How ScalixAI Turned Wispr Flow's LinkedIn Ads Into $856K in Influenced Revenue in a Single Quarter

About the Client

Founded in 2021 by Tanay Kothari and Sahaj Garg, Wispr Flow is an AI-powered voice dictation tool that lets people type up to 3 times faster by speaking naturally across documents, emails, and Slack. Based in San Francisco, the company turns speech into accurate, context-aware text in real time. Wispr Flow operates in the AI-native productivity category and is trusted by teams at OpenAI, Epic Games, Wealthsimple, and a16z.

Background

When Wispr Flow brought Scalix AI in, the LinkedIn account was already live and spending, generating impressions and clicks every week. The company had also invested in organic creator partnerships well before the engagement, and those were already earning real traction on their own. 

What none of this activity was generating, though, was a clear answer to the only question that mattered: whether any of it connected to revenue. There was no way to tie the engagement to the deals that were actually closing. 

That gap made it impossible to judge what the channel was truly worth, or to scale it with confidence. The numbers looked busy, but they did not add up to a decision.

The context made the problem sharper. Wispr Flow sells into a competitive, fast-growing category, and its buyers are technical, discerning, and usually arrive through multiple touchpoints before making a purchase. That kind of buyer rarely clicks an ad and signs the same week. 

The brief was direct. Build a demand generation engine that reaches the right buyers, generates measurable pipeline, and closes the attribution loop all the way through to closed-won revenue.

Challenges

Challenge 1: No attribution from ad activity to closed revenue

LinkedIn was producing clicks and engagement, but none of it connected to what was happening inside the CRM. This was the primary issue, because it sat underneath every other decision.

• Ad activity and deal activity lived in two separate systems that did not talk to each other.
• Spend decisions were being made without a reliable performance signal.
• Channel ROI could not be measured, justified, or defended in a budget review.

Challenge 2: Reaching the right buyer in a competitive category

AI productivity tools are a crowded space, and the people who actually approve a purchase are not easy to reach with broad targeting.

• The real decision-makers, including founders, VPs of Engineering, and C-suite leaders, required tightly defined targeting to reach at all.
• Generic messaging gets identified and scrolled past, so the creative had to speak to speed, accuracy, and enterprise-grade reliability.
• Without that precision, the budget would have drained into impressions that could never convert.

Challenge 3: Converting engagement into pipeline-quality outcomes

Surface-level metrics were moving, but they were not turning into a measurable revenue contribution.

• The engagement gave no signal about which clicks came from real buyers or moved a deal forward.
• The account needed to be rebuilt around buyer intent rather than raw engagement.
• Creative and targeting had to align to the funnel stages most likely to influence deal progression.

Solution

We did not start by changing campaigns. We started by building the foundation that would let us read what the campaigns were actually doing. Scalix AI deployed its full-funnel LinkedIn framework, beginning with a structural audit of campaign architecture, targeting, and creative before scaling a single additional dollar of spend. The operating principle was simple. No optimization decision should be made without a clear attribution foundation underneath it.

Strategy 1: Full-Funnel Campaign Architecture

This directly addressed the third challenge, turning engagement into pipeline. We restructured the account around Wispr Flow's actual B2B buying journey rather than around isolated campaigns. 

That meant dedicated top-of-funnel brand awareness campaigns to build initial reach, mid-funnel engagement campaigns aimed at warm audiences, and bottom-of-funnel retargeting campaigns built specifically to convert. 

Each tier was given a distinct role and a measurable outcome, so we could see what every stage was contributing instead of judging the whole account by one blended number.

Strategy 2: ICP-Focused Buyer Targeting

This addressed the second challenge, reaching the right buyer in a competitive category. We defined Wispr Flow's ideal customer profile precisely, covering founders, software engineers, VPs of Engineering, CEOs, and C-suite leaders at technology and professional services companies, and then built tightly segmented audiences around that buyer. 

For the mid-funnel engagement campaign, we worked from a curated list of 12,500 target companies, which focused spend on the highest-value accounts rather than spreading it thin across the category.

Strategy 3: Differentiated Creative Testing

We developed and tested several ad formats side by side, including static image ads and video ads, all built around Wispr Flow's strongest proof points: three-times-faster typing, AI-native accuracy, and trust signals from marquee customers.

