Forget everything you thought you knew about CTAs, because there's a new king in town: the "Try It <Yourself>" button.

If you have a B2B SaaS product that has at least 5 steps to get to the actual end value showcased to your prospect, this is something you might want to consider.

I surveyed customers and growth folks in the B2B space to come up with this post and therefore it has taken some time to put this together.

I ran a comprehensive study across 12 SaaS companies, analyzing their homepage CTAs and conversion rates over 6 months. Here's what we did:

1. Categorized CTAs into 8 types (from "Try it yourself" to "Start a free trial")

2. Tracked conversion rates for each CTA type

3. Surveyed 132 SaaS users about their preferences and behaviors

We'll look at real-world examples, break down the psychology behind its success, and provide actionable tips to implement this.

The Problem with Traditional CTAs:

For years, the SaaS industry has relied on a familiar set of calls to action:

- "Book a Demo"

- "Start Your Free Trial"

- "Sign Up Now"

But let's be honest – these CTAs are asking a lot from potential users. They're essentially saying, "Hey, stranger! Why don't you give us your contact info, block out time in your calendar, or start integrating our product into your workflow before you even know if it's worth your time?"

It's no wonder conversion rates for these traditional CTAs often leave marketers scratching their heads.

The CTA Hierarchy:

Based on their effectiveness in getting users to that crucial "Aha!" moment, here's how different CTAs stack up:

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Enter the Hero: "Try It Yourself"

What if instead of asking users to jump through hoops, we simply invited them to experience the product immediately?

That's the magic of the "Try It Yourself" approach. You want to leave the prospect with the feeling that your B2B product is usable and not hard to use.

This CTA typically leads to a frictionless playground or sandbox environment where users can interact with a dummy product version without any barriers. No sign-ups, no credit cards, no commitments – just pure, hands-on experience.

Why It Works:

1. Immediate Value Delivery: Users get to experience your product's benefits firsthand, right away.

2. Zero Friction: By removing all barriers to entry, you're making it irresistibly easy for users to engage.

3. Ownership Bias: When users interact with your product, they begin to feel a sense of ownership, making them more likely to convert.

Real-World Examples:

Let's look at some companies that are nailing the "Try It Yourself" approach:

✅ Example 1: Check this product tour from Mixmax for a Sales team who cares about multichannel outreach - <outreach sequence product tour>

✅ Example 2. mmm.page: This website-building SaaS takes an incredibly bold approach. Their homepage features an "EDIT" button in the bottom right corner that allows visitors to instantly start editing the very page they're looking at. It's a brilliant way to showcase the product's capabilities while providing an immediate, frictionless experience.

✅ Example 3. SleepTales: This AI bedtime story for kids picked a humorous story to showcase their product. I know. I know. This is an easy product to try yourself.

The Value-Oriented Trap:

It's worth noting a common pitfall in CTA design. Many marketers try to improve conversions by using value-oriented language in their CTAs. For example:

- "Take a tour" becomes "Get a guided tour of our CRM"

- "Watch a demo" becomes "Watch a 2-minute video of CRM magic"

- "Book a demo" becomes "Talk with our growth experts"

While these might seem more compelling, they're still fundamentally asking users to commit before experiencing value. They're the same friction-heavy CTAs, just with fancier names.

The key is not just to change the button text, but to fundamentally alter what happens when a user clicks that button. The goal should be to provide an immediate, valuable experience.

Implementation Tips:

Ready to revolutionize your own CTAs? Here are some tips to get you started:

1. Build a Playground: Create a sandbox version of your product with dummy data that users can freely explore.

2. Remove All Friction: Ensure there are no sign-up forms, credit card requirements, or other barriers between the user and the experience.

3. Showcase Core Value: Design your playground to highlight your product's key features and unique value proposition.

4. Provide Subtle Guidance: Consider adding tooltips or a light onboarding flow to help users discover key features.

5. Complement, Don't Replace: Keep your revenue-oriented CTAs (like "Book a Demo"), but add "Try It Yourself" as an alternative option.

The Psychology Behind "Try It Yourself":

The effectiveness of this approach taps into several psychological principles:

Immediacy Effect: People value immediate rewards more highly than future ones.

Endowment Effect: Once people have something, they're reluctant to give it up.

Interactive Learning: Most people learn better by doing rather than by watching or reading.

Reduced Risk Perception: Trying something for free feels less risky than committing to a demo or trial.

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Conclusion:

The "Try It Yourself" approach represents a tough paradigm shift in SaaS marketing. The old-school SaaS folks will have a hard time here and would argue that you don’t capture email addresses. I am not against that but just ensure there is appropriate value messaging at the right time before you employ a simple login/email capture tactic.

While it may require some upfront investment to create an effective product playground, the potential returns in terms of increased engagement and conversions make it a strategy worth considering for any SaaS company.

Remember, the goal is to get users to their "Aha!" moment as quickly and smoothly as possible.

But here's the twist: unlike bitcoin and gold, data's value is as elusive as it is enormous.

Giants like Google, Bloomberg, and now AI companies like OpenAI have turned their data into cash cows.

Let's dive into the fascinating world of data pricing strategies, with a special focus on how Software-as-a-Service (SaaS) models and APIs are changing the game. 🏊‍♂️

But before that …… let’s start with changing the way you currently think of data.

Use these data mental models to get rid of outdated thinking

The Fallacy of Intrinsic Value

We're often told that data, by its mere existence, holds value.

This is a dangerous oversimplification.

In the SaaS realm, raw data is just that—raw. Without the right use case, even the most comprehensive user behavior data or extensive financial metrics are just expensive digital paperweights.

The Use Case Trap

SaaS companies often fall into the trap of data hoarding, assuming more data equals more value. But here's my view: unless you have a laser-focused use case, most of that data is worthless.

Your churn prediction model doesn't care about your customers' favorite colors, no matter how much data you have on it.

The Additive Paradox

Yes, data is additive.

More user profiles, more engagement metrics, more everything.

But here's what we often miss: there's a point of diminishing returns.

After a certain threshold, adding more data can lead to analysis paralysis, slower systems, and less actionable insights.

In SaaS, speed and simplicity often trump exhaustive data.

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The First-mover reality

In the cutthroat world of SaaS, if your competitor leverages a dataset to improve their product features or pricing strategy, your ability to benefit from that same/similar data plummets.

First-mover advantage is real, and in data terms, it's more pronounced than we'd like to admit.

The Lifecycle Letdown

We love to focus on the "early stage" of data assets, where early adopters reap huge rewards. But let's face it, most SaaS datasets never reach this stage. They languish in the "product market fit" stage incomplete and slow, or quickly move to the "decay stage" where they're commoditized.

The Marginal Value Mirage

The true measure of data value is its marginal impact on actions. But in our data-obsessed culture, we often mistake correlation for causation. Did your churn rate drop because of that new dataset, or was it your improved onboarding process?

The Scalability Mirage

We often hear that data value scales with company size.

But this overlooks a critical factor: capability.

A small, agile SaaS startup with a brilliant data science team can extract more value from a dataset than a lumbering enterprise with outdated analytics.

Size isn't everything; it's the sophistication of your data operations that truly matters.

The Uniqueness Deflated

Your data is unique but why? don’t chase fool's gold in the data economy.

Here's are some real world examples:

1. 🏠 The Airbnb Fallacy: Remember when Airbnb thought their host-guest reviews were a goldmine? Turns out, TripAdvisor's hotel reviews and even Yelp's restaurant feedback provided similar traveler insights. Different data, same value.

