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Use these data mental models to get rid of outdated thinking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

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.

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

• $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.