The Racecar Growth Framework

There are four concepts that Casey Winters and Kevin Kwok go deep on in Growth Strategy at Reforge: Growth Loops, Growth Catalysts, Growth Methods, and Sequencing.

Dan Hockenmaier(Basis One, Reforge Partner, ex Thumbtack) and Lenny Rachitsky (Ex Airbnb) brought these concepts together in a super elegant way called The Racecar Growth Framework. The four pieces of the framework are:

1. ⚙️ The (Growth) Engine

   Self-sustaining growth loops that drive most of your growth (e.g. virality, performance marketing, content, and sales).

2. 💥Turbo boosts

   One-off events that accelerate growth temporarily but don’t last (e.g. PR, events, Super Bowl ads).

3. 💧Lubricants

   Optimizations that make the growth engine run more efficiently (e.g. improved customer conversion, a stronger brand, and higher customer retention).

4. ⛽️ Fuel

   The input that your engine requires to run (e.g. capital, content, users).

Knowing When To Focus On What

Knowing when to focus on what is the key and where most mistakes are made. We summarized the general take in context of the S-curve of growth:

The main mistakes Dan and Lenny walk through:

1. Focusing on Growth Engines when you usually need Turbo Boosts

2. Mistaking A Turbo Boost for a Growth Engine

3. Focusing Too Much on Lubricants When you need a new Growth Engine

4. Focusing on a new growth engine when you need lubricants.

5. Not understand what type of fuel your business needs.

Read more here...

What Product Market Fit Indicators Don't Capture

A lot of focus in the past few years has been on the quantitative and qualitative indicators of product market fit. In a recent Reforge discussion, I realized they don't capture the three toughest things that I've seen around validating product market fit.

1: True Validation

A lot of people approach MVP's and de-risking with qualitative interviews. They typically end with some version of "do you want this?" Most people will say yes, even though they don't truly mean it misleading teams down dead ends. Andy Johns enlightened me that this not only exists at the startup phase, but also in scaling product in larger orgs.

But true de-risking involves finding a way to get people to put their money where their mouth is. This is easiest in B2B. by having people who say they want this, put money down (that you will return if you don't deliver the product by a certain time date). People who say they want the product, but won't put money down is where the real conversation begins.

2: Solving From First Principles

When you are building a new product in a larger org (Product Market Fit Expansion) you can typically leverage a strength of your existing company to enter a space which means you don't need to necessarily solve everything from first principles.

For example, with the HubSpot CRM we didn't try to invent a completely new type of CRM, we made a better CRM, made it free, and used our distribution muscle to enter into that space. When you are trying to find initial product market fit you don't have this luxury. So you need to dig much deeper on the problem and solve in a new way from first principles vs leverage a bunch of knowns. I see a lot of seed pitches that are incremental better versions of other products out there but not a leap forward.

3: Sequencing Your Hypotheses

Often times to reach product market fit you need to validate multiple hypotheses around the product, market, model, and distribution. I cover this in the 4 Fits Series. But even within each fit (especially product) there tend to be multiple hypotheses to get a new idea to work. The trap is not thinking about sequencing. You need to know what is the most important hypothesis to de-risk first? What I often I see instead is validating the easiest hypothesis rather than the most important hypothesis first which can lead to a false feeling of validation.

The Feeling of Product Market Fit

I've done 4 different startups now around initial product market fit.  Even though I'm more comfortable with numbers, I believe there is a feeling of product market fit.  In my first 3 startups, there was not initial PMF out of the gate.  We had to really work for it.  With Reforge, it was a different story.  There was obvious PMF from the beginning.  Interestingly, I think my first three experiences biased me in too conservative of a direction and if I had to do it all over again, I would have been much more aggressive in the first two years of Reforge. But more on that in a future quick take.

Related Posts: The Never Ending Road to Product Market Fit, Why Product Market Fit Isn't Enough, Market Product Fit, Not Product Market Fit

Defining Anti-Persona's

Note: Thanks to Behzod Sirjani (Fmr Head of Research & Analytics at Slack) for his thoughts on this. Learn more from Behzod in Reforge's Advanced Customer Insights program.

One of my favorite tactics to drive clarity is to explore the opposite. When it comes to strategy, initiatives, target audience that means not only defining what you are doing, but what you aren't doing. The contrast of the two provides a more complete and clear picture of where teams should be focused.

When it comes to thinking about your target audience that means defining anti-personas. A Reforge Member recently asked in the community the best approach to defining an anti-persona? Behzod Sirjani (Fmr Head of Research & Analytics at Slack) and I chimed in with some thoughts.

Anti-Persona's Are People Who THINK They Are Your Target Audience, But Aren't

When you define an anti-persona, stating the obvious is not helpful. For example, if I'm a new SaaS tool for marketers, an anti-persona of VP's of Finance is not helpful. The team knows that isn't who they are targeting (hopefully).

Anti-personas instead are people who think they are in your target audience but actually aren't. That means, they look similar but have some variation of the problem that makes your product not a fit as a solution.

For example, at one point HubSpot's target persona for the marketing tool were VP's of marketing in mid-market companies of certain types. The anti-personas were things like technical marketers, an enterprise marketer, or a small business owner.

They all generally had the problem that led them to a tool to be more effective at marketing, but they had variations of the problem that the tool did not serve well and was not designed for. As a result, these anti-personas would be high sources of low converting leads, customer support requests, feature requests that didn't make sense, and ultimately higher CAC and lower LTV.

What Do People Believe Your Product Does, But Doesn't?

Behzod added some good thoughts to this:

"From your business' perspective, there are certain things people need to know about, care about, and have the ability/agency/autonomy to do in order to be successful with your product (for a range of definitions of "success"). While you can educate people and help them understand why your product matters, there are people who won't have the right levels of ability/agency/autonomy to be a healthy, retained user. Being clear about what these abilities look like so that you can identify potential obstacles will help you define the kinds of people who are not your targets.

One way to do this from your customer perspective is to think about the various Jobs (as in Jobs to Be Done) that your product offers and think about where people believe your product does a job, but it doesn't. What are the conditions necessary for your product to be hired for that job and where do they fall short?