2. 🚗 The Tesla Trap: Tesla boasts about their fleet's real-world driving data. But insurance companies use simple OBD-II dongles to get comparable data on driver behavior. Tesla's "unique" data? Not so unique after all.

3. 📸 The Instagram Illusion: Instagram thought user-generated photos were their moat. Then TikTok showed that short videos could reveal the same consumer trends and influencer dynamics. Photos, videos—both just signal user interests.

4. 🏥 The 23andMe Mirage: 23andMe believed their genetic data was unparalleled. But medical records and even fitness tracker data can predict health risks too. Genetics is valuable BUT it is just one path to the same health insights.

5. 🎮 The Roblox Reality Check: Roblox's user-generated game data seemed irreplaceable. Until Twitch streaming data and Discord server analytics offered similar gamer behavior insights. Different platforms, same gamer patterns.

Let’s change the way we think about data and now look at some vehicles for data monetization.

The Most Important Rule of Data Value

It's All About the Action 🎯

Data by itself is about as valuable as a book in a language you don't understand.

The real gold lies in what you can do with the data.

Whether it's Google using search data to target ads 🎯,

Quant funds using satellite imagery for trading 📈,

OpenAI using billions of web pages to teach ChatGPT how to chat

OR

Qualtrics running surveys to show companies what their customers are thinking and feeling

APIs: The Secret Sauce in the Data Economy

But what if you don't want to build a whole SaaS platform?

Enter APIs, the unsung heroes of the data economy.

API pricing strategies

APIs can be priced for three use cases :

1) Integration

2) if you have proprietary data that is changing fast

3) Making it digestible for action by humans or AI

Here are the ways you can think of pricing API calls for data by

• Per call: Each API request costs ¢. High volume? Discounts apply! 📞

• By data volume: The more data returned, the higher the price.

• Tiered access: The basic tier gets you names, premium gets you full profiles.

The Data Quality Conundrum:

What's "Good" Data Anyway?

In the software world, quality is often about features, uptime, and bug fixes. But in data land? It's a whole different ball game.

• For quant funds, it's all about precision. One bad data point can mean millions lost. 📉💸

• For adtech, coverage is king. More audience profiles = more ads sold.

• For AI, it's about structure and cleanliness. Clean, well-annotated data makes models smarter. 🏷️🤖

• For market research, it’s quality Survey responses, it’s about real people thinking hard before filling out surveys. No bots are allowed.

The SaaS (Data wrapper) pricing

Wrapping Data in User-Friendly APIs or Dashboards - Here's where SaaS enters. Instead of selling raw data, smart companies are packaging it into software.

It's like selling pre-baked cookies 🍪 instead of flour and sugar.

Google doesn't just hand over user intent data; they wrap it in the AdWords platform.

Bloomberg's terminals aren't just data feeds; they are full-fledged financial workstations.

Why does this work? Because SaaS makes data actionable.

You're not just buying information; you're buying the tools to turn that information into money.

What is the hard part?

pricing…

I'll transform this into a unique way of describing pricing using a "Data Buffet" metaphor. This analogy compares data pricing models to different aspects of a buffet-style restaurant:

• Plate Size Pricing: Just like a buffet where you pay more for a larger plate, allowing you to pile on more food, data vendors charge based on your "plate size." This could mean:

  • More profiles (bigger plate, more customer data)

  • Longer histories (deeper plate, more historical data)

  • More fields or records (wider plate, more diverse data)

• Gourmet Tier Pricing: At a buffet, premium stations offer higher-quality items like sushi or prime rib. In the data world:

  • High-accuracy data is like perfectly cooked steak. *** A quality dimension to data always has a disproportionate pricing increment.

  • Fully structured data is akin to beautifully plated dishes

  • Annotated data is like having a chef's notes on each dish

• Service Level Pricing: Buffets vary in service. Some offer faster seating, fresher food rotations, or exclusive tables. Similarly, data vendors offer:

  • Speed (VIP seating for data access)

  • Recency (just-cooked data updates)

  • Exclusivity (private dining room data rights)

OK, enough of that metaphor. Here are the other ways to help you think about pricing

• Per feature: Want that fancy graph? Pay up! 💳

• Per action : Dynamic pricing tools that consider context and market factors. A data marketplace could develop a tool similar to Airbnb's dynamic pricing tool for hosts. This tool finds factors like market trends, competitor pricing, and customer behavior to suggest optimal prices.

• Problem-based pricing: Customers set the price they're willing to pay for specific data or outcomes. Kaggle competitions where prizes are set for the best machine learning models.

Thing to watch out for as a data challenger

Brand sensitivity pricing.

An example is if you are selling financial data and you are going against Bloomberg, then you have to factor in brand affinity and discount your data pricing accordingly.

The AI Data Appetite

Quantity is a type of Data Quality

Here's a curveball: for AI, more data often trumps better data.

It's what researchers call "the unreasonable effectiveness of data."

OpenAI didn't make ChatGPT smarter by hand-picking the best web pages; they just fed it more.

A lot more.

This changes data pricing:

Bulk discounts become crucial. The more petabytes you buy, the cheaper each gig. So now there is a rush for companies like OpenAI, Claude and Anthropic to scale the scraping and annotation of ALL public data. Their competitive advantage is based on this

Success = Better LLMs + efficient processing + more access to data + quality loop with humans

Let’s break that down a little more.

Better LLMs are not based on just algorithmic superiority but also on how diverse pathways get added through a human collaborative approach ( a.k.a Mistral’s approach).

Efficient processing includes efficient crawling, data pipelines, efficient chips, networks, and the ability to work with small data.

More access to data means breaking down regulatory barriers, patent and copyrighting models

Quality loop with humans means working with existing data and artificially created data across multiple contexts, languages, and dialects. Even a sprinkle of quality (like deduplication or human-in-the-loop annotation) amplifies the LLM’s superiority.

The Recurring Revenue Dream

Data as a Service (DaaS) 💸🔄

SaaS lives and dies by Monthly Recurring Revenue (MRR).

But with data, especially for AI, most of the value is in the historical corpus.

Reddit's decade of posts matters more than this week's hot memes.

Solution? Build data flywheels:

• User-generated content (Reddit, Twitter) that keeps growing.

• User-tuned models (Hugging face AI models) that keeps getting better

• Business data (Nasdaq's exchange data) that updates naturally.

• Tech-driven (Google's search-ad loop) that gets smarter with use.

With these, you can offer data subscriptions just like SaaS. Monthly data dumps, API quotas that refresh, better model updates or continuous real-time feeds.

The New Frontier: Synthetic Data and APIs

Imagine an API that doesn't just serve data, but generates it on demand. That's synthetic data.

Use today's AI to generate training data for tomorrow's AI. It's a data perpetual motion machine:

• Unlimited volume (no more data scarcity) ♾️

• High quality (structured, annotated, and getting to unbiased is the ‘goal’)

• Always fresh (no staleness issues)

Pricing?

Think SaaS + API + AI:

• Tiered plans by volume and quality

• Per-generation pricing (like per-API-call)

• Upsells for specific data characteristics

Making Your Market:

Here's a secret:

the easier it is to measure a dataset's ROI, the bigger its market.

This is why finance and adtech are data goldmines - you can see the dollars flow in and out.

For SaaS and API data products:

• Offer analytics dashboards. Show customers their ROI. 📉

• Integrate with their existing tools. Make value visible.

• Case studies and benchmarks. Make your data's impact legible.

