The Old Way

Traditionally, handling data in a business setting goes something like this:

• Gathering Data: You pull information from everywhere—CRM tools, product analytics, marketing platforms, and spreadsheets.

• Cleaning and Storing: You tidy up this data and store it in a centralized place like Snowflake or Redshift.

• Building Insights: You then churn through this data with specialized tools. If you're code-savvy, you might use SQL; if not, perhaps a no-code tool or even good old pivot tables in spreadsheets.

• Charting: From there, you produce various charts and graphs. These visuals are supposed to show what’s working, what isn’t, and indicate next steps, often ending up in BI dashboards for viewing.

• Acting on Insights: When it’s time to act— let’s say, launching a targeted upsell campaign for enterprise customers or a specific ad campaign for a customer segment—you often need to leave these tables and charts behind. You might export a CSV or script some automation to move data into CRMs or ad platforms.

• Evaluating and Iterating: Finally, to see if your efforts are bearing fruit, you start the whole process over again, adding new analyses to your dashboards as needed.

This approach is costs more than just money:

• Details Get Lost: Switching tools can mean losing valuable insights.

• Information Gets Stuck: Each department’s data ends up siloed, hindering collaboration.

• Systems are Costly and Slow: Companies pour millions of dollars and months of engineering work into setting up and integrating these data tools.

• Actions are Delayed: Disconnected tools make swift, informed actions difficult.

All this effort and expense often just leads to more complexity, particularly because many tools cater primarily to those with technical expertise, leaving others in the lurch.

The Airbook Way

Airbook offers a full-stack, seamless approach that bridges the gap from exploring insights to taking action. Imagine a single collaborative workspace that integrates pulling data from any source, jointly exploring insights using SQL or no-code options, building dashboards for presentations, and creating workflows to activate these insights across different tools and teams. And all of this comes in one intuitive, simple, affordable solution.

Consider this scenario: You’re the head of product at a tech startup. You’ve noticed that adoption rates for a new feature are disappointing, despite initial positive feedback.

With Airbook, you can quickly aggregate data from CRMs, product databases, and more to analyze user behavior and pinpoint why specific user segments aren't engaging. You can then seamlessly work with your marketing team to launch an in-app tutorial campaign to guide users on the benefits of the new feature, all without switching tools or waiting on technical support.

Our aim is to make working with data not just more straightforward but more connected, ensuring that insights lead directly to actions that drive growth.

In our case- we’re a product-led company, and we spend our days knee-deep in data from the moment someone clicks onto our site, to each customer call, down to every issue raised to the point they make payments and everything after as well. We see patterns and we've come up with a plan to keep our customers around, not just by reacting to problems, but by being proactive when it comes to retention.

Support with Usage & Revenue Impact

Here’s a practical look:

Think about how your customers use your product. The ones logging in daily, using all the key features? They're not just users; your product is a core part of their operations. A hiccup could throw a wrench in their day-to-day—something we aim to avoid at all costs.

• List of Unresolved Tickets: We track all open tickets. Long unresolved high-priority tickets? Big alarm bells.

• Revenue Impact: Each ticket is tied to a customer. More tickets may mean more problems. We find patterns and fix them. We also look at the revenue at risk tied to each ticket. A high-revenue customer with urgent tickets gets priority to prevent major revenue loss.

• Product Usage: We look at our customers’ behaviour on the product. What are we solving for them, their use-case as well as their activity. This is crucial to understand how critical are we in their day to day operations. Highly active users facing issues get utmost priority.

List of Unresolved Tickets

We’re vigilant about tracking every ticket that comes in. It’s not just about counting them—it’s about understanding the urgency. Tickets that have been unresolved for too long set off alarm bells. These are potential crisis points that could escalate into customer departures if not addressed promptly.

Here's also where ticket severity comes in. A high priority ticket is a red flag, indicating a big problem that needs to be addressed on priority.

Every ticket is more than an issue; it's a window into the customer's experience. By linking each ticket directly to a customer profile, we can see patterns emerging. Are there recurring issues for certain users? Are specific problems tied to recent updates or system changes? This linkage helps us diagnose systemic issues that might be affecting multiple customers or pinpoint specific areas where a customer might need more guidance.

Revenue Impact

Understanding the financial weight of each ticket changes how we prioritize. A high-revenue customer facing critical issues isn't just a support ticket; it's a priority that could impact our bottom line. Below, is a chart we use to see not just the number of tickets, but their potential revenue impact. This helps us make calculated decisions on where to allocate our resources most effectively to mitigate risks.

Product Usage

The activation score is a pivotal metric, derived from product usage or behaviour. It quantifies how deeply integrated our service is into each customer's daily operations. A high activation score means a customer isn't just using our product; they're relying on it heavily, making it a critical component of their workflow.

Here’s how the understanding customer activation guides our support strategy:

1. Early Detection and Support: By monitoring product activation scores, we can identify which customers might be most affected by even minor disruptions. For a customer with a high score, our system can trigger an alert to our support team to conduct a preemptive check, ensuring everything is running smoothly.

2. Customized Communication: Customers with high activation scores receive more tailored communications. For instance, if an upcoming update might affect features they rely on heavily, we ensure they’re the first to know, along with providing detailed guides on managing these changes.

3. Priority Issue Resolution: When these customers face issues, their tickets are automatically flagged as high priority due to the potential impact on their operations. Our support team is prompted to respond quicker and with more resources, if necessary.

4. Feedback Loop for Product Improvement: High activation scores also help us gather focused feedback. Because these users rely heavily on our product, their insights are incredibly valuable for refining features. This feedback is often fast-tracked to our product development teams to help prioritize updates that will benefit our most active users.