Brian's example of marketers who need a tool is a great one. I'll share an example of an anti-persona for Slack (based on my own POV, not the company's). Lots of people want a tool/service to engage/manage a large online community so they think Slack will be a great fit. However, most people don't pay for communities and often times community governance is quite loose. Slack has a message history limit on the free tier and no ability for people to block others or mute DMs, as well as few tools to moderate content. You could argue that given these features, communities are an anti-persona for Slack in its current form."

Concierge Onboarding - Is It Scalable?

A year ago, Superhuman did something that surprised people. They forced new customers to go through a live 1:1 onboarding call with a Superhuman rep. I wrote about it in How to Launch A Product to Maximize Growth, and Kevin Kwok talks about here in Superhuman's Acquisition Loops. Since then, I've seen a lot of companies start to copy the tactic. It led to a Reforge Member asking the following in the community: 

I am at day 14 in the superhuman's onboarding cadence. The question I have is how they made it possible to maintain this 1:1 concierge onboarding model? Or are they just pouring down money from investors until they get to the point where they have a strong user base and be able to build monetizing features?

Here is a summary of my response: 

I don't know the exact economics, but my take is that it probably is scalable. But that does not mean it is scalable for you or others. It is important to understand why it might work for Superhuman, and see if the same things apply.

Why It Might Work For Superhuman

There are a few reasons: 

1. Multiple Points of Compounding Impact - The impact of the onboarding has multiple points of impact. One, it likely increases activation rate substantially. That then flows through to not only retention but also their viral loops (WOM and their casual contact loop of Superhuman signatures). As we know, retention increases all your other metrics.

2. High Frequency, Long LTV - Email is one of those things that once you build a habit, you stick on the product a very long time. I've been using Gmail for 15 years. But, building a habit on a new email product has high switching costs.

So even if the costs of concierge onboarding increases CAC and payback period, it might be worth it in the long run due to the really long lifetime. This dynamic is not the same for other $30 per month products which might not have same the LTV dynamics.

3. Additional Products - Email is a high frequency, high habit product. If you nail it with a big enough audience it gives you a ton of leverage to expand product market fit, and launch new products on top with additional monetization opportunities.

4. Offsetting Costs - They've used the onboarding as customer feedback and support. So it's not 100% additional costs because if they weren't doing the onboarding, they would be spending time on those things elsewhere.

5. Productizing What Works - In addition they likely productize what they find works and make everything else in onboarding more efficient over time. 

What Scales For Others, Doesn't Scale For You

So for all of the combined above I think there is a reasonable chance that it is definitely ROI positive for Superhuman, and fairly scalable. It probably increases payback period, but thats ok if you have the capital fundraising strategy to go with it (they've raised $33M). But that doesn't mean it works for you and your product. Deeply understand why something might work for another product, and it will lead you to a more growth.

The Symptoms and Root Causes Of Failing Data Efforts

Crystal Widjaja's (Reforge Partner) story is pretty amazing. She joined Gojek (one of the largest super apps in SE Asia) when it was at 20K orders per day. Over the course of a few years as the SVP Growth and Business Intelligence, she helped it scale to 5M orders per day. To give you a sense of that scale, Gojek completes more daily food orders than Grubhub, Uber Eat, and DoorDash combined plus more trips than Lyft per day 😳. She recently wrote a post on Why Most Analytics Effort Fail with some counterintuitive advice. Here is the quick summary:

Symptoms of Data Issues

It's important to recognize the symptoms of data issues, and separate these from the causes:

1. Lack of Shared Language - When people describe the same experience in different ways using different terminology. This causes disconnects and time to understand and discuss data.

2. Slow Transfer of Knowledge - When it takes more and more time to make someone new fully productive. Compensating with more training, is like trying to fix a bad product with more onboarding screens.

3. Lack of Trust - When people in the org don't trust the data.

4. Not Being Able To Act on Data Quickly - The longer it takes to get the data, understand it, and discuss it, the longer it takes to act which leads to the data being used even less.

Root Causes of Data Issues

To fix the symptoms, you need to fix the root causes.

1. Tracking Metrics vs Analyzing Them As The Goal - Many teams view the goal of data initiatives is to track metrics. The real goal though is to analyze those metrics. Those two things are very different. The latter is how we make information actionable.

2. Developer vs Business User Mindset - A core principle of building any good product is deeply understanding and empathizing with your target user/customer. When building data systems many teams lose sight of who their customer is, or don't have one in mind at all - the business user.

3. Wrong Level of Abstraction - One of the toughest things to get right with tracking is the right level of abstraction on what to track. Bad tracking is when our events are too broad, good tracking is when our events are too specific, great tracking is when we have balanced the two.

4. Written vs Visual Communication - Good teams will at least have a shared dictionary that is updated with consistency. But great teams combine visual communication with the written.

5. Data As A Project vs Initiative - You have to treat your data systems as product that you constantly iterate on. Over time your product will change, your goals will change, and the business changes. As a result, if you aren't constantly iterating it results it results in the Data Wheel of Death.

Crystal goes on to share her step by step process of how she thinks about instrumenting data, along with an event tracking dictionary template here.

Fighting Off Rising CAC

A friend in VC recently emailed me to get my take if I thought CAC would increase/decrease/trend over time for a specific product and business. The question isn't if it will increase? It is when, by how much, and how you keep growing. There are a few forces I think about when thinking through this question.

Saturation - Things That Increase Costs

The primary headwind that increases costs will be one or more forms of saturation. The main types of saturation are:

• Audience Saturation - How quickly you saturate your current target audience. Most importantly, how quickly do you saturate high intent customers to a lower intent audience? As you saturate your audience or move from high intent → low intent your costs will increase due to decreasing performance (The Adjacent User Theory from Bangaly Kaba).

• Channel Saturation - How quickly does your channel or specific growth mechanism saturate? Each channel is able to access a portion of the total target audience, but not the entire audience and is therefore a subset of audience saturation. Each channel has a ceiling, and as you approach that ceiling CAC will increase.

• Market Saturation - Simply put, more competition. The more competition, tends to mean more friction to convert potential customers and therefore costs increase.

All of these will happen over time for a successful product. So the real question at the early stage is how quickly it will happen and is there enough ceiling room to get the growth you need to get to the next venture round and/or enable the below.