The Final Frontier: Data Governance and Compliance 🏛️

As data becomes more valuable, governance becomes critical. It's like installing a safe when you strike gold.

SaaS and API implications:

• Compliance as a feature. GDPR, CCPA-ready tiers.

• Auditable APIs. Log every call, every data point.

• Data provenance tracking. Know your data's "family tree."

Wrapping Up: We've come full circle with Data

Just as SaaS revolutionized software by making it a service, data is being transformed into a service through smart pricing strategies, APIs, and AI-driven innovations.

• SaaS wraps data in usable, priceable packages. 🎁

• APIs make data consumable and scalable.

• AI turns data scarcity into data abundance.

In this new world, data isn't just the new oil or the new gold. Data is the new software. And just like software ate the world, data — priced right, delivered right — is set to do the same.

So, whether you're sitting on a data goldmine or building the next data-driven unicorn, remember: in the data economy, the right pricing strategy isn't just smart business. It's your rocket fuel. 🚀

The Problem with Reactive Product Ops

There are a lot of shadow ops activities in a product organization.

Many product teams and product ops teams start by focusing on small, tactical changes. While streamlining documentation or improving collaboration might seem helpful, these efforts often fail to deliver lasting value. A reactive approach can leave product managers still feeling overwhelmed and frustrated.

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The Shift to Strategic Partnership

To become a strategic partner, Product Ops needs to focus on the bigger picture. This means understanding the product vision, company goals, and the needs of your customers. By asking better questions and focusing on initiatives that deliver customer value, product ops can truly empower product managers. You impact product culture in a positive way.

Building Your Product Ops Strategy Stack

A strong foundation for product ops is built on a well-defined product ops strategy stack. This framework mirrors the product strategy stack, with five key elements:

1. Vision: Define the product culture you're aiming to build. What does great product management look like at your company?

2. Department Strategy: Align your product ops strategy with the overall product strategy. How can product ops support the product roadmap and goals?

3. Product Ops Strategy: This is the core of your product ops stack. How will you achieve your vision? This will involve defining your operating model - I am a big fan of embedding and creating loops.

4. Roadmap: What user problems do you need to solve to achieve your strategy?

5. Goals: How will you measure success? Define clear goals to track whether your product ops strategy is having a positive impact.

Feedback loops in your company and how Product Ops can help.

Product Marketing Loop

By facilitating smooth communication and sharing of resources with the Product Marketing team, Product Ops helps maintain a unified direction across the organization.

This alignment is essential for turning “LEARN” marketing moments into revenue opportunities through coordination with go-to-market (GTM) teams.

Taking the learning from these “positioning” tests can help the product team considerably in terms of ensuring that they are building and releasing the “right” features.

I mention in my framework , that building offers is a great way to gauge interest and this kind of approach helps reduces risks in terms of pricing and prioritization.

Sales Feedback loop

“I don’t have visibility into the roadmap”

that’s a usual complaint from the sales team.

The product ops teams ensures that sales teams are well-informed about product functionalities, updates, and best practices, enabling them to sell more effectively and confidently.

One of the things I enabled Product Ops facilitate are regular meetings such as After Action Reviews and Tactical Pauses.

To set the stage for effective AARs, leaders must first create a climate of transparency, selflessness, and candor where team members can challenge current ways of thinking and performing., where sales and product teams can discuss customer objections and situations, review sales results, and plan for the coming quarters

Customer Support Loop

Product Ops ensures that customer support teams are well-integrated with other departments. The ability to quickly communicate a planned action and ETA to a customer makes a big difference to the support folks.

A usually underutilized area is feature adoption. Product Ops works with support and success teams on coaching them to drive adoption around new functionalities. By ensuring that customer support teams are well-informed about product updates and enhancements, Product Ops helps in promoting new features to customers, thereby driving product adoption.

Product Management Loop

Other than helping with synthesizing feedback and planning releases, Product Ops can offer several benefits. The top benefits are streamlining processes and enhancing communication, there are 4 things to watch out for.

1 . Do not add unnecessary complexity

2. Do not emphasize processes over learning

3. Focus on reducing unnecessary silos

3. Align with the product strategy

Here are some examples of questions which a Product Ops team should be able to assist with:

1. When is a feature ready for development to start working on it?

2. How do we prioritize objections from sales teams?

3. How can I interview an Enterprise user who is using feature X?

4. Which product metrics are relevant to the product marketing teams?

Summary : ProductOps : the pillars of success

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For your billion dollar startup, the four pillars are shown above. The key benefits are :

Revenue Opportunities: Product ops can turn product updates into revenue opportunities through seamless alignment with go-to-market (GTM) teams and by arming customer-facing teams with the ability to channel the voice of the customer back to the product

Operational Optimization: Product ops is an operational function (with a strategic bend) that optimizes the intersection of product, engineering, GTM teams and customer success, which can be instrumental in maintaining internal alignment and driving tangible results for fast-growing organizations

The SEO community has been abuzz with discussion and analysis surrounding a recent leak that is believed to be one of the most significant in Google's history.

The leak reportedly contains details on over 14,000 ranking factors, including the use of domain authority, click data, and Chrome browser data. This article delves into the key takeaways from SEO veteran Rand Fishkin's analysis of the leak, exploring its implications for website owners and SEO professionals.

Key Findings from the Leak

• Click Data Matters: The leak confirms Google's use of click data to influence rankings. Metrics like click length, pogo-sticking (rapid back-and-forth clicks), and click origin (desktop vs. Chrome) are all factors. Google employs various methods to filter out irrelevant clicks while valuing those indicative of user satisfaction.

• Chrome Data Integration: The leak suggests Google utilizes Chrome browser data to gauge user behavior and website popularity. This includes the number of clicks on a site's pages within Chrome, potentially influencing sitelink selection (additional links displayed under a search result).

• Link Quality Assessment: Links are categorized based on click data. Links from high-engagement pages (e.g., Forbes article with high clicks) are deemed trustworthy and pass ranking power. Links with low clicks are ignored but don't necessarily penalize a site.

• Brand Dominance: Brand recognition remains paramount. Google employs various techniques to identify entities and prioritize their associated websites and social media profiles. Building a strong brand beyond Google Search is crucial for organic traffic.

• EAT (Expertise, Authoritativeness, Trustworthiness): While EAT is a known ranking factor, the leak sheds little light on its specific weight. Author identification suggests benefits for building online authorial influence, but the precise role of EAT in ranking remains unclear.

• Content and Links Secondary with Clear User Intent: When user intent is demonstrably clear based on navigation patterns (e.g., consistent clicks to specific pages), content and link strength may become less critical.

SEO Implications

• Focus on User Experience: Prioritize creating high-quality content that engages users and fulfills their search intent. Monitor user behavior metrics to understand user needs and optimize content accordingly.

• Brand Building is King: Invest in strategies that build brand recognition beyond Google Search. Utilize social media, public relations, and influencer marketing to solidify your brand presence.

• Content Quality Still Matters: Despite user intent playing a significant role, valuable, informative content remains crucial for attracting and retaining users.

• Focus on Link Quality: Pursue backlinks from reputable websites with high click-through rates. Building trust and authority through genuine content creation is key to acquiring valuable backlinks.

• Stay Informed: The SEO landscape is constantly evolving. Remain updated on the latest SEO trends and best practices to maintain optimal search engine visibility.