A high score indicates high dependency, which, paired with unresolved issues, could spell trouble. This measure helps us preemptively intensify support for our most reliant users, aiming to resolve issues before they even become aware of them.

Helping CSMs Prioritize better

Monitoring our Customer Success Managers (CSMs) gives us insights into both their workload and performance. If a CSM is managing a high volume of high-risk tickets, it might indicate that they need more support or resources. Conversely, it can also highlight the effectiveness of our training programs and support structures. Balancing their workload ensures that no single CSM becomes overwhelmed, maintaining a high level of service across all accounts.

Let’s consider a practical scenario:

1. CSM A: Handles 30 open tickets with a total revenue at risk of $50,000.

2. CSM B: Handles 10 open tickets with a total revenue at risk of $100,000.

On the surface, it might seem that CSM A is more burdened. However, CSM B’s tickets represent a higher potential revenue loss.

Therefore, CSM B might need more immediate support to resolve these high-impact issues. Knowing both the number of open tickets and the associated revenue helps in prioritizing which issues to address first. CSMs can focus on resolving tickets that, if left unresolved, could result in substantial revenue loss.

This combined view helps in balancing the workload among CSMs. If one CSM is handling many high-risk tickets, it might indicate they need additional resources or support to manage effectively.

By stitching together these various data points, we don’t just react to problems—we anticipate them. Our approach ensures that our support isn’t just timely but targeted, addressing potential issues before they become critical.

Customer Priority Framework

Every customer is important but in a growing startup where resources are constrained, we need to segment our support efforts by priortization.

We use a simple method to decide who is most in need of urgent support:

• High Usage, High Revenue, High Severity: Top of the list. Immediate action needed.

• High Usage, Low Revenue, High Severity: Also crucial. They depend heavily on us, so we can’t slack off.

• Low Usage, High Revenue, High Severity: Less frequent users but big spenders. We keep them happy.

• Low Usage, Low Revenue, High Severity: We address these as we can, ensuring everyone gets good service.

Retention isn’t just on customer success or support; it’s on the whole business.

Think about it this way—every piece of the puzzle from how often someone uses our product, to the revenue they bring in, and how we handle their problems, tells a part of the story. It’s not just about solving issues as they pop up; it’s about understanding and integrating every interaction they have with our product into a broader strategy.

So, here’s how we see it: retention is a team sport. It’s not something you can shove into a corner of the customer success team's office and hope for the best. Everyone from product managers who build roadmaps, to the marketers crafting messages, to the sales folks on the front lines—they’ve all got a stake in this game.

We track how our users engage, how much value they get from us, and how effectively we solve their issues. And we make it everyone’s business to know these numbers because when one of us wins, we all win.

We have made this template ready for you to use. You can try it out here.

Before we dive in, here are some cool pictures from launch day!

We knew our product wasn’t perfect.

We also had no clue who we were really building for yet.

• Were we targeting enterprises? SMEs? Startups?

• Who were the decision-makers? The power users?

• Would anyone actually pay for this? And if so, how much?

We had read all the advice:

But we did the opposite, at first.

Going Wide, Not Narrow (At First)

When we launched, we didn’t position Airbook for just one specific type of customer. Instead, we basically said:

Our assumption was that Airbook could be valuable to many functions within SaaS businesses—maybe not today, but eventually.

With the wide range of connectors and collaboration capabilities we offered, it felt like we could appeal to a lot of teams.

Why We Took This Approach

To be honest, we knew the beta product wasn’t perfect.

It had plenty of bugs. There was no billing page, the backend was glued together in a hacky way, collaboration was broken, and we didn’t even have a proper website.

Here’s how Baby Airbook looked, btw -

We launched anyway.

Why? We wanted to stress-test the idea.

We wanted to get people on the product, hop on calls with them, and see how they viewed the potential value of Airbook.

Casting a wide net was the quickest way to eliminate people who didn’t see any value.

It also helped us identify patterns among those who did see potential (even in the glitchy MVP we had).

This way, we could start figuring out where to focus next.

The Big Questions We Wanted to Answer

From this launch, we were trying to answer a few key questions:

• Is there a real need for Airbook?

• Who’s getting interested in our product, even if just a little?

• What use-cases stood out to them?

• How did they see us being different from other tools?

• Were they willing to pay? If so, how much?

• What was the “delta”—the change—between what they were doing before Airbook and what they saw possible with Airbook?

• What’s the right way to charge for Airbook that makes sense to customers?

The Feedback Flooded In

We got a ton of feedback—some good, some not so good. Some people wrote us long emails about what wasn’t working. Others told us the product was too glitchy to use.

Some complained about the lack of transparent pricing, while others said the product was too confusing to navigate. A lot of people were just curious and signed up for fun.

But there were a handful of people who were genuinely interested and gave us some real good feedback!

They told us about their current workflow and how Airbook could save them time and money once it was more polished.

They weren’t ready to pay just yet, but they gave us valuable insights about how they were currently spending their time and budget—areas that Airbook could potentially improve.

Patterns Started to Emerge

Soon, we started seeing clear patterns from the people who signed up and provided feedback.

We learned a few things:

• Product, Analytics, Engineering, and Marketing teams saw the most value initially. (So, that became our initial focus areas)

• The ability to cross-query from multiple sources was the feature people valued the most.

• Better visualizations, dashboards, and improved collaboration were the top features people wanted.

• Very small startups (1-20 employees) didn’t see the need for Airbook yet—they were happy with Excel for the time being.

• Large companies (10,000+ employees) had built their own in-house systems. Switching to Airbook would’ve been too costly and complex for them.

Our target customer profile started to look more clear now -

• SaaS startups with 50-1000 employees in the US or UK.

• These companies were in growth mode, rapidly increasing headcount across multiple functions.

