Not Every Conversion Is a Good Conversion

Key Takeaways

• Cloudways rebuilt its paid search program to optimise for long-term customer value rather than conversion volume, feeding more than ten LTV-weighted customer personas directly into Google and Microsoft Ads.
• 1st Central built its targeting from lookalike audiences modelled on its own high-value customers rather than optimising broadly for as many quotes as possible.
• Cloudways’ customer value rose 66% and conversions rose 78%, ahead of the company’s eventual $350 million acquisition by DigitalOcean.
• 1st Central’s direct sales rose 33% and direct quotes rose 50%, though its own case study reports those as volume figures rather than a measured value uplift, a genuine difference in what each company was able to prove.

“More conversions” sounds like an unambiguous win, until two companies publishing case studies on this site both restructured their paid search specifically because it wasn’t. Cloudways rebuilt its entire paid search program around long-term customer value instead of sign-up volume. 1st Central built its targeting from its highest-value existing customers rather than casting the widest possible net. Both moves assume the same thing: that a conversion is not a conversion, some are worth chasing and some aren’t, and a campaign optimised purely for volume will happily bring in more of the wrong kind.

A hosting platform that stopped counting sign-ups

Cloudways, the managed cloud hosting platform that DigitalOcean would go on to acquire for $350 million, needed its paid search program to do more than drive sign-ups as it scaled. It needed campaigns optimised for the long-term value of the customers they brought in, not the volume of conversions those campaigns generated. Bind Media restructured Cloudways’ paid search accounts and built a custom conversion-data endpoint that fed more than ten LTV-weighted customer personas directly into Google and Microsoft Ads, with bid adjustments set by country rather than a single global target. The agency also worked on conversion-rate optimisation and landing pages to support the new targeting. “Working with Bind Media has been a refreshing experience. They managed Cloudways’ paid media and broke my recurring experience about agencies lacking expertise and ownership. Bind Media’s in-depth knowledge of the paid media landscape to drive SaaS growth and strong sense of ownership helped us achieve record numbers,” said Joakim Holmquist, Marketing Director at DigitalOcean. Customer value rose 66%, conversions rose 78% anyway, and conversion rate improved 20%, evidence that optimising for value didn’t come at the cost of volume.

An insurer that stopped optimising for the widest possible audience

1st Central, a car insurance provider, relied heavily on price-comparison channels for new business and was carrying acquisition costs that made growth expensive. Found built its Everysearch framework and used it to unify PPC brand and non-brand search, Google Performance Max campaigns and demand generation under one strategy. The targeting itself was built from segmented first-party customer data and lookalike audience modelling based specifically on 1st Central’s existing high-value customers, rather than a broad audience built to maximise reach. “Working with Found has significantly improved our customer segmentation and digital marketing at 1st Central. Their AI-powered approach helped us uncover valuable insights from our first-party data, allowing us to deliver highly personalised experiences in paid search and social. As a result, we have seen impressive results in overall performance,” said David Judic, 1st Central’s Digital and Marketing Director. Direct sales rose 33% and direct quotes rose 50%, and the campaign won a UK Search Award for Best Use of Search, B2C.

How “value” actually got built into the bidding

The two approaches sit at different points on the same spectrum, and the difference is instructive on its own. Cloudways’ system is the more explicit of the two: a custom conversion-data endpoint measuring value directly and feeding more than ten distinct LTV-weighted personas straight into Google and Microsoft Ads, with bid adjustments set per country rather than a single blanket target. That’s a company with enough historical customer data, and enough confidence in that data, to let an ad platform’s own bidding algorithm optimise against a value signal it built itself, market by market, rather than against the platform’s default proxy of a completed sign-up.

1st Central’s version is a step less direct, and reported that way in its own case study. Instead of feeding a measured value figure into the bid system, Found built lookalike audiences modelled on 1st Central’s existing high-value customers, alongside Google’s own AI-driven audience tools, and let the platform find more people who resembled that group. That’s a value signal encoded once, at the audience-definition stage, rather than continuously fed into the bidding the way Cloudways’ endpoint does. Both are legitimate ways to stop optimising for raw volume, but they put a different amount of trust in an ad platform’s ability to keep chasing value once the initial targeting is set.

