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  • Writer's pictureAditya Sudhakar

Growth Product Management for B2b Saas

Updated: Jun 2, 2019

Building to solve for acquisition, retention and monetization by definition means we're solving for data that customers aren't automatically giving us, like feature requests. I can't remember the last time I filled out an exit survey (Sorry to see you go!) as I rapdily closed a tab on my browser. Between my time at seed and Series C/D stage b2b saas ventures, these are topics I've been immersed in through work and reading over the last four years.


I like 2x2 matrices (like the Ansoff Matrix), and I think of this similarly. On one axis there is before they, customers, hear of you (BHOY), and after they hear of you (AHOY.. there matey). On the other axis you have before product market fit (BPMF) and after product market fit (APMF). While the objectives are different in each box, what's common is that the work of a growth product manager is data and hypothesis driven. We started by acknowledging that we have no data, so it's up to us to collect it and act on it.

Customer strategy depending on the situation

1: The growth product manager is the CEO, as many of us are when we first started our companies. Our growth objective is 1; get 1 customer or a handful at most. More than that and it's like trying to collect water before we have a bucket... we'll be drenched but that's about it! Our hypothesis is that we'll get at least one prospect willing to cross the chasm with us. This is what Eric Yuan did with Zoom. He spent 15 years at Cisco Webex. Stanford was willing to work with him and investors gave him $3mm to go build something better.


2: We've acquired our first customer/s. Our objective is to hear them say "Love It". The hypothesis is that if they're that happy, they'll bring us our next customers and buy more from us. Like firing up kindling to get a bonfire going.

A dedicated growth product manager at this stage is useful in cases like Zoom; a technical product that also needs to be built for ease of understanding and repeated use. Iterating on customer feedback and engagement data from a tool like Google Analytics will prevent guesswork.

Stanford was happy enough with Zoom that they told three other colleges about it. It took Eric twenty months to get there. It took Jyoti Bansal at AppDynamics twenty four months to get there.

Beware what Suhail Doshi at Mixpanel calls the sharkfin effect at this stage, ie, the shape of a graph that shows rapid usage growth and drop-off over a short period of time.


3: We're onto something. We're getting unexpected demo requests and trial signups. There's increasing usage among existing customers. The kindling is now burning. We need to retain the heat or it could die out again. Ever seen Bear Grylls do this?

Our objective is to retain these visitors, ie, optimize conversion. We're reacting to frequency and recency of conversion data and behavior flow (user journey) data leading up to conversion. See Mixpanel's industry benchmarks report here to see how you compare.



Behavior flow example

'4' is when retention efforts start to work like in the graph below. Each cohort is stickier than the previous one.

Retention cohort analysis

Our objective is now revenue. Our hypothesis is that with a go to market (GTM) effort, even more customers will benefit from our product. GTM involves a variety of outbound and inbound activities to generate conversion. Attribution is important so we know which ones to stop, tweak or double down on. This needs attention to detail.

Acquisition efficiency

As we push deeper into stage 4, we face new challenges. In addition to customers hearing about us, our competition has too. They've started mimicking our features and calling on our customers! In addition to the external data we're collecting above, we now also want to collect internal data to fine tune our operations machine. Do we know what our sales ramptime yield loss is, what cadence has been on closed-won deals so we can improve sales velocity across the sales floor? Are we proactive when a prospect researches our product category so we're ne increasing conversion volume? Are there automation opportunities to increase volume, margins and cash flow that can be fed back into product development?


David Skok, GP at Matrix Ventures writes about Saas metrics here. As growth product managers, our features and processes move the needle on the important ones.


So... being a good growth product manager is really about having high customer empathy and being obsessed with their needs (spammers and robo callers instrument for growth too). We complain easily, poor experiences bother us and we'd probably make good Airbnb hosts.

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