• As they scaled, the number of different data sources multiplied quickly.

• The Excel world they once relied on was now becoming frustrating and inefficient.

• They were at a point where they needed to build a flexible, scalable data architecture.

• Moving fast, they didn’t have the time, bandwidth, or resources to build a complex architecture from scratch.

• They needed something that could scale with them but wasn’t overly complicated to set up, onboard or maintain.

The Teams Who Showed the Most Interest

Here’s a breakdown of the teams who saw the most potential in Airbook, and why they got interested:

Teams that showed most interest-

• Product (PMs) - They wanted a full view of the customer journey—from initial signup (e.g., Hubspot) to activation (e.g., Snowflake) to payment (e.g., Stripe)—all in one place.

• Analytics (Product Analytics, Mktg Analysts) - They needed to cross-query data from multiple sources, build charts, and collaborate easily, all in one tool.

• Engineering - Building pipelines manually was a pain and expensive. In smaller companies, engineers were frustrated with using their time for reporting requests from non-technical teams.

• Marketing - They had tons of data sources (ads platforms, CRMs, website tracking tools) but found it chaotic to bring everything together. They struggled to justify their investments because of unreliable data.

What We Learned Post-Beta Launch

After launching the beta, we came out with much more clarity:

• Airbook is most valuable for Product, Analytics, Engineering, and Marketing teams.

• The ability to cross-query multiple data sources is our most valuable feature.

• Small startups don’t see the need just yet, and large enterprises are too complex to switch.

• Our sweet spot is SaaS companies with 50-1000 employees in the US and UK.

By casting a wide net at first, we were able to quickly learn who wasn’t a fit for Airbook, and who saw value in the product—even in its rough beta stage.

We found the signals we needed to focus on the right customer profile and use-cases moving forward.

Recently, we launched again on Product Hunt to signal that we’ve come out of Beta.

Although this one went well and we got the #1 Product of the Day & #5 Product of the Week, our first launch taught us much much more.

More on this in another post!

But at Airbook, we’re doing things a bit differently—our engineers lead the demo calls.

Sounds odd, right? A little unconventional?

Well, that’s by design.

Here’s why we think it works better and why this approach makes us a different kind of company.

Product Builders Should Be Talking about it too

People who build the product should be part of pitching and demoing it.

Why? Simple—no one knows the product like the engineers who’ve spent hours writing code, fixing bugs, and designing the system from the ground up.

They don’t just know the product—they live it.

When an engineer gets on a call with a prospect, they’re not there to just deliver a polished sales pitch.

They’re there to solve problems.

And that genuine desire to solve the customer’s issues—whether or not they buy Airbook—builds trust faster than any sales script.

It’s real. It’s authentic. And it’s unscripted.

Engineers can dive deep into how things work, why they’re designed that way, and how Airbook can solve problems in ways that standard sales pitches often miss.

The First Time We Tried It

The first time our engineer led a demo call for us was actually an accident.

We were doing founder-led sales, and I had to step out at the last minute. Our engineer, Danish, was the only one available to run the call.

He didn’t know the sales script.

I gave him a quick briefing on the prospect and the company they worked for, and then we decided to just roll with it.

Later that night, I watched the recording.

And honestly? It went way better than expected.

Danish’s deep dive into the tech was exactly what the prospect needed.

Instead of a polished sales pitch, they got a genuine look under the hood.

He answered questions no one else could, walked them through real-time use cases, and even coded on the spot to show how adaptable Airbook was.

It wasn’t perfect or polished, but it was real.

And by the end of the call, the prospect was sold.

Not just on the product, but on the fact that they’d just spoken to the person who built it.

(Although Danish thought we got lucky, I knew it was just not luck)

Sales needs a different energy

Here’s the thing: engineers bring a completely different energy to demo calls.

They’re not there to close deals or hit quotas.

Instead, closing the deal becomes the byproduct of a genuine, problem-solving conversation.

In fact, we’ve had prospects ask specifically to speak to our engineers after realizing how much they can learn in just one call.

It’s refreshing for them to skip the sales fluff and get straight to the tech talk.

Bringing Engineers on the frontlines

In most companies, engineers stay in the background.

They’re only brought out when there’s a technical question or when something goes wrong. But that’s a missed opportunity.

At Airbook, having engineers lead demos has led to some real benefits:

• Faster trust-building: Prospects love the transparency and authenticity of hearing directly from the people who built the product.

• Deeper technical conversations: Engineers handle complex questions on the spot—no need to bring in a separate team later.

• Better product feedback: Engineers get firsthand insights into what customers care about, which helps us build better features.

This has become a part of our culture at Airbook.

Engineers are encouraged to hop on calls, share what they’ve built, and get direct feedback from the people who might use it.

It’s not just about sales—it’s about building the product and the company.

Nuances

I get that this approach might not work for every company.

But for us—being a product-led, engineering-driven team where our customers are fairly technical—this approach fits.

Having our engineers lead demo calls has been a game-changer, and it’s something we’re continuing to lean into.

It’s not traditional. It’s not polished. But it’s real. And for us, that’s what works.

Show The Big Picture First

At a high level, I’m tracking:

• Avg Monthly Traffic (Google Analytics)

• Avg Monthly Leads (HubSpot)

• Avg Monthly Pipeline Created (Salesforce)

• Avg Monthly Closed Won Deals (Salesforce)

At this point you already have an idea of what you’d want to look into deeper. However, while doing a full funner overview, everything from top to bottom is almost always connected and we need to now go one level deeper at each stage of the funnel to spot trends and bottlenecks.

Top Funnel: Acquisition

This is where it all starts.

