In search for a data-informed way to build product vision and positioning: part 3
This is the last part of a series on building product vision and positioning by researching decision-drivers — factors that your customers evaluate and make a decision on in regards to selecting a solution. Here I wanted to discuss some of the questions that haunted me a bit after we had completed our own research and were able to use it to create positioning.
Recap of the first 2 parts
There are 5 steps to craft a product vision and positioning based on a data gathered directly from people that are in the market for a solution like yours:
1 — Describe a decision that your target audience needs to make
The first step relates to who you are building your product for, and the decision this audience needs to make. Be as specific as possible with your target audience definition, identify ICP, identify the role of a person that makes a decision that is relevant to you. And then state that decision or reframe it as a question, such as: “Which solution from [product category] should we select?”
2 — Discover the decision-drivers
This step revolves around trying to gather data on how different solutions were evaluated, how the overall evaluation process has gone, what sources of information were used, and other context. The main point is to gather decision-drivers from multiple relevant representatives of your target audience.
3 — Rank the decision-drivers and get context on the most important ones
Here you need to come up with a list of 10–15 decision-drivers and ask your interviewees to rate them on a scale from 1 to 7 (or 1 to 5 — whatever you prefer). You will also need to get the context on 3–5 most important decision-drivers for each interviewee.
You can ask for ratings either via survey before the second round of interviews or do it within the interview.
No less important is to gather context on the most important decision-drivers in a structured, pre-built way. You’ve got to ask very similar questions for each driver to be able to get the same type of information — it shouldn’t prevent you from getting additional context, but get to the heart of the drivers by reusing the same type of question — examples are in part 2.
4 — Calculate average, median, standard deviation for each decision-driver
That will help you get a prioritized list of decision-drivers to make decisions in the crafting phase. Select up to 3–5 decision-drivers that ranked the highest. At this step, if you had multiple audiences that you want to understand deeper, simply calculate the numbers for them separately. Keep an eye on decision-drivers that are high in standard deviation — it might be a signal that you have a sub-segment of your target audience within your interviews, and they highly value a decision-driver that might be of no interest to other segments.
5 — Make the product vision and positioning
Now you’ve got it all: ranked decision-drivers and lots of context on them through interviews. That should give you an understanding of what you should build and what’s not important for your audience. This should guide you towards product vision that makes hard decisions on what not to include. As for positioning, use context — and use specific language and words that your interviewees used, when they described decision-drivers and how solutions were evaluated based on those decision-drivers.
Scaling it up
As (if) the business grows, you are going to be looking to expand your target audience. The methodology still holds, and you can approach scaling up from two different perspectives:
- Reconsider the decision drivers that you’ve gathered and start covering more of them either within the main product or as an add-on
- Perform the research once again, but with a specific segment in mind
In the first case, the idea is to use the data that you have already gathered to see what decision drivers can be covered that potentially unlock a bigger chunk of the market. An easy example here is advanced compliance with multiple regulations (if that was a decision driver for your audience, but wasn’t high enough on the list) if you are in a product category with different levels of data sensitivity. A company that has it as a decision driver just can’t select you, because it’s simply a blocker. You need to study the companies that rated it high, just to see what else they require. Is it enough for you to cover the decision drivers that you initially covered and do you just need to cover one more? In that case, it seems like a good opportunity.
If you conducted the research a long time ago, the results may not be applicable, so approach it with care.
In the second case, you just go through the full cycle of research once again and just follow the outlined steps.
What if you are creating a completely new market category?
The whole article was dedicated to building product vision and positioning for a product in an existing market category. A fair question is whether we can follow this methodology to create the same artifacts for a product that seems so novel that it is going to create a completely new market category?
In short, as related to this specific methodology, I don’t know — I haven’t tested it on completely innovative products. Regardless, I’m going to speculate about that.
In theory (I warned you I’m going to speculate here!) you could start with a problem for a specific target audience, rather than a product or product category. But in this case, what exactly should we research for building our product vision and positioning?
Each existing problem has a solution. Sometimes the solution is to do nothing, and sometimes there are workarounds employed by people or companies who feel the pain of the problem. We can study how the problem is currently solved, and do it in two ways:
- Find decision drivers for their current solution (which you believe to be not optimal)
- Find gaps: what exactly people or companies you research are unhappy with? What prevents them from solving the problem completely?
Essentially, the research will be split into two parts, because there are now two lists to be ranked. Follow the same steps, but in the end, you’ll be building product vision and positioning not only based on what they value in their sub-optimal solution, but also on what they miss in the current solution.
Can it work for an existing product?
The challenges of working with an existing product don’t lie in the research area.
Instead, your challenge will be multiple stakeholders that need to be involved as well as sheer organizational effort to use the insights to build product vision and positioning, as well as use them later on in product management, marketing, and sales.
You might run into executives that will dismiss your research just because ‘some research was done already’ or something along the lines. It’s just not going to be a priority for many executives. It will be your work to prove that the research is necessary.
Going back to the research dimension, working on an existing product will provide additional insights because you will be able to talk to your users or decision-makers from existing accounts. Those interviews should be run separately from the interviews with users/decision-makers of competitive tools, or at least separated within the analysis stage. But you will not only learn what the specific segment of the market cares about but also what were the decision drivers for the existing accounts as well.
Gratitudes
Thanks to Alan Albert, MarketFit president, who has decades-long expertise in building products, crafting positioning, and optimizing pricing and who led the way for me to dive deeper into discovering customer values.
Thanks to Dmitriy Amosov, Sinisa Kravarascan, Maja Blazek, Tomislav Jus who were an integral part of the initial research that was a foundation to my learning of decision-drivers.
