operations.

Number one, be clear and honest, what you do with the data, right? If you’re going to share it, just say that you’re going to share it say who it’s going to be shared with how it’s going to be shared.

We recently did a joint promotion paper with talk Walker and on the bottom of the form it very clearly in the same size font, as the rest of the form, says, hey, you’re sharing this with both organizations, both organizations will be contacting you.

And the information will be used specifically for marketing purposes.

So just be clear and honest, it’s a it’s required by law.

But be it’s also ethical be use and collect the minimum amount of data that you need.

So one of the things that marketers should be doing is running an analysis of what variables actually matter for predicting or forecasting, what’s going to convert.

So if you have a lead gen form, there’s 44 fields on there.

You can run a type of analysis, called driver analysis, you have your conversions, and they have all the other data, and you say to the machines, show me what variables actually matter to conversion.

So it has this called feature selection, or predictor estimator strength, whatever you want to call, there’s a number of different names in data science for it, that tells you what you need, and then stop collecting what you don’t need.

Right? If it’s not useful for predicting an outcome, don’t collect the data, right? It is a storage cost, it is a security risk.

And it is an efficiency drain if you have data that isn’t useful.

So number two is be collecting only what you need.

Number third, number three, B, and C is specificity.

The more specific information you have, the less useful it is for building those machine learning and data science models for predicting and forecasting marketing effectiveness.

Knowing someone’s birth year gives you a rough generation of what they’re in, right, knowing the exact birthday, that’s not useful from our predictive perspective, because there are going to be that many other people in that year that are going to have that exact birthday.

And that’s not helpful.

Right? Knowing someone’s job title.

Sure, that’s helpful knowing the middle name, not really useful.

In fact, their name itself isn’t really predictive, right, knowing my name is Christopher Penn does not accurately in any way forecast the behavior other people named Christopher Penn, right.

So that’s not super useful.

Now, knowing that I am a co founder of a startup, that is a useful predictor, right.

It’s more general information.

But there are going to be certain problems that you know, a startup phases that I will also have so you from a marketing perspective, that is useful information.

So be less specific, and the data you collect.

And it’s more useful for modeling within reason.

Number four, encrypt and encode, protect users privacy by encrypting and encoding the data that you collect.

there’s a there’s a technique called one hot encoding, that is actually very useful for not only changing how the data is represented in the database, but also what makes it useful for machine learning.

You should be D identifying data whenever reasonable.

And having clear access controls on who is allowed to see the personally identifying information.

Again, when you’re doing advanced analytics.

Most of the time, you don’t need the personally identifying information, you can have just the characteristics of the descriptors of the person and not the person’s data itself.

Again, go back to Google Analytics, you know, an awful lot without ever touching PII.

Number five, delete which don’t need and capture just the model.

So when you’re making advanced analytics models, statistical models, machine learning models, capture the models, capture the interactions capture the data patterns, but you don’t necessarily need the data once you’re done with it.

And if you can safely dispose of it, you should, again, unused data is laying around as a security risk and operational problem and an efficiency drain.

So get rid of it as as quickly as reasonable, or at least put it in encrypted safe cold storage somewhere, you can encrypt it and stick it on a hard drive, lock it up in the office safe, please do not put it in an Amazon s3 bucket and just leave it unencrypted and password free for the world to find.

It’s just a bad idea.

Finally, you and most organizations are going to need or are required already to have a data protection officer, like the HR person at companies, the DPO has a tendency to be relegated to either a part time role, or, you know, sort of quarantine their own little corner.

And nobody wants to work with them, because they represent essentially administrative overhead for a company to be compliant with regulations, particularly GDPR.

Don’t do this, hire somebody who’s good at data to be the DP Oh, but also in that role.

Ideally, you hire for somebody who can not just tell you what to do, and not to do but give you better go guidance about how to do it so that you’re not only regulatory, aligned, but you’re also making the use of their talents to get better data to work with better data.

So again, most large organizations have this tendency to want to, you know, put the, the regulations person in the corner.

Don’t do that.

Think of that person as a resource to improve your business, not just from regulation, but also from how can they help you make your data better.

So as more companies are facing regulations like GDPR, and CC, PA and all the different variants of privacy requirements, you’re going to want a person who can interpret those who has a background, obviously in law and regulation, but also a person who has experience in data so that they can say, hey, while we were doing the compliance review for this customer table, I noticed that it’s in second normal form, and it will be a lot more efficient, not to mention safer, but efficient.

If we transition to the third normal form.

Let’s get the DPA and the data team in here and see if we can do this and see what the efficiency game would be.

And that person could lend performance increases to our marketing and to our data.

The DPO, for example, is absolutely somebody who should be overseeing a customer data platform to make sure that you’re compliant, but also to help with the governance to like, hey, like, here’s where all our data lives, right, we need to define it in order to use a system like a customer data platform.

So those are the six things I think are useful for informing how we can be privacy compliant as marketers, how can be ethical marketers and still be effective marketers.

Again, I keep coming back to that example.

Google Analytics provides tremendous value with no personally identifying information as long as you’re using it properly.

How can your marketing do the same? As always, please leave your comments below.

Subscribe to the YouTube channel to the newsletter, I’ll talk to you soon.

Take care.

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