Make Better Design Decisions with Data

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Behind every data point, there is a human being. Our job is to translate those numbers to create connections with customers.

Take a second to think about your relationships with your friends or folks you work with. You maintain relationships with the people who treat you with respect and consideration, right? You probably avoid relationships with people who don’t listen or are difficult to communicate with. Relationships between customers and brands shouldn’t be that different. Responsibly leveraging data and industry metrics in your design decisions can show customers that you’re paying attention to what they need and improving their overall customer experience.

I have always considered myself a mixed-methods researcher, but not long ago I realized I wasn’t leveraging all the customer data available to me. While qualitative research and findings came very naturally, I realized I needed to strengthen my quantitative research muscle.

For context, I am a researcher and designer at Salesforce working within our consultancy, Ignite. I help companies create better experiences for their customers and employees. In my work, I became curious about how these companies are using data to inform their understanding of their customers. More specifically, I wanted to understand how they use different types of data when thinking about what success looks like for customers.

Preface: Before getting into anything else, first and foremost, designers (and anyone else involved) have an ethical responsibility to be clear and transparent about what data is being collected from customers and how it will be used. Some customers want recommendations to be hyper-specific to them, but the tradeoff can require sharing lots of very personal information. On the other hand, some customers are okay with less-tailored experiences to maintain their privacy. Whatever their preference, it’s critical to let customers know when data is being collected and how it’s being used. Furthermore, users should be able to opt-out at any point in the experience. The goal is to steward data (given consensually) to make better customer experiences, not to use information solely for the benefit of business.

Now let’s explore how designers can turn data and metrics into actionable insights to build a stronger relationship between brands and customers:

Determine what metrics matter to your industry.

Yeah, I know you might be rolling your eyes right now and thinking, “duh, Austin,” but here’s the deal, metrics are not a one-size-fits-all solution. I see businesses looking at metrics that may not be pertinent to their industry or even just too surface-level all the time. Every industry has nuanced metrics that tell you different things about the customer.

For example, in hospitality, loyalty metrics look like frequent flier miles with a specific airline, points from a hotel, consecutive stays, and meals in the hotel restaurant. Whereas in financial services, loyalty focuses on who is “first in wallet” or the brand a customer reaches for first when they go to pay for something. In both cases, these customer behaviors can help you better understand your users and design solutions to better serve them.

Let’s look more closely at the metrics that are relevant to hospitality. The things a traveler wants to do when they travel for business are very different from what they may want to do when traveling with their family. To help a hotel brand make better recommendations for travelers, I want to understand the number of nights a customer might spend at their hotel(s) and maybe even start to segment those stays into work and pleasure. A designer can start to better understand the customer by looking at the length of stays, how many folks a person might be traveling with, or even the types of activities customers book for the trip. All of this information helps me design a better and more relevant experience for customers, and their success is what’s most important.

One last note: It’s important to get clear about the data you want to collect and metrics you will leverage sooner than later, and only collect what’s absolutely necessary. If you don’t know how you’re going to use the data in the first place, you can’t be transparent about it, which can lead to unethical usage of data without informed consent.

Photo of a woman with dark hair sitting at her laptop looking at her smartphone with graphs and charts on the screens

Implement metrics that are grounded in real-time data.

So I am going to say something a little controversial here: while NPS and CSAT are go-to metrics for many industries, these metrics aren’t the most actionable on their own. Before you stop reading, hear me out. Here is a quick refresher on NPS and CSAT:

  • Net Promoter Score (NPS)
    “The NPS method, which is based on a two-minute survey, gives insights about customer loyalty by measuring customers’ willingness to recommend a business to a friend or acquaintance,” Salesforce Learning Centre.
  • Customer Satisfaction Score (CSAT)
    “You can assess key indicators of customer satisfaction: overall satisfaction, loyalty, attribute satisfaction, and intent to repurchase,” Salesforce Learning Centre.

What does it mean when a company’s NPS drops from 8.5 to 8 or even from 10 to 5? What does that actually tell you about how to remedy the situation? It depends on when the questions were asked — was it after purchase? or a customer service interaction? Also, these types of metrics are reactive. With NPS and CSAT, by the time the score is calculated, customers could already be frustrated, and now there is more work that has to be done since you didn’t get ahead of it. In an ideal world, we can be proactive with our design decisions by leveraging real-time data to address customer needs.

Let me give you an example. Let’s say a loyal, frequent traveler has recently stopped booking stays at their preferred chain of hotels. The hotel sends the age-old NPS question, “How likely are you to recommend [the hotel where they stayed]?” The customer responds with an 8 out of 10. Unless that survey asks a lot of follow-up questions to get to the root cause of their score, the hotel chain doesn’t actually know what they could do better in the future. It could be that they changed jobs and don’t need to travel for work as much, or perhaps they had a terrible experience during their last stay and have decided to book with a competitor down the street.

So instead of just looking at metrics like NPS and CSAT, I encourage designers to proactively explore real-time data. Things like email open rate, web analytics, and social media engagement can all be great compliments to NPS and CSAT, helping you understand the context around ratings and the customer experience in real-time. Again, a gentle reminder, all of this should still be clearly communicated, and customers should be given the choice to opt-in or out.

Now let’s go back to our frequent traveler scenario. Upon looking at the hotel website analytics in real-time, it comes to your attention that many folks are abandoning their cart when it comes time to enter their loyalty number. This is actionable information for a designer: it’s time to take a look at that process and see what’s going on. Maybe there’s a bug or a bigger issue you need to address.

One more quick call out: NPS and CSAT put the onus on your users to fill out a survey, generally based on hypotheticals like “would you recommend,” rather than observing actual behavior. I may say I love something by scoring it a 10, but if I never actually recommend the product — does it matter? Furthermore, I wouldn’t recommend the same things to my parents that I might recommend to a friend, but that nuance isn’t reflected in NPS or CSAT. A better measure of this might be a referral program or something grounded in behavior.

Photo of a Black man with a beard sitting at a desk working on his laptop with graphs and charts on it

Continue to explore opportunities to leverage quantitative and qualitative customer data to inform design decisions.

Let’s be real here, there are many methods you can use to learn how to better serve your customer. I would be remiss not to mention a tool my team affectionately refers to as the quantitative/qualitative sandwich. It works like this:

  • First, you use quantitative data to frame your target group and gain a better understanding of where you’re trying to focus.
  • Second, do qualitative research within that group to learn about how users behave, and why, by listening to them and observing how they do things.
  • Then you can wrap up with some more quantitative research like user-testing to give you specific, real-time feedback on how people use tools such as websites or apps.

Although I do love going out into the world and talking to customers (or conducting video interviews these days), there are other data-gathering techniques that I can use in parallel. Whether I look at real-time data like email clicks or frequent purchases, each data-collecting technique allows me to understand people better.

Ultimately, (I hope) we all want to foster healthy and thoughtful relationships, whether they’re with our friends and coworkers or by designing excellent user experiences. There is no one-size-fits-all solution when it comes to leveraging data in designing these great relationships, but in every case, it is important to look at both hypothetical and real-time data to inform your design decisions. If you’d like to discover how design can help build better relationships with customers and communities, check out our Relationship Design Trailhead module.

Thank you to Susan Emerson, who made analytics seem way less scary as well as Margaret Seelie, Karen Chan, Chris Weber, and M Sigma for making sure this made sense.

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