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How a B2B SaaS startup increased feature adoption by 80%

A global payroll and compliance platform improved its pay via crypto feature adoption by gaining a deeper understanding of user concerns, revealing underlying issues beyond simple feature awareness.

November 5, 2022
Rahul Mallapur

Consider this scenario. Last week, your team released a highly anticipated feature. The entire team worked hard to ship the feature. But your customers haven’t shown similar enthusiasm. The feature adoption is low. The business objectives haven’t been reached. Sounds familiar?

Since the normalization of remote work in early 2020, companies like Amazon and ServiceNow use Mungo(name changed for competitive reasons) to access cheaper and abundant talent in international markets. Freelancers and gig workers prefer to be paid in cryptocurrency as international payments are processed faster and with much smaller transaction costs, as against traditional banking.

Mungo launched a feature for freelancers to request payment and for companies to pay in cryptocurrency. But adoption was low on both sides. The low adoption by freelancers who had requested the feature was more baffling.

The Head of Product and a Lead Designer, jumped to deeper investigate the low adoption. The investigation was structured into two parts: understanding the low adoption by the freelancers who had requested the feature and identifying the concerns of companies. In this situation, having an in-product research tool like Blitzllama made the research fast and feasible.

Here is how Mungo identified and fixed the top issues and increased the feature adoption rate by 80% within weeks.

Step #1: Identifying the right users to research.

The team previously ran a survey to test the adoption of crypto payments. Of the 2600 freelancers who responded, 30% were considered highly likely to use the pay-via-crypto feature. A follow-up cohort of these freelancers, filtered further on who hadn’t tried out the feature was the right group for the first study.

The account managers had identified about 100 Seed to Series B funded companies that were most likely to pay employees and contractors via crypto. The payroll and finance team members in these companies were the target of the second study.

Step #2: Launching in-product surveys.

In order to hone in on the root causes, Mungo sought to answer the following questions:

  • Are the freelancers and teams aware that Mungo offers pay via cryptocurrencies?
  • Do they desire to receive or make payments in crypto?
  • What's holding them back from using it?

Research studies can be performed more efficiently with surveys delivered in-product as the responses rates are typically 5x of traditional email surveys and the collected feedback data is highly contextual.

Step #3: Analyse the results.

As the results from the in-product surveys rolled in, the reality came within grasp.

90% of freelancers were aware of the feature. So the issue wasn’t a lack of feature awareness.

But the biggest hindrance was waiting to try out the payment through a small amount. The freelancers did not want to risk their client payments going into the wrong wallets. Seemed obvious in the hindsight!

The results from the second study indicated that about 60% of teams in Series A and B weren’t interested in making crypto payments. The number was much lower for Seed funded companies at only 25%.

Payroll and finance teams process payments in Series A and B companies so following standard accounting practices and compliance was more important than experimenting with cryptocurrency. While in Seed funded companies, founders manage payroll and were more open to experimenting.

The team still had some work to do on educating the teams, and hence room to improve the feature adoption by companies.

Step #4: Take action based on the discovered insights.

In the following weeks, an option to test the payment route was added for freelancers.

The content team published articles addressing the most significant concerns from the first study. The articles were easily available on configuring the payment method screen.

On the companies’ front, the team took a more cautious approach, adding a “talk to our experts” prompt prominently on the process payment screen.

Step #5: Review and iterate after feature updates.

Mungo’s engineering team within weeks shipped a feature to enable freelancers to test the payment route by transferring themselves a small amount of any cryptocurrency. By the end of the month, 20% of the freelancers had shifted to accepting crypto payments. An 80% increase from 11% adoption earlier.

The product team on the feature continues to test out new ideas around referrals paid in crypto, etc to boost adoption further.

With just content updates in the product on the companies’ dashboard, the head of customer success reports a 3x increase in weekly calls from the payroll teams to learn more about the nuances of crypto payments. Quick wins!

How can you replicate the success to improve your features’ engagement?

1. Validate assumptions sequentially. Start by determining if the users have the problem that the feature desires to solve. And so on.

2. Ask in context. Ask questions to the users about the most relevant point in the product journey. This will provide the highest quality of actionable feedback.

3. Review results and iterate. Your product and users are continuously evolving. So keep learning from your users how their experience with the product evolves and the problems that they are seeking your product to address.

What did not work?

Tooltips and nudges that directed users’ attention to the feature did not improve the feature’s usage. Why the traditional nudges didn’t work? The reasons for low adoption were much deeper than just a feature discovery the issue, which is the case most often.

Disclaimer: We modified the name of the company and redacted the names of the people involved for competitive reasons.