What we learned when we asked our users to clean up their data
Even though interfaces get redesigned at a regular basis, the foundation of the average platform in your marketing stack is probably over a decade old. When rethinking and redesigning our database model, we asked our users to clean up their existing database to be able to transfer to the new platform.
I want to share with you the reasons behind database chaos, fears and thresholds our users had to overcome when faced with that question. Why is it so hard to bring structure in your customer data? And why would those efforts pay off?
Why this article
This isn’t a sales pitch. As our product — an email marketing platform — continued to grow, we were struggling with the fact that we had a large customer base who were stuck in old habits. We really wanted to change the way we dealt with email addresses and mailing lists by putting the “end” user first: the recipient.
In order to do so, we wanted to rebuild our database model from scratch. This would mean that our current customers — the marketeers — had to convert their old data to the new platform. This was an action the had to do themselves. We knew we were asking a lot and had to take into consideration all thresholds our users had to overcome when faced with that question. And it’s those thresholds I want to share with you.
Background — How marketers are using email platforms
Traditionally marketeers create new mailing lists for every campaign, with data coming from god knows where — CRM, old excel files from sales, exports from another database.
Email addresses can be duplicate across those mailing lists. Maybe even with different segments on those mailing lists. The campaign is then sent to a collection of those different groups.
Next, unsubscriptions are managed throughout those different lists.
The fact is, marketers often don’t know where their own data is coming from, and whether it’s accurate. And that info is then used for personalization.
Then came GDPR
… And not much has changed.
Needless to say, the previous process doesn’t seem to be GDPR compliant at all. We have customers of all sizes, and we chose not to leave them hanging. We wanted to help them be GDPR compliant and force them to adopt a new way of working:
- One central database
- In which every contact should be unique and up to date
- They (the users) can then use segments on that database, but all data always leads back to a person.
We need to change the way we look at contact data.
Can we make it possible for consumers to capture the value of behavior prediction for themselves? Or at the very least, give them access to the information we use? The key is to view their behavior not as an impenetrable murk, but rather as an ongoing series of distinct actions or choices.
- Let’s say we use the email address to identify that person, rather than have a customer id or purhase as a unique value. There is a person behind that email address. Who has interests, needs, makes choices and wants certain things.
- That person should be central in your strategy, not that data. The data is that person.
- Consumers don’t have eyes that light up when they see a website form asking them to share their personal data. We’ve all entered an email address only to receive a bombardment of unwanted emails from companies we’ve never heard of, for products we don’t want.
- Consumers have lost confidence in sharing their data and they don’t wholly trust companies who ask for it. This isn’t a problem that can be fixed overnight.
Let’s look at a typical Flexmail user.
Let’s face it: people don’t use Flexmail because they love working in it, or because they love sending email. Their most important need is to REACH other people. And be as relevant as possible while doing it.
They know that communicating to their audience is vital, and this isn’t something that is their full-time responsibility.
Their goal is to make sure they get the right message across to the right people. They need to see in their reports that they’ve reached that goal. Collect answers or other contacts through forms. Who are those people? They want to get to know them and understand them, to create better campaigns.
Usability is a commodity. They work with many different tools, Flexmail is just one of them. They are not married to us. They want to spend as less time as possible to learn how to work with Flexmail. We should be as integrated into their daily habits as possible.
They want to be able to rely on and have faith in Flexmail. They need to feel safe and secure. They are always scared to do something wrong, because they’re handling personal data. We need to give them confidence and make them better in their job.
So, how to get from A to B
We knew that we were asking a lot from our customers. They had to adopt a new way of working in Flexmail that wasn’t typical in other mailing platforms. Also, they had to clean up their existing mailing lists to adopt that way of working. We didn’t want to leave them to having to solve everything for themselves, so:
- We introduced the concept Sources. To help you keep track of the origin of your contacts and how you came into contact with them.
- Sources are also added by the system when a contact fills out optin forms.
- We developed the Contact Converter. This allowed them to automatically clean up their databases by choosing which mailing lists they wanted to keep, and merge conflicting data.
Well, when you’re a product company, you want to avoid building these kinds of things to have people take the step towards a new platform, because it asks a lot of effort from your user.