Model Expansion - Things That Decrease Costs or Increase Floor of Affordable Costs

One way to fight off saturation is by making model improvements. The main ways would be:

• Growth Model Expansion - You are able to sequence to another part of the growth model that has a higher ceiling, ability to access another portion of the audience, or able to do it at a lower cost. For example, HubSpot went from an inside sales model (content + sales), then layered on a VAR program that unlocked a new ceiling of growth, then layered a product led motion. This method is harder than most teams think it is and is one we teach in Advanced Growth Strategy.

• Audience Expansion - You layer on new features and use cases in the product that expand your target audience. This fights off audience saturation by expanding your target audience. This is what most early stage companies are doing going from some small segment of early adopters to the majority.

• LTV Expansion - You layer on new functionality/use cases that increase the LTV of customers and therefore increases the floor of what you can spend on CAC holding LTV:CAC the same. Taught in Product Strategy and Monetization Deep Dive.

• Capital Expansion - Raising more money and applying it to existing growth model. This doesn't decrease CAC, it actually increases CAC, but still creates growth. Expanding the pool of capital available to increases the CAC and Payback Period you are able to afford.

Scale Effects - Things That Decrease Costs and/or Increase Floor of Affordable Costs

This is the age old question of how does the company get better with scale? The common answers:

• Network Effect - As a network effect kicks in, conversion rates somewhere in the growth model will increase, which will decrease costs and/or increase LTV.

• Economies of Scale - As economies of scale kick in, costs decrease or the experience gets better in a way that increases conversion rates in the growth model which leads to decreased costs or an increase in LTV.

• Brand - If Brand is an important aspect of the category it can increase conversion rates, which decreases costs. It can also help increase retention, which extends LTV and increases the floor of the CAC you can afford.

We go deep on each of these in Advanced Growth Strategy.

Net Effect of Saturation, Model Expansion, Scale Effects

The net effect of Saturation, Model Expansion, and Scale Effects is what determines when CAC will increase, by how much, and how to keep growing despite it.

Related Posts: Your Average CAC is Lying To You

The Entertainment Value Curve

Ravi Mehta (Former CPO at Tinder, FB, TripAdvisor) is an EIR at Reforge, leading our Product Strategy program. We've been talking about product strategy in the renaissance of the B2C social category. Ravi wrote an excellent piece on what he calls The Entertainment Value Curve, explaining why TikTok is on 🔥 and Quibi is 📉. (Tweet thread here, LinkedIn conversation here).

What Is The Entertainment Value Curve?

People use social content products based on the entertainment value they derive. But what creates entertainment value? The key point is that people enjoy content differently based on who is creating and sharing.

• Entertainment Value = Social Value + Production Value

  • Social Value = The level of personal connection the viewer has with content. Measured by creation participation rate.

  • Production Value = The quality of the content relative to the highest quality in the genre. Measured by a content view distribution curve (longer tail for lower production value, shorter tail for higher production value).

Successes Live Along The Curve

There are a lot of successes that live along the curve in different combinations of social and production value. The two extremes being:

• Snapchat - From Ravi: *"Snapchat is at one end of the spectrum. Their AR lenses offer tools for anyone to be able to create fun content. Your friend's baby face isn't going to win any Emmys, but it will make you laugh. The production value is low, but the social value is very high. Snapchat takes a "creation first" approach by opening directly to the camera and encouraging people to "Send To" close friends as an integral part of the creative process. The result? A fun, low-stakes platform that puts the emphasis on sharing, not flexing." *****

• Netflix - From Ravi: "Netflix is on the other end of the spectrum. The production value of Netflix shows is very high—so high that only a handful of elite Hollywood content creators are able to achieve Netflix's bar for production value. As result, there is limited social connection between Netflix viewers and content creators. In addition, Netflix hasn’t enabled much social interaction within its products. But, Netflix content does have social value."

Failures Live Below The Curve

This is where Quibi has gone wrong. From Ravi:

"The problem with Quibi is that the product is not optimized for the Entertainment Value Curve. Quibi added format, length, and viewing constraints that made it harder to compete with the production value of Netflix content, but did not supplement those constraints with increased social value.

Quibi is a solitary experience—both online and off. The solitary nature of Quibi’s experience is reflected in the app’s design. There are no signs of life—no signals that anyone else in the world is watching. In stark contrast, Twitch, YouTube, Instagram, and TikTok are brimming with activity. A flurry of reactions, comments, and messages make those apps feel alive.

So Where Does Quibi Go From Here?

If you were the product leader at Quibi, what would you do? Ravi's take:

"Quibi wanted to create a Netflix-killer. But users love Netflix and didn't want a Netflix killer. What they did want was an mobile-native social entertainment they could enjoy and share with their friends — TikTok.

Creating new products is not just about pulling from a lengthy menu of features as Quibi did (mobile + short + vertical video + Netflix), it is about finding a new, self-reinforcing formula for creating value in people's lives.

If I were in Quibi's shoes, a few questions come to mind for their present quandary:

1. How might we… make content more shareable?

2. How might we… make the app feel more alive?

3. How might we… make the audience part of the creative process?

4. How might we… spark conversation between the audience and the A-list community of storytellers, actors, and artists Quibi has assembled?

These are challenging questions, but loaded with opportunity. Like any good Hollywood story, Quibi might have a surprise ending."

Doing The Opposite

In both Marketing Strategy and Product Strategy, there is often a question of how you differentiate. Differentiation can help you grab a foothold and grow more easily rather than grinding head to head with others. Categories tend to converge on value props, features, messaging, and even design over time. It's why every SaaS website looks exactly the same. This means you need to reinvent yourself on a consistent cycle.

A Converging Category Creates Opportunity In The Opposite

When a category converges, there tends to be an audience looking for something completely different. One of many ways to find this open space is to ask what the opposite is:

1. List out all the main characteristics/elements of the category.

2. Ask "If we were to do the exact opposite for each of these, what would that look like?"

When we started Reforge, this is exactly what we did. In 2016, there were some common themes among most professional education programs:

1. They were designed for entry-level (helping someone get a job).

2. They were cheap.

3. Open and available to anyone.

4. The typically promised big results, in a very short period of time.

5. They all advertised a "certificate" as the gold at the end of the rainbow.

This converging led to what I called the "X-minute abs" problem. Everyone was promising "Get X, in Y time." where X kept going up, and Y kept going down — until the point that many were flat-out advertising false claims (and still are).

