Why You Need a Data-Informed Product Strategy

Having a data-informed product strategy will help you create sustained, long-term growth. Here are the metrics that get you there.

Best Practices
May 30, 2020
Image of Andrea Wang
Andrea Wang
Principal Product Manager, Growth
Why You Need a Data-Informed Product Strategy

The best product strategies exhibit a deep understanding of customers. They reflect how customers get value out of your product. After a product strategy has been set, teams should be able to not only measure the impact of product initiatives, but also discern how users get value from the product offering (and tie that value to concrete business outcomes).

Insights—gathered from quantitative and qualitative data—are foundational for business growth. This is true in marketing. It’s true in sales. And it’s definitely true when it comes to building products that create real, sustainable, profitable growth.

Simply gathering insights is not enough, though. You need to be able to translate those insights into a measurable product strategy. So as you refine your plan, consider the ideas and metrics listed below. And make sure you balance qualitative and quantitative insights to develop a product strategy your team can execute on.

What is a data-informed product strategy?

A product strategy is a high-level plan for what your product will accomplish. It generally defines three things: how you’ll meet customer needs, reach business goals, and provide unique value.

A data-informed product strategy takes your product strategy to the next level. As the name suggests, a data-informed product strategy is based on data, qualitative and quantitative. So instead of guessing what customers need, you use data from their behavior, feedback, etc., to understand what they need.

The reason we call this style of product strategy data-informed rather than data-driven or data-backed is that data should be one of many inputs in decision-making. Decisions about product strategy can’t be made by one input. If you base your product strategy on one input — like data or personal experience — you’re missing the bigger picture. That might cause your product strategy to not achieve any of the goals you’ve set out to achieve.

Why does data matter so much?

Even though data is just one input, it’s a crucial input to your strategy. Data helps you understand your customers in a way that your personal experience does not. It also helps you understand your customers better than they understand themselves.

A data-informed product strategy is founded on a simple truth: The better you know your customers, the better you can serve them. And the better your data, the better you know your customers.

According to a survey by Forbes, companies that effectively use data are “six times more likely to be profitable” year over year. Higher-level data can help you make product and feature decisions. For example, if you operate an English-only digital product but notice a significant portion of your active user base is located abroad, you can set an international expansion strategy and decide which languages to prioritize.

Or, let’s say you’re running a media company. You find that 50% of your customers abandon renewal halfway through the process. With that in mind, you should revisit how renewals work in your system and spend time understanding what those non-renewals have in common.

Intuit used a data-informed product strategy to their benefit when data showed them that customers were struggling to get invoices paid on time. Their data also told them that the customers who were struggling the most had one thing in common: they used Gmail for their email provider. That presented a customer problem that Intuit knew they could solve.

Intuit’s solution resulted in the fastest-growing product in their 35-year history: a personalized invoice tool for Gmail domains. The tool doubled the number of invoices paid on time for Gmail users. Without data, Intuit may have never known why customers were struggling and what they could do about it.

The metrics you need to inform your strategy: North Star and Input Metrics

When building your data-informed product strategy, there are two categories of metrics that you should focus on — your North Star metric and your input metrics.

North Star Metric

Your North Star metric is your guiding metric — it’s the one metric that defines the relationship between the customer problems you are solving for the customers and sustainable, long-term business results. If you can improve your North Star metric, you should deliver business results and customer value.

To figure out your North Star metric, start by asking two key questions:
What is the core value customers get from my product?
Is this metric I’m considering predictive of medium to long term business success?

As we’ve said before, “Your product north star should be the leading indicator of future business outcomes.”

Now, sometimes your North Star metric is pretty high-level. It might be the number of new customers signed or the percentage of customers retained. When you’re in a situation where your North Star is too high-level, that’s when input metrics come into play.

Input Metrics

If your North Star metric is your leading indicator of future business outcomes, your input metrics are the leading indicators that show you you’re on the right track to improving your North Star metric; they’re more granular than your North Star metric.

In an ideal world, it works like this: Improvements to your input metrics lead to improvements to your North Star metric. Those improvements lead to better business outcomes. Each level you go up — from inputs to North Star to business outcomes — takes a little longer to see results. That’s why you’ll see the quickest results at the input metric stage.

Netflix’s former VP, Gibson Biddle, explains that input (what he calls “proxy”) metrics show results more quickly than the high-level North Star metric because of their granularity. Input metrics should work to influence your North Star metric. A big improvement to an input metric should result in a smaller change to your North Star metric.

So, if your North Star metric is net promoter score (NPS), your input metrics would be smaller metrics that strongly correlate with NPS. You might track the amount of time customers spend using your product, or how many times per week they log in to your app. The more specific your input metrics, the smarter your teams can be about tracking and improving them.

For Biddle at Netflix, the high-level North Star metric was customer retention. And their input metric was, “the percentage of new customers who add at least three titles to their Queue during the first session.” Those two metrics had a strong correlation, so instead of focusing on the high-level goal of retention, which could encompass so many things, the team focused on increasing the number of Netflix users who added three titles to their Queue.

You’ll notice that if you sign up for Netflix today, they ask you to choose three titles that interest you—before you even get onto the screens where you can start watching. Cleverly, it helps new customers find stuff they like right off the bat while also pushing more users into the three-titles-in-the-Queue category.

How You Can Get Started

Getting strategy right starts with data—and the tools you need to gather, view, interpret, and understand that data. This is where Amplitude can help. The best product teams use our product to set and evaluate their north star metrics, and it’s easier than ever to get access to the crucial insights you need to guide your team to success.

Leverage our product intelligence platform to learn more about your most valuable user base and discover untapped opportunities.

About the Author
Image of Andrea Wang
Andrea Wang
Principal Product Manager, Growth
Andrea is a Principal Product Manager, Growth at Amplitude, driving product-led growth for Amplitude’s business and leading the growth product pillar. She currently focuses on activation and monetization.