It doesn't matter which bucket you fit into, there's no denying the logic behind the flywheel metaphor.

Flywheels are self-sustaining and build momentum with each consecutive rotation. This is a really helpful way to look at the things we build to drive growth - and while this model will not fit every program you build, it’s still a helpful exercise to think through how you can build things that take on a life of their own, vs needing you and your team to always be there to give it another push.

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What does this have to do with go-to-market teams?

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Well … Consider this 🧐

Your employees are your greatest asset.

When you enable each individual in your company to monitor, explore, come to conclusions, and take action (on their own) you are investing in continuously educating them about your customers and your business 🤩

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With each iteration, they will learn📚

As they learn they will make stronger, more informed decisions 💪

And with each sound decision, your business gets stronger 📈

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Consider the impact that this strategy will have when executed at scale across your entire business. The potential is enormous 🤯

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This article explores this concept and talks about how companies can begin to enable their teams to continuously learn and improve their ability to make sound business decisions and deliver more amazing customer experiences.

Stage 1: Monitor

Monitor Key Performance Indicators

All go-to-market teams have a core set of key performance indicators (KPIs) that they monitor and track against their goals. And while these vary from business to business and from team to team, the fact is everyone is monitoring their progress against an established baseline and/or target.

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Teams monitor their KPIs: 

• to understand their team's contribution to the business
• to understand how campaigns perform
• to know when something is on fire (good or bad)
• to identify new opportunities
• to measure individual performance
• to mitigate risk
• to learn

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Teams build reporting and dashboards around this core set of operating metrics. They choose the metrics they feel most strongly align with their business strategy, and press forward measuring their ability to positively affect these metrics.

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Often these metrics and dashboards remain unchanged for quarters or longer. While the actual target may get updated monthly or quarterly, the level of granularity and specificity of the data rarely changes.

Stage 2: Explore

Conduct Exploratory Analysis

Once companies have an established set of metrics and are tracking their day-to-day performance they come to a place where they understand what is expected. 

👉 They know how many leads to expect.

👉 They know how many new support tickets to expect.

👉 They know how many intro calls are needed to hit the target.

👉 They know what their CAC:LTV ratio should be.

👉 They know their average deal size.

👉 They know what their NRR should be. 

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And so on…

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When one of these guiding metrics is off, exploratory analysis is needed to better understand what has changed that is positively or negatively affecting the business.

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The thing with exploratory analysis is … it’s ad hoc.  Chances are you’re going to have to create something that you don’t already have. This can become problematic depending on the flexibility of your business intelligence tools and the coding skills of your team. Many popular setups will require you to submit a request to your data/engineering team to have a new query built or to have additional data connected to your system.

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This process is slow and inefficient – it wastes time and money. And, depending on how long it takes to gain access to the data you’ve requested, you may end up having to act before completing your analysis, rendering the whole loop useless.

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Even worse … the data returns and doesn’t provide the anticipated insights, sending you back for rounds two and three with your data/eng team. This can be paralyzing for GTM teams and deadly for data-driven organizations.

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But the worst thing about this model is it passes the buck.

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When your business leaders throw analysis jobs over the wall to data teams, they are effectively wiping their hands clean and moving on to something else while they wait for conclusions from an analyst that is often disconnected from the larger picture. They are passing up an opportunity to learn (through free data exploration) and as a result, their mindset and understanding of the business and customer do not evolve.

Stage 3: Action

Turning Insights into Action

Businesses that can turn data-driven insights into action have a significant competitive advantage. These businesses are able to monitor their core KPIs, react to changes and conduct exploratory analysis in real-time, derive insights, and take action.

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Here’s an example to illustrate the point:

Let's assume, Kim is a customer success manager at a software company.

She’s responsible for managing a portfolio of 100 high-priority customers 🏋🏾

Recently, Kim has noticed the average CSAT score across her portfolio is declining 📉

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Here is how Kim might approach this problem to figure out what's going on:

1. Create a new report to group her portfolio by CSAT score
2. Add a filter to show only companies where CSAT score has declined by more than 5% over the past 30-days
3. Order by the greatest rate of decline

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Now that Kim knows which accounts CSAT is declining at the largest rate, she can take things to the next level 🔎

4. At this point, Kim will likely try to identify common threads or trends in the data to help her diagnose the problem. By now she has probably isolated the problem to a handful of accounts that have the greatest rate of decline.
5. Next, she might examine some individual customers to see what their most recent engagements look like across all of the data she has access to, and look for things like:
   👉 Is product usage declining?
   👉 Is sentiment positive or negative?
   👉 Do they have open support tickets? How many?
   👉 Have they been talking about your company on social?
   👉 Have there been any In-App chat conversations?
   👉 What is their tone?

As Kim continues to identify common threads she gains confidence in the signals that lead to a declining CSAT and is soon ready to take action. 

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In this case, she builds a series of workflows to help prevent and/or address this type of issue in the future.