So, to create Reforge we did the exact opposite:

1. We designed for experienced professionals (those with a job).

2. We priced the programs at the premium end of the market.

3. We actively advertised "you get out what you put in" and the effort required.

4. We required you to apply and accepted only a small percentage of applicants.

5. We did not give out meaningless certificates.

The end result is that we tapped into an audience that did want to be challenged, who did want to put in the work, and had the willingness to pay for that experience. Four years later, there are a lot of new players entering the market with similar messaging to Reforge. The space is once again converging, and as a result we need to reforge Reforge (terrible joke I know). More on that another time, though.

Navigating Reinvention

Navigating reinvention is not easy. It is hard to let go of something that has worked for you. It takes significant energy to pull away from the gravity of whats been established. But a lot of the best companies find a way. Some other great examples of this are HubSpot and Drift.

• HubSpot went from an Inbound Marketing tool → All In One Marketing Platform → All In One Marketing + Sales Platform → Growth Platform

• Drift recently went from Conversational Marketing → Revenue Acceleration Platform

The WOM Coefficient

Yousuf Bhaijee (Former VP Growth @ Eaze) and Tomas Pueyo (VP Growth at Course Hero) wrote an excellent piece on defining a metric to measure and monitor word of mouth called The Word of Mouth Coefficient. Here is my quick synthesis and some thoughts.

Core Problem: WOM is Critical But Hard to Measure and Therefore Hard To Influence

Why define and use this metric? Yousef and Tomas lay out a good argument that:

1. Word of Mouth is becoming more critical as an acquisition channel as the core 3 (Facebook, Google, Amazon) become more saturated, competitive, and less open.

2. Historically, word of mouth has been hard to measure. Most teams use some version of NPS, attribution surveys, or social listening tools to get a sense.

3. Because it is hard to measure, teams tend to ignore word of mouth, lean into more measurable channels, which leads to being less capital efficient.

The Word of Mouth Coefficient

The WOM Coefficient is pretty simple. It is the number of new organic users divided by active users (returning users + non-organic new users). If your denominator is WAU and the WOM Coefficient is .2, it tells you every 5 WAU's generates 1 new user per week via word of mouth. Their goal was three things. Create a metric that was:

1. Tied To Active Users - It was tied to active users. The premise is that active users is the predictor of word of mouth, not new users. This make sense, as retention is at the core of every growth loop.

2. Stable - The metric was stable, and therefore we could use it to forecast.

3. Able To Be Influenced - We were able to find the inputs and influence it with product and marketing initiatives.

The Data + The Key

Yousuf put together some data across three different product categories, gaming, EdTech, and a music mobile app and found in all three cases that the above was true. Active users did predict word of mouth and the metric was stable enough to use in forecasting. Here is the example showing the WOM Coefficient from three different gaming genres:

But as with any output metric, the key is can you find inputs that you can influence to improve the output. I'm interested in the next post in the series to see cases where Yousuf and team were able to do that.

Related Posts: How to Launch A Product To Drive Growth, Sequencing Target Audiences

The Universal Growth Loop

In Reforge, we spend a lot of time on Growth Loops. Self-reinforcing systems create compounding returns. They are a powerful force. While every company's Growth Loops are different, there is one that they all share in common.

The steps of the loop are:

1. The product grows.

2. That growth attracts a higher volume and quality of resources (money and people).

3. Those resources enable the company to solve new, meaningful problems that create more growth.

It sounds simplistic, I know. But it explains the core reasons why companies lose momentum:

• They don't leverage that growth to attract a higher volume and quality of resources.

• They fail to hire the right people.

• They misallocate those resources to projects that don't result in growth.

Play The Loop Forward

One of the parts of loop thinking, is to play the loop forward multiple cycles, then work backwards to what you need to be doing in this cycle to enable future cycles. Not doing this leads to some common mistakes:

• Not having an idea of the number or size of bets needed to maintain growth.

• Not acquiring the resources needed to enable new bets (hiring and raising capital take time).

• Getting to a place where you need to create growth, but not having the resources to enable the bets that maintain momentum.

All of these lead to a loss in momentum and the loop starts to reverse.

Loops Can Work Against You As Much As Help You

The challenge with self-reinforcing systems is that they can reverse on you and rather than creating compounding benefits, they instead create compounding destruction. Here's a common scenario:

• A company misallocates resources to projects that don't result in growth.

• Company growth starts to flat line.

• Flat growth makes it harder to attract the right resources.

• Which makes it harder to both maintain current initiatives and solve new problems that create growth.

So what do you do in these situations?

"Resetting" The Loop

The counter intuitive thing is you typically need to "reset" the loop which typically creates contraction before it drives growth. A hypothetical scenario. Which company would you rather invest in or join as an employee?

Most people choose Option B because the company is showing new growth and momentum which gets the positive reinforcement of the loop spinning again.

Resetting the loop requires cutting initiatives and/or people in order to focus the fire power in a concentrated area with higher return potential. But a lot of teams don't do this. They try to keep all the plates spinning in the air while also fixing whatever issues created the flat line growth in the first place. It feels like you are using all your energy just to maintain and stay afloat. This is a losing strategy. You need to cut some weight in order to move forward.

"What do we need to do now, to have +50% YoY growth in 5 years?"

That was the question the HubSpot exec team was asking that led to hiring me in late 2013 (about a year before the IPO). They were playing the loop forward. At the time, what they realized is that it was unlikely they would be able to produce that growth with just the inbound marketing product line. What they concluded was:

1. They needed to create new product lines.

2. Those product lines would take years to build to a point they were contributing meaningful revenue.

3. It would probably take multiple bets to find one that works. That included multiple product bets, but also a bet on a new growth motion.

