![\"\"](\"https:\/\/www.mazurchak.com\/pictures\/1x2-2.jpg\")
\nMy Pre-Interview Setup<\/h2>\n
For research like this, I build the process from scratch: first I align terms and expectations → then I collect facts → after that I write testable conclusions and turn them into concrete decisions with clear priorities. This removes subjectivity and makes it easier to pass the result between teams.<\/p>\n
The project had four steps:<\/p>\n
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- Aligning views inside the company.<\/li>\n
- Quantitative segmentation by LTV and ABCD segmentation.<\/li>\n
- Deep interviews in JTBD logic.<\/li>\n
- Writing clear customer jobs.<\/li>\n<\/ol>\n
Step 1. Running an Internal Team Workshop Alignment<\/h2>\n
At the start, I ran a workshop where I set the JTBD frame:<\/p>\n
People choose a product as a tool to solve a specific task. It is important to see the context, triggers, success criteria, trade-offs, and alternatives — not a list of desired features.<\/i><\/p>\n
Then the team described its current view of segments: who they see as key, what they promise them, where the product magic is, and which jobs it covers. We compared the maps, captured the differences, and agreed: from here we test hypotheses with data and interviews, not with personal beliefs.<\/p>\n
Step 2. Economics: LTV and ABCD segmentation<\/h2>\n
To make sure interviews did not turn into talks with random people, I started with the customer base. Using seven months of data, I split users by LTV ranges and calculated group size, average number of purchases, revenue, average LTV.<\/p>\n
\n![\"\"](\"https:\/\/www.mazurchak.com\/pictures\/2x2-2.jpg\")
\nThe analysis showed that customers with LTV 200–500 bring 56% of revenue. The goal of the research was to understand why they choose the product and what tasks \/ jobs they use it for, so we can learn how to attract and retain this group.<\/div>\n<\/div>\n\n![\"\"](\"https:\/\/www.mazurchak.com\/pictures\/3x2-2.jpg\")
\nI applied the ABCD framework to the collected data.<\/div>\n<\/div>\nThis is where the first “cold shower” happened, and it made the discussion clear right away: a clearly defined share of LTV customers brought most of the revenue. That gave the research an honest focus. We wanted to understand:<\/p>\n
- \n
- who these people are;<\/li>\n
- why they choose us;<\/li>\n
- how to scale this success without drowning in endless service for low-value customers.<\/li>\n<\/ul>\n
Step 3. In-Depth Customer Interviews to Identify Segment Patterns<\/h2>\n
The goal of the interviews was to understand why the client chose our product:<\/p>\n
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- what happened before the purchase;<\/li>\n
- what became the trigger;<\/li>\n
- what options they compared;<\/li>\n
- where they hesitated;<\/li>\n
- when the feeling appeared: “aha, this works”;<\/li>\n
- how the customer defines success.<\/li>\n<\/ul>\n
In each session, I captured the profile, trigger, aha moment, problems, customer jobs, and signals of disappointment. At the same time, I collected live quotes: the phrases people use to describe their goal and progress. Later, this language works great in marketing, onboarding, and product messaging.<\/p>\n
I ran the interviews in Google Meet, and I recorded and transcribed them with Loom. Some conversations were in Spanish: I used a new Google AI<\/a> service that does live, two-way translation in real time from Spanish to English and back.<\/p>\n
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