Introduction

The scaling phase is critical for start-ups and new businesses when the risk of failure is very high. According to a Harvard Business School study, the failure rate of US companies after five years is more than 50% which climbs to over 70% after ten years.

Doing more with less is imperative to scale businesses beyond a certain size efficiently and profitably. Although it is a strategy that seems obvious and frequently discussed in boardrooms, the journey towards its realisation is often far from well-understood and transparent as we desire.

In this blog post, we delve into the strategy of achieving more with less in the tech business realm, particularly in challenging economic times. We explore the concepts of scaling and growth, the challenges emphasising the importance of increasing revenues while effectively managing costs with automation.

Prerequisite for Growth

It's important to understand the "why" behind the imperative. Scaling is the process of increasing revenues at a more rapid rate than costs, while growth refers to the process of increasing revenues and resources at an even rate. Scaling allows a company to grow larger at a rapid rate because a relatively small investment can yield outsized returns; i.e., achieve more with less.

Challenges in Scaling

In a resource-constrained world, companies carefully have to manage the trade-offs between three core value pillars: operational efficiency, customer intimacy and product leadership.

A founder or a CEO in a scaling tech business is typically focused on product leadership and/or customer intimacy value pillars to maintain differentiation in the market. In times of scarcity, this means little-to-no time for operational efficiency and scalability in preparation for growth. In addition, often tech startups starting with a suboptimal tech stack and capability due to time pressure or resource availability, carry significant tech debt that slows down the business often until it’s too late - creating a bottleneck in scalability. This can be turned into an opportunity rather than a blocker when ready to scale up.

Leveraging operational efficiency and automation for scalability doesn’t have to be difficult.

The Opportunity Indicators

What indicators or benchmarks can be used to quickly assess how much room for improvement is there?

There are lots of complex metrics floating around every level of the business, however, to quickly assess the opportunity in the operational efficiency space, often simple high-level metrics are sufficient. An example would be "annual revenue per FTE" benchmarks, which have been proven a powerful performance measure, especially for SaaS companies. The general rule is to target $200K or more when SaaS companies reach scale (see below). This helps to evaluate the performance and set a target. A stretch goal inspiration can be taken from exceptional ones like ServiceNow ($375k revenue per FTE), Zoom ($500k), and Aha! ($800k+).

Realising the Potential

There is often no one silver bullet, despite the promises of many COTS options that aren't fit for purpose, or complete bespoke solutions that can be cost prohibitive. The best path forward is to examine the business as a whole and also in parts to identify opportunities to improve operational efficiency at every level. A careful assessment of all the areas may open up the opportunity achieve more with less and incrementally leverage "fit for purpose" tools and technologies - including generative AI for automation.

Automation in business is the silent symphony of progress, where efficiency dances with innovation, composing a harmonious future.4

Thoughtful automation helps with the operational efficiency so resources can be freed up to focus on value generating business activity to create competitive advantage and prepare to grow. Scaling businesses can focus on implementing various types of automation for operational efficiency.

Key areas to consider

1. Infrastructure Automation
   Automating the provisioning, configuration, and management of infrastructure resources such as servers, networks, and storage. This can be achieved through tools like infrastructure-as-code (IaC) frameworks (e.g., Terraform) and configuration management tools (e.g., Ansible).
2. Deployment Automation
   Streamlining the software deployment process by automating the building, testing, and deployment of applications. Continuous Integration/Continuous Deployment (CI/CD) pipelines, containerisation (e.g., Docker), and orchestration tools (e.g., Kubernetes) can be used to automate the deployment workflow.
3. Testing Automation
   Automating the testing process to ensure software quality and reduce manual effort. This includes unit testing, integration testing, regression testing, and performance testing. Test automation frameworks (e.g., Selenium, JUnit) and continuous testing practices can be employed for efficient and reliable testing.
4. Monitoring and Alerting Automation
   Automating the monitoring of systems, applications, and infrastructure to detect issues and generate alerts. This involves implementing monitoring tools (e.g., Prometheus, Grafana) and setting up automated alerting mechanisms to proactively identify and resolve problems.
5. Data Integration Automation
   Automating the extraction, transformation, and processing of data within your organisation. This enables seamless data movement, consolidation, and transformation across different systems and databases. Tools like Apache Kafka, Apache Airflow, and ETL frameworks simplify data integration and automation tasks.
6. Workflow and Task Automation
   Automating routine and repetitive tasks, workflows, and business processes. Robotic Process Automation (RPA) tools can be utilised to automate manual tasks such as data entry, report generation, and data processing, freeing up human resources for more value-added activities.
7. Analytics and Reporting Automation
   Automating the collection, processing, and visualisation of data for analytics and reporting purposes. Business Intelligence (BI) tools, data pipelines, and dashboarding platforms help automate data analysis, generate insights, and create visual reports and metrics.
8. Security and Compliance Automation
   Automating security measures and compliance reporting to ensure the protection of systems, data, and customer information. This includes automating security testing, vulnerability scanning, access controls, and regulatory compliance checks.

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Starting with the Best Value Opportunity

"Automation" like many buzzwords in the tech industry can often be misunderstood and wrapped in marketing jargon that may derail the initiative. To stay focused on what matters most, it's good to adopt some simple exercises to identify the best ROI opportunity.
The team can develop a scorecard similar to the one below that can be validated against top-down metrics and prioritised accordingly.
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Ready to take the next steps?

This is how we can help in days - not months - leveraging pre-built Nimbly frameworks and white-label solutions:

• Day 1: Technical assessment
• Day 2: Service mapping
• Day 3: Prioritise opportunities
• Day 4: Decide build, buy or customise
• Day 5: Kick-off implementation and deployment automation

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References

• Why Some Startups Succeed (and Why Most Fail)
  https://www.entrepreneur.com/leadership/why-some-startups-succeed-and-why-most-fail/288769
• Customer Intimacy and Other Value Disciplines
  https://hbr.org/1993/01/customer-intimacy-and-other-value-disciplines
• 4 Startup Challenges To Avoid When Scaling Your Business
  https://online.hbs.edu/blog/post/scaling-startup-challenges-to-avoid
• 2022 OpenView SaaS Benchmarks Report
  https://openviewpartners.com/2022-saas-benchmarks-report/