As a product manager, you’re always tracking key metrics and hunting for indicators about what’s working, which new features are succeeding, and how customers are interacting with your products and services. But as you sift through data to search for the why and the how, the one significant factor you may be overlooking is data onboarding and how it affects those metrics.
Why product managers should care about data onboarding
Data is fundamental to every SaaS offering and unfortunately importing data is often a slow, manual, and frustrating process.
This common pain point hinders product growth. In a survey Flatfile conducted, 23% of software companies said that importing data takes them weeks or months, and a staggering 96% of all surveyed companies said they’ve had issues at some point onboarding customer data.
Just because this is a widespread issue doesn't make it any less critical. A poor data onboarding process is completely at odds with one of your core responsibilities as a product manager - to increase the efficiency of your customers’ use of your product, and ensure that their experience with it is overwhelmingly positive.
With that in mind, your data onboarding processes should be fast, buoyed by automation, and painless. That may take the form of a self-service data onboarding tool, or something more comprehensive, such as a third-party data onboarding solution.
Asking better product usage questions
When you add data onboarding to your overall focus on product metrics, there are new questions you should ask to help you get a handle on how customers are using your products.
- How long is it taking customers on average to import their data?
- How many actions on average are being taken to ensure a successful file import?
- What is the import success rate for my customers? (That is, how often are files being successfully imported as a percentage of the total import attempts?)
- Where specifically are my customers getting hung up when they use a self-service data importing tool?
- How impactful is a customer’s data onboarding experience on future, longer-term success on the product?
These are specific questions, and it’s likely that you haven’t given them much thought before now. But they align tightly with so many other metrics you’re probably already looking at, including activation rates, retention rates, time to value, and so on, because they all fundamentally address friction in your customer’s journey. In the customer experience, friction causes you to lose people, and a rough data onboarding experience to start that experience is a huge factor to consider...
How data onboarding affects metrics you already know and love
As you’ve no doubt ascertained, these new data onboarding questions impact many of the existing metrics you already track as a PM.
Once your customer gains access to your product, data onboarding is one of the first meaningful interactions they will have. If you don’t have a streamlined data onboarding process, you may end up with lower activation rates, or any core activity on your product that indicates a customer has successfully begun using it.
Even if and when customers push past a suboptimal data import experience, you’re going to see a decrease in time to value (TTV). You simply aren’t going to get anything fruitful from customers until they’re done getting set up with your product or service.
Although it’s not necessarily always correlated, longer TTV can lead to higher customer acquisition costs (CAC). If an “acquired” customer is one who both signs up and interacts with your product in a meaningful way, and your team has to intervene in a clunky and manual data importing process, you’re unnecessarily burning resources and increasing costs.
There are other related metrics at this step. Actions to value (ATV), for example, essentially looks at how many actions or clicks are in the way of activating a customer. Even if your team can move quickly to help customers along on their data importing journey, these metrics can reveal if there are a lot of little things that are (or should be) irrelevant to getting users activated.
These issues crop up all before you’ve generated any value from a new customer.
The longer game: retention
Retention rate is the broad metric that gives you the most insight into whether or not your product has market fit and if the changes you make please your customers or nudge them away over time.
You can judge the retention rate by tracking usage signals. These will vary depending on the type of product you’re examining. For example, for Slack, it may be the percentage of activated users who send a message within a certain time span, whereas for Gmail it would be what percentage of users send an email within a certain time span. When it comes to importing data, a positive experience here will lead to higher retention rates, because your customers are set up for success to begin getting value out of our product.
A related and more targeted metric, net revenue retention (NRR), is designed to look more closely at revenue your customer base generates over some time period, such as month over month (MoM) or week over week (WoW). You can look at retention and NRR via cohorts–eg, putting customers who signed up in January in one group or “cohort,” putting February signups in another, and so on.
Cohorts make it easier to track how changes to your product, including your data onboarding process, affect customer behavior. A positive data import experience will lead to better retention, so if you improve that process, you should see more retention over time. You can compare retention rates between cohorts both from their initial experiences as well as from ongoing interactions with your data import process.
You’ll find that retention rates correlate well with net promoter score (NPS), too. Essentially, you get this information by asking existing customers to rate your product and determine how likely they are to recommend it to someone else. You extract NSP scores throughout the user journey, so it’s useful ongoing feedback.
Even if you manage to retain customers, if the data onboarding experience isn’t great, it will leave a lasting negative impression, and you’ll see this show up in lower NPS scores. (Note that if it’s a painful experience, and customers have to endure it regularly, those NPS scores will be lower and lower.)
Along the same lines, natural usage frequency (NUF)--a measure of how often a customer engages with your product–is somewhat of a precursor to retention. If your NUF is lower than you expected, that means there’s an issue that’s causing your customers to use your product less, which can help you sniff out a suboptimal data onboarding experience.
Data onboarding is a core necessity of SaaS products today, and every product manager should focus on making it the best possible experience for their customers. There are numerous metrics that can help you determine how your customers feel about your data onboarding process so you can improve upon it. Don’t underestimate its importance in the overall user experience.