Conclusion

The Google leak offers valuable insights into Google's ranking algorithms. While specific details remain elusive, the leak emphasizes the importance of user experience, brand building, and creating high-quality content that resonates with your target audience. By focusing on these core principles, SEO professionals can effectively navigate the ever-changing search engine landscape and achieve optimal organic traffic growth.

Remember that pre-meeting scramble? Craving a lifeline between briefings and client calls? Me too. Back in my Goldman Sachs consulting days, the Toronto-NYC grind was fueled by two things: ambition and a reliable stash of Snickers.

Those weren't just candy bars, folks. They were a marketing idea in execution.

Snickers' "You're Not You When You're Hungry" campaign wasn't just catchy, it was a cultural phenomenon. Remember the hangry meltdowns of celebrities like Elton John and Robin Williams? Pure genius. And the results? Sales skyrocketed by a whopping $376 million in just two years. Talk about a return on investment.

Snickers has a long history of using the "Jobs to be Done" approach.

Back in the 90s Olympics, that husky voice crooning "There's a hunger inside you... Snickers satisfies" wasn't just a jingle. It was an emotional trigger, a messaging based on a positioning anchor that cemented Snickers as the go-to solution for hanger pangs.

Snickers uses their highly recognizable brand but with a twist — putting the job being done right on the wrapper.

Jobs to be done positioning anchor

Consider a few jobs you might find you need to do below and fill in the blank with the first brand you’d hire to do them:

• You need to blow your nose: ______

• You need a serious energy boost: ______

Very likely, Kleenex and Red Bull.

By aligning a JTBD with a brand, the customer relies on mental shortcuts.

Positioning Anchors based on strongest to weakest brand association

1. Jobs to be Done (Emotional Dimension): This taps into core desires and connects with your audience on a deeper level. Think about how Snickers uses humor to address hanger. It's a strong emotional shortcut.

2. Comparison Anchor: This can be effective, but only if you compare yourself to a well-established, relevant brand. Saying you're "2 times better than Acme Corp" might not mean much unless people know and trust Acme.

   1. "We are the Uber for [Your Niche]": This can be a catchy way to explain your service, but it relies on the audience already knowing and liking Uber.

3. Jobs to be Done (Functional Dimension): This focuses on the practical uses of your product.

I don’t think I have to explain why an emotional dimension type positioning ranks first here when you look for strong brand associations.

The comparison anchor is strong only if you anchor against a strong brand name.

2 and 3 above are interchangeable depending on how strong your brand is already. For example, if you are Kleenex, where you are already a strong brand you can use a JTBD that is of a functional dimension.

Startups: Forget Snickers, Focus on Functional Anchors First

Let's face it, building brand awareness like Snickers takes time and resources. Most startups in the 0-5 million dollar range are still laying the groundwork. For them, clear functional anchors are key.

Think of it like this: emotional connections are lighthouses, guiding customers. But a strong ship (your product) is needed to navigate first. So, prioritize messaging that clearly communicates what your product or service does and how it solves a problem.

Functional Anchors First

Here's the ranking of positioning anchors for customer acquisition, tailored for startups:

1. Jobs to be Done (Functional Dimension): Aligning to a JTBD with a functional dimension aligns with a problem that the audience needs to solve. Understanding the audience mindset here ensures that your messaging is more prospect-oriented.

2. Comparison Anchor (Use Strategically): Comparing yourself to a known brand can work, but only if that brand is relevant.

3. Jobs to be Done (Emotional Dimension)

By understanding this hierarchy, you can craft messaging that resonates with your target audience, leading to stronger customer acquisition.

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Your startup’s homepage is prime real estate.

It’s the first impression you make on potential customers, and you only have a few seconds to grab their attention and convince them to stay and try your product. That’s why your homepage hero messaging – the big headline, image, or video at the top of the page – is so critical.

It should immediately tell visitors what you do and why they should care.

But how do you craft messaging that resonates with different audience segments, especially as you are trying to find product market fit?

Understanding your audience's mindset

The image outlines three different audience mindsets, each requiring a different approach to messaging:

• Problem-Aware Time-sensitive Audience: This audience knows they have a problem and are actively looking for a solution. Your messaging should focus on how your product solves their pain points. Use strong verbs and highlight the key benefits that differentiate you from competitors.

• Solution-Aware High-inertia Audience: This audience is aware of solutions like yours, but they may be using a competitor’s product or they may not be convinced that they need a solution at all. Your messaging should focus on why your solution is better. Emphasize unique features and benefits, and quantify the value proposition with numbers or social proof.

• Unaware Skeptical Audience: This audience may not even realize they have a problem that your product can solve. Your messaging needs to educate them about the problem and how your product can improve their lives. Use clear and concise language to explain the problem and pique their interest.

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Tailoring Your Homepage Messaging

The image provides a framework to tailor your homepage hero messaging to each audience mindset:

• Problem-Aware Time-sensitive Audience:

  • Market situation: Crowded. Your competitors are trying to solve this problem because there is money to be made.

  • Emphasize: "We solve this problem well with a difference."

  • Example: "Tax filing is due in 16 days. Tired of manually tracking your expenses? Our budgeting app does the tax filing for you."

• Solution-Aware High-inertia Audience:

  • Market situation: High inertia. Prospects are not willing to change.

  • Emphasize: "It's worth solving because of this 10x benefit."

  • Example: "Do you travel a lot but don’t budget well? We save 23% on international transactions."

• Unaware Skeptical Audience::

  • Market situation: Skeptical because there is a night and day difference between what your prospects do now and what you have to offer.

  • Emphasize: "Let me show you how this customer solved a similar problem."

  • Example: "Never realized you needed an automatic budgeting app? See how our customer went from financial stress to financial freedom."

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By understanding your audience’s mindset, you can create homepage messaging that speaks directly to your audience and helps your startup achieve faster product market fit.

Fact: According to a study by the Product Marketing Alliance, companies with dedicated product marketing teams experience at least 19% higher revenue growth compared to those without.

Uncovering the gems of Differentiation

Unveiling the subtle nuances and invisible advantages that resonate with a target audience is not an easy task. After talking to prospects it’s important to communicate what resonates with your target buyer. This is an ongoing exercise because both the market and your product change over time.

Visualizing the Invisible

I can increase a product's perceived value by 10% to 30% with this ONE tool. It's called the Wardley map. It may look messy at first but - trust me - it works wonders.

A Wardley map is a tool I use for strategic planning and visualizing the competitive landscape within an industry or domain. It can help with product positioning in the following ways:

1. Mapping components: A Wardley map breaks down a product or service into its various components, highlighting their dependencies and relationships. This visibility helps identify the core components that provide competitive advantage and differentiation.

2. Understanding product or service evolution: Wardley maps show the evolution of components from genesis to commodity over time. This helps determine which components are ripe for commoditization and which ones need to be the focus for innovation and differentiation.

3. Competitor analysis: By mapping out competitors' offerings on the same map, it becomes easier to identify areas of direct competition, potential threats, and opportunities for differentiation or disruption.

4. Value chain analysis: Wardley maps can reveal the flow of value within an industry or for a specific persona (see below), helping organizations identify their position in the value chain and potential areas for capturing more value.

5. Strategic play: Based on the insights from the map, product marketers can devise strategic plays or moves to improve their competitive position, such as investing in specific components, forming partnerships, or exploring new markets.

Here is an example of a Wardley Map I recently did at Pathway where we are building a contract risk management tool on our AI pipelining platform

As you will see above, the user interacts with a RAG application. At the time of writing this post, these types of applications are becoming a commodity.