I was one of many hires to help create the new product lines like the HubSpot CRM and the HubSpot Sales tools, as well as transition the growth model from marketing led to product led. It took the team multiple bets to get those successes. But I'd say it worked:

Related Posts: Substack's Core Growth Loop, Why Focus Wins

Choosing What To Work On

In 7 Principles to Mastering Growth, one of the principles was about choosing what to work on. What you work on impacts what you learn and the perceived impact of your work inside your org. So what you work on is an important choice. In that post I argue a great place is to work on High Impact, Low Popularity projects: 

A typical mistake is to optimize for the popularity of a project over its impact. Working on high impact, low popularity projects does a few things:

1. You learn a lot simply because the more challenging a project is the more you tend to learn.

2. Messy projects require a deeper level of understanding to untangle whatever the mess is. You instantly become an expert on something that is high impact in the org.

3. You put yourself at the center in focus. These types of projects tend to have high visibility in an organization. You are seen as a leader/savior because you are tackling something no one else wanted to touch. Everyone wants these types of people on their team."

So what are common areas in the company that fit this criteria? Elena Verna (EIR @ Reforge, Advisor @ Miro, MongoDB, others) argues in Making Monetization Your Superpower, that monetization is one of those areas in almost every company. I agree, I saw the same thing at HubSpot.

Elena points out that nobody wants to work on monetization typically for three reasons:

1. Fear or Customer Revolt - Everyone has fear changes they make around pricing and monetization will be met with a customer revolt.

2. Complicated Set of Stakeholders - Monetization changes requires you to manage a large set of stakeholders (product, sales, marketing, customer service, etc).

3. High Visibility Impact - The impact you make (good and bad) is highly visible across the org and to executives.

In other words, it's high impact, low popularity. Where others moan and groan, is your opportunity to embrace it.

Related: Monetization and Pricing Deep Dive, Free Trial vs Freemium, Monetization vs Growth? It's A False Choice

The Adjacent User

Bangaly Kaba (EIR @ Reforge, Former Head of Growth at Instagram and Instacart) helped Instagram grow from 400M to over 1B+ users. One of the core concepts he has taught at Reforge that helped them grow to that level, is The Adjacent User Theory.

"The Adjacent Users are aware of a product and have possibly tried using it, but are not able to successfully become an engaged user. This is typically because the current product positioning or experience has too many barriers to adoption for them." 

The Adjacent User is critical for three reasons:

1. Solving for the Adjacent User helps you capture the full potential of your current product-market fit.

2. The impact solving for the Adjacent User compounds over time.

3. The Adjacent User focuses product efforts in a different way than what other core product efforts are typically focused on.

You solve for the Adjacent User by:

1. Know who is successful today and why. This gives you the attributes that you can tweak to define your adjacent user.

2. Define who the adjacent user might be based on bottoms up data analysis.

3. Figure out why they are the adjacent user. In other words, why are they struggling? To do this there are four techniques Bangaly recommends:

   1. Be the adjacent user by simulating their environment.

   2. Watch the adjacent user through research studies.

   3. Talk to the adjacent user through customer discovery.

   4. Visit the adjacent user to watch them in their actual environment.

Most importanly, you need to choose which Adjacent User to solve for. Sequencing is important. There are a few important points:

1. The Adjacent User should only be different on one or two attributes. "Let's say you have 5 different vectors you can expand on. If your adjacent user definition is different on all 5 of those vectors, or even the majority, choosing that segment is a bad choice. That is like trying to hit a home run on every swing."

2. Make sure the Adjacent User aligns with the strategic direction of the company. Just because they exist, doesn't mean you should solve for them.

3. Solve in-house problems first. "These are users that are already showing up in your funnel and product vs brand new users who aren't there yet. Those that are already showing up are displaying intent, but having trouble finishing."

4. Look at the growth trajectory of the segment. Fareed Mosavat (EIR @ Reforge, Former Slack) - "At Slack, we found that both users in France and India had far worse monetization. A lot of teams would have probably chosen to solve for French users since they are a higher income audience. But users in France weren't growing and we didn't have a clear hypothesis of why they weren't monetizing. On the other hand, India was growing way faster and had a clear hypothesis as to why they weren't paying. When looking at it on a slightly longer term horizon, solving for users in India was clearly the higher ROI opportunity."

Related: Defining Target Audiences, Product Market Fit Expansion

The Impact Of GPT-3

By now I'm sure you have heard of GPT-3. But if you haven't, simply put it is an AI API released in private beta by OpenAI. You give it a a bit of text, and then it returns text (code, SQL, or writing). Justin Gage has an excellent primer here and so does MIT Tech Review here. But more importantly, you should just watch the demos like these ones:

• GPT-3 writing an email response for you based off a few bullet points.

• GPT-3 completing an essay as Paul Graham. (bold text is the input, non-bold text generated by GPT-3). Note how it mimics his writing style with decent accuracy.

• GPT-3 generating a resume for you.

• GPT-3 generating a mini application.

• GPT-3 generating a prototype in Figma based on plain text description.

Disclaimer: I do not have access to the API yet. I'm still wrapping my head around the details of how it works. All my thoughts are based on what I'm seeing in the demos, and looking at some of the documentation. So from here on out, take these thoughts with a grain of salt.

The Good

There is a lot of menial work across marketing, sales, and customer service. My guess is this is where we see the most immediate impact. I imagine the first order applications to be things like:

• Content Creation: Give GPT-3 an outline/primer, it generates V1 of a fully written blog post and copy for an email, tweet thread, LinkedIn post to promote it. It probably even generates a bunch of title ideas for you.

• Competitive Analysis: Prompt with "people love using competitor xyz because..." and GPT-3 returns a synthesized competitive analysis. Credit: Serge Doubinski

• Customer Service: Auto generate a draft of a response to someones request. Rep checks it over.

• Sales Outreach: Auto generate a unique sales outreach by prompting with customer, company, and other bullet points.

Anywhere you are writing copy, doing basic research on the web, etc GPT-3 looks like it can generate a decent V1. This is the low hanging fruit, first order use cases. There will be plenty of 2nd and 3rd order use cases.

These things might sound small, but when you add up the hours that professionals in marketing, sales, and customer service spend on these things it is quite significant. So the optimistic view is that GPT-3 eliminates these things and frees up professionals to focus on more complex and higher impact problems for the org. 

The Bad

It is unreasonable to expect it is all good. There will definitely be some bad. For years, the majority of the content marketing ecosystem has been focused on aggregating other people's content in a more comprehensive way in order to rank SEO (popularized by Brian Dean's Skyscraper Technique). We've been living in a giant game of telephone which has been driving noise up and average quality down. To make things worse, companies have a hard time understanding the value of unique content and therefore justifying the cost.

