How Asana used Hightouch Match Booster to lift match rates up to 57% across every paid channel and realize estimated 15× ROI

Results at a glance

The challenge

Maddie Ogletree is the Senior Manager of Technical Marketing Operations at Asana. Her team supports Asana's paid media program by building the suppression and lookalike audiences that determine campaign targeting and optimization.

Those audiences directly impact campaign performance. Match rates affect whether Asana can reliably suppress existing customers from acquisition campaigns. And low match rates made it difficult to build lookalike audiences large enough for ad platforms to target effectively.

For years, those low match rates created wasted spend and limited lookalike experimentation.

Asana is a B2B software company, and people often sign up with work email addresses. But major ad platforms rely on personal identifiers, and that mismatch showed up across every audience list. On Google Ads, Asana was matching about 10% of uploaded audiences. Meta was closer to 21%.

With suppression lists matching at those rates, ads intended for prospects were still showing up to active Asana customers. At the same time, the team struggled to generate high-quality lookalike audiences because the seed audiences weren't large enough to meet platform thresholds.

The process didn't help either, as audiences had to be pulled directly out of the database by the data team, dropped into spreadsheets, and uploaded by hand to each ad platform on a quarterly cadence. Updates were rare, and the data was always behind. Maddie's summary of the before-state was "infrequent, very manual, and very low match rate."

"It's something we knew but didn't have a solution for at the time."

— Maddie Ogletree, Senior Manager, Advertising Technology, Asana

The solution

Maddie ran an evaluation for a solution that would solve the identity problem and the manual workflow problem at once.

The first requirement was a strong B2B identity graph. Most data onboarders didn't handle business email data, which made them effectively unworkable for Asana. Maddie compared the shortlist that survived on the practical criteria: cost, speed, and match rate improvement.

Hightouch met every requirement on their list.

Match Booster runs records through Hightouch's B2B identity graph, matching business emails to the personal identifiers the ad platforms have on file. Pricing is record-based, with no upcharges for B2B-specific matching or "enhanced" features. A new audience model takes about fifteen minutes to set up, and lists refresh daily after that.

The team was also impressed by integration speed. New integrations with Reddit, CTV platforms, and conversion APIs were shipped before Maddie had to ask for them.

Across every paid channel Asana ran on, match rates increased between 12% to 57%. LinkedIn match rates increased from 22% to 75%. Meta climbed from 21% into the 50–60% range. DV360 went from 30% to 65%, TikTok ran past 85%, and Google Ads doubled.

The numbers

With suppression matching at usable rates, Asana's prospecting budget could reach more actual prospects instead of its own customers.

Speed accelerated too. The quarterly upload cycle went away, and every audience automatically refreshes daily now. New audiences hit production fast enough that Maddie's team can finally test ideas that used to live in a backlog.

A recent example: Maddie's team built a niche, granular lookalike for a specific campaign. From request to available in the ad platforms took about a day. Before Match Booster, an audience that specific would have been too small to activate and would never have run, and that discovery would have happened after taking the time to manually build and upload the audience.

The transformation

Before Match Booster, Asana's time often went into making audiences usable: extracting data, formatting it, uploading it, and working around low match rates. Even when the strategy was right, execution and low match rates slowed the work down and limited what they could test.

Now that constraint is gone.

Maddie's team builds and activates audiences that refresh daily. Match rates are high enough that prospecting budget isn't spent on current customers, and optimization ideas reach the experimentation stage quickly instead of stalling. Work that once took weeks or months now takes a day or two, freeing the team to focus on targeting strategy rather than working around system limitations.

The biggest change is in how the team operates. Instead of asking "is this worth the effort to build?", they can ask "is this worth testing?" — and get an answer quickly.

That shift shows up in the volume and quality of experimentation. More niche audiences. Faster iteration. Ideas that used to sit in a backlog are now live in-market.

"I was able to build that audience and push it into Hightouch all over the course of a day and a half. And now it's already populating in the platforms — versus before, we probably wouldn't have even been able to do that."

— Maddie Ogletree, Senior Manager, Advertising Technology, Asana

Every B2B business is familiar with the downstream effect of poor match rates: wasted spend, lookalikes too small to run, ideas that never reach production. Match Booster closed that gap for Asana and returned an estimated 15× ROI. But the bigger payoff was that Asana stopped working around the data and started working with it.