Introducing Data Experience Design
Chapter 2: Principle 1—Growth comes by Capturing Situational Markets
Dear Friends,
Time sort of got away from me today and when I realized how late in the day it was I came to an abrupt stop and chose to post. There’s more to come tomorrow. This topic, data experience design, is one that I will return to multiple times in future chapters. There’s a lot to unpack.
Mary Putman, Stone Mantel’s chief consultancy officer, says that the data experience design framework is her favorite Stone Mantel framework. It’s deceptively simple. Few companies do it well. But if they did, they’d be so much more successful with data capture and analytics.
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Introducing Data Experience Design
There’s another important lesson to glean from the principle that growth comes through situational markets. It’s a reminder that the data companies collect about their customers should be used to make the experience better for the customer.
It seems so obvious but so many companies do just the opposite. They use the data they collect to monetize the customer, not to create more value for the customer.
And every time companies monetize customer data, the customer creates barriers of engagement between them and the company, making the data type harder to come by and the relationship more strained. Let’s use an example that everyone is familiar with.
You shop online at a new store for a shirt you saw in a social media feed. Here’s the sequence:
1. Click on the social media ad
2. Go to site landing page
3. View popup offering a discount if you would just provide your phone number and email address
4. Provide information requested and get discount code through email message
5. Buy the shirt with discount code
Now, what happens next? The transactional nature of the data share is so obvious. You know perfectly well the new store is going to hit your inbox with spam. Your text messages shall be overflowing.
Do you?
a. Cancel your ‘subscription’ right away
b. Move your ‘subscription’ to the spam folder
c. Wait to see if there’s another discount and then cancel
d. Keep the ‘subscription’
If you use Gmail or Outlook, the tools will automatically hide emails sent by the retailer behind an ‘other’ folder. Thus, email – and to some degree text message tools – create the barriers to engagement for the company’s customer. They know you don’t get real value from sharing your data with the retailer and so they treat the ‘subscription’ as spam.
The reason why customers give companies data is to benefit the customer, not the company. Thus, companies, if they want to keep and use that data, need to find ways to do so that truly benefit the customer. Knowing and supporting their situation does that. Customizing experiences based on situational needs creates that value.
The analytics teams at most companies spend far too much time trying to leverage the data gathered about people and their experiences to target the customer. Companies need to spend far more time using that data to target the situations where individual needs meet business opportunities.
It doesn’t matter whether its biometric data or demographic data, companies should be focusing the data first on the benefit for the customer and, consequently, focusing on the situational markets that the data can help create. A smart ring creates new situational markets. People want to know how to improve their abilities when they are exercising. And they want the experience tailored to their age, location, gender and other demographic information. Biometric rings do a great job supporting the situation and the job to be done.
When data is used to improve the situation of the customer, the solution becomes smart. We will spend a whole chapter focused on smart and genius technologies—as well as a dumb and stupid tools. But for now, what we need to focus on is how to use data to identify and improve situational markets.
I believe that data, like all resources at a company’s disposal, can be designed to support the experience. Companies today capture data, analyze data, and build tools to use data. What only a few do well is to design systems for using data to make experiences better.
The starting point for any organization who wants to build value for customers through data is the data experience design framework below. There’s at least a decade of research that we at Stone Mantel have put into this framework. It works.
Step 1: Know the jobs the customer is trying to get done
The first thing a data strategist should know is the job the customer is trying to get done. People share far more data with companies when they know that data will be used to help them accomplish their goals, their needs. Before the strategist starts to analyze data, he or she should pull out the when statement board for the solution and become acquainted again the real job the customer is trying to get done. When you decide the shop for a shirt, what are you hiring the retailer to do for you? The strategist should know and then think about the types of data that would help the retailer to fulfill on that job.
Step 2: Use techniques that encourage data sharing
The next step in building a data experience that creates value for customer and company is to identify techniques that encourage the customer to share data. If you need location data to get the job done for the customer, what techniques can you use to gain the customer’s trust in sharing that data. Do you, for example, allow the user to turn off/on the feature so that they have control over the when? Do you create features that only work if the customer shares location data? The retailer may include a feature that fast tracks the delivery of a tailored shirt to a store near the customer, but only if the customer turns on location data sharing.
Step 3: Support point of use
We all know what point of sale is. It’s the moment when the customer buys the solution. So much data is generated at the point of sale. Personal data, transaction data, communication data, logistics data. The prospect becomes a known customer. Consequently, companies have invested heavily in supporting the point of sale. Their analytic dashboards positively glow with data supporting point of sale.
But, remember, the customer didn’t give the company their data so that they can be targeted with sales. They gave the data because they have a job to get done. They have a need. The data that is collected should be cultivated and applied to help the customer get the job done at their point of use. If point of sale is the moment of purchase, then, obviously, point of use is the contextual moment, or situation, in which the solution is used. Strategists should use every analytical capability to understand what point of use means for their customer and how they can use data to help those customers get the job done.
Step 4: Anticipate the needs that customers have
If the company knows the job to be done, they’ve got the right type and amount of data, and they have focused their analytics on the point of use, then the next time the customer interacts with company, they should be able to anticipate needs or do more jobs for the customer. Of course, the retailer should preset sizes for the customer based on the past purchase. That’s basic preference data being deployed.
But the truly successful company should be able to anticipate new and similar situations that the customer will likely need additional shirts. If they bought a t-shirt, perhaps the next time they will need a long sleeve t-shirt, or a hoodie—both of which help people stay casual and comfortable (the job to be done) but work better for Winter situations.
To Be Continued …
Click here for book outline with links to posts
This post about Experience Strategy Certification is also helpful.
LOVE this quote: "the data companies collect about their customers should be used to make the experience better for the customer. It seems so obvious but so many companies do just the opposite. They use the data they collect to monetize the customer, not to create more value for the customer."