GPT-3 is going to make this 1000X time worse by drastically decreasing the cost and friction. One key will be if/how Google deals with SEO ranking. Compounding returns in content marketing stem from ranking in SEO. So even if you produce unique content repeatedly, if you don't rank, it is hard to make the ROI equation work.

Moving From Execution → Complex Problem Solving

Even if it turns out that GPT-3 can't do the above things with decent quality, the message is clear, that AI with the ability to do so is extremely near. What that means for a lot of professionals is that execution starts to get commoditized, the real value will be placed on those who can solve complex problems.

Generalist vs Specialist

7 years ago (damn I'm old) I wrote How To Become A Customer Acquisition Expert. It has been one of my most popular posts where I defined what a T - Shaped Marketer looked like. I need to do a massive update, because there is a lot that I consider wrong in that post now after seeing a lot of people mis-apply it.

The most common mis-application is that it leads people to thinking the best approach early in their career is to be a generalist - know a little about every channel. Or even worse, thinking that they are an expert in every channel. It would take a life time to become an expert in every channel, so if you come across those who say they are...run in the opposite direction.

Being A Generalist Does Not Maximize Your Career Path

But being a generalist almost never maximizes your career path. A good quote from Matt Greenberg, CTO @ Reforge and Former VP Engineering at Credit Karma:

"Impact is the most valuable thing you can grow in order to grow your career. The bigger the problem the bigger the impact needed to resolve it. In our career, learning to tackle the biggest problems well gets us the most personal and professional return. But the bigger the problem, the deeper the expertise is required to solve it. For simple problems in our bathroom, you can do it yourself. When the problems get large enough, you hire a plumber. As a company scales it is almost always looking to replace generalists with specialists who can do something the generalist could never do."

The Trap Of "Knowing" Something

A separate trap is the illusion of knowing vs doing. I know a decent amount around branding, positioning, and naming. I can tell you what great looks like and why. I can pontificate on examples all the day long. But creating from scratch? Oof...I struggle. It just isn't how my brain works and I haven't had enough reps at it to become great.

I hear this from others all the time. "Oh, I already know that." But have you actually tried to do it? Once you try to do it, you realize the depth of nuance that exists and how much there is to learn. There is nothing that highlights that your knowledge is actually surface level than trying to do it.

Thinking In Experiments

Even if you know how to do something well, doing it in a new context often requires revalidating your assumptions for a new industry and a new company or even just a new set of target users. This is where having an experimental mindset and a hypothesis-driven process can be so valuable. It forces you to declare your assumptions, state your reasoning, and validate them with data. Thinking in experiments isn't about A/B tests, it's a consistent and disciplined approach for solidifying and expanding your knowledge more quickly. As Elena Verna (Ex SVP of Growth @SurveyMonkey and Malwarebytes) likes to say:

" There's always a gap between perception and reality. Experimentation is how you collect knowledge to close that gap. If you take a hypothesis-driven approach to selecting company strategy, setting OKRs, launching features, etc, you will avoid being blinded by your own biases and false assumptions."

Crossing The Marketing Leader Canyon

Last week, Mark Fiske (Former VP Marketing @ CreditKarma) and Brittany Bingham (VP Growth @ Guru, Former SurveyMonkey) published Crossing The Canyon: From Senior Marketer To Marketing Leader. If it sounds familiar, thats because I covered the product version of this a few weeks ago.

Where General Management Fails

It highlights how this canyon from senior individual contributor to leader exists in every function. Some of the key transitions you need to make are shared across functions, but some are very functional specific. This is where general management courses fail in my opinion. It might help you with the shared transitions, but completely misses on the functional specific ones that make or break your success.

Four Transitions For A Marketer To Make

For marketing, Mark and Brittany covered four key transitions:

1. Being an expert in one channel → Building a strategy across all channels.

2. Being good at your job → Guiding others to be good at theirs.

3. Focusing on your area → Collaborating across functions.

4. Playing an instrument → Conducting the orchestra.

Being An Expert In One Channel → Building A Strategy Across All Channels

This transition stuck out to me, because it is where I see most of the friction happen. Most marketers start their career by becoming an expert in a single channel (content, paid social, lifecycle, etc). To be a leader, you have to manage a strategy across multiple channels. The problem - what you learn in one channel doesn't apply to other channels.

This is where most strategies fail and team friction occurs. Performance marketers don't understand the role of Brand (and vice versa) is the most common manifestation of this, but it occurs in many other forms.

You need to quickly understand what bad vs good vs great looks like across all channels, how they all come together in a cohesive strategy, and manage the intersection of all them for your team. Thats why it is better to be generalist early in my career, right? No....(see next quick take)

Free Trial vs Freemium and Other Monetization Decisions

Free trial vs freemium is a common question for subscription companies. I recently saw some advice given on how to make this choice - if you want revenue quickly, go with free trial and if you want users quickly, go with freemium.

This is bad advice. Framing the decision this way pits monetization vs growth, and as Elena Verna (Miro, SurveyMonkey, MongoDB) and Dan Hockenmaier (Basis One, Thumbtack) point out, thats a false choice. The question you need to answer when making monetization decisions is, what model will create the most long term healthy customers? It is not about what you get now. It is about what model helps you create the best for the long term.

An Example From HubSpot

In the first 9 or so years of HubSpot's life, the free trial was one of a few key lead touch points. It actually performed worse than the other touch points. Why? Because the friction to experience and understand the value prop was so high. Had the free trial been pushed, there might have been more free trials and customers, but they would not have retained as well. The thing that created the most long term healthy customers was getting someone on the phone and showing them the product in context of what they were trying to achieve. The price point of HubSpot and other aspects of the business supported this motion.

Over time, the company has shifted to more of a freemium model. But that transition has been in progress for at least 6 years. It took developing new products, developing new lower friction use cases on current products, new muscle on the team, new pricing, and much more.

How To Answer Monetization Model Questions

Answering the free trial vs freemium question (and any question on your monetization model) comes down to having deep hypotheses on:

1. Your use cases.

2. Your growth model.

3. Your costs to support the model.

You monetization model is one piece in a multi-piece puzzle, and trying to answer questions with out understanding the other pieces leads you to flying blind. When you understand all the pieces, you can answer important questions like:

• What is the friction for my customer to experience and understand the value prop?