We’re not just interested in how many people land on the site—we care about where they come from, what they do, and if they convert not only to leads but qualified leads that convert to Opps/Deals.

• Monthly Trend of Leads by Source: I want to see where leads are coming from—organic, paid, email, etc. It tells me where to double down.

• Monthly Trend of Leads by Stage: How are these leads progressing? Are they getting stuck in a particular stage?

Mid Funnel: Opportunity Pipeline

Now let’s talk about opportunities. Leads are great, but until they turn into deals in the pipeline, they’re just names in your CRM.

• Monthly Pipeline Created: I’m tracking both the number of opportunities and their dollar value. What’s entering the pipeline?

• Monthly Pipeline Closed Won: How much of that pipeline is actually closing? Are we moving deals fast enough, and how much are we winning?

  

• Monthly Closed Lost: This is important too—how many deals are we losing, and more importantly, why?

• Revenue Lost by Reason: Understanding why deals are lost gives us clear areas to fix—pricing, product fit, competition, whatever it may be.

Sales Team Performance

Finally, we need to keep an eye on how the sales team is performing. After all, they're the ones converting pipeline into dollars.

• Pipeline Coverage Ratios by Reps: Are individual reps keeping up with the average? This shows who’s ahead and who needs help.

• Avg Days to Close by Reps: How quickly are deals closing? Speed matters. The faster a deal closes, the quicker you can reinvest in growth.

  

• Days to Move Between Stages by Reps: This helps us see if there are bottlenecks in the sales process and who’s getting stuck where.

• Win/Loss Ratios by Reps: Who’s winning more often? This tells us not just who’s hitting targets, but why they are (or aren’t).

Nuances

To be honest, the funnel isn’t a linear, one-size-fits-all process.

There are nuances at every stage, and you won’t always get it right the first time. But the beauty of this kind of analysis is that it gives you clarity.

Ultimately, this is about constant iteration. You’re not just tracking numbers for the sake of it. You’re looking for patterns—where are you gaining momentum, and where are you bleeding potential revenue?

If you’re willing to face the data head-on, you’ll find opportunities to grow faster, smarter, and more sustainably.

It’s not glamorous, but it’s how you win.

This one is the most popular use-case amongst our customers, so we decided to create a template for you to get started easily.

I wanted to share a bit about what we’re building, why we built it, and how we think it can really help you cut through the noise when it comes to working with data.

I know from firsthand experience that pulling data from a bunch of different tools—whether it’s marketing, product, sales, or customer support—and trying to get a clear picture is a nightmare.

I’ve spent way too many hours jumping between CSVs, dashboards, and SQL queries just to answer simple questions.

That’s where Airbook comes in.

We built it to simplify that entire process by creating a collaborative workspace where both business and data teams can work together to explore insights and build reports—without the hassle of managing complex data architecture or juggling disconnected tools.

Connect

Airbook connects to over 100 different sources of data, from tools like Google Analytics and HubSpot to databases like Snowflake and BigQuery. Once you’ve connected them, Airbook syncs your data automatically, so you’re always working with the freshest numbers.

Let me give you an example.

Say you want to track how a visitor goes from landing on your site, reading a blog post, signing up for a product, moving through your sales funnel, and eventually becoming a paying customer.

Normally, you’d have to pull that data from at least four or five different places. But with Airbook, you connect those sources once, and it brings all the data together for you to explore in one place.

Explore

I know not everyone on your team is a SQL pro, and that’s why we designed Airbook to work for both technical and non-technical users.

Analysts can write SQL queries to get exactly what they need, while business users can explore the same data by creating visualizations and reports with a simple drag-and-drop interface.

Your marketing lead might want to see how many visitors came through a campaign and how many of those converted into leads.

They can just drag the data from Google Analytics and HubSpot into a chart—no SQL needed.

Meanwhile, your data analyst can dive deeper, running queries on the same data to figure out more complex insights.

And the best part is, it’s all happening in the same space.

Dashboard

One of the biggest pains we’ve all faced is outdated reports. You pull data, build a report, and by the time you present it, things have changed. With Airbook, your dashboards are always live.

They update based on the sync frequency you set—whether that’s every 6 hours or once a day—so the numbers you’re looking at are always current.

Let’s say you’re a customer success team keeping an eye on high-risk accounts. In Airbook, you can connect Mixpanel to see product usage, Zendesk for support tickets, and HubSpot for revenue impact. All of that can be pulled into a single dashboard that automatically updates, so your CS team knows exactly who to prioritize, without constantly needing to rebuild reports.

Together

Airbook was built with cross-functional teams in mind.

Whether you’re in marketing, sales, or customer success, it lets you combine data from different sources and get a full view of what’s happening across the board. No more bouncing between 10 different tools or struggling to piece together a customer’s journey. It’s all in one place, ready for analysis.

We’ve all been there—waiting for analysts to pull the data, or the data team waiting for business users to explain what they need.

Airbook cuts through that by letting everyone work together in the same space.

You can invite team members to collaborate in real time.

Analysts can write SQL, while business folks can build charts and add notes, all in one place. No more back-and-forth emails or delays.

What’s Next?

As companies grow, staying on top of your data can feel overwhelming, and it’s easy to deprioritize it when things get busy. Specially if you’re building a fast growing startup!

We don’t want that to happen. That’s why we’re committed to building tools that make data accessible and actionable for everyone—without the complexity.

The idea is to keep you from feeling like you need 5 different tools just to get the insights you need to make decisions.

We intend to make Airbook the go-to tool for every startup from 0 to IPO and beyond.

Whether you’re a small team or a scaling company, we want to make sure you always get all the information you need at any time to help you make the right decisions.

That’s our mission with Airbook.

We were in the trenches, trying to pull data from multiple sources, build insights that spanned those sources, and present those insights in a way that made sense to leadership.

