It’s almost become law that every company in the software industry must loudly claim to be some degree of “customer-centric.” You’ll also find companies make a slightly softer stance of being customer “focused,” while others decide to go all-in and tout their customer-obsession.
No matter what verbiage your company uses, it’s only by being truly data-driven that any degree of customer-centricity can be proven.
The reality for many companies is that data is merely viewed as a byproduct—something generated and stored but rarely utilized to its full potential. This is unfortunate since so much of the data we collect can serve as a critical asset for driving product development, increasing feature adoption, and enhancing user experience. Let’s look at how software companies can better utilize data, drawing parallels with Netflix’s inspirational approach.
The Netflix Model: A Masterclass in Data Utilization
Netflix has set a high bar when it comes to leveraging customer data. They employ sophisticated algorithms and machine learning models to analyze a multitude of data points used to make better-informed business decisions, provide targeted recommendations, and deliver insights into their content creation pipeline. This is true customer-centricity, or “obsession,” if you will.
For those interested in the technicalities, Netflix’s engineering blog and published research on their recommendation engine provide insights into their impressive approach. Their commitment to using data for customer satisfaction serves as a compelling example for software companies in any industry.
While most companies may never have their own algorithms and machine learning models that would stand up in complexity or performance to those found at Netflix, those also aren’t required to deliver a memorable, personalized experience for your customers.
Data Points: Beyond the Obvious
In the software industry, particularly in the engineering, testing, and developer visibility space, we have access to a wealth of data that often goes underutilized. This includes not just user interaction metrics but also API call logs, error reports, and performance metrics. Moreover, there are less obvious data points being collected outside of engineering. Everything from the details within customer support tickets, product feature requests, UX beta testing/research, and even marketing surveys—all of which contain information that any team could find valuable.
For instance, API call logs can reveal how often certain features are being accessed and in what sequence, offering clues to user workflows that may not be immediately apparent. Error reports can be a goldmine for understanding not only what broke but also what might break or simply what’s causing an uptick in wait time, user frustration, or feature abandonment.
Customer support tickets often contain nuggets of information about what users wish the software could do, which can be a rich source of inspiration for future development. Feature requests, often relegated to a backlog, can be prioritized based on how often similar requests come in or how they align with the data on feature usage. UX and marketing research often contains data that lives outside of numbers but within equally important, emotion-capturing user stories.
The problem isn’t that businesses aren’t collecting enough data or even that they aren’t using the data they’re collecting. The problem is thinking too small about who else in your org could do something impactful with that data if only the team who collected it had thought to share it.
Cross-Functional Data Sharing
One of the challenges in effective data utilization is the siloed nature of departments. Sales, Marketing, Customer Support, and Engineering often operate in isolation, and each can often be found sitting on a mountain of mutually beneficial data.
While new technical solutions may be required to enable “sharing at scale,” it’s far from the first step to making this a reality in your org. The first step is fostering a culture of openness and a shared responsibility to your customers and just as importantly, to each other as coworkers. Coworkers who aren’t necessarily looking for more meetings but who would never turn down more meaningful interactions.
Teams can set up regular but brief check-ins where the focus is on sharing key data insights that have led to successful outcomes. Another approach is to create cross-departmental working groups that are focused on specific customer-centric goals. These groups can serve as conduits for data sharing and collaborative problem-solving.
The key to successful cross-functional data sharing is not just the act of sharing but also the follow-through. When teams are informed that the data they shared had a tangible, positive impact, they are more likely to continue sharing, and with greater numbers of people and teams in your org. These wins should obviously be shared by the teams that were involved, but sharing those wins with your entire company at something like a regular “all-hands” or “town hall” event is an even better idea.
Employee-centric orgs deliver customer-centric releases
Data is not just a byproduct of your customer’s interactions with your software or your brand—just ask Netflix. It’s a goldmine of insights that every customer-centric team can leverage to elevate customer experience to an art form.