Where the two case studies genuinely differ

The two companies made the same underlying bet, that targeting toward value beats targeting toward volume, but only one of them reports a measured value figure to back it up. Cloudways’ case study states a specific customer-value uplift, 66%, produced by an LTV-weighted targeting system built to measure exactly that. 1st Central’s case study reports sales and quote volume, up 33% and 50% respectively, alongside a targeting strategy built on high-value lookalikes, but it doesn’t report a customer-value or lifetime-value figure the way Cloudways’ does. That’s not a flaw in 1st Central’s result, a 33% rise in direct sales is a real and creditable outcome, but it’s worth being precise about which claim each case study actually supports. Cloudways can show its shift from volume to value worked on its own terms. 1st Central can show its shift toward higher-value targeting produced more volume, a related but distinct claim.

The assumption both approaches depend on

Neither case study addresses this directly, but both approaches share the same structural dependency: the value signal is only as good as the historical data used to define it. Cloudways’ LTV-weighted personas and 1st Central’s high-value lookalikes both work by assuming that a customer’s past behaviour, or the past behaviour of customers who resembled them, is a fair predictor of what a new customer will be worth. That’s a reasonable assumption for an established SaaS platform or an insurer with years of claims and renewal data behind it, which both of these companies are, but it’s worth naming as an assumption rather than a guarantee. A newer company with thinner historical data attempting the same shift from volume to value would be building its bidding system on a much shakier base, even using an identical methodology.

What this means for a paid search team reporting on conversions

Read together, these two case studies argue for the same discipline from two different directions. Cloudways shows that a campaign built around customer value doesn’t have to sacrifice conversion volume to get there, its conversions rose 78% at the same time its customer value rose 66%. 1st Central shows that narrowing targeting toward a company’s best existing customers can lift volume rather than shrink it. Neither result supports the instinct to treat “conversions” as a single, interchangeable number on a dashboard. A marketing team reporting conversion totals without weighting them by what those conversions are actually worth is measuring the same thing Cloudways and 1st Central both deliberately stopped measuring.

The channel problem sitting underneath 1st Central’s numbers

1st Central’s shift wasn’t only about how it targeted customers within a channel. Its own case study is explicit that the underlying problem was the channel itself: the company “relied heavily on price-comparison channels for new business, carrying high acquisition costs that made growth expensive,” and the entire campaign, unifying PPC brand and non-brand search, Performance Max and demand generation under Found’s Everysearch framework, was built with the stated aim of “reducing 1st Central’s reliance on third-party platforms.” That’s a materially different problem from the value-versus-volume question the rest of this article has been about. It’s a question of who owns the customer relationship at the point of sale.

A price-comparison channel is a reasonable place for an insurer to start. It supplies volume and demand without the insurer having to build its own audience from nothing, which is precisely why 1st Central was relying on it heavily enough for the cost to become a problem worth solving. But a comparison site sells the same customer to every insurer bidding on that page at the same time, which is a structurally different position from a direct channel an insurer controls end to end: it competes primarily on price within the comparison site’s interface, has no first-party relationship with the customer until after the sale, and pays the platform for access to demand it didn’t generate itself. First-party channels, PPC to 1st Central’s own site, direct search, its own customer data used for lookalike targeting, invert that: the company keeps the margin the platform would otherwise have taken, and it owns the customer data from the first interaction rather than the point of purchase. 1st Central’s direct sales rising 33% and direct quotes rising 50% are, read this way, less a paid search result and more a measure of how much new business moved off a channel it didn’t control and onto one it did.

For a startup building its first acquisition channels, the caution embedded in 1st Central’s case study is less about whether to use a comparison site, aggregator or marketplace early on, that kind of channel can be exactly the right way to get initial volume and the transaction data a young company doesn’t yet have, and the previous section in this article already flagged how thin that early data usually is. The caution is about treating that channel as a permanent home rather than a starting point. 1st Central didn’t abandon price comparison altogether; it built a parallel first-party channel specifically to reduce, not eliminate, its dependence on the one it didn’t own. A startup reliant on a single external platform, a marketplace, an app store, a comparison site, a lead-gen aggregator, for the bulk of its customer acquisition is in the same position 1st Central was in before this project: paying for demand it can’t fully see the economics of, and with no owned channel to fall back on if that platform’s costs, algorithm or terms change. The lesson from 1st Central’s own case study isn’t “avoid third-party channels.” It’s that a company should be actively building toward a first-party alternative before the acquisition cost on the channel it started with makes that build too expensive to fund.