We’d put together nice-looking dashboards, but they were always one step behind the data because of how clunky the tools were.

It felt like the Wild West—a bunch of scattered tools and manual processes just to get some basic numbers together.

Every time, we had to jump between different platforms, reformat data, redo the same work over and over again.

It was messy. It was frustrating. Not only for us, but also for the one’s waiting for insights to be able to make decisions.

And it was slowing down the decisions that needed to be made to drive growth.

The Problem We Built Airbook to Solve

That’s where the idea for Airbook came from. We knew there had to be a better way, so we set out to build it.

The initial vision was simple: create a tool that pulls data from multiple sources, builds insights seamlessly across those sources with or without code, and presents everything in clear, customizable dashboards. And making this whole experience less scary and more collaborative, easy to build insights together and confidently.

Airbook was designed to be the tool we wished we’d had back when we were leading data teams.

Our Own Customer Zero

Now, we’re using Airbook ourselves. And honestly, that’s one of the coolest parts of the journey—because we’re solving the exact problems we struggled with before.

As our own Customer Zero, we track everything inside Airbook.

We track:

• Marketing metrics: Keeping an eye on how our campaigns perform and where we get the most traction.

• Funnel conversions: Knowing exactly where our users drop off or convert helps us optimize our onboarding.

• Product usage and activation: Seeing how users engage with Airbook helps us tweak features and improve the overall experience.

By using Airbook internally, we stay on top of our metrics without the hassle of bouncing between platforms.

We’re able to make data-driven decisions faster and more efficiently.

Finding Areas to Improve

Of course, using Airbook day in and day out also helps us spot areas where we can improve the product.

Because we’re living the same experience as our customers, we’re quick to find points of frustration—whether it’s a bug in the dashboard, a feature that doesn’t quite work the way we want it to, or a missing integration.

We meet regularly as a team to discuss these pain points and figure out how to make the product better, not just for us, but for every user out there.

Every frustration we solve for ourselves is one less frustration for our customers.

It Starts with the Founder

As startup founders, we’re not just building products—we’re shaping the culture of how those products are used, both inside and outside the company.

And if you want your team to deeply believe in the value of what you’re creating, it has to start at the very top.

When founders and leadership actively use the product, it sets a powerful example for the entire company.

It shows that you’re not just selling something—you believe in it. You’re eating your own dog food because you know firsthand that it solves real problems.

Here’s how to foster a culture where everyone from the top down is using, testing, and improving your product.

Create a Product-First Culture

To get the whole company involved, you need to make using the product part of the company’s DNA.

This culture doesn’t just happen overnight—it needs to be intentionally cultivated.

Here are some ways to create that environment:

1. Integrate the Product into Everyday Tasks: Make sure that your product is part of your team’s daily workflow. Whether it’s tracking sales, monitoring marketing campaigns, or using internal collaboration features—whatever the core functionality is, make it essential for your team.

2. Make it Fun: Encourage a culture of experimentation. Let your team play with the product, discover new features, and try things out. You might be surprised at the creative ways people find to use it. Reward teams or individuals who come up with innovative use-cases or suggestions for improvement.

3. Encourage Feedback Loops: Using your product internally is a great way to identify bugs, pain points, and opportunities for improvement. Set up a feedback loop where every team member can report on their experiences and suggest changes. Make sure this feedback is heard and acted on.

4. Transparency: Be open about the product’s strengths and weaknesses. Create spaces where team members can discuss what’s working and what isn’t. Celebrate successes but also embrace the struggles openly. By being transparent, you empower the team to feel like they’re part of the solution.

Build Cross-Functional Engagement

When you get multiple teams across the company using the product, you see it from all angles. Sales might use it differently than marketing. The engineering team might have a completely different perspective than customer success.

That cross-functional engagement gives your team invaluable insights into how well the product serves various needs.

Leadership can encourage this by facilitating interdepartmental collaborations.

For example, have your engineers sit in on a customer demo to see how sales uses the product.

Or let your marketing team play around with new features in beta mode, giving early feedback before it’s released.

When everyone has a vested interest in making the product better, it drives a deeper connection to the company’s mission.

Testing to Tweaking, all the time!

Using your product internally gives you a continuous source of feedback.

And that means you’re always testing, tweaking, and improving.

This cycle of constant iteration becomes part of your company’s daily rhythm.

It’s not just about finding bugs—it’s about anticipating where the product can improve before customers even notice.

Introduction

Analytics has become a key element for decision making for businesses in the current world. But the role of an analyst has been changing with the advancements in technology and data being the focus of every decision. In this blog, we speak to modern analytics teams to understand how the role of an analyst is evolving and the emerging need of the data solutions to help teams move from insights to taking action.

As we speak to modern analytics teams, we are observing a pattern in how the role of an analyst is evolving where data is at the centre of every decision.

Challenges & Technological Advancements

The definition of an analyst is under ‘identity crisis’. Traditionally, analysts were regarded as the ones who can use their technical wizardry to query, wrangle, clean large chunks of data and then being able to convert raw data into visualizations via dashboards for business stakeholders to consume.

As data took the centre stage in the previous decade, we found that the expectation to ‘being data-driven’ had started to become a pre-requisite in several business facing roles as well.

Although the dashboarding solutions were being designed for ‘self-service’, many deployments of these solutions ended up being “report factories” offering only a single-player environment where the semantic layer was used by technical experts to create reports for others. This often reintroduced the bottlenecks and frustrations that self-service BI was supposed to get rid of.

With the emergence of more advanced code abstraction layers (ofcourse, ChatGPT and NLP)- teams will directly go from 'describe to action'.

