Company
Duration
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UX
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Research
Personas
At DataCamp, there was no clear idea of who our customers were, and many different business units used their own versions of personas. Proto-personas were developed and refined for use in product.
Role: Principal IC, leading persona definition through stakeholder workshops, lightweight research, and validation to align product, design, and commercial teams.
Overview
Rapid persona development using insider insights.
Initially, I created quick proto-personas, believing that something was better than nothing, and that we didn’t have the time or budget to do a deep dive into a research project of this nature.
Once crafted, the product organisation and design team used the proto-personas as part of the design process I established with Marvel@DC. A key facet of this approach is the creation of customer journey maps using personas and specific scenarios to map out the user experience.
I created these personas by running a number of collaborative workshops with key stakeholders across the business, including PMs, EMs, CS, sales, and curriculum leaders. I started with around 10 personas and revised them down to as few as possible and landed on 3x B2C and 2x B2B.
Proto-personas — crafted in a week using stakeholder inputs, validation, and prior research.
Personas V2
Evolving personas with data to make them more accurate and relevant.
After about 6 months I decided we needed to improve the quality of our personas in order to better understand our customers' needs. I used a simple pop-up survey on our website to validate the personas. This gave firstly a much clearer picture of who our customers were but equally it gave us insights into what problems we still needed to solve for them. Many of the key initiatives I developed focused on solving these problems.
Enriching persona with research data
We used simple filtering to match the audience to our existing set of personas. Part of what I was keen to understand is also the representation of each group within our audience. Our largest segment fell into career switchers which are people who have little or no knowledge of the data space and work in different sectors or functional areas. The second largest group was upskillers, who are people who are in a related role and seeking to improve their data skills.
I created a short survey with filtering questions and few sets of card sorting to better understand pain-points, motivations and habits. While the list was not exhaustive it still provided some strong signals for pain points of each persona, which aligned with the type of persona so was self validated. e.g. career switchers biggest fear was not landing a role which aligns with someone who is not familar with the industry, upskillers’ focus was more on real-world examples.
Key filtering question
Segmenting users into our key persona types
We also dived into the breakdown of manager roles as this gives a good idea of who we need to service and also feeds into the sales process as enterprise purchases typically have a large number of stakeholders and influencers who need to be engaged to complete a sale. Again we were interested to understand in the B2B administrators space what was the level of data skills vs learning & development. We also initiated a separate project to dive into the B2B audience as our numbers of responders were lower than we would like.
Outcome
Aligned teams around a shared, data-informed understanding of core customer segments, improving decision-making across product and design.
Interestingly this also gave us a good insight that our core base of customers indexes very heavily to early learners(novices). This helped to rationalise my hypothesis why our new products like the IDE struggled to gain traction with our core base.
Additionally many of these insights informed the roadmap e.g. looking at how we can better support Career Switchers to land their first roles. Looking at how we might build a community and allow novices to create their data portfolios.
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