• What model and experience get someone over that friction the best?

• Where and when is it best to place the friction of my monetization model in my growth model?

• What indirect value would free users provide over the long term?

• Many more..

Monetization is a very hard thing to work on for many reasons. Get it right and it acts as a tailwind. Get it wrong, and it will be a strong headwind. With the shift in focus from user growth to revenue growth across the tech industry, I'm seeing more and more companies looking to answer these types of monetization questions, which is why we built a program for it at Reforge.

Substack's Core Growth Loop

The key to any fast growing company is having one or more compounding growth loops. In a previous quick take, I wrote about why good creators should never post on medium. As part of that I mentioned why I think Substack (or something like it) will take over Medium. That triggered a bunch of questions on why.

Substack's Primary Growth Loop

Substack's primary growth loop is what we call a User Generated, User Distributed content loop within Advanced Growth Strategy program.

1. New Creator Joins

2. Creator Creates Content

3. Creator Distributes That Content

4. Other Creator's See Creator Is Using Substack

5. New Creator Joins

Substack has other loops, but this is their central one.

Why Does This Loop Work For Substack?

There are plenty of other options and places for good creators to create. So why is this loop working for Substack? The nuances of what makes a loop work in one situation, but not another come down to what lives beneath the steps of the loop. This loop works for three primary reasons:

1. Good Creators Consume Other Good Creators

   The title says it all. If I'm a quality creator, I consume the work of other quality creators. That makes the connection between the distribution and new creator steps work.

2. Substack's Presence Is Just Enough

   A key is that the emphasis of the experience is on the creator's brand, not Substack's. But that doesn't mean Substack's presence is hidden. This is key. Substack's brand is just present enough that other creators recognize what the original creator is using, but it isn't so present that it turns creators away. They do this through things like the url (creator.substack.com) and "powered by" branding in emails and on the site.

3. Substack Addresses The Why Of The Main Constraint

   The key constraint in this loop is getting new good creators to choose their platform. If that happens, the rest of the loop takes care of itself. Substack addresses the key motivations of the creators:

   • Ability to own their audience.

   • Easy ability to monetize their audience.

   • Reduced friction of creation and distribution all in one.

   • Emphasis of experience is on the creators brand, not Substack's brand.

If you'd like evidence of this, look no further than this twitter thread from Matt Sherman who just switched from Medium to Substack:

"In the 3 months since I've been very active on Substack, I have made more money than I have on Medium in the entire 5 years I was writing on it. I have a quickly growing emails list whereas before, none of the metrics were mine."

Substack's Monetization Model - Accelerator or Hinderance?

Substack takes 10% of a creator's subscription revenue. It's not yet clear to me if this will be a long-term enabler or hindrance to its growth model. One thing that we emphasize in the Monetization program is that your model enables or disables your growth loops.

For Substack, the percentage of revenue as the model enables a few things:

1. Low CAC Loop - The model enables the loop above. It reduces friction in converting to a new creator (nothing to pay upfront). But also enables free newsletters which drive the loop even more.

2. Aligns w/ Outcome - The best pricing models align as closely as possible with the outcome of the user. This is important for a variety of reasons, one of which they are able to spend more supporting a creator as that creator grows.

But once you get to a certain scale, that 10% becomes a pretty big barrier for creators because Substack fees become more expensive than running something like Ghost or piecing together your own solution using a CMS + ESP + Paywall. I've talked to a few creators making tens of thousands of dollars per year who are already questioning the 10%. This could create a 'graduation' problem for Substack where their most successful creators eventually leave.

Sequencing Loops, Discovery, and Bundling

One thing that Substack probably has to think about is combining this initial success with some additional form of a growth loop that provides defensibility and justifies the 10% as you scale. This typically comes from some type of network effect. To be a platform rather than just a tool, Substack needs to answer the following question: as another new creator joins the Substack, how does it become better for all the other creators as well?

One value prop they could enable is discovery. Creators would then choose Substack not only for the reasons above but also because they also drive more subscribers and revenue than building your own platform. They already do this a little with their leaderboard.

A different way to enable this would be through enabling features like bundling. If I'm on Substack, I can easily bundle my newsletter with other Substack creators to drive more subscribers and revenue for all of us. But if I don't create on Substack, then that becomes harder. I've already seen some creators doing this manually, but my guess is that a bundling feature on Substack would remove friction and enable far more creator bundles.

There is a lot more they can do. But alas, I'll keep this to a quick take, and not an essay :)

Product Market Fit Expansion - Why Us?

As I've gone throughout my career, I feel like I can attribute the source of most problems to one of two things (or both):

1. Misaligned Incentives

2. Applying the wrong tool to the problem.

Casey Winters and Fareed Mosavat wrote about the second this past week. I think Fareed's quote sums it up well.

"One of the most common conflicts I've seen is when product leaders try to apply a single development process, measure of success, and strategy to all product work. For instance, while some of the steps can be similar, Growth and Feature work are fundamentally different and a lot of energy is wasted trying to make them all fit into the same process, success metric, and approach." - Fareed Mosavat, former Director of Product at Slack

They detail out that after product-market fit, there are four types of product work that product leaders need to continually evaluate. Each with their own process, measure of success, and set of strategies.

The 4 Types Of Product Work Beyond Product Market Fit

• Feature Work - Creating and capturing value by extending a product's functionality and market into incremental and adjacent areas.

• Growth Work - Creating and capturing value by accelerating adoption and usage by the existing market.

• Scaling Work - Focusing on bottlenecks to ensure the team can continue to move forward and take on new levels of feature, growth, and product-market fit expansion work.

• Product-Market Fit Expansion - Increasing the ceiling on product-market fit in a non-incremental way by expanding into an adjacent market, adjacent product, or both.

The intersection of all these form the strategy of the product.

Product Market Fit Expansion

During my time at HubSpot I helped build the division responsible for new product lines. We were squarely focused on product-market fit expansion. There were three choices:

1. Same Product, Expanding to an Adjacent Market - Keeping the product value prop the same but enabling it for a new adjacent market.

