Over the past ten years, I have worked with many startup CEOs. One of the most common questions I get, by far, is where and how to focus their time and resources to effectively grow their startup.

My answer depends on where they are on their startup journey. Along the way, there are several strategic shifts that the CEO and their executive team need to make: failure to do so means their company will remain stuck in its current stage – often with fatal results.

Much has been written on the first shift: from ideation to problem/solution testing. Startups need to shift from thinking and building their initial idea, to a stage of rapid iteration where they must interact with customers to test their fledgling product, gather feedback and quickly adapt. This is the main focus of the Lean Product literature – one of my favorite books is The Lean Product Playbook by Dan Olsen.

Over time, you know your product starts having a good fit with your customers’ needs because you feel sales getting easier: You are able to find customers, reuse sales materials, the pricing seems right, and the number of features new customers demand starts going down. This packaging and standardization are the key to turning a ‘solution’ into a ‘product’.

Startups at this stage start getting traction and revenue starts increasing. Founding teams set their eyes on lofty goals, and the most successful ones manage to attract venture capital to accelerate: A large opportunity is there for the taking before competition emerges. The race is on!

 

The Scale Shift

It is at this point, however, that a second shift is crucial: We call this strategic shift the Scale Shift, and it consists of shifting the focus from the product to the company.

The same way CEOs had to let go of the idea of building the product they wanted, to build instead what the customer needed, they must now delegate product responsibilities and expand their thinking to the whole company.

Building a great product is not enough, you need to build a great company.

There are multiple aspects of this challenge. In this article we focus on three very important, interrelated ones:

  1. Improving the customer journey
  2. Becoming a data-driven company
  3. Implementing tooling for scale

Improving the Customer Journey

To align on terminology: We define the Customer Journey as the end-to-end relationship customers have with your company, encompassing both the Buying Journey (leads/prospects) as well as the Success Journey (customers).

So how do you know you are ready for the Scale Shift? Our Growth VP, Danielle, likes to look at sales losses and customer churn post-mortems. Is the reason your customers churn a product issue or is it a customer journey issue?

Product issues include:

  • Lack of clear definition of what you’re selling
  • Lack of perceived value of the solution
  • Low opportunity conversion rates
  • Resistance to pricing
  • Product variation across customers (everyone buys something a bit different)

Customer journey issues include:

  • Long sales cycles
  • Inefficient customer onboarding and time-to-value
  • Sales expectations vs. delivery mismatches
  • Reactive customer support
  • High volume of support calls/email/chat
  • Low satisfaction scores
  • Low product utilization and/or customer engagement

If the majority of your sales losses and churn issues relate to the second category – congratulations, you may very well have product/market fit! However, you probably need to improve your Customer Journey before you are ready to scale and be able to efficiently convert capital into growth.

To do this, just like you thought long and hard about product features during the product/market fit stage, you now need to think about customer processes.

This transition begins by the mapping the entire Customer Journey and identifying key external and internal elements that must be worked on. As part of the transformation, the Customer Service team needs to evolve into true Customer Success and shift from a reactive stance to proactive management of the customer experience.

In the same way you iterated your way to product/market fit during the previous stage, you must now standardize and implement a continuous improvement approach to iterate your processes until they can systematically deliver on your business goals.

On the buying journey side, your processes are working when you can see sustained reduction of the sales cycles, improved conversion rates, and a high percentage of quota achievement from your sales reps.

On the success journey side, you know you are on the right track when your onboarding backlog and time-to-value metrics go down, your churn/retention rates show continuous improvement under a predictable cost-to-serve model, and your customer experience metrics show a good number of promoters with little to no detractors.

Becoming data-driven

The second aspect of the Scale Shift has to do with the role of data and metrics in your company. Up until now metrics might have been following your decisions, telling you if they worked or not, they must now be strong enough to lead your decisions.

Having a scalable business means you know how to convert capital into growth, and you can demonstrate that knowledge by pointing to actual, compelling performance trends in your company.

Every successful scaling company is able to tell a compelling growth story. This growth story must include unit economics and metrics that highlight how you:

  1. Make money
  2. Increase capacity
  3. Make customers happy

This growth story needs to highlight the part of your business that will scale to become the core of your business in the future. As you searched for product/market fit, you probably signed up some customers that are not strategic nowadays, and maybe developed a few revenue sources that support the emerging business – but that are not scalable or aligned enough to accompany you to your desired objective.

