Financial and Operational Achievement in the Post-Free Money Age; Introducing the Moneyball playbook for outsized business performance.

Every CEO is trying to figure out what to do in an environment of fiscal tightening—getting deals done, whether new sales or financing to enable the company's growth strategy, used to be easy because we were in an era of free money. However, with interest rates nearly tripling in 2022 and separately full-year 2023 looking even more uncertain, CEOs cannot be sure of anything. In addition, new clients and funding for 2023 are no longer certain things. In economic wartime, CEOs cannot afford to bring on the wrong customer as it will create a waterfall of challenges that will be hard to overcome.

Introducing the Moneyball playbook for business for operational and financial achievement in uncertain economic times 

Healthy growth requires identifying the right fit customers and improving acquisition efficiency, implementation, and customer success efficiency. Why? The challenge for these businesses is the growth itself, i.e., too much and too little to ensure that with scale, the company retains traction on onboarding and on-time and on-budget implementations. If you don't get the post-sale part of the business right, then you have a high degree of likelihood that the company will experience an early death.

Poor growth requires an emphasis on the right-fit customer business analysis and execution because the business can only afford to spend a single dollar of invested capital. Once you have the right fit, Product, Sales, Marketing, and Customer Success will achieve operational and financial achievement with efficient capital utilization.

How do you define right-fit customers?

The following playbook was developed while I was engaged, as CEO, in turning around a 24M Annual Reoccurring Revenue SaaS that had burned through $42M in invested capital and had high churn while also having very little cash in the bank. Based on its success, we raised 16 million in new funding, and the results were improbable when we sold the business 3.5 years later for a multiple of Annual Reoccurring Revenue (ARR).

The First Step:

The path to defining the right fit customers is to subscribe to an off-the-shelf Automated Machine Learning Software; GiniMachine is my platform of choice. These platforms make it easy to quickly build predictive models based on your customer data with no AI/Machine Learning training or resources required on the client side.

Author Call Out: Start by loading your CSV of Customer Data to identify the following customer groups:

Bad-fit:

Once you load your CSV files and critical metrics, you can define bad-fit customers based on empirical data.

The definition of a bad-fit customer is that they require a lot of resources and investment to acquire. It then stresses the organization with heavy retention efforts, service costs, and, eventually, churns. So naturally, this leaves you, the team, and the investors disappointed, disillusioned, and demoralized. It's the main reason employees leave — because they don't trust that management knows what they're doing.

Next Step, Analyze the Data:

Determine the number of customers that churned that were a bad fit (i.e., churned because they didn't have a good chance of success in the first place)

The number of customers that churned was right-fit (this, plus bad-fit, should equal 100%

Bad-fit customers likely prevented you from focusing on the right-fit customers, and that's what caused the right-fit customers to churn

What the data and model will tell us:

It will tie churn to bad-fit customers. Once these have been identified, we can confidently say that X percentage of our customers will predictively churn, not renew, or expand and will not advocate or serve as a reference for us.

Right-fit:

The predictive model will also define a right-fit customer based on as many data points as you like. I like to start with crucial metrics and run multiple cohort analyses. Laser focus on right-fit customers versus bad-fit establishes a remarkably efficient machine that creates enjoyment through value creation for employees and clients.

Author Call Out: With the data, company leadership will now know the following:

· Where Marketing should invest dollars

· Where Sales should spend their time

· Where should the Product should focus their roadmap

· Which clients Customer Success should focus their energies on

· How to improve onboarding experiences to achieve improved time to value

The Last Step is to operationalize the data implementation.

If it still needs to be done, the business must track employee time spent with customers. Nothing overly detailed or cumbersome; focus on time spent. Communicate why you're doing this with employees so they will buy in from the start. Remember, nobody likes micromanagement, so you will get some resistance if you don't explain why you're doing this. No employee wants to sell to, onboard, or build products for bad-fit customers. Employees know it wastes everyone's time and limits professional and income advancement.

Do this for a week and then a month. Asking for a week at a time creates an accountability framework but keeps employee satisfaction intact.

Author Call out: Analyze and Normalize the Data for Easy Consumption:

• Time spent with bad-fit customers is time not spent with right-fit customers.
• If your business is too far in favor of a bad fit, it becomes apparent that it's taking away from the right fit.

Operationalize the Model to Gain Organizational Buy-In

1. Develop the Executive Presentation for Board, Investors, Employees, and Department Heads. Tailor the presentation to your audience. In my experience, too much information presented during an all-hands can create doubt (i.e., why are they selling this idea to me now, and what does it mean for my professional growth opportunities?).
2. Work with finance to establish a central point of truth and model the financial plan to reduce the negative impact of bad-fit customers.
3. Develop an executive presentation that outlines how much bad-fit customers cost the company across all departments, including Human Resources, i.e., talent acquisition and employee churn.

Author Call Out: Key reports should include the following:

• Pipeline Good vs. Bad-fit Customers
• Churn (all calculations) Good vs. Bad-fit Customers
• CSAT/Expansion Good vs. Bad-fit Customers
• Time to Close / CAC Good vs. Bad-fit Customers
• Profitability between Right vs. Bad-fit Customers
• Employee Resources Invested Good vs. Bad-fit Customers

Create a 90-day action plan to test the various models in the real world.

Marketing, Sales, Product, and Customer Success will need to work together to produce a method for communicating to Right-fit customers and prospects. Additionally, you can create communications in which bad-fit customers automatically drop out of the pipeline or offer ways to append their process to meet right-fit standards. For sales, this is particularly necessary with the existing pipeline, as the focused direction will take time to be tested and modified. We want to keep all deals because they already have a high sunk cost. The goal is to get them as close to the right fit as possible.

Author Call Out: Outcomes from the process in less than 90-days:

• Marketing and Sales Professionals will have a much easier time honing your value proposition and will only focus on the right prospects. Measured by: Improved CAC and Sales Velocity
• Product UX teams will stop splitting hairs over what features to develop next and instead focus on delighting the right customer. Measured by: Improved Sales and Marketing KPIs and Reduced Churn
• Your Customer Success team members will know how to help customers find value in the Product and achieve their goals. Measured by: Reduced Churn and Upsell
• Your Customer Success team can focus on serving high-value customers at a potential churn risk.

Does it work? Below is a sample of how it worked for me as CEO, which resulted in a private equity sale at a multiple of ARR:

This example is from my accomplishments while CEO of an Enterprise Fintech, which had experienced incredible growth but was challenged by slow time to value and longer than necessary sales cycles.

  As a CEO, the most significant challenge is that our teams focus on their role-specific accountabilities rather than the enterprise agenda. It's human nature, i.e., marketing relies on marketing data, which waterfalls the entire company. The prescription outlined establishes a central point of truth based on actionable quantitative data that will create lean models where fatal blind spots are addressed. The action plan will make a collective sigh of relief in your next Board meeting as all stakeholders are working as a single unit rooted in fact. 

"The age of free money may not be gone forever, but, as CEO, for the next two to three years, you and the board must consider deploying a data-driven lean organizational strategy or risk losing it all."

About the Author: 

Marc Pickren

• 25 Years of Outsized Executive Management Success in both Public and Private Companies. 
• Economic War Time CEO, 2X Exited Technology Executive.
• Raised over 50M from Institutional Investors. 
• Nominated for Austin's Mid-Market CEO of the Year 2020.
• Named Top CEO for Women 2019 by Comparably.