The goal of this three-part series is to add one more skill to your toolset: creating product vision and positioning based on customer research. The series will outline the steps that can take you from relying on basic intuitions about what will speak to buyers, to a completely research-based positioning and product vision.
Using customer decisions to build positioning and product vision
One of my most memorable working experiences comes from some significant research I undertook with my colleagues and a consulting firm called MarketFit, led by Alan Albert. It was about product vision and positioning – a tricky topic, even for the most experienced product managers.
Many product managers and product marketing managers don’t know how to tackle crafting product vision and positioning in their company. The problem seems to be cloaked in mystery.
At best, companies run internal workshops and brainstorming sessions to devise product vision and subsequent positioning based on their understanding of the market, customers, and the product they’re building.
Many business books, including Blue Ocean Strategy, describe the same way to get at positioning or product vision. The implication is that people working on a product should know enough collectively to create good product positioning. While this approach is better than a top-level manager making the decision alone, we can do much more to get product vision and positioning that reflects what our market segment truly needs.
We typically position what we create instead of creating what is easy to position
In general, positioning follows a product vision.
After all, we start with a vision and only then get to the details: what features to build, what positioning our product will have, how we’ll appeal to the needs of the user, and how to make sure our potential audience realizes we can solve their problems.
But we shouldn’t wait until after we’ve delivered a product to start positioning. If we start with both product vision and positioning, we’ll be able to focus our efforts on features that the target audience will appreciate. Some features won’t make the cut. That’s fine!
Alex Walton
Default product vision and positioning aren’t optimal
The product vision, positioning, and value proposition are usually decided by an individual or a group. Managers write positioning statements and documents and then push those to GTM teams. There are lots of problems with this approach.
A sole decision-maker introduces too many biases
Industry, market, and client knowledge are nothing to sneeze at, but our experiences within an industry and even insights from interviews introduce biases to our thinking. These biases shape the way we see our product and the needs of our existing or potential customers, resulting in positioning that misses the mark.
A group comes to a compromise between multiple biases
Collaborative work is a much better way to make a decision than following one person’s gut feeling. Triaging multiple data points results in a much more consistent and relevant output, although the biases are still present. You just remove the most egregious offenders by doing the work together. The result is a compromise between the biases of different people.
Sometimes the standard approach works – against the odds, we nail the positioning, product vision, and value proposition. The problem isn’t that we can’t ever succeed with this approach; the problem is it’s not consistent and depends on luck, even when we involve experts. So many products fail despite the names behind them.
A data-informed way to build positioning
In this series, we’ll go through the steps you can take towards a data-informed product vision and positioning. But why is the data-informed approach so much better than the standard approach described above?
First of all, instead of guessing what product features and benefits we should highlight for our target audience, we’ll take those points directly from those who have recently selected a solution like the one we’re building.
Secondly, we will have to say ‘no’ to things that don’t align with our audience’s decision drivers. Maybe you’re thinking of introducing a cool machine-learning algorithm that will make you stand out but isn’t relevant to the audience you are building your application for – that’s a no. You might want to think about finding an audience that cares about that algorithm, but you still can’t escape the burden of validation.
Thirdly, we won’t just uncover decision drivers with our interviews; we’ll also quantify those decision drivers.
As the main idea of the series is to show you how to create a product vision and positioning based on decision drivers, that’s what we’ll spend the most time on. If target audience definition, ideal customer profiles (ICPs), and roles within B2B are not already defined within your organization, you might want to read an additional article or two.
Step one: What decision does your target audience need to make?
Define an ICP first
At the outset, you need to have an idea of the most fitting customer segment for your new product. You may be wrong at that point, and interviews may show you that you can’t respond to the decision drivers of the audience that you thought was best for you, but a rough image of an ideal customer profile is necessary.
The problem your product solves is felt most by a specific set of people. Who are they? What do they do day-to-day? What kinds of jobs do they have? Create a portrait and try to screen your interview participants through it in the next steps. For B2Bs, you’ll start with the company description first, and the person description second – you will need both.
If you have no experience in defining a persona or ICP, read up on it or find a person within the organization that can help you. Don’t skip this part just because you think you know your audience.
Preliminary research on segments of your target audience may also be necessary, or you may use existing data to build your ICP.
Restate the decision into a question
It’s vital to know what kind of decision your audience needs to make. A decision can generally be restated as a question. A simple example for B2B SaaS would be: “What product from [product category] do we want to select?”
Specific needs, values, criteria, and motives drive an answer to this question. The next steps will shed light on the specific decision drivers that help your target audience answer that question.
Let’s say you’re creating a B2B SaaS application that allows your clients to accept payments on a website or in an application. The target audience’s decision, in this case, is the answer to this question: “Which payment provider should we introduce to our product?”
Restating the decision as a question is my preference, not a hard rule. Knowing the decision is enough, but I feel it’s much easier to empathize with the audience when the decision is restated as a question.
Define the role to focus on
Multiple roles are involved in the selection of B2B SaaS solutions: decision-makers, evaluators, end-users, and others. Sometimes they overlap; sometimes they don’t. For our purposes, we need to hone in on the roles that can answer the above question.
You might discover that the evaluator and the decision-maker are different people within the company, but the evaluator will be crucial. The decision-maker can only approve or reject what the evaluator suggests.
Often, an evaluator will provide a selection of options a la carte for a decision-maker to select from, so you need to decide: do we want to just get on the list, or do we want to be selected from that list?
This concludes part one. In the next chapter, we’ll go about actually discovering decision drivers and their levels of importance as a foundation of the positioning and product vision.
At this point we know:
- What our ICP is for the product we’re considering building (or for the problem we’re trying to solve).
- What decision an ICP, or their representative, needs to be make (or what question they are answering).
- Which role we should talk to (for B2B).
In part two, we’ll cover:
- Ways to come up with the list of decision drivers (quick and dirty vs. long and robust).
- How to perform interviews that are aimed at ranking decision drivers and getting deep context on each important decision-driver.
- How to rank decision drivers with supporting context across all interviews.
- How to build a product vision and positioning based on all the data you’ll have gathered up to that point.
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