How a professional services firm moved from scattered data and instinct-based decisions to a clean analytics infrastructure — in under 90 days.
This case study is a composite of real engagements. Identifying details have been changed to protect client confidentiality.
Client profile: B2B professional services firm providing HR consulting and compliance services to small and mid-market businesses. Annual revenue: approximately $2.8M. Team of 22. Data existed in HubSpot CRM, a project management platform, Xero for financials, and a custom client intake system. No analyst on staff; the owner managed all reporting personally.
The firm's founder had grown the business to nearly $3M in revenue through exceptional service delivery and a strong professional network. He knew his clients, his pipeline, and his business intuitively — but as the team grew past 20 people and the client roster expanded past 60 concurrent engagements, the intuition that had served him for years was starting to feel insufficient.
He couldn't answer basic questions about his business without significant manual work. How long was the average client engagement? What was the revenue concentration risk across his top five clients? Which service lines were growing and which were plateauing? Which referral source produced the highest-value clients? These questions took him two to three hours each to answer, which meant he was asking them less and less often.
The firm was at an inflection point. A private equity firm had approached him about a potential acquisition — not an imminent deal, but a serious conversation that revealed how quickly "we can pull that" becomes "we can't produce that" when someone is evaluating your business as an investment.
We began with a scoping sprint: two weeks of structured discovery to document every data source in the business, the questions leadership most needed answered, and the gap between what the current systems could produce and what the business actually needed.
The gap was significant — but the underlying data was better than expected. HubSpot had three years of clean pipeline and client records. Xero had complete financial history. The project management platform had detailed engagement timelines for every client going back 18 months. The problem wasn't missing data. It was that the data lived in four unconnected systems and nobody had the time or infrastructure to connect them.
We built a unified data model that pulled from all four systems on a nightly schedule and normalized everything into a consistent schema. Clients, engagements, revenue, and pipeline all shared a common taxonomy. For the first time, a query like "show me revenue by service line, broken down by client tenure" could be answered in seconds rather than hours.
On top of this foundation, we built an executive dashboard covering five key domains: pipeline health and conversion rates, client revenue concentration and retention rates, service line performance and margin by engagement type, team utilization and capacity, and a rolling 12-month revenue forecast. The dashboard updated every morning before 7 AM.
In the final phase of the engagement, we ran a knowledge transfer session with the firm's most analytically inclined team member — building her capability to manage and expand the reporting layer independently after the engagement closed.
Eighty-three days after the engagement started, the firm had a live, trusted reporting environment that the founder described as "the first time I've felt like I actually know what's happening in my business." The questions that had taken hours to answer were now answerable in under two minutes.
Within the first month of using the dashboard, three significant decisions were driven directly by the data. Client revenue concentration was higher than the founder had realized — the top three clients accounted for 41% of total revenue, a risk he hadn't quantified before. He immediately began a structured diversification effort, prioritizing new client acquisition in under-represented segments.
The service line data revealed that one practice area — compliance audits — was generating 28% of revenue but consuming 40% of senior team capacity. The unit economics weren't working. That finding led to a pricing restructure that improved the margin on that service line by 19% within two quarters.
When the PE conversations resumed six months later, the firm could produce a clean, auditable data room in two days instead of two weeks. The founder entered that process with confidence instead of anxiety. That, he said, was worth the engagement cost on its own.
If you're running a growing business on instinct and spreadsheets, a clean analytics foundation changes everything — not just the numbers, but how you lead. Let’s show you what’s possible.
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