As a Product Manager, a big part of your job is collecting and making sense of data to inform product decisions. These are decisions about building a new feature, diagnosing something that isn’t quite getting traction, or tracing UX or technical bugs in a product.
We’ve faced all of these situations at Pathao and the question that inevitably comes up is:
What data should we look at?
Here are a few data sources to get started:
Customers (short-term)
Usage analytics
The first (probably most accurate) way to understand how customers are using your product is to look at usage data. This can (rather easily) be done through Google Analytics/Firebase for your website or app.
By inserting a few lines of code into the <head> section of your website, you get near full visibility on users’ behavior – isn’t that magic?
What pages are most visited?
Which part of your funnel are most users dropping off?
Which features are least used?
There are a plethora of questions this data can help answer.
If you want to get fancy and track more, here are a few options:
Lastly, usage analytics is the best quantitative data source you will have. Regardless of what the other qualitative sources indicate, be sure to cross-check using these numbers.
Sales/Ops feedback
If you’re building a consumer app, feel free to skip ahead since you likely don’t have a Sales team. If you’re building a B2B product, and have a sales team, leverage them to get customer insights.
This should not be mistaken for “Build what the sales team wants” because that can quickly lead to building for hyper-specific use cases and clutter your product. Rather, treat this as another source for qualitative data.
At Pathao, we’ve received a lot of feedback from drivers via our Operations teams over the years. Some employees in being ardent advocates for the drivers, have acted as voices of the customer on their behalf. They’ve helped us push a handful of UX features on the Driver app over the years.
Whenever you can, find and utilize these internal champions instead of letting their feedback fall on deaf ears.
Focus groups
Focus groups, when correctly assembled, are a useful tool to capture customer insights.
Bear in mind this stuff is qualitative data and is likely to be directionally correct rather than specifically accurate. In the early days of Pathao, we did a lot of focus group discussions and learned a few things from our early users and drivers.
For example, the fact that our waiting screen did not have any indication of progress left users feeling uncertain about whether their request was placed and being assigned. These were early days for ride-sharing in Bangladesh and these simple UX cues went a long way in getting users comfortable ordering.
Customer interviews circa 2017
Support feedback
Customer support can provide good data points to understand customer pain points.
For instance, take a look at the most frequent support tickets. You’ll find a bunch of broken UX issues or feature candidates that, if shipped, can help customers instantly.
For us, the most recent example was updating carts. Whenever an item is unavailable or a restaurant closed, the deliverymen had to call Customer Support to update or cancel the order. On the other hand, customers usually wanted to alter their orders and take something else instead. This back and forth was previously mediated by Support agents and consisted of a large chunk of their tickets.
What if drivers and users could sort this out themselves? I’m glad you asked…
Business (medium-term)
Where does the business want to go?
Does your company intend to stick to what it’s doing now and get 10x better at it?
Or are there plans to expand into adjacent industries/geographies/use-cases?
These strategic questions should inform how you should shape your product, brand, and indeed the tech stack.
What are competitors doing?
While it’s usually advised to be customer-obsessed, it helps to be informed of what your competitors are doing to benchmark how better/worse off your startup is performing.
In fact, it can help you understand several interesting things, such as:
Are customers defecting to a competitor for a specific feature (technical or quality-related) that you don’t cater to yet?
Are there specific geographies your competitors are out-performing you?
Vision (long-term)
Where do technology & culture trends seem to be headed?
Where do competitors seem to be going?
What novel (or at least different) innovation can you and your product/company deliver?
In summary, to me, being “data-driven” means being in an effective feedback loop between all of these data sources and then making short, medium, and eventually long-term product/company bets.