Your growth story is different from your overall financial and operational performance in that it showcases only this scalable core, which is where you are hopefully now intensely focused.

Although the top-level metrics used to describe this scalable core tend to be the same across companies (customer acquisition cost, customer lifetime value, cost to scale, NPS, etc.), it’s the next few levels of granularity that make all the difference: More often than not, we come across board presentations, top-level metrics and quarterly plans that are completely disconnected from the operational reality of the day to day.

Your strategy needs to be connected with day-to-day metrics. We call this process “operationalizing the growth story”. How can you tell if your strategy is operationalized? Well, if every time the Board asks you for the evolution of your customer lifetime value, you need to spend days working in Excel to figure it out – you are not there.

Becoming data-driven is not easy: The organization must start by having a common language where things are clearly defined and have precise meaning. A foundation of data and analytics, tied to processes, has to be built that is powerful enough to answer the right questions and trusted enough that the team feels confident to act on it. Finally, daily governance must evolve: Weekly business reviews, status reports and proposals must be rooted in data – and participants need to be comfortable explaining past behavior and expected impact of future plans on the metrics.

From a capability perspective, this demands the startup move from a report-centric view with reports from many systems and dozens of spreadsheets, to an explorer-centric view based on a Modern Data Warehouse.

This is because reports highlight point metrics, but your team needs the ability to explore the data from multiple angles, connect the dots to understand what happened and truly support their decision-making. For example, it’s one thing to have a report tell you usage of your platform went down last week – but it’s another to be able to understand why and how to fix it.

Of special note is the urgent need to start capturing data as soon as possible. Many operational systems contain only up-to-date data – and unless this evolving data is captured and stored away, the company’s historic record is lost forever.

Implementing tooling for scale

The third key change relates to the systems powering the go-to-market and data capabilities of the company. This includes sales, marketing, customer success and service delivery, as well as analytics and data warehousing. During the product/market fit stage, systems were typically added gradually as simple complements to the work being done by a handful of people in sales, marketing and customer success. Flexibility was key, so processes were undefined, and data lived everywhere – including only in people’s heads.

As the company starts scaling and begins to add people, processes need to become institutional, and data must be captured in systems to prevent chaos and confusion. The inexpensive, simple choices made during the product/market fit stage in most cases do not scale well into the scaling stage. Here startups face a difficult choice: continue to build with duct tape upon a patchwork of systems, or transition to robust platforms that can be customized and truly support their needs.

A common mistake is to underestimate the time, cost, effort and know-how required to successfully implement these platforms – whether it is a CRM system or a Data Warehouse, for example. A full cost analysis needs to include not only tools, licensing and data hosting costs, but internal and external people associated with the infrastructure buildout and maintenance as well.

Planning for a successful implementation based on best practices is key. Every day we come across botched CRM implementations, or analytics infrastructures out of control with hundreds of dashboards filled with data nobody trusts. The impact of these failures on the startup is severe: months and hundreds of thousands of dollars are lost as they are eventually forced to rebuild them again properly from the ground up.

The architecture for these new systems needs to be implemented according to best practices, and the tool selection done considering key principles such as standardization, modularity, portability, security, robustness and support for good development practices to effectively support the organization’s needs for the long term.

We often see selections being done simply because “that’s the system I used in my previous role” or “this is the tool I am familiar with”, leaving on the table massive potential gains with better alternative solutions, or skimp a few dollars on inexpensive tools and end up with solutions that have short lifespans and low scalability.

These system transitions involve many people in the company and are essential to the other two shifts we discussed. As such, leadership is critical – these are not “engineering projects” that can be simply assigned to the CTO/IT lead. They demand instead the involvement of a large part of the CxO team and the CEO herself.

Conclusion

Achieving a successful capital raise is an exciting time for a startup. However, we see many startups stumble badly as they try to scale their company by using the new money to expand their existing practices and drive growth with their existing capabilities, instead of taking a step back and thinking about the shifts needed to create a scalable organization.

To avoid this, we recommend startups in this stage consider:

  • How to shift management focus from product features to customer processes
  • How to move from a report-centric world, to a data driven, explorer-centric
  • How to implement scalable infrastructure and best-in-class, robust platforms.
  • How to leverage best practices and partner with companies that have experience doing these changes.

At Fractal River, we help startups undertake the traction-to-scale transformation as they go through the Scale Shift. If you or one of the companies in your portfolio are ready to scale, we’d love to hear from you!