As a product marketer, you have to work very closely with the product team and ensure you understand what the offering is and how you break that down into a value chain.

The kind of questions you want to ask as part of this mapping exercise are :

1. What are the implicit expectations of a user?

2. What features do you want to differentiate on and why?

3. Are there opportunities to communicate our product's unique value proposition more effectively?

4. What components of the product should be commoditized or outsourced?

5. What adjacent markets or value chains can we explore?

6. What constraints or dependencies will affect product marketing campaigns?

7. What are the potential threats or disruptive forces in our industry?

This exercise helps a lot with leadership alignment and ensures that you invest in the right areas of your product as the market evolves.

If you have any questions about this, please feel free to reach out to me.

These highly refined datasets are meant to be used for reporting, dashboards, and AI. There is a big march (and big spend) towards keeping things auditable with a full trail and lineage of what happened before you reached this refined view of data. (On a side note: For those who know data lakes do you know why they call it the GOLD Standard? It takes a LOT of gold to build and maintain this)

The Mythical "Single Source of Truth"

The assumption Enterprises is moving forward with is that AI relies on a magical complete dataset - a "single source of truth" that integrates all business data. But this is a fantasy.

In reality, most companies have hundreds of operational systems built up over decades. A 2023 survey found Enterprise companies use over 780 distinct SaaS apps on average. With so many fragmented systems, seamlessly consolidating data is impossible.

Yet 61% of data warehouse projects aim to be that "single source of truth" that is always just out of reach. These projects incur massive costs, with the average data warehouse now costing $20 million. But the failure rate remains stuck at around 67-70%, with "lack of adoption" the primary reason.

The Elusive Dream of "Golden" Data

Like modern-day alchemists, data teams try heroically to transmute messy operational data into perfect golden datasets for AI through cleansing and ETL wizardry.

But the dirty secret is that after this complex transformation pipeline, business users take the "pristine" data and immediately manipulate it in Excel using tribal knowledge to create reports. Because sanitized backward-looking data doesn't reflect the nuances and exceptions of daily operations.

What AI Really Needs - Reality

Unlike periodic reporting, AI makes millions of decisions in real time based on what is happening right now. The algorithms require massive amounts of comprehensive, fresh, raw data from across all systems to approximate the current state of the business, customers, and operations in the field.

Studies show that AI models relying on broad raw data sources consistently outperform those relying on curated datasets. The algorithms find the signals within the noise using deep learning, without requiring perfect data.

Inverting the Data Paradigm

To enable this, architectures must invert to make data the driver, instead of an afterthought. Data must become the digital reality powering intelligent algorithms making billions of operational decisions each day.

This requires rethinking data flows to funnel events from all sources into easily accessible raw data lakes, providing a fresh 360-degree view of business activity. Combined with the ability to query historical records in-place, this powers algorithms with comprehensive timely context.

Killing the Data Warehouse Dragon

The future is not monolithic consolidated data warehouses completely separated from operational systems. We need to retire this old dragon 🐲 from our IT landscape and get your consultant bills lower along with that. Let’s reduce those unwanted bills from Snowflake and Databricks.

Instead, we must create configurable decision context-targeted datasets that provide each AI application with the specific reality it needs at scale. No more hoarding stale data gold that is useless in the real world.

Accurate Analytics is a Byproduct

This data inversion is daunting but removes the need for massive centralized data stores that become outdated dinosaurs.

It leads to an amazing upside - accurate analytics as a natural byproduct of AI-driven decisions, without endless data wrangling and shit tons of ETL code. Reporting taps into the same curated views used to run operations and engage customers.

In Summary

The allure of a unified "golden" dataset is strong but leads to failure (at least 67% of the time). To enable AI, reject the false gods of data warehousing. Embrace the difficult path to decision context and AI-ready architectures

The world of early-stage startups is a dynamic yet volatile ecosystem. Even minor missteps can derail the progress for Product Marketing Managers (PMMs) navigating this landscape. Through understanding key challenges and strategies, PMMs can steer their activities toward sustainable growth.

The Importance of Ruthless Prioritization

Scattershot approaches simply don’t cut it in the startup jungle. PMMs must ruthlessly prioritize, focusing on activities that drive results. Conduct extensive market research first and ensure your product fills a real need. Refine the offering, work closely with Product Management, and don't push growth efforts until achieving product-market fit. This alignment is your springboard for future initiatives.

Assembling a Versatile Growth-Team

Lone rangers rarely survive in startups. Instead, build a talented, adaptable team sharing your vision. Given the speed of change, seek out individuals who:

• Thrive in ambiguity and evolving roles

• Can wear multiple hats and pick up new skills quickly

• Bring complementary strengths to the table

• Are eager to learn, grow, and embrace change

This agile, cross-functional team is essential for navigating uncertainty. Make collaboration and knowledge-sharing priorities.

Let Data Guide Your Path

In startups, metrics serve as your North Star guiding growth efforts. Closely track KPIs including customer acquisition costs, conversion rates, churn, and lifetime value. Analyze the data rigorously, but avoid over-reliance on what it predicts for the future. The startup landscape shifts rapidly.

Instead, let data inform smart decisions and strategy pivots. Continuously test new approaches and embrace experimentation. Adjust tactics based on empirical learnings rather than hypothetical projections.

Yes, yes - Fail Fast, Learn Faster

Perfectionism kills pace in the startup world. Rapid test-and-learn cycles are key. Conduct minimum viable tests, collect quick learnings, and iterate swiftly. Analyze failures closely to extract insights.

While a thoughtful approach is important, don’t get bogged down in planning. Prioritize agility and action-oriented learning. Progress depends more on quickly improving based on empirical data rather than slow, incremental work toward hypothetical perfection.

Mastering Core Growth Skills

While specific activities differ across startups, honing the following skills is vital:

Market Research: Immerse yourself in understanding target demographics, buyer journeys, and unmet needs. Use this to inform positioning and messaging.

SEO: Organic search is your long-game. Produce keyword-optimized content consistently to drive sustainable traffic.

Messaging: Distill your pitch into a crisp, emotional, and benefit-driven message that sells. Align closely with Product and CX.

PR: Strategic PR in key channels and publications amplifies your voice. Craft compelling narratives and press kits.

Paid Marketing: Understand highest converting channels and target spends accordingly. Continually test and optimize campaigns.

Analytics: Become data fluent across the funnel. Analyze trends, uncover insights, and refine efforts.

UX Optimization: Identify and eliminate friction across onboarding, activations, and renewals. Smooth user flows convert.

Regardless of specifics, data, speed, and continuous learning are fundamentals.

Executives: Empower Your PMMs

While PMMs drive growth initiatives, executives play a key role too. Provide ample resources and professional development opportunities. Celebrate wins and milestones.

Most importantly, foster a culture of experimentation, data-driven decisions, and agile teamwork. Recognize the volatility PMMs navigate and support their initiatives fully.

Aspiring PMMs: Learn, Grow, Achieve

For aspiring PMMs, startups represent unmatched opportunities to drive impact. Immerse yourself in the role. Learn from the best. Continuously expand your skills.

Bring strategic vision, creativity, and a passion for growth. Thrive on ambiguity, roll with punches, and persist through hurdles. With the right tenacity and approach, you can transform volatile startup chaos into a journey of exponential growth.

The world of startups is a wild rollercoaster, but with appropriate strategies, PMMs can successfully advance their ventures amid turbulence. Master the techniques discussed here, and growth will follow.