Eventually non-data teams would be able to operate on data themselves more effectively AND finally the role of an analyst will be respected more as 'advisors' of data rather than 'report factories' for ad hoc reporting. Hence, an emerging need within data solutions to have the ability to explain insights, direct a narrative, create and close analytical feedback loops and eventually helping reach from insights to decisions quickly and together will become inevitable.

Collaboration & Modern Solutions

Having previously lived the life of a frustrated analyst, I relate strongly to this problem.

In our interviews with data teams, we've found that an average data practitioner hops between at least 4 unique tools to query, explore data and 5 communication channels to gather requirements, explain insights, work on follow-ups and present data which almost always gets lost in transit.

In our recent conversation with Dhruv Kadakia who is a Senior Data Analyst at JioSaavn we spoke about how the role of an analyst is not only pertaining to be able to query data but is evolving more towards understanding what the insights mean in business context.

Speaking more about the analytical workflow, analysts today are skilled at querying data from various sources using SQL, R or Python but eventually all of the gathered data is communicated to larger business stakeholders via disparate excel sheets and the actual knowledge sharing over data happens over multiple communication channels (messaging apps, meetings, emails).

‍

Conversations around data where the actual knowledge lies all exist within different channels so - what we learn from data, context around numbers and the thought process behind making a particular decision is difficult to retrieve or refer today. There is an opportunity for modern data solutions to make collaboration the centre of everything data related so that teams can move towards decision-making faster.

Today, with more focussed managed services for ETL/ELT and reverse ETL existing in the Modern Data Stack, there may be an exciting opportunity to help teams move from insights to actually taking action from one collaborative interface.

Conclusion

In conclusion, the role of an analyst is evolving to be more data-driven and focussed on understanding the business context. Today, with the emergence of more advanced code abstraction layers, teams are able to move from insights to taking action quickly and collaboratively. As data becomes the focus of every decision, it is important for analysts to be able to understand the business context for the data, communicate their insights effectively and collaborate with stakeholders to take action.

In today's fast-paced business landscape, marketing operations are crucial for optimizing efficiency, driving revenue generation, and ensuring the overall success of a company. Avinash, who leads Marketing Operations at Chargebee, possesses valuable insights into these key aspects. In a recent interview, he shared his expertise on optimizing costs, ensuring predictability, aligning goals, hiring strategies, overcoming challenges, and the impact of technical debt. Let's delve deeper into Avinash's perspectives to gain a comprehensive understanding of the role of marketing operations in modern business.

Role of MarketingOps in 2023

In the post-covid era, efficiency lies at the core of effective marketing operations, encompassing various aspects such as cost optimization and the elimination of operational leakage. Avinash emphasized the importance of aligning tools, processes, and people to achieve operational excellence and drive optimal utilization of resources. When these elements are in sync, it minimizes operational inefficiencies and allows the marketing operations team to deliver results more effectively.

Aligning tools and processes involves selecting and utilizing the right technology stack for marketing operations. This includes marketing automation platforms, CRM systems, analytics tools, and other software that streamline processes and enable data-driven decision-making. By aligning these tools with the specific needs and objectives of the marketing operations function, teams can maximize their efficiency and effectiveness.

Predictability is another crucial aspect of marketing operations. Avinash highlighted the significance of effectively handling the tech-stack, processes, hand-offs, and Service Level Agreements (SLAs) to achieve predictability. When marketing operations can accurately forecast and anticipate outcomes, it allows them to proactively address challenges, allocate resources strategically, and optimize campaigns for better results.

By focusing on efficiency and predictability, marketing operations can drive better outcomes, optimize resource allocation, and respond effectively to changing market dynamics.

Driving Accountability and Collaboration through shared metrics

Avinash emphasized the significance of aligning marketing and sales efforts around revenue generation metrics rather than solely focusing on the high level metrics such as quantity of Marketing Qualified Leads (MQLs) generated. While MQLs are an important component of the marketing funnel, they may or may not translate into revenue. By shifting the focus towards revenue generation metrics (e.g. opportunity creation) marketing and sales teams can foster a culture of accountability and collaboration, working towards a common goal that impacts the bottom line.

When marketing and sales teams share the same goals and metrics, it creates a sense of shared responsibility. Rather than viewing marketing and sales as separate entities, they become two interconnected parts of a cohesive revenue-generating machine. This alignment encourages both teams to work together, leveraging their respective strengths and expertise to drive tangible business outcomes.

Additionally, aligning goals and metrics between marketing and sales teams enables more accurate performance evaluation. Instead of evaluating marketing solely on lead generation numbers, the focus shifts to evaluating marketing's contribution to revenue growth. This evaluation framework promotes a data-driven approach and enables marketing operations to assess the effectiveness of their strategies and tactics in driving revenue.

Trust, Context & Alignment

In today's rapidly changing business landscape, maintaining alignment and ensuring contextual understanding are critical challenges for marketing operations. Avinash highlighted the difficulty of effectively sharing and documenting contextual information related to processes, metrics, and decision-making. Without clear and consistent alignment on key metrics, e.g.  definition of a Marketing Qualified Lead (MQL), misunderstandings can arise, leading to inefficiencies and hindering effective teamwork.

"Hey Dashboard! You look cool, but can I trust you?" is often what comes to our mind when we look at data today.

One of the key factors that underpin alignment and contextual understanding is data trust. Data serves as the foundation for decision-making and analysis in marketing operations. When teams lack trust in the data they rely on, it can have detrimental effects on decision-making processes, resource allocation, and overall operational efficiency.

Data trust refers to the confidence that stakeholders have in the accuracy, reliability, and integrity of the data they use. When data is trusted, it becomes a valuable asset that informs strategic decisions and enables organizations to respond quickly and effectively to changing market dynamics.

