Businesses changing shape, and what changed.

Profit felt lower than it should. The founder's instinct was that the team was spending too much time on non-billable work and not enough on delivery. He brought me in to look at how time was being spent and identify where to pull back.

He had data. Quite a lot of it. He'd even invested in a bespoke reporting app the business had built properly.

And still, when I asked "which clients are actually making you money?" - silence.

The team was working hard. The real leak was happening around clients, not within the team: unqualified proposals that went nowhere, meetings that consumed time without producing work, and a handful of clients who generated steady revenue on paper but absorbed a disproportionate amount of senior attention, amends, and goodwill.

None of it showed up in the averages. It never does.

Almost nothing - and that turned out to be the point. The process was solid. The tools were fine. I pulled data from their existing systems and restructured it so the picture was visible at project level and client level.

They called it the magic spreadsheet. Their words, not mine.

The owner can now see which clients are worth the revenue they bring. They changed how they qualify new proposals. The expensive reporting app didn't go anywhere - but now it sits alongside something that actually answers the question they were trying to ask.

A CEO had taken over a marketing and lead generation firm following an investment round, expanded the team from 10 to over 30 people, and driven strong UK sales growth. By most measures, the business was doing well.

But lead quality was falling as volume rose. Clients were unhappy. Order volumes were slipping. The supply team were hitting their volume targets. The sales team were dealing with the fallout. Both sides were working hard - in opposite directions.

The supply team optimised their paid search campaigns using the previous month's average sale price. It was a reasonable system - except averages were hiding everything that mattered.

Some of their highest-spend keywords were generating the lowest-quality leads. Others, barely noticed, were consistently producing strong conversions. The data to see this existed. It just lived in two separate places that had never been connected.

The supply team couldn't see downstream performance. The sales team couldn't see where their leads were coming from. Each team was optimising for something the other team couldn't measure.

I built a connected dataset that linked lead generation keywords to downstream conversion and quality data. For the first time, both teams were looking at the same picture. Live dashboards gave the supply team visibility of sales outcomes, and the sales team visibility of lead source performance. Leadership got a single high-level view of volume, quality, and profit in one place.

Once everyone could see the same thing, the optimisation was obvious. Higher spend on what worked, less on what didn't - and a differentiated pricing model that charged more for leads that converted.

Month-on-month profit increased. Pricing went up - and clients accepted it, because they could see the results. The firm retained 100% of its key clients.

Supply and sales stopped negotiating in monthly meetings and started collaborating. Data became the bridge between two teams that had been working at cross purposes.

A European Director at a global consulting firm noticed that when she travelled with her team, her days were half empty. The performance data seemed to confirm it: too many "one-visit days". The target became cutting them by a quarter. The conclusion felt obvious - the Account Executives weren't scheduling enough meetings on each trip.

The company's core performance metric -"one-visit days" - was painting a misleading picture. It wasn't accounting for local meetings near the company's base, the first or last days of multi-country trips, or any context around why a trip was structured the way it was.

Strip out those outliers, and the "inefficiency" looked very different. The team wasn't nearly as far off as the numbers suggested. But there was still room to improve - it was just in a different place than anyone had been looking.

First I rebuilt the numbers without those outliers, so the Director could see the real size of the problem. Then I asked the people booking the meetings what made it hard. They wanted to book more - they were paid on it. They just couldn't see which clients and prospects were near each other, in towns they'd never heard of. So I built a map of every client and prospect by territory, so they could see who else was nearby while they were on the phone.

Nobody was asked to work harder.

The Director had the real picture: a smaller problem than the data suggested, with a different cause. The map was piloted with one team, who liked it, and was forecast to add up to $1M a year in sales if half of joint trips gained one extra visit. The full rollout was deferred to the following year, after I'd moved role, so that forecast was never tested.

The UK operations of a global market research firm were under pressure from every direction: clients demanding earlier reports and budgets tightening. The teams had already improved their processing speed significantly. It still wasn't enough.

The natural answer was effort: if people worked faster, reports would go out sooner. And every delayed report was a contract retention risk.

I mapped the full reporting process from raw data intake to client delivery. Each stage was owned by a separate sub-team, each doing their part well. On paper, it was working. In practice, something wasn't adding up.

When I talked to each team, the picture became clear. Each stage was waiting for all the data to arrive before starting. But not every product group needed every retailer, and nobody had written down which retailers fed which product groups, or when each one's data usually arrived. The slowdown wasn't inside the teams. It was in the gaps between them.

So I mapped it: which retailers fed each product group, and when each was due. A product group could then start as soon as its own retailers were in, instead of waiting for the full batch. A short weekly stand-up across the three teams showed what was ready and what was coming.

Nobody was asked to go faster. Each team could see what was coming, and got longer to do its part.

Then we changed the target itself. The old score rewarded publishing as many product groups as early as possible, not getting the client's report out. Once the goal became client reports by 2pm Thursday, the share of weekly reports out by the end of Thursday rose from 55% to 75%, and by 2pm from 23% to 58%. Clients got their data in time to act on it the following week.

None of it needed extra people.