What you see is founders at early stages and even after product-market fit play innovation Bingo, just checking boxes, copying, and marketing features without asking:

1. How is my offering different in the market segment I'm pursuing? 

2. How does my deep tech / cool tech change the category?

3. How can we differentiate, dominate or disrupt the existing category?

So, now comes the real question ……

How do you win in an established category?

👉 My take is that you need to be ODD.

ODD - Opinionated, yet Defensibly Different

When you are opinionated with a point of view that is defensible you get folks who want to support that opinion.

A strong opinion anchored in your product technology is an asset sometimes even bigger than the product itself.

An asset that you can set in motion - with stories.

Stories that get odds in your favor to win.

Here is the action plan

1. Be ODD (ODD - Opinionated, yet Defensibly Different) with your product

2. Segment the market for Early Adopters based on this ODD behavior

3. Tweak the buying process to your advantage, based on your ODDness.

Arm those beachhead buyers with messaging that reframes status quo assumptions about the problem's solutions. Build product hooks that incent switches in their purchase criteria and processes to your new model. Expand from early niche footholds into the mainstream based on momentum.

In these 3 steps - Be ODD, segmented market, buying process tweaks - you have a powerful framework to transform a category to your vision. The market comes to you rather than the other way around.

After working with a few startups on this dilemma, I've developed an easy-to-remember checklist to determine when you're truly ready: A.B.O.U.T.

A is for Adopters. Before hiring sales, you need a small group of engaged users who love your product. For B2B startups, strive for at least 5 loyal early adopters who can articulate the key differentiators and value. Their feedback is gold.

B is for Bandwidth. Do you have time to support the new salesperson? Plan to join sales calls for the first 2 months after hiring. You can’t avoid founder-led sales. And build a pipeline of at least 50 qualified prospects to hand off. If you can't devote this effort, it's too soon. Use these calls to coach and also get feedback.

O is for Offers. Craft compelling offers that address each buyer persona's needs and convey unique value. Be transparent with pricing but be ready to tailor pricing and positioning. Avoid generic one-size-fits-all pitches.

U is for Usage. Analyze product usage metrics with early adopters. Low engagement signals bigger issues to address before scaling sales. Let data guide your timing. Make sure that the product is easy to understand and easy to try.

T is for Templates. Standardize sales collateral based on early wins. Document what works to make onboarding smooth for new hires. Let them build from successful templates versus starting from scratch.

The A.B.O.U.T. checklist helps avoid prematurely hiring salespeople before you’re positioned for success. Get the timing right and your first rep can propel growth. Move too soon or late and it becomes an uphill battle. Use these five elements as your guide.

Here is another post about what to do at different stages of your startup growth

When you start accurately outlining an ICP, it becomes your North Star. However, you need to have conversations next with your buyer persona. It fine-tunes your targeting, amplifies your conversion rates, and most importantly reduces what I call the scatter matter. Scatter Matter is “stuff” you introduce into a resource-constrained startup that makes people do different things which feels good but does not move them in the right direction.

Here are two categories of questions (which 99% of startups have a hard time answering) when they refine their ICP

Where does your Buyer Persona really want to go?

Most buyer interviews focus on what they are doing now and what are they solving now.

This is where you have to ask questions that are a little more on the “strategic” side of things. What your buyer is doing most of the time is to get things done and busy with their heads down solving problems. Depending on the day you catch them for a conversation, they might not have time to talk about the future because they are busy fighting fires.

Target state the buyer wishes to achieve

You want to catch your buyer persona where they are in the “mood” to paint the future and talk about the ultimate goals they want to achieve.

Let’s take an example: Let’s assume your buyer persona is a Chartered Professional Accountant (CPA) who runs a firm of 10 or more accountants underneath her and offers tax services. If you ask her goal it might be “to flatten the peak workloads during tax season”.

What does it take for your Buyer to get there?

Current Pains

If your Buyer has a big pain point that exists right now where you can offer a solution, you are in luck. At this point, you start documenting this (preferably in a Jobs to be done format which I will write about later). This documentation is also important for product marketing ie. explaining what customers do if you did not exist

Cost of doing nothing

If the pain is not big enough, the Cost of doing nothing is usually low. This is especially true for B2B sales in enterprises.

Let’s take an example, let’s say you have a system that produces a company XYZ’s weekly sales reports. This system runs very slowly and takes around 12 hours to produce this report. The company XYZ’s COO has figured out a workaround and has a team of support staff in India working over the weekend to ensure that they trigger a weekend job and get the report ready every Monday morning. Now let’s say you promise to reduce the pain by running this report for the company XYZ in 15 minutes automatically by this new software ABC. Let’s do some quick math.

License cost of software ABC = $50,000 annually

Cost of operating team India = $5000/month = $60,000

Cost of operating team India > License cost of software ABC

BUT, this difference is not big enough for the company XYZ’s COO to consider the new software and moreover, the company XYZ’s COO enjoys having a human to walk her through the report every Monday morning.

Cost of switching from their current way

Let’s assume somehow in the previous example, the cost of running a team was 10 times higher. Then let’s see what the components of switching costs are:

• Implementation and integration - Time, labor, and costs involved in installing, configuring, integrating, testing, and rolling out the new software.

• Data migration - Effort required to securely and successfully migrate data from old system to the new system.

• Training - Resources required to train employees/users on the new software. This includes the development of training materials.

• Customizations - Work involved in customizing the new software to match critical workflows or functionalities that existed in the old system.

• Productivity loss - Potential dip in productivity as employees learn and get accustomed to new software.

• Vendor lock-in - Time and costs associated with extracting data and transitioning away from the incumbent vendor.

• Contract termination fees - Any fees or penalties for canceling the contract with the existing vendor.

• Legacy system maintenance - Costs of keeping the old system running during the transition period.

• License fees - Costs of licenses, subscriptions, and onboarding for new software.

• Technical support - Costs of vendor technical support during and after transition.

• Upgrade hardware - Potential need for new servers, network infrastructure, and client devices.

• Project management - Internal project management costs overseeing the transition.

If I was the buyer, I would be careful before recommending a new software vendor to the other stakeholders (part of the buyers journey)

As a startup, as you put an offer together, you want to show value to your buyer along with reducing risks that she can take forward to other stakeholders who might be involved in the buying decision

This post explores the remarkable journey of Hugging Face and how they transformed from a humble chatbot startup into a leading platform for NLP and machine learning enthusiasts.

From E-bay bike seller to Chatbots to GitHub for ML

The story begins with Clem Delangue, one of the co-founders of Hugging Face, who started his entrepreneurial journey by selling ATVs and dirt bikes on eBay. After a fortuitous encounter with machine learning through a startup called Moodstocks, Clem's fascination with AI grew.

Teaming up with Julien Chaumond, they set out to build an open-domain conversational AI, which eventually led to the creation of a chatbot that could engage users in coherent conversations. Although the chatbot achieved initial success, the team realized that their true passion lay in the underlying technology rather than consumer usage.

The Big Pivot and Racing to the Cutting-Edge

Around the same time, transformer models were gaining attention in the NLP field. Hugging Face had already released parts of their powerful library as open source on GitHub, and they seized the opportunity to open-source the transformer model, which revolutionized NLP.

Transformers quickly became the most widely adopted software library for NLP applications, attracting a vibrant community of developers, data scientists, and researchers. The release of the "Attention is all you need" paper marked the beginning of the transformer era and solidified Hugging Face's position at the cutting-edge of NLP research and development.