2. Same Market, Expanding to an Adjacent Product - Keeping the market the same, but launching adjacent products.

3. Diversification - Launching a completely new product to a completely new market.

Expanding Product-Market Fit At HubSpot

At HubSpot, we chose #2 - Same market, expanding to an adjacent product. But we didn't get it right from the beginning. One of my key early mistakes was to push the strategic direction of the new Sales product toward the prosumer market. I was ignoring our key strategic advantage: our distribution and brand among mid-market companies. We ended up course correcting and the product is successful today, but I learned this the hard way. I wrote about the full story of this here.

Why us?

The most important question for PMF Expansion is why us? You have to create something new, while leveraging a key strategic asset that you've already built. That could be brand, user base, technology, or something else. In other words, you aren't wiping the slate completely clean. You are building the best possible product, holding some set of variables the same.

More Resources and More Distribution Tempt You To Make Mistakes

Something we did get right from the start was that we carved out a small team of 8ish people, and separated them from the rest of the core functions. This allowed us to iterate fast without the constant overhead that you experience in a larger team. As we started to prove things out, we slowly added to the team. Eventually, we had enough traction with the new product to merge back into the core functions of the company.

I've seen similar efforts at other companies pour too many people or too much distribution too quickly in an effort to accelerate product-market fit expansion. Other times companies will even skip steps entirely. When you do that, it is like adding too much wood to the fire too soon, which smoothers the flame you already had going.

Take Ownership Of Your Career

Last week I moderated a discussion between Mark Fiske (Former VP Marketing at Credit Karma) and Brittany Bingham (VP Marketing at Guru, Former SurveyMonkey) on making the leap from Senior Marketer to a Marketing Leader. At one point, Mark said something I love:

"You need to be in the driver seat of your career. You need to be a hunter versus a gatherer. Don't go to your manager and say, 'I'm not getting career development.' You need to come to them with ideas – This means being very proactive about networking within your organization. I have always respected the people who ask 'hey, can I grab coffee with you this afternoon? I'd love to understand your function better."

At some point I think we should write a whole series on what it means to be in the driver seat of your career. But a key part of owning your career is not depending on the feedback of others, and instead building your ability to self evaluate. It reminded me of something Henry Ward (Founder/CEO of Carta) wrote:

"Self-evaluation is the most important skill you can teach an employee. I am happy to offer my perspective, but only as feedback on theirs. They can evaluate themselves every day, minute, and second. I am lucky if I see their work once a week. This may seem strange after years of receiving report cards and employee performance reviews. Companies (and schools) have convinced us we should be graded."

This hits home for many reasons. But the primary one is that as I got distance from my prior companies, the more I learned about what I could have done better by self evaluating what I did in those moments. Those learnings have been far more valuable than any type of manager or peer feedback that I've received (despite having had great managers and peers).

How To Build The Ability To Self Evaluate

Building the ability to self-evaluate is like any other ability. You need many purposeful reps. Here are some tips:

1. Put It On The Calendar - First, carve out time on your calendar. Every couple of months is a good starting point. You need a forcing function to build the habit around this. Eventually you won't need the calendar and you will do it as a natural habit.

2. Inputs Not Outputs - Self-evaluation isn't about whether or not you hit a goal. That is an outcome. Instead, focus on what helps produce the outcome. Those are the skills, abilities, and behaviors to achieve the outcome. Simply start by asking yourself what did I do well? What didn't I do well? Why? Is there a theme? Which one of these is most important?

3. Focus On The Good - It's important to identify what you are good at. Being in the top few percent of professionals at something is far more valuable than being generally ok at everything. A good strategy is to double down on what you are good at, and try to improve the things that were or will be major roadblocks.

4. Supplement + Validate With Outside Feedback - Supplement your own self-evaluation with outside feedback from those involved in the process. You do not need to wait for a company's performance review cycle to do this. Just go to those involved and say "Here is what I think I did well, here is what I think I could have done better. Agree, disagree, why? Anything I'm missing?" The primary purpose of this is to validate. If you are lucky, you'll get something that you missed.

This Is Not An Excuse

Let me be clear. This is not an excuse for companies or managers to have terrible review processes. Providing feedback, clarity, and purpose is critical for any healthy company. But if you don't have that at your company, it is not an excuse for your lack of progression. Your career is just that, yours. Treat it that way.

Alternatives, Not Competitors

A common question I get from people interviewing to join Reforge, is “who are your competitors?” My answer - you are asking the wrong question.

You should rarely think about competitors, because competitors (by your definition) are rarely who you are truly competing with. Most products are competing with alternatives. Alternatives are the other ways your target audience are solving the problem today.

Examples of Alternatives, Not Competitors

• Slack was not going up against Hipchat, Flowdock, and the many chat products that came before them. The primary alternative for their use case was email.

• Pinterest was not going up against iHeartThis and other early clones of Pinterest. The primary alternative for their users was cutting/pasting pics out magazines or copying/pasting digital images into document files.

• DocuSign did not compete against HelloSign and other e-signature companies. The primary alternative for their customers were pen-and-ink signatures and FedEx-ing documents.

If you focus on competitors, you are likely to make three critical mistakes:

1. You Will Lack Differentiation - When competitors look at each other, they will gravitate towards the same features, the same messaging, even the same design.

2. You Will Play Too Small of a Game - Alternatives typically have 10X to 1000X the usage of competitors. It is a much bigger ocean to fish in.

3. You Won't Understand Real Psychology Of Your Users - Most of your audience has a habit built around the alternative with very specific actions, workflows, and motivations. You need to build against those things to break the habit with the alternative and establish it with your product.

Defining Your Alternatives

So how do you find your alternatives? You should be doing three things:

1. Ask your existing customers, what problem does the product solve for them? Get it in their own words.

2. Then, go to non-customers in your target audience. Ask them "When was the last time you had this problem? Walk me through step by step how you solved the problem."

3. Then ask yourself, how do you provide a 10X experience to those alternatives.

It’s possible in step #2 that your competitors come up. More often than not, they are not the most popular answer. If they are the most popular answer, you are likely competing in a red ocean. I love thinking about strategy. We go a lot deeper on it in Advanced Growth Strategy, Product Strategy, and Marketing Strategy programs in Reforge.