She builds workflows to:

1. Notify the CSM
2. Update the CRM
3. Trigger an email
4. Trigger an in-app CTA
5. Etc.

And, as a result of conducting her own analysis (customer education) to track down the root cause of her portfolio’s declining CSAT she now has a wealth of learnings to feed back into the Monitoring Stage 🙌.

Completing this cycle will leave Kim and her team better prepared to deliver on customer expectations and keep satisfaction high 🤩

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For this model to work, non-technical business pros must to have access to accurate and timely customer data AND be equipped to explore it freely.

Stage 4: Improve

The Glue That Holds It All Together

The final stage is often the most painful for organizations. In order to continuously improve our ability to understand and react to customer data insights, we need to be iterating at the top, constantly. And this is hard.

Here’s why:

1. Rigid Business Processes
   In the Monitor stage we’re not dealing with reports that belong to a single stakeholder, these are typically established dashboards, designed for a department or team, with an agreed-upon set of guiding metrics that are used to measure progress.
   This makes innovation painful, and forces one of two outcomes:
   
   Mavericks 🧗🏼
   Will deviate from the norm and go rogue - building a new set of reports as they come to conclusions. They do this with the best intentions, but the result is often misalignment and confusion.
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   Followers🧍🏽
   Will stay in their lane no matter what. They’ll continue to execute to plan and press forward, even though they know there is a better way.
   
   That is a pick your own poison type of situation. There really is no winner, and businesses are almost always a mix of these personas. This causes teams to deviate and come out of sync.

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2. Gatekeepers 🔐
   Gatekeepers are just that … they keep the data under their control. There are different degrees here, but all of them slow innovation and progress. It could be that gatekeepers are needed to conduct exploratory analysis because of technical limitations of the analytics platform, it could be that all top-level reporting is locked down and can only be changed through a special request, and it could be a different variation or some other combination.
   
   In SaaS speed is a competitive advantage. As is data. If one (or both) of these are governed then innovation stalls out and teams grow frustrated. Depending on the individual's personality, the result is a mix of Mavericks and Followers, and teams that continue to drift away from one another.
   
   
3. Annual Planning Cycles 🐌
   ‍Sure, these are necessary for BIG change, objective setting, team building, modeling, etc, but we can’t wait for annual planning cycles to make improvements to our GTM.
   
   This change must occur in real time⚡️
   
   SaaS moves too quickly. Go-to-market teams need to continuously improve their readiness and preparedness to react to new opportunities and challenges as they present themselves. Not weeks, months, or quarters later.
   
   The data we consume about our business should be continuously educating and improving your team's ability to assess challenges, develop solutions, and execute plans. If this is not happening, you’re at a disadvantage.

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We Created LoudNClear to finally give GTM teams a customer analytics tool they can operate independently (no code, no devs, no analysts) and continuously learn from without having to lean on a formal data team.

The Promised Land

The LoudNClear Customer Cloud

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The LoudNClear Customer Cloud will give your business a significant competitive advantage.

Today, everybody wants to be data-driven, but the truth is, many businesses are still struggling with enabling their teams to operate with the speed and flexibility needed to truly be data-driven. 

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We solved this problem by creating a platform that enables our customers to complete the full data analysis cycle with no help from engineers or dedicated analysis. Your team can now go from monitoring top-level KPIs to free data exploration and ad hoc analysis, to taking action all with LoudNClear 🔥.

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The benefits of this process are many, but two immediate benefits your organization will experience are:

1. By enabling your team to freely explore data, with no help from analysts or engineers they will become more educated and in-tune with both your customers and your business. This is invaluable.
   
   One of the biggest challenges in becoming a truly data-driven organization is closing the skills gap. With LoudNClear we have taken the technical burden off the table for you. So any business user can explore data and derive insights, freely.
   
   Your team will: 
   ‍✨ Learn more efficiently
   ✨ Make more informed decisions
   ✨ Understand the business letter
   ✨ Innovate faster

 In short, they will thrive.
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2. The second thing that will quickly become apparent after adopting the LoudNClear Customer Cloud is that, as your team becomes more educated (through data consumption), they will start to iterate in every stage of the customer intelligence lifecycle.
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   In other words, they will begin to continuously feed learnings from the free data exploration stage, into the monitoring stage. This means with every cycle your team is smarter and more educated, making every iteration more impactful on the business 🤩.
   
   The business impact of continuous iteration is huge:
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   Your team will:
   ‍✨ Be more closely aligned
   ✨ Execute with more confidence
   ✨ Iterate more frequently
   ✨ Be more nimble
   ✨ Achieve more
   

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LoudNClear is more than a customer intelligence platform. By choosing LoudNClear you’re investing in a Customer Cloud designed to help your GTM teams continuously improve by providing them with easy access to all of your customer data.

The better your teams understand your customers, the more quickly your business will grow.