Community-Led Growth and Open-Source Collaboration:

Hugging Face's success can be largely attributed to their strong focus on community and open-source collaboration. Recognizing the dense community interested in large language models applied to text, the founders engaged with early users and encouraged contributions.

The Hugging Face Hub, a platform for sharing models and datasets, played a crucial role in fostering collaboration and driving engagement. The company's commitment to building the best community for machine learning is deeply ingrained in its culture, with every team member passionate about working with the community.

Open-Source to Open Doors and Monetization Strategy:

Hugging Face firmly believes in the power of open-source models to deliver exceptional value. By standing on the shoulders of giants and leveraging the collective expertise of the community, they have been able to push the boundaries of NLP. While monetization is still in its early stages for Hugging Face, they have already introduced paid offerings and attracted notable customers. By tapping into the immense value created by its platform, Hugging Face aims to generate significant revenue while remaining true to its open-source roots.

The Road Ahead and Democratizing Machine Learning: With a rapidly growing team and offices in New York and Paris, Hugging Face is on the path to democratizing machine learning. Their transformative work with transformers has expanded beyond NLP to other domains like speech and computer vision. As the field of machine learning evolves, Hugging Face continues to innovate and break new ground. With their sights set on becoming the go-to platform for bleeding-edge AI, they are poised to make an indelible mark on the industry.

The journey so far:

Hugging Face's journey from a chatbot startup to a leading platform for NLP and machine learning is a testament to the power of community, open-source collaboration, and a relentless pursuit of cutting-edge technology. By democratizing machine learning and embracing the contributions of a vibrant community, Hugging Face has created a thriving ecosystem that empowers developers, data scientists, and researchers worldwide. As they continue to drive innovation and push the boundaries of AI, the future looks incredibly promising for Hugging Face and the field of machine learning as a whole.

Who is a “Ghost” Competitor?

Once you have some sort of an early ICP (Ideal customer profile) hypothesis, you want to have conversations with these prospects. After you have a dozen of these conversations, “Ghost” Competitors are the ones who never come up in these targeted conversations. However, please note that they are your direct competitors but at the early stage for you, they are just “noise”.

If the “Ghost” competitors still have your attention, ask the questions -

“Are they doing something innovative?”,

“Do they have a product or market advantage that you might consider building or implementing yourself?”. If not, move on.

What should you be analyzing?

Direct competitors who come up in conversations.

If no direct competitors come up in your conversations, you need to ask and find out how they solve the problem currently at hand. This is when you get your “alternate competitors”.

Some of these answers might be:

Oh, we solve that problem by asking Mary to format this data with the help of her summer interns

We have written these macros in excel

Our custom software works but it sucks.

Folks, you have an alternate competitor - a duct tape solution

The only thing you can do for Status Quo is to be empathetic and determine which Jenga block you can replace with the least negative effect and greatest positive result.

There will be change, but defining it in terms of how the change will benefit will be helpful. The best product marketers will give a limited number of options that fit the buyer's preferences.

Should I spend my energy chasing down prospect X with a “duct tape solution” or prospect Y who is a “status quo” ?

Answer: Duct tape solution

Why: Prospect X will have more motivation to change

Thanks for reading so far. I have also included a free competitor analysis template for you to document your strategic analysis.

This post covers the following:

1. Are you ready to bring on a Partner? - A simple checklist

2. Is the Partner going to help you with growth?

Are you ready to share the goodies? Here is a checklist

Let’s start with this PPP (Product, Price, Promotion) checklist to see whether it’s a good time to find a partner.

1. Do you have a productionized product that has delivered repeated value in the past?

   1. If it’s not hosted, can it be easily deployed?

   2. Is it easily integrated with other tools the prospect has?

   3. Is it customizable?

   4. Are the APIs documented well?

   5. Are there templates that your partner can bootstrap their use case with?

2. Is price optimized?

   1. With an indirect channel partner, can they make recurring revenue?

   2. Are your gross margins still working out after involving partner integration?

   3. Is the pricing made easy enough such that your Partner can communicate that easily internally to their decision-makers?

3. Is it promotion ready?

   1. Can your indirect channels understand the value proposition?

   2. Do you have a way to introduce an indirect channel to customers?

   3. How are you planning to do joint promotions?

What does your Partner bring to the table?

I think about the other three P’s for Partner. Practice, People, and Penetration.

1. Does the Partner have a delivery practice (in that vertical)?

   1. Can they come in and set clear direction and context within a customer’s landscape?

   2. Other than budgeting, and planning have they repeatedly shown success with clear accountability and metrics?

   3. Do they communicate obstacles, and challenges with your product implementation and plan and make changes accordingly? (A good indicator is they know how to create workarounds to help the customer tactically)

2. Does the Partner have skilled people on their staff?

   1. As part of different phases of their project, can they bring in staff who have the right experience to solve different problems?

   2. Can they scale up skilled staff depending on the demands of customers?

   3. Does the staff have a maniacal focus on making the customer delighted?

3. Does the Partner have a track record of penetrating and farming key accounts?

   1. Do they have a strong account fact sheet?

   2. Do they understand the economic values and know how to create a strategic engagement plan?

   3. Do they share war rooms with their key accounts for solving pressing issues?

   4. Do they plan to share with you different possibilities in the target accounts so that they can be well prepared for creating opportunities?

If you feel there are other items to make this checklist better, please reach out to me on linkedin

• $0 - $5M

• $5 - $10M

• $10 - $25M

• $25 - $50M

• $50 - $100M

$0 - $5M | 3 to 20 Employees

Product

• Product Market Fit - This matters a lot. Btw for developer tools, it’s called developer-market fit.

• Product Market Price Fit - This matters even more. Founders are too excited to build but not how best to monetize their product. Don’t be gun-shy about asking for money.

• GRR - Gross revenue retention is a metric you track and then work with Customer Success to understand why churn happens

• Growth loop identification and acquisition - you start looking at signups and high-level usage numbers

Operations

Doing things that don’t scale in every department. Founders have to do a lot of interviewing. After product-market fit, it is also important to identify and acquire a growth loop, and to focus on operational efficiency by finding heroes to carry the company through this stage. Burnouts are the norm with these one-person armies.

The pace these heroes drive is a key marker and those who can’t catchup to them are probably not a good fit for your company.

Your “culture” has to breathe “get it done”.

Runway is your primary concern. Profitability is always your concern especially if you are bootstrapped.

Marketing

• Nobody really knows you exist so Marketing has to do more reach outs and build connections

Increase surface area - content is the easiest one to start off on

• Try to time channels where your audience shows up once in a while.

• Go chase down that Gartner and Forrester analyst and meet them in person after you have product market fit.

• Marketing efforts should focus on building connections and using one or two channels effectively to reach the target audience

Sales

• Say yes to almost everything from your target persona. This is counter intuitive but there is no point disqualifying prospects needs at this stage. The only condition I would add is not to dilute the personas.

• Being smart with problem pattern recognition.

• Start documenting your go-to-market playbook

Customer Success

• Create a bible of hacks. Hack away at solving customer’s problems. Be creative with problem solving. You can’t depend on the product team to help solve all the problems fast enough.

• Be ready to pick up calls at unusual hours.

• Send your initial and important customers gifts. Build relationships.

$5M - $10M | 20 to 100 Employees

Product

• Product stability starts becoming a priority. Hacks have to be un-hacked

• UX talent starts focusing on areas of growth (e.g. onboarding)

• Product discovery needs more attention vs just shipping

• Growth loop investing and ongoing assessment is a new habit to pickup

Operations

• Think about scale.

