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In conversation with Riley Timmins, Director of Marketing at Cacheflow. | Better identification of LinkedIn’s impact on the pipeline ⅔ reduction in attribution to ‘unknown sources’
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http://www.getcacheflow.com/
Revenue Operations
Global
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LinkedIn Campaign Management, Account Identification, Analytics, Alerts
Cacheflow, a Palo Alto startup, offers a deal-closing platform that unifies CPQ, renewal management, and billing, and integrates CRM and ERP reporting. Cacheflow simplifies the entire deal lifecycle, from quoting to ARR reporting, decreasing admin workload for RevOps, sales, and finance teams.
Read on to learn how Cacheflow managed to improve ROI from LinkedIn advertisement and kickstart warm outbound using first-party signals provided by Factors.ai.
LinkedIn Ads fueled our lead gen at Cacheflow, but murky attribution data often kept us in the dark. Tough to optimize spend without knowing what worked.
Riley Timmins
Director of Marketing at Cacheflow
Limited visibility into LinkedIn Ads performance
Cacheflow lean GTM team relied heavily on LinkedIn Ads for inbound leads. While leads came in, the lack of detailed attribution data made it difficult to understand which specific aspects of their LinkedIn ad strategy were driving results. This limited data made increasing the advertising budget a gamble.
Transition to Google Analytics 4 (GA4) Worsens the Problem
The transition to GA4 from Universal Analytics further compounded the issue. GA4's user interface lacked the intuitiveness of its predecessor, making it difficult for the team to navigate and extract the insights they needed. Additionally, GA4's depth of information fell short compared to Universal Analytics, particularly in terms of tracking conversion rates effectively.
Cacheflow’s Attempts to Improve Lead Source Attribution
To tackle these challenges, the team implemented a multi-pronged approach. They used UTM parameters and custom cookies to track user activities on their website more effectively. Additionally, they employed offline conversion tracking within their ad platforms to capture conversions that happened outside of their website.
However, despite these efforts, a significant blind spot remained – 30-40% of their lead sources still couldn't be identified. This lack of clarity forced Riley to manually review all deals every quarter, a time-consuming and often inconclusive process that yielded minimal actionable insights.
Clearbit’s Limitations
They then tried using Clearbit, a tool designed to identify companies visiting their website, which offered some improvement. However, Clearbit's limitations became apparent. The free plan could only identify the top 20% of visiting companies, with a maximum of 100 accounts identified weekly. Additionally, the data required manual analysis in spreadsheets before being usable. Even after filtering out irrelevant firms to refine targeting, the limited data from Clearbit meant their audience reach remained restricted.
These persistent challenges highlighted the urgent need for a solution like Factors.
With Factors, our LinkedIn Ads went from a guessing game to a laser-focused strategy. They integrated view-through data and engagement metrics, giving us deep audience insights for pinpoint targeting. This newfound clarity boosted my confidence in ad spend, and the results were incredible
Riley Timmins
Director of Marketing at Cacheflow
Cacheflow's journey with Factors began with a chance encounter between the chief executive officers of both companies at an industry event in the US. This sparked their interest in a solution to Cacheflow's marketing challenges, and competitive pricing made the decision to adopt Factors an easy one.
Understanding LinkedIn Ad Performance and Audience
From the outset, Factors provided valuable insights into Cacheflow's LinkedIn ad performance. By integrating view-through data from LinkedIn with account engagement data across Cacheflow's other channels (both first-party and third-party), Factors helped them understand how LinkedIn ads influenced accounts to explore their product and at which stage of the buying journey.
Furthermore, Factors provided demographic data for over half the companies engaging through LinkedIn. This enabled Cacheflow to fine-tune their campaigns. With more precise job title targeting through third-party data and the removal of irrelevant companies facilitated by Factors, Cacheflow's confidence in ad targeting soared. This allowed them to increase their ad spend while improving conversion rates.
Enhanced Outbound Campaign
Factors' benefits extended beyond LinkedIn. Their capabilities enhanced Cacheflow's new outbound campaign with better account identification and engagement tracking.
Improved Conversion Rate Optimization
The solution further aided conversion rate optimization (CRO) by tracking account visits to new website pages. It also seamlessly integrated with HubSpot to monitor progress in the sales funnel. This enabled measurement of conversion rates at a company level, a significant improvement over their previous setup.
Generating First-Party Intent Signals
One innovative use of Factors was in generating first-party intent signals. By bidding on competitors' terms, the Cacheflow team could identify interested companies that didn't initially convert but showed interest in similar products. This data was automatically sent to HubSpot, providing them with valuable intent signals traditionally purchased at high costs from third-party sources.
Exceptional Support
Cacheflow's experience with Factors' support has been exceptional. Regular sessions with their dedicated CSM, provided not only customized reports but also empowered them to build their own. This fostered self-sufficiency in their marketing efforts.
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Cacheflow now also aims to bolster brand presence through content marketing and thought leadership. This includes brand ads, data-driven industry benchmarks, and insights showcasing their differentiation. Tracking long-term ROI with Factors on this brand awareness content allows for a more balanced marketing strategy, driving both immediate pipeline and long-term brand value.
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