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Being a ‘data-driven’ SaaS product manager

Clement Kao AMA
Continued: Part 2

Have a look at Part 1 in case you missed it.

Q1. Question: What are the best data tools to invest for a B2B SaaS product?

A1. Great question! For B2B SaaS products, it depends on what kinds of platforms you’re using. If you’re using a web-based platform, then you’ll likely benefit from implementing something like Mixpanel, Amplitude, or Mode Analytics.

If you’re using a mobile-based platform, you’ll want to work off of mobile analytics tools such as Apptentive, since the data tracking is a bit more robust there. In my experience, I’ve found that using non-mobile-optimized analytics tools can lead to some heartburn in trying to make it work for mobile.

Also, keep in mind that different tools specialize in different things. For example, if you’re specifically interested in NPS, use something like Delighted, which enables you to ask NPS questions across both web and mobile.

 Q2. What are the meaningful metrics to track a product from the product management perspective?

A2. This is a great question that warrants a much deeper discussion! The quick answer is: “it depends on your product”. I’ve written about it in more depth in these two articles at Gainsight:

Always start from what the business needs, and then narrow it down to the metrics that will really make a difference.

Q3. In a B2B or a Saas scenario, mostly it is a longer and slower product/sales cycle. How should a PM distribute time for strategy vs. operational aspects?

 A3. This is an interesting question – it’s not necessarily true that B2B or SaaS are slower! I’ve found that I’ve had to pivot within hours, because we learned something about our customers that we didn’t know about before.

Rather, what I would say is that B2B requires you to be particularly thoughtful about the kind of data that you’re getting. You’ll need to lean heavily on qualitative information that comes from your key customer executives, rather than solely relying on product analytics.

Q4. Can you share your experience on how you went about capturing data (defining product KPIs, integrating a data tool with the product) and using the captured data to make decisions on product features (which ones to invest on, which ones to discontinue etc.) for any of your products? What were some of the mistakes herein which we can learn from without repeating them? How different is this for a B2B vs a B2C product?

A4. In terms of defining product KPIs, take a look at my previous response A2.

In terms of using the captured data to make decisions on products, check out this case study I wrote with Amplitude here: key mistakes that I made:

  • I assumed that I could run A/B tests for B2B products. That’s not a good assumption because B2B products have much smaller user bases, and their transaction times are much longer. It’s much more worth it for you to speak with end-users directly.
  • I assumed that the problem was sitting with our end-users, but the problem was actually sitting internally with us! We were training our users incorrectly, which caused them to struggle to use our product in the most effective way.

I speak more about the nuances of B2B analytics in this presentation, and how that differs between B2B and B2C.

Q5. What are the ways in which Product Management will change in post covid scenario?

 A5. Great question!

Due to COVID-19, consumer habits are quickly changing (and B2B users are themselves the consumers). The challenge is in understanding “how to identify the new needs of your users”.

I’ve found that quantitative data and qualitative data work synergistically together, because they address fundamentally different questions. Quantitative data tells you “what” – it informs you about the behavior that is happening within your product. On the other hand, qualitative data tells you “why” – it gives you an understanding of why users are doing what they’re doing, and why they may not yet be using your product, or why they’ve decided to adopt your product.

In speaking with hundreds of product managers since the start of COVID-19, I’ve found that the most successful product teams have been more proactive about gathering the “why” – in other words, reaching out to end-users and to customers to seek qualitative data through interviews, phone calls, surveys, and feedback collection. Passively monitoring the “what” within the product is not sufficient to drive success in these turbulent times – it’s time to reach out to speak with customers as frequently as possible.

That said, now is a fantastic time to double down on instrumentation and quantitative data collection, especially as you continue to learn about the new needs of your customers and end-users. When you learn about new needs, you’ll need to create new features, and none of those features are engineering-ready right now. So, while you dive into synthesizing that research and creating compelling new functionality, leverage this downtime to implement robust quantitative data tracking and analytics. That way, as soon as you’ve shipped new functionally to address the new pains that you’ve discovered, you’ll be able to refine and iterate much faster than your competitors can.

Q6. To keep down on costs on data collection in these times of the COVID-19 pandemic, do you have any open source data tools in mind?

 A6. Great question! Unfortunately, I don’t have a good answer here, since my Finance team is the one that deals with costs. We believe that powerful analytics capabilities are worth their weight in gold, so as long as our product team is benefitting, we’re happy to pay those costs.

I realize for much smaller teams, or for much earlier-stage organizations, costs can be a significant factor.

That said, even though you may run into difficulties in finding less expensive data collection functionality, you can always consider alternatives to collecting data automatically through your product.

For example, you can set up a survey at the end of one of your product workflows, and ask your end-users to respond to qualitative questions.

You can always send out an email to your registered user base to ask them for time to jump on a call.

All of those are free ways to gain crucial information about your product.

Q7. In your experience what are the data interpretations mistakes that PMs make in the B2B / Saas space? (in terms of assumptions, mindset, biases etc…)

 A7. I dive into this deeper in one of the earlier presentations that I shared! Here’s the presentation link:

Key mistakes that happen:

  • PMs expect to get to statistical significance – that’s not always possible in B2B. Rather, get enough data that you can move forward and make progress.
  • PMs expect to have experiments wrap up quickly – that’s not likely to happen. Either expect to have long test times for rigorous tests, or use leading indicators to accelerate your experimentation times.
  • PMs expect that users are independent. They’re not. Some users can influence other users within your product, especially if it’s a B2B productivity platform.
  • PMs expect that their product is the sole driver of adoption. That’s not true. Sales, marketing, customer implementation, customer success, and customer support can all dramatically change your adoption patterns. Learn to work cross-functionally internally to shift adoption patterns. Sometimes, making changes outside of your product in terms of processes is what will net you the biggest wins in terms of product metrics.

Q8. As we have come to the end of this session, Clement Kao do you have final tips for our members?

 A8. Really glad to be here today!

Final thoughts:

  • Product managers cannot harvest value for themselves without first creating value for others. If you want to be a product manager, then demonstrate to your company that you will provide them unique value as a PM, and that they’re going to lose a bunch of money if they don’t hire you as a PM. If you want to ship a successful product, obsess over unlocking value for your customers and your end-users first, before trying to figure out how much to charge or how to monetize.
  • Don’t try to learn everything yourself. Ask others for help, and join communities. Be active! Take advantage of this community and others to ask the questions that are on your mind. Everyone is here to help one another out!

Stay safe, stay healthy, and stay happy! Thanks for a fun AMA session, everyone!