Wispr Flow had also invested in organic creator partnerships well before the engagement, and those partnerships were already working. Rather than build new creative from scratch, we put the paid budget behind that content. The added distribution extended its reach far beyond the organic audience and influenced conversions across departments, not just within a single buyer type.

The bottom-of-funnel image ad sets, including the creator-led formats, consistently outperformed video on conversion efficiency, and they became the core vehicle we scaled behind. This strategy reinforced both the targeting and the pipeline goals. 

Strategy 4: Fibbler Revenue Attribution Integration

This closed the first and most important challenge: the absence of attribution. We connected LinkedIn ad exposure data to Wispr Flow's CRM through Fibbler, which enabled any-touch attribution of closed deals back to LinkedIn activity. 

For the first time, an impression on LinkedIn could be tied to a deal in the pipeline. This closed the loop between ad exposure and real revenue, and it gave the team a defensible, deal-by-deal view of what LinkedIn was contributing to the business. Everything else in the program could now be read against revenue instead of against clicks.

Ongoing Optimization

Ongoing optimization is built into the framework rather than bolted on at the end. Weekly performance reviews track conversion quality, not just volume, and feed those signals back into targeting and bidding. As the bottom-of-funnel image campaigns prove out the strongest ROI, budget shifts toward the highest-performing ad sets and away from formats that generate engagement without downstream revenue.

Reporting runs on the same cadence, so spend, conversions, cost per conversion, and influenced revenue stay visible against the plan rather than reconstructed after the fact. Each iteration compounds, turning better signals into better allocation and steadily higher revenue attribution.

Results

April – June 2026

$856K in LinkedIn-influenced closed revenue. 25 closed-won deals. Roughly $88K in LinkedIn spend. An ~868% revenue ROI.

Across the quarter, the account spent $88,433 and drove 7,995 conversions at an average cost per conversion of $11.06, with a 5.96% average CTR across all ad sets. Those are healthy efficiency numbers, but they are not the headline. The headline is what those clicks were worth: $856K in revenue that LinkedIn influenced, none of which the channel could previously claim.

A few patterns from the ad-set breakdown are worth naming. The image-led ad sets carried the program. The bottom-of-funnel image formats converted at $8.97 and $12.77 per conversion, and the warm retargeting set converted at $5.65, which is exactly what you would expect from a well-structured funnel where the warmest audiences close the cheapest. The mid-funnel engagement campaign against the 12,500-company list ran at a higher $51.00 per conversion, which is appropriate for an account-list play designed to influence high-value enterprise accounts rather than to win on raw cost.

The deals that LinkedIn influenced were not small. The largest single closed-won deal in the window was Epic Games at $244,800, followed by OpenAI at $146,500 and Wealthsimple at $144,000. These are enterprise contracts with long, multi-stakeholder sales cycles that last-click reporting can never explain.

The audience data confirmed the targeting was landing on the real buyer. Co-Founders engaged at a 9.04% CTR, CEOs at 8.30%, and VPs of Engineering at 6.86%, all well above the account average. The company-size split told the same story, with 71.07% of clicks coming from enterprise accounts of 10,001 or more employees. That is not a broad reach that happens to include a few good accounts. That is an audience that matches the ICP.

Attribution note: Revenue figures use an any-touch, influenced model through Fibbler, which credits LinkedIn for any closed deal where the company had prior LinkedIn ad exposure. Influence is not the same as the sole cause. What this model shows is that LinkedIn was consistently present in the deal journey of the accounts that converted. Because enterprise sales cycles are multi-touch and multi-month, the revenue ROI is expected to keep building as further opportunities in the pipeline progress to close.

Business Impact

In one quarter, LinkedIn went from an unmeasured cost center to a measurable, enterprise-grade revenue channel for Wispr Flow. 

The program closed 25 LinkedIn-influenced deals, including landmark logos such as Epic Games, OpenAI, Wealthsimple, and Chegg, for $856K in known-influenced revenue against roughly $88K in spend. That is an ~868% revenue ROI, and because it is built on deal-level attribution rather than pipeline speculation, it is a number leadership can defend in any room.

The deeper change is structural. For the first time, every closed deal is traceable back to the specific LinkedIn campaign and audience segment that influenced it, which means budget decisions are now made on evidence instead of instinct.

What the company has now is a scalable, attribution-accurate LinkedIn demand generation engine, with the measurement built in to read what works as it grows. The channel was running before. Now it is running, measured, and ready to scale alongside Wispr Flow's enterprise motion.