The ability to solve someone’s problem is what makes a product great, not the number of features it offers. As Theodore Levitt, an American economist and professor at Harvard Business School, once said: “People don’t want to buy a quarter-inch drill, they want a quarter-inch hole.”
During the design phase, many organizations do a poor job of focusing on outcomes and instead focus on outputs. They lead their product teams via product roadmaps—sets of outputs requested by various parts of the business—and forget that the reason behind roadmaps is to deliver solutions that solve business problems.
These organizations need to change their mindsets. Discussions about features should focus on outcomes. Sales, operations and engineering need to work together and share the belief that products exist to serve people. The question everyone should ask is, “What do we want to achieve by building this product?”
In a B2B model, where commerce transactions occur between two or more businesses rather than between a business and its end users, it can be especially tricky to answer the question “Whom do you serve?” Imagine that Mr. Merchant, a busy retailer, asks your software company to deliver an online-shopping platform for his customers. Do you serve
Mr. Merchant or his many customers?
It’s easy to fall into a trap and forget that those many end users drive the business. If they aren’t happy, it doesn’t matter how happy Mr. Merchant is with the product you deliver.
You could argue that it’s Mr. Merchant’s business and he knows his customers well. They come to his stores, buy his products and offer feedback. But will online customers behave the same way? There must be a reason why they shop online instead of going to Mr. Merchant’s store. Are they too busy? Is their size always sold out? Is the physical store difficult to access? Mr. Merchant won’t know the answer because he hasn’t met these people.
In the software industry, third-party businesses and end users shouldn’t sit in silos. The world demands collaboration. If you add your e-commerce platform to the equation, it’s your responsibility to collaborate with Mr. Merchant and to understand his customers. It’s not about going the extra mile, it’s about necessity.
Fortunately, finding out about customers is easier than ever. For example, shoppers provide supermarkets with terabytes of information about themselves and their shopping habits in exchange for small discounts through loyalty programs. Most customers have no clue how valuable this knowledge is for retailers.
Consumers are creatures of habit who automatically repeat their former behavior. These habits have an impact on almost all their shopping decisions. But the habits are unique to each individual. By closely following customer buying habits, analysts can tell what is happening within their homes and create demographic of each one. This helps create exactly the right offers tailored to the specific items someone needs to buy. Think about it: If retailers could understand shopping habits of specific customers, they could convince them to buy almost anything.
Researchers discovered that buying habits usually only change when people go through big life events, like getting married, having a baby, moving houses or getting divorced. Although consumers often don’t realize when their buying patterns change, retailers are aware and see the potential: They could gain new customers who return for years. That’s worth a lot of money. In fact, pregnant women and new parents belong to one of the most highly profitable consumer groups.
When Target hired a statistician named Andrew Pole in 2002, his marketing colleagues asked if he could determine which customers were pregnant, based on their shopping habits. The goal was to get a step ahead of the competition. Once a baby is born, it is already too late, because any company can easily access public birth records.
Pole began analyzing how the average woman’s buying habits shifted as she approached her due date. His algorithm was so effective that he could actually predict when an individual woman was due to give birth.
The pregnancy-prediction model continued working well until one day when an angry man walked into a Target outside Minneapolis and demanded to see the manager. He was holding coupons that had been sent to his daughter, and he was angry, according to a 2012 New York Times article by Charles Duhigg.
“My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?” The manager apologized and then called the man a few days later to apologize again. On the phone, the father sounded uncomfortable. “I had a talk with my daughter. It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”
Such valuable information allows retailers to know exactly what their targeted audience needs and wants before they know it themselves. These predictions can continue for years beginning with maternity clothes, followed by diapers, baby food and clothes, then toys and school equipment. And it all comes from data.
Although analyzing data is a great way to understand and predict your market, it’s not all about numbers. Real people are behind the data.
It’s also important to remember that working with data is a reactive process. You’re analyzing things that already happened, and some decisions need to be made up front.
That’s where the concept of personas can help. They put a human face on otherwise abstract data and represent groups of end users who may use your product in similar ways. Personas are fictional characters based on research, previously collected data and interviews with real people. Typically, their segmentation is based on goals, attitudes and behaviors. Personas become realistic when you provide them with names, photos and demographic details. They work because they bring teams together, create a shared vision of the target audience and encourage everyone involved to think about the actual users.
Consider the persona of Emma, 39, an accountant who works in London. Emma feels guilty about not spending enough time with her husband, James, and daughter, Lia. She has a busy job and long commute and gave up traditional shopping a while ago. She now buys her groceries online. Emma values her time—“Time is money,” she says—so she wants to shop online as quickly as possible.
Now imagine an e-commerce platform that could learn what she needs on a regular basis and help her complete the shopping process more quickly. She would have more time to spend with her family. Even more efficient would be the ability for her to shop on the train, where she already spends a lot
of time. Imagine an e-commerce platform optimized for her smartphone.
There are plenty of Emmas out there, and there are plenty of other personas as well. Before adding a specific feature request to the product backlog, it’s important to consider how that feature will solve a persona’s particular problem. By focusing on the needs of the personas most likely to use your product, teams can make better product decisions and decide what is truly important.
Data helps you recognize and quantify needs. Personas help you remember the real people behind all the data. Combining the two will help you build great products that solve the problems of real people and get your product well on its way to success.