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15 Mar

The User Persona is a damn lie.

You are a Marketer at Zoom. You have to gauge who your target audience is and what groups of customers will buy the product.

So, you are tasked with gleaning insights to help craft a marketing campaign for your target audience. If you do the task well, Zoom unlocks more business from that group of customers. And everyone lives happily forever after in paradise.

So, you draw up different user personas. And they look somewhat like this:

Using different descriptors — psychographic (culture, social status, lifestyle), behavioural (light, medium, heavy user), geographic (urban, rural, global), and demographic  (age, income level, gender, family size, religion, etc.) — you create a set of fictitious profiles or characters, i.e., user personas.

These user personas will help you segment your audience, understand each target audience's end goals, and how to communicate the value of your product to them.

So, basis all your market research and user interviews, you generalize the priorities, pain points, and needs of each of these segments as a whole.

And you design a marketing campaign based on this segmentation.

But soon after the launch, you notice that the audience sets you targeted — based on the insights gleaned from your user persona research — don’t end up buying Zoom.

Puzzled, you try to understand what you are missing.

Here’s what you find out :

1. While the Rithik Yadav audience segment wants to host and run effective and productive meetings, this segment also uses Slack for its intra-company comms. What ends up happening is that people within the company often use Slack huddles as they're more convenient and most meetings do not require many bells and whistles Zoom offers. For the cases where Zoom's additional functionality is needed, the free plan suffices.

2. The Sofia Joseph audience segment wants to stay connected with family and friends and attend online workshops, but they are usually participants in Zoom calls hosted by others and rarely host Zoom meetings and events themselves.

What changed? Nothing. You just missed some crucial context while formulating your abstracted and fictional persona.

In other words, you over-generalized at the expense of missing key contextual nuances that make or break a purchase decision. When you felt secure about the extensive research you did on prospective target audiences, you failed to incorporate the most subjective factor that determines buying behaviour — the context in which buying occurs.

And that made your segmentation useless.

Now, I may be exaggerating when I say useless, but I hope you understand. And at the risk of repeating myself — CONTEXT MATTERS.

Let’s look at Spotify and how they achieved better results by incorporating the context of how buying occurs.

We all listen to music. So, for a business like Spotify, the market initially seems to be “Everyone.”

And it would be the correct answer, too, because it is available both as a free and paid product. It is also used by people on different devices across different countries.

But the cool thing is, in spite of how wide and general Spotify’s market is, they are able to provide a customized experience to every user.

And to do so, they, too, created personas, albeit more meaningfully.

Firstly, Spotify made personas a dynamic artefact that would flexibly accommodate conversations around the product and adapt to newer insights, as they were being collected over time.

It also took Spotify two years to accomplish the process.

They started by analyzing their US listeners owing to the market size and variety of listening behaviours that emerged from there.

Here's the crucial bit:

They consciously moved away from the idea of clustering behaviours from the data they had gathered because they realized it only revealed superficial knowledge about the listeners. And superficial information about the users conceals the real contextual reasons why potential customers listen to music.

In order to gain richer insights, they used a combination of Diary studies (research where the participants log their thoughts, experiences, and activities over a period, usually a few days to several weeks) and contextual inquiries.

The team noticed that while reasons for listening to music were common even in different segments — killing boredom, feeling productive, entertaining themselves — their attitudes towards music consumption differed.

How much value people saw in paying for music streaming, which device they used, and the context in which they listened to music were data points that differed wildly.

So, they ruled out the use of segmenting based on needs alone and focused on transcribing minute-by-minute user interviews to codify and segment into needs, attitudes, device habits, contexts, and other dimensions.

They tried to identify a pattern of segment combinations instead of operating from a siloed perspective, which segmentation is often at risk of.

They followed up the rigorous data collection into their target audiences with a literature review of theories from sociotechnical systems and adaptive computing. In the next phase of better segmenting, they spent time understanding how people listen to music together.

Spotify’s blog emphasizing the need for context, explains:

“In this phase, we sought to unpack the nuances and complexities that arise when people listen together at home, in the car, with kids, and more. And since this work was built on our previous research, we once again kept our sampling within the US. We included roommates, empty nesters, partners with and without kids, households with toddlers, teenagers, and others. Our goal was to ensure we had an extensive variety of situations where people came together to listen to music.”

And instead of creating a stray document on consumer personas, Spotify created an internal website detailing the results of the research, which could be used by teams operating independently across the globe. It became a resource that could be updated continually and customized as and when new information arose.

The Spotify example is necessary to understand that —

  • Descriptive and factual information on your customers, while necessary, may not be sufficient.
  • Customer preferences could be understood better from the context or situation in which they do a particular activity.
  • Systems are better built using dynamic thinking — how context evolves over time — versus static or snapshot thinking, where a document is treated like a conclusive end.

Additionally, this case from Dharmesh Ba’s What’s cooking makes a better case for understanding contexts while segmenting.

Darshil, living in Gurgaon with his parents and two sisters, highlighted three instances of his relationship with food:

Instance 1

“My mom has been cooking for a long time, and she knows what everyone likes. So my mom plans everything. My elder sister is very particular about what she eats. So my mom actually plans on that. She would really feel bad if everyone was eating something and if one particular member didn't have anything to eat of their own choice.”

Instance 1 reveals that Darshil’s mother is the real decision maker and that has more impact on how Darshil eats as opposed to his demographic, income-based factual information.

Instance 2

“I really wanted to eat Kerala Paratha. We went to a hotel, and my friend ordered Beef. I was like nah, I can't eat it. I know it's already dead, but I can't eat it because my family believes in it, and I don't want to break their trust. My friend was like, you only live once and all that. Then I took just a small piece of it and I tried to eat it. I couldn't even swallow it even though it was just another meat coated in masala. My upbringing was in such a way that I could not just eat that.”

This bit is the most context-specific. If Darshil were only categorized based on his desire to try new things and his recent hotel visits (via digital ads), it would lead to an inaccurate assessment of his preferences.

Instance 3

“When we came to Gurgaon, my mom was not used to this sort of lifestyle here because everything is located far from each other. Even the closest shop is really, really far compared to Delhi. So she thinks like there's no point in going till there. She compared the price and quality, and there wasn’t much difference between offline and online items. So she was like, okay with it.”

This anecdote, if generalized, can be a key factor in dictating which cities adopt online grocery shopping more than others. But if you limit your segmentation to a customer's individual traits like age, gender, region, pain points, needs, motivations, income, or profession and don't pay attention to the totality of the context they inhabit, you might derive totally different conclusions.

See, the thing is: we as marketers often fall prey to the Fundamental Attribution Error.

FAE is our tendency to explain the behavior of people in terms of internal and permanent characteristics (such as character traits) and to underestimate the influence of situational and external forces.

It is our tendency to take a contextual observation and generalize it as a static character trait about the person.

My suggestion:

Instead of focusing solely on static user preferences and behaviour, focus on the context around which the said behaviour occurs.

Where and when do people buy your product?

Why then and there?

Why does the need for your product surface in that situation?

Narrowing down on your user persona is not a static one-time activity, but an ongoing process.

And staying with a mess and not rushing to generalize is doubly important if you’re working with a product that could appeal to a variety of audiences.

No matter who you are, I think that you will always benefit from spending more time studying the various contexts in which your customers buy your product or service.

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