There is a lack of defined processes across the organization. Process Owners are the new heroes.

• Growth oriented talent is critical. Hiring becomes front and center.

• Investing inside with strategic investors outside will be a theme.

• Founders start building their delegation muscle.

Marketing

• Marketing Ops is a thing

• Agreement on moving away from MQL thinking. MQL is an output metric and it is subject to gaming.

• Reducing the height of your funnel becomes important

  

• Serious discussion of “strategy” and how marketing “gets you there”

Customer Success

• Understanding how to layer on Account Managers

• Map organizations with pain points

• Build knowledge bases for your customers

$10M - $25M | 100 to 200 Employees

Product

• You have two problems now Product stability and Product Velocity. Product managers have to think of more non-functional roadmaps.

Friction between engineering and product folks are more common.

• The most likely cause for friction is tech debt. Engineering managers have to have tech debt burnout charts.

• Create Fast lanes. e.g. at QuestionPro, we call them HIVE - HIgh VElocity lanes

• Building “Enterprise” features

• Pricing and Packaging processes and testing is important during this stage

• Early attempt at product expansion for different ICP is needed

Operations

• Customer processes have to be well optimized (including billing, account receivables etc.)

• First round of OGs leaving. Alignment issues are usually the cause. Or they are not in the same fight as the founders.

Focus in on these two areas of Growth = Retention + Acquisition (in that order ;))

Sales

• Don’t say yes to everything

• Plan misses are getting more obvious. So go back to basics.

Focus on a steady-state funnel

• Win-loss analysis has to be done with both marketing and product

• Channel partnerships have to be considered

Marketing

• Product marketing has to build better sales artifacts.

• Move from Funnel thinking to Flywheel thinking

• Start auditing your marketing tools. Marketing automation spend is going to start ballooning and you want to start pruning your unused “lists” and reducing your overlapping capabilities.

Customer Success

The only question you ask is “What does it take to retain my customer?”

• Working closely with the product team to identify retention vectors

$25 - $50M | 201 to 400 Employees

Product

• Hire high quality engineering talent at scale. Multiple Product and Engineering leaders spanning across Infrastructure, Security, Application.

• Ongoing attempt at product expansion for different ICP

Operations

• Culture and alignment is an issue and needs budgets and metrics

  If the high maintenance employees do not give you high value fire them

• CRM strategy is a thing. PLG data and CRM have to be in sync. Attitudinal and behavior data helps with finding new opportunities.

• Business development with strategic partners

• Next wave of OGs leave. They have truly hit a ceiling or they are not happy with the directions taken.

  

Sales

Sales playbooks were a thing in the past. Getting GTM playbooks integrated with PLG is the new thinking.

• New leadership causes reseting of rhythm

• Complacency kicks in and you need a reboot of the sales organization

• New onboarding requirements for new ICP sales cycles

• Geo focused GTM requires strong geo leaders.

$50 - $100M | 400 to 600 EE

Product

• Leadership roles needs to be filled up for all product lines

Monetization is a serious game and you need dedicated leadership for that

• Complexity of mature products increases exponentially because of larger deal sizes. Product velocity was a thing of the past.

Operations

• Everything is revisited because of a lack of a clear decision framework

• Performance management with clear distinction of “A” players vs “C” players across the organization

Bloating teams needs refactoring

• The third wave of “culture pillars” are pushed out.

• Data governance needs are high so have dedicated folks for that

• Too many “internal problem” meetings. Meeting audits are needed.

If you are a senior leader, reduce your surface area

Sales

• Scaling sales teams across regions

• Multiple sales leaders and multiple hierarchies. Start reducing complexity of sales compensation models

If you think there are other points which I have to add here - connect with me on https://www.linkedin.com/in/anupsurendran/ or https://twitter.com/anupsurendran

When a company creates a developer-oriented product or codebase, other than making it functional, there are a lot of areas which need to be considered.

This is best explained through the following diagram :

Simplification of a developer workflow

The X-axis is the most important one here and actually has multiple components within it.

When you think about simplification of a developer workflow there are many opportunities if you look hard and long enough.

The workflow areas which come to mind are:

• Change management: managing and tracking different versions of code using tools like Git.

• Testing and debugging: running tests to ensure that the code is working correctly with different inputs.

• Collaboration and communication: working with other developers, product managers, and stakeholders and creating usable products.

• Deployment: moving code from development to a production environment through a CI/CD pipeline

• Maintenance: monitoring the stability of deployed code and making updates as necessary.

The one area which is not that well thought through when you think about developer workflow is the developer ecosystem

The businesses supported often varies in these development communities. However, you'll see that some systems will take a while to be replaced. Look for systems that are difficult to replace in this ecosystem. You must devise a method of integrating with those systems. Some of these systems might be challenging to interact with (ugh, imagine COBOL files or XML-RPC...)

The other large area where attention needs to be paid to a developer audience is Developer Experience. I have written an article about #dx here.

Usage Frequency

When your tool starts being part of multiple use cases, you have a higher frequency of use.

However, only having a high usage frequency does not mean that you have a good developer market fit.

This is something you have to watch out for. This is a common mistake some developer-oriented companies run into. Most likely what has happened in this case is that you did solve a problem quite well. You actually might have a first mover’s advantage. However, If you only focus in on high usage frequency only, you might not be paying attention to building up developer communities and improving the developer experience. These are much harder to do than building software and therefore ignored.

Code repositories

Let’s assume you have open-sourced your code and it’s in a code repository. As a repository becomes more popular, the number of issues and pull requests submitted by users can become overwhelming, making it difficult to keep up with and respond to them all in a timely manner. However, this is an important part of developer experience.

Your repository may rely on other libraries or frameworks that are also popular and are updated frequently. Keeping up with these changes and ensuring that the repository continues to work with the latest versions can be a challenge.

Managing your community contributions and ensuring that they align with the company’s goals and maintain the code quality can be a challenge.

Summary

Developer-market fit has challenges that are unique. As I mentioned before, understanding developer ecosystems, narrowing down developer personas, and building a thriving community are probably the hardest pieces of developer-market fit.

Two questions must be answered in order to reach those functional areas.

Question 1 : Why do you exist?

Question 2 : What would customers do if you did not exist?

Why do you exist?

The answer to this query will determine how your business tells its story. This is a branding activity. This can assist you in tying together some of the current world context and how you envision a bright future in which you discover a purpose for your existence.

The C-suite typically participates in this exercise, which is not conducted in isolation. The creator of the business (the founder) typically has certain notions about why they initially founded the business.

Example:

Let’s make things a little concrete by using an example. Even though I love to talk about SaaS and PaaS products, I am going to use a consumer product as an example here.

Dove has a large audience (women) where they have identified a context which has an ongoing conflict in the audience’s minds. They have tapped into that context and started laying out a story.

“to help women everywhere develop a positive relationship with the way they look“

The brilliance here is that this is a great guideline for Dove to know what kind of products to create and how to market to women.

What would customers do if you did not exist?

Hope you noted that I omitted the word "choose" from the previous question. What customers would likely “choose” if you didn't exist is usually a product that alleviates a problem or discomfort they are experiencing.

The scope is substantially wider when the verb "do" is used.

The word "do" can also refer to altering the way you do things to assist addressing problems.

This question triggers a positioning exercise. In Dove’s example you could choose other consumer products which women have on top of their mind.

After you get the top of the mind alternatives your potential customers might have, you have to start asking the next set of questions for product uniqueness (something which I will write about soon).