Transparency and documentation are crucial for establishing data trust. Clear documentation of processes, data definitions, and methodologies ensures that all stakeholders have a shared understanding of how data is collected, analyzed, and interpreted. This documentation provides context and ensures that the right information is available to support decision-making and collaboration.

The Impact of Technical Debt on Decision Making

Technical debt is a concept that refers to the accumulated inefficiencies, outdated processes, and suboptimal practices that accumulate over time in systems and operations. When it comes to data sources and reporting systems, technical debt can have a significant impact on data integrity and reliability. As businesses scale and rely more heavily on data-driven decision-making, addressing technical debt becomes crucial to maintain trust in the accuracy and credibility of the data.

When technical debt accumulates in data sources, it introduces uncertainty and raises questions about the quality of the data. Data sources burdened with technical debt may suffer from issues such as inconsistent data formats, duplicate or missing entries, or outdated data collection methods. These issues can lead to data inconsistencies, inaccuracies, and an overall lack of confidence in the reliability of the information being used for reporting and analysis. Organizations risk making flawed decisions, allocating resources inefficiently, and missing out on opportunities due to unreliable information.

Here's what the Head of Data at Postman said about Technical Debt!

What goes behind building a great MarkOps team?

In the realm of marketing operations that are often behind the scenes often go unnoticed, yet they play a crucial role in ensuring the smooth and efficient functioning of operations. Avinash provided valuable insights into the hiring process for marketing operations roles, shedding light on the qualities and skills he looks for in potential candidates.

It is when the ship is sinking is when Marketing Operations as a function really shines. Hence, one of the key attributes that Avinash highlighted is the ability to handle pressure. Marketing operations professionals often face tight deadlines, complex challenges, and high expectations. They need to be able to remain calm under pressure, make sound decisions, and keep operations running smoothly even in challenging situations.

Another essential quality Avinash emphasized is strong problem-solving skills. Marketing operations professionals encounter various issues and obstacles that require effective problem-solving abilities. They must be able to analyze problems, identify root causes, and develop creative and practical solutions. These problem-solving skills enable them to address operational inefficiencies, optimize processes, and drive continuous improvement within the marketing operations function.

A fast-learning mindset is also highly valued in marketing operations roles. Given the dynamic nature of marketing and the ever-evolving technology landscape, marketing operations professionals must be quick learners. They need to stay updated on industry trends, new tools and technologies, and emerging best practices. A fast-learning mindset allows them to acquire new skills, adapt to changes, and effectively leverage innovative solutions to enhance operational efficiency.

In summary, marketing operations professionals are the hidden heroes behind successful marketing initiatives. Their ability to handle pressure, strong problem-solving skills, fast-learning mindset, and focus on minimizing operational risks are essential qualities for driving operational excellence. By seeking candidates with these attributes, organizations can build a strong marketing operations team capable of adapting to change, optimizing processes, and contributing to the overall success of the business. Recognizing and valuing the contributions of marketing operations professionals is crucial for fostering a culture of appreciation, growth, and success within the organization.

Closing Notes

Avinash's insights provide valuable guidance for marketing operations professionals aiming to enhance efficiency, align goals, and generate revenue. By optimizing costs, ensuring predictability, and aligning marketing and sales efforts, marketing operations can become a driving force for business growth. Additionally, prioritizing the recruitment of individuals with strong problem-solving skills and the ability to handle pressure contributes to the overall success of the marketing operations team.

To gain a better understanding of how Postman looks at analytics, we interviewed Prudhvi Vasa, who leads a team of data analysts and engineers at the company. Prudhvi is an analytics leader with extensive experience within various industries- including retail, e-commerce, technology, and telecommunications.

In this blog, we will learn more about Postman’s current data stack, how do they structure their data team, what is the core skill they look for while hiring and how they enable different functions to make sure everyone in the organization is aligned with everything data! Let’s dig in.

Postman’s Data Stack

At Postman, product and data is at the centre of every conversation. Understanding user behaviour on the product and offering the best experience for developers is key. As you can imagine, there is a tonne of data coming from everywhere- to use data to make decisions, Postman has invested in some of the best solutions, which includes the following-

1. BI & Dashboards- Postman uses Looker to build interactive dashboards to track key KPIs and metrics for key stakeholders in the company

2. Data Ingestion- There are a tonne of application and event data to be gathered; Postman uses an in-house developed data ingestion engine called Fulcrum and Hevo Data for 3rd party data ingestions

3. Data Cataloging- It’s important to have one home for data teams to map lineage, maintain a data catalog and make the best use of metadata capabilities. Postman uses Atlan to solve for this.

4. Data Warehouse- Postman uses Amazon Redshift as their data warehouse.

What is at the core of Postman’s data team?

As we are witnessing numerous technology disruptions in the data space, the role of a data team member is evolving at a rapid pace.

In order to keep up with the changing times, a data team member must be versatile and adaptable to new and emerging technologies. For instance, the advent of cloud computing, machine learning, and big data analytics has brought about significant changes in the way data is stored, processed, and analyzed. In addition, with the emergence of code abstraction layers (e.g ChatGPT) -the role of an analyst is evolving but what remains at core is to have natural inclination towards using data-driven insights to identify and solve problems.

This requires a deep understanding of the business and the data itself, as well as an ability to think critically and creatively. Furthermore, data teams must possess excellent communication and collaboration skills in order to effectively communicate their findings and work with other teams within the organization.If you’re looking to be a data analyst in 2023- here’s what you should know.

How Postman looks at building data products?

Postman values the core fundamentals of the software development lifecycle (SDLC) to build various data products or artefacts. This ensures that the analytical processes are well-defined and optimized for efficiency.