But we did it because we strongly believe in the value of data privacy and a data culture. We had 2 driving forces helping us for clients to see that change was necessary and actually good: GDPR and the promise of new features and a better way of working. During our release cycles, we saw that the GDPR alone wasn’t enough motivation on its own.
We did see that customers who converted were able to transform their way of working very easily: they had all their data in one place, and it made for easier management, but also better targeting and campaigns.
But let’s take a closer look at people that weren’t actually jumping on that converter immediately: what were their reasons for not doing it immediately:
- Afraid of data loss
- Didn’t understand the need for centralized data. “our way of working works for us”. Wrong use. Bit of laziness: I don’t want to make the time to do this.
- Afraid of the unknown: they don’t actually know how they should continue on working in the future. Uncertainty about impact.
- Multiple people managing the data: “I’m not allowed to touch that person’s data”
- Receiving lists of different teams and not knowing which data is accurate.
What did we do to change this?
Lots of webinars, in group and individually
Show them what new system would look like, through trial accounts with their own data.
We can group those reasons under 5 different barriers. Let’s have a look at them:
Barrier #1: Confusion
Most introductions to data are confusing and overly technical.
Complicated words can alienate people that are just entering the field of working with data. Pick your words carefully to welcome them.
Barrier #2: Not Knowing Your Data
Sometimes you don’t even know the data you have.
Sometimes people look at the data they do have too closely, and cannot make sense of it anymore. When did you last do anything with that data? Will you ever be able to do something meaningful with it? How accurate and relevant is that data still? And how does that translate to the customer journey, what does that data tell you?
Barrier #3: Organizational Silos
People will fight efforts to work across silos.
This can be a case of job protection, but it doesn’t always have to be. Working with data doesn’t sound sexy, it sounds difficult. Sometimes people think they own the data, and are hoarding it. Or there isn’t a system in place yet to make that data visible.
Most organizations suffer from these silos. You have to acknowledge these walls in order to break them down. Try to work together with your colleagues and make the experience of your customer the focus.
When you have an example of a project where you already (successfully) worked across solos, hold it up as an example and focus on results. Get people enthousiastic and involved.
Barrier #4: IT-Centric Thinking
Data gets locked away in the IT department.
You need to make sure people don’t have to go to IT to pull out the latest numbers they need. Building a data culture means making sure every part of your organization can use data, for a variety of reasons.
Just because IT owns the data technology, it doesn’t mean they should or have to own the process of creating a data culture.
And as people keep adding more and more SaaS in their stack the pains linked to software interconnection, data migration, stack management, workflow integration, experience customisation etc… keep growing as well.
Also: to get back to the silos from the previous point: to get maximum ROI out of your data, you need that data to be centralized. Even DPO’s love centralized data and breaking down silos: it means that they can look at a single source.
Barrier #5: Boredom
Data is seen as a boring chore.
Spreadsheet-driven activities are boring to the majority of people. Communicating in charts and graphs is the default for presentations. However, these don’t tell a story. Encourage your organization to put the data in context, and talk about impact. People like telling stories, and get interested and engaged in hearing them.
How to clean up your data
There isn’t a one-size-fits-all solution, but I want to end this article with a couple of tips and questions you can use to find your own solution to your data problem — or communication problem?
- What makes a person a person in your data? Don’t see the data as just numbers, but as characteristics, needs and choices of that person you want to learn more about. Put that person in the center. always.
- When looking at the data you already have: how relevant and accurate is that data?
- The customer journey is never linear. Don’t look at it as journey from start to finish. A customer can come in from multiple places and in various stages. Is the information you’re giving them clear? And are you collecting the right data? It should be valuable.
- When you look at your touchpoints, what impact are you making? And what are measurable characteristics of your audience there? Can you use any of that information for personalized communication?
- Do we know enough about our audience to send them something relevant? Should we ask them?
- Do you know how people are using your data? How they want to keep in touch with you?
- Do we do enough for our customers in moments where they don’t need us? Or when we’re not usually reaching out?
- Are we pleasantly surprising our customers? Or reassure them in moments when they need that? Eg directly after making a purchase?
- Are we taking into account discrimination, disability and transparency? How does privacy fit into all of this? And please, don’t ask that question in the end. I only put it here to make sure you don’t forget about it.