Adapt Your Personas to the Emotional Moment

Developing personas is a great start to putting yourself in your customer's shoes. But in today’s world of e-commerce, you could fail to adapt the experience during those unpredictable, emotional moments your shopper lives in. Personas can't capture the unexpected journey customers take when they shop online. Fortifying your technology with deep learning and machine learning can help improve the customer experience in real-time, especially when they deviate from the script, writes  Diane Burley, VP of content, Lucidworks.

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Identifying with customers is one of the key tenets of effective selling. And most of us are still relying on a technique that was invented in the mid-‘80s, back when you would comb the rack for fingerless gloves and leg warmers.

The father of customer personas was software and UX designer, Alan Cooper. Fed up with poorly designed computer products, Cooper wrote about designing for the user - vs simply creating what could be coded. His user-based personas were composite characters who best embodied key traits of a product’s target audience. 

The promise of these fictional characters was to make it easier for product designers or marketers to stand in the shoes of these users, to identify and understand their broader needs. Consultants at the legendary marketing firm, Ogilvy loved the idea and customer personas were born.

Fast forward a few decades and we are still in the throes of creating personas so we can map out the buyer journey. These personas represent a cohort of individuals who will purportedly watch, buy, or interact with the same things.

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E-commerce brands spend oodles of time modeling what a person is likely to purchase based on these generalizations, and merchandisers are expected to create rules that to promote a product for people to buy. Typically they create these personas off previous buying history.

Once personas are identified, projections are modeled, inventory is ordered, and promotions take place. And theoretically, the sales should just roll in the door.  But what happens when sales don’t materialize? Do you throw out the personas, look to see what went wrong with your projections, or analyze what the customer experience was really like. Instead, maybe you should consider moments?

Personas Are a Start - Not an End Goal

Personas are a terrific way to put yourselves into the shoes of your customers - although approaches vary between B2C and B2B buyers. The goal is to map the journeys customers would take to buy your products. And while personas offer a good guideline for overall direction, they can’t capture all the detours customers take in real life as they shop, evaluate, purchase, and use our products.

Relying strictly on personas is somewhat of a fool’s errand. How often do any of us fit neatly into a generalized segment of an audience?

Further, we are emotional creatures, heavily influenced by emotional events. It’s these events, or moments, that retailers need to be on the lookout for.

Consider a mom of young children, who is normally budget conscious. But her child’s coat ripped, and a new one is needed. All bets are off as to how she will react on your site. The key is to help her find what she is looking for while she is in that particular moment with that particular need.

If she goes to the search bar will she be able to search by size, by color, by style (and all three!)? Can you autosuggest for her, so while she is in that harried state she can be guided to what she is looking for?

In order to personalize a persona, you need to know more about your customer, and that is less about mapping a journey at the outset and more about gathering more data along the way so you can figure out the “moment” that persona is in.

Connecting With Customers in the Moment

When a person comes to your site with the intent to buy they typically rely on the search bar. If you are relying on merely keyword search, in 80% of the cases, they won’t find what they are looking for. And chances are you won’t get a second chance to get that harried mom to stay.

The problem with keyword search is that your customers need to type in the word(s) exactly as your team has written them. Misspellings and synonyms are just two of the reasons why customers can’t find what they are looking for.

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Determining Intent

One of the ways savvy retailers are determining what moment a person is in is by using customer signals.

User signals include what a person clicks on, what they don’t, what they search for -- and how they rewrite queries to pinpoint what they’re looking for.

In a 2019 benchmark survey of retailers doing more than $100M in annual sales, nearly two-thirds said they were collecting some sort of signals or user behavior, but only 49% said they were using AI for query intent detection. That puts them at a disadvantage. Machine learning allows you to understand intent by figuring what a person is looking for -- despite how they spell it or describe it.

By combining machine learning and signals you can see that when a person types wintrecoat or parka -- she means wintercoat. Typical thesauri are created by manually embedding metadata, machine learning of user signals can dynamically detect synonyms.

Further, with smart devices like Siri and Alexa -- people are searching in natural language -- a girls red wintrecoat size small.

Combining machine learning with deep learning will allow you to understand your user’s query no matter how they phrase it.

Having personas and static journeys have gotten you pretty far. But today’s technologies including search fortified with machine learning and deep learning and combined with signals, help you better adapt to the buyers’ moments — so you can truly deliver on their wants.