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Welcome to Stuff and Things! In todayâs post, weâre discussing biases you will need to overcome to be a fierce customer advocate.
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Usually the customer's loudest advocate in a room, a marketer is the first one to feel the pain of a badly built product. To avoid lipstick on a pig situations, marketers go to great lengths to ensure that products and campaigns are built with the user in mind. They do this by:
Leading focus groups and interviews with users
Championing their needs in product meetings
Writing marketing briefs that capture the right user tension
Ensuring that creatives resonate with customers
While representing users in these forums, it's only human to default to âwhat we knowâ, as our beliefs are shaped by our personal experiences. Today, Iâm sharing 3 biases to watch out for as a fierce customer advocate.
Sampling Bias
Sampling (or Selection) Bias has an outsized role when you define your audience - in the PRD, your marketing brief, or the creative kickoff.
This occurs when you choose a few users to stand-in for a broader set of customers. Your selected users may not be a good representation, leading to insights that donât fit your audience well.
And since, to uncover relationships between customers and profits, most direct marketers rely heavily on data about actual customersâwho are only a subset of the total pool of possible customersâthey are bound almost by necessity to estimate the relationship between acquisition, retention, and profits incorrectly. As a result, they will once again ignore the potential of customers whom they have not yet succeeded in acquiring or overinvest in the ones they already have.
As a marketer, we use investigative tools such as UX research, qualitative studies & user-diaries to understand user needs & wants. In the real world, these tools come with trade-offs. With limited time, people & money, the more you invest in understanding an individual user, the fewer users you can understand. Some marketers optimize for these constraints and cherry-pick users who, they believe, will provide the right inputs to define the product strategy. You can imagine how a few successive research cycles with cherry-picked users could result in hyper-optimization to the marketerâs biases.
Choosing the right set of users, & the right insights from these users has a multiplier effect on the learnings you get from your research. As a rule, the more users you select, & the more diverse their experiences & behavior is, the better they will collectively represent your targeted audience.
Successful companies use qual research to power quant research. A moderately deep & wide qual study generates a set of candidate insights & hypotheses. The quant research powered by these outputs tells you the relative importance of each insight - What proportion of users does the insight apply to, & how strongly does it affects their behavior?
Do this right, and you have a virtuous cycle that keeps your product true to user needs, even as your business evolves.
Survivorship Bias
Survivorship bias is the false representation of reality when we choose to learn only from the lucky few who survived, instead of the numerous others who didnât.
We need to ask ourselves what stories are not being told because no one is around to tell them.
Not recognizing survivorship bias can lead to faulty decision making. We donât see the big picture and end up optimizing for a small slice of reality. When the stakes are high or the result important, stop and look for the stories of those who were unsuccessful. They have just as much, if not more, to teach us.
In marketing, we tend to amplify the stories of existing users. We donât ask ourselves the important questions - why do customers leave, why do we not have a larger addressable market, and what important information (& users) are we missing out on?
Belief Bias
If a new piece of information goes against your experience, youâre more likely to reject it. For example, If you believe emails are the best channel to reach customers, and your channel team tells you that your email open rates are much lower than the industry average, youâre likely to recommend creative and message testing instead of exploring new channels.
As humans, we tend to default to what we already know. When you see a conclusion that doesnât align with your beliefs, ask yourself - Why do you believe what you believe? Were emails the best channel in your previous organization? Do you personally prefer to receive emails over other channels?
Understanding the root of your assumptions goes a long way in removing emotions from the equation & makes you more receptive to new information.
If youâre interested in learning more about biases and how to keep them in check, here are a few resources to get you started:
This thread on 40 powerful concepts for understanding the world
101 Cognitive Biases & Principles that affect your UX
An infographic of every single cognitive bias
Until next week,
Shrikala