SDLC is a process followed by software organizations for managing software projects. It includes a detailed plan for developing, maintaining, replacing, and enhancing specific features. Similarly, to address complex data challenges, data products should follow a similar lifecycle.

Postman operates under a service model, providing data to internal consumers in order to meet their needs. This model allows Postman’s data teams to identify repeating patterns in data usage, which is then leveraged to create new products. For example, Postman developed Fulcrum, an in-house data ingestion tool, to better serve internal consumers' ETL needs. In addition, the team is dedicated to continually improving their service offerings and expanding the product line to meet the ever-evolving needs of internal data consumers.

“We are primarily in a service model serving internal consumers with their data needs - and when we see a repeating pattern, we build products (ex. Fulcrum for data ingestion).”

As a part of building on the above fundamentals and make the data function successful- Postman has observed that hiring for an Analytics Engineer has proven out to be a valuable asset for the data organization.

What does an Analytics Engineer do at Postman?

At Postman, the role of an Analytics Engineer has proven out to be successful. The primary objective of an Analytics Engineer at Postman is to ensure that the analytical code is performant. This means that the code should be optimized to run as efficiently as possible, so that the system can handle large volumes of data and complex queries without slowing down.

“Primary objective of the Analytics Engineer is to make sure-
‍
-The code is performant
-Keep tech debt in control and improve our overall code.
-Provide guidance and training to the analysts for best practices.
-Monitors key metrics around query times, load on our warehouse, run time of our analytics jobs and more."

In addition to optimizing the code, an Analytics Engineer is responsible for keeping technical debt in control and improving the overall quality of the codebase. This requires a deep understanding of the system architecture and the ability to identify areas for improvement. By continuously improving the codebase, the Analytics Engineer helps to ensure that the system remains scalable and reliable.

A key part of the role is to guide and train analysts on best practices. This includes helping them to understand the underlying data model, designing efficient queries, and interpreting the results.

Finally, monitoring key metrics around query times, load on the warehouse, run time of analytics jobs is a key part of the role. By keeping a close eye on these metrics, the Analytics Engineer can quickly identify potential issues and take proactive steps to address them before they become a problem.

Analytical enablement to bring alignment

Enabling everyone in an organization to consistently gain insights from data is a challenging task. This requires a multifaceted effort that involves investments in technology, process improvement, basic business intelligence (BI) training, and change management. However, these initiatives are often implemented in silos, resulting in tools that are under-utilised, proof of concept graveyards, report factories, and gaps in processes.

To avoid these common problems, it's important to take a holistic approach to digital transformation when it comes to analytics programs. This means not only investing in the technology and training needed to achieve success, but also creating a culture of data-driven decision making throughout the organization. This can involve regular training sessions, data literacy programs, and cross-functional collaboration to ensure that everyone is on the same page.

In addition, it's important to recognise that digital transformation is an ongoing process, and not a one-time event. As such, it's important to regularly assess analytics programs and identify areas for improvement. This can involve gathering feedback from stakeholders, monitoring key performance indicators, and conducting regular audits of the analytics tools and processes in place.

One of the enablement programs that Postman utilises is to create use-case based templates using Confluence on ‘how to’ do particular analyses for different teams. This practice ensures that all team members are equipped with the necessary knowledge to conduct analyses and generate insights. Moreover, it fosters a sense of collaboration and knowledge-sharing, which can lead to better decision-making and more effective problem-solving.

Furthermore, Postman's investment in data solutions extends beyond just the technical tools. The company also places a strong emphasis on collaboration and generating user studies, which helps ensure that the data being generated is useful and actionable. Through these efforts, Postman is able to stay ahead of the curve when it comes to data analysis and management, and is well-positioned to continue its success in the future.

Conclusion

For many years now, companies have been making efforts to become more data-driven. However, despite these efforts, the results have been mixed. The success of such endeavours is not always immediate and takes time to play out within organizations.

What distinguishes the companies that ultimately succeed from those that continue to struggle is their persistence, resilience, execution, and relentless drive to employ data to make more informed business decisions. These companies are determined to integrate data into their decision-making processes, and to foster a culture that values data-driven insights.

This requires a multifaceted approach that involves investments in technology, process improvement, basic business intelligence (BI) training, and change management. But, most importantly, it requires individuals and teams to be fully committed to leveraging data to improve decision-making at all levels of the organization.

So, there is a lot that happens behind the scenes to make an organization truly ‘data-driven’ and Postman is a great example to follow! Postman has successfully implemented various processes to continuously improve its analytical capabilities, and has created a culture of data-driven decision-making throughout the organization.

I’m writing this note on behalf of the team building Airbook!

Typebar began as a simple Notion document I shared with my team and here’s how it started..

“I often feel like we're all in a vast desert, searching for that elusive oasis where we can find water, food, and perhaps even a place to build our future city. It's a treacherous trek. Move too quickly in the wrong direction, and it could spell disaster.

In this desert, we’re observing every sign—the direction of the wind, the feel of the terrain, the faintest hint of green on the horizon—to guide us. Once we’re sure we've found the right path, we advance with urgency. The truth is, we’re desperately thirsty, hungry, and yes, sweating through every step. Shelter can’t come soon enough”

We know the journey is rough. It's full of signs that are hard to read and directions that are often misleading.

But that’s where our strength lies—we are living this journey just as other startups are.

We share the same thirst for clarity, the same hunger for growth, and the same drive to find that spot to build our city around.

Here at Typebar, you’ll get honest stories, experiences and learnings about how we’re finding our way, how others like us are finding their way…while we’re all finding our way!

We plan to journal, write and discuss while we try to make sense of the chaos, making each step less wrong than the last.

Let's move forward, together.

Subscribe now