Loved. Martina Lauchengco
a story that shapes how the world thinks about your product. That is where positioning and messaging comes in.
Chapter 5 Storyteller: Shape How the World Thinks About Your Product
Remember how the Word team talked about feature improvements focusing on how people actually used word processors? Or how Pocket showed how saving content to view later was part of a bigger behavior shift driven by the rise of mobile devices? Both are examples of positioning through a bigger story narrative.
Building a great product isn't enough to succeed if you don't also take the time to position it in the market. Don't make the mistake of assuming the world knows how to think about your product and why it's valuable. You must frame its value. If you don't do it, other market forces will.
That said, positioning a product well is much harder to do than it looks. It's more than just data, stories, claims, or a positioning statement. It's the collective outcome of everything you do to bring your product to market over time.
Positioning and messaging are both important and often get conflated with one another. The differences are:
Positioning is the place your product holds in the minds of customers. It's how customers know what you do and how you differ from what's already out there.
Messaging includes the key things you say to reinforce your positioning, making you credible so people want to learn more.
Positioning is your long game. Messaging is your short game.
Part of the confusion between the two comes from a formula that was popularized for writing positioning statements. You can find it easily if you look online for “positioning statement generators.” They all churn out some variation of the following:
This formula became the straight jacket of positioning. Teams took its output and applied it—as is—all over product materials. They assumed because it's called a positioning statement, they'd checked the box on positioning.
This overly simplistic approach is unhelpful to customers or worse, confusing. It's why shaping how the world thinks about a product is Fundamental 3 of product marketing and one of the most important parts of the job.
Use Formulas as Input, Not Output
Positioning starts with knowing the story you want to tell about your product—and having the evidence to support it. The most visible place this happens is in a product's messaging.
Formulas can push teams to think through their audience, a product's unique value, and the reasons to believe claims. It doesn't work well when teams rely on formulas for their final messaging. For starters, formulas tend to generate messaging that's derivative, dense, and jargon filled. It makes what something does or why to care hard to decipher.
But second, and more important, formulas focus teams on what they want to say and not on what is most important for customers to hear. The right level of detail or if you should orient toward technical or business value depends entirely on the audience and how well known a product is.
Let's look at two companies at the height of their head-to-head competition in the business analytics space. Both served the same audience and offered the same value proposition. One of them did okay despite having a four-year head start. The other got acquired by Google for $2.6 billion after just seven years.
Can you tell which statement belonged to which?
“[Company A] is the best tool for running a data-driven online business. Data-driven decisions lead to better results.”
“[Company B] re-invents business intelligence. Our modern data discovery platform takes a markedly different approach to analytics. Because it operates in-database, all your data is inherently drillable and explorable.”
Company A is messaging something everyone knows to be true: “Data-driven decisions lead to better results.” Does that help a data analyst understand why they should pay attention to this product? It could be talking about Microsoft Excel.
While the message is simple, it does nothing to help a data analyst—someone more analytical than most—understand how better results are achieved. It doesn't say anything that might make an analyst more curious to learn more.
Company B chose to be longer and more concrete—a good choice for this audience. They are specific about what was different and say so upfront: “Our modern data discovery platform takes a markedly different approach.” Then, how it “operates in-database” and what you can do better as a result: “data is…drillable and explorable.”
Even if you don't know what this means from a technical perspective, you do know what they're claiming is unique. Note how it includes elements from the positioning formula but doesn't present them in a formulaic way. Instead, they provide concrete examples of what a data analyst could do differently. It's helpful information for an analyst deciding if they want to learn more.
Company B's messaging is better for its audience for all these reasons. It belonged to Looker, who built a fantastic product customers loved and had good messaging to go along with it. Company A was RJMetrics; their messaging and eventual outcome was average.
Modern teams test messaging, but that's not enough to guarantee a good result. It's easy for teams to test variations on a theme and not probe the landscape of possibility enough. Testing should reveal outer bounds of what might work as well as tradeoffs in approaches.
Let me be clear: messaging isn't what makes a product good. But a good product can't succeed in the market without good messaging. It won't come from a formula; it comes from knowing what your audience needs to hear. A product marketer needs to know that to be good at their job.
A Better Process
Good messaging is honed and chiseled by multiple teams. It is never an instant masterpiece created by one team in a room. Messaging is revised numerous times with input from tests on a variety of platforms (web, in app, email, ads, and sales conversations) before it's done.
I don't advocate a formula, but offer CAST as a guide to check if your messaging is grounded in what customers want to hear. The concepts are:
1 Clear. Is what you do clear and is there a reason to be curious? Is being comprehensive getting in the way of clarity?
2 Authentic. Is the language evocative and meaningful to your customer? Is it said in a way makes them feel known?
3 Simple. Is it easy to understand what's compelling or different? Will customers know what's better?
4 Tested. Has it been tested and iterated in the context customers will experience it?
Teams often iterate messaging in documents, assuming it's honed because product, sales, and marketing all got input. That is only a starting point. When messaging is tested in a web page or email, not only do you get better customer input, it's easier to see unnecessary or confusing phrasing.
Beware: simple and compelling often get confused with jargony and promotional, even among experienced product marketers. Imagine if Looker's “Because it operates in-database, all your data is inherently drillable and explorable” was “Because it's a collaborative data platform, there are no limits to what you can explore!” The original doesn't have trendy buzzwords, but it is much clearer for a data analyst.
I go into significant depth on this process and many more examples of great messaging in part 4.