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Trendkite

by Eric HuddlestonLaunched 2013via Nathan Latka Podcast
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The Spark

Eric Huddleston joined Trendkite as employee number seven in mid-2014, a year after the company was founded by a team that had gone through the DreamIt accelerator program. The startup had raised its initial seed round from Austin-based Silverton venture firm—with a notable catch: their investment was contingent on bringing in an external CEO. Eric, who had relationships with the Silverton partners dating back decades, was brought in to lead the company after already introducing them to a handful of customers who loved the product. Mercury Fund (then a DFJ affiliate) co-led that early round. Rather than creating friction, the transition was smooth because the company was still tiny and the founding team was "radically aligned" on the vision: to help brands and agencies understand the impact of PR across their media efforts.

Building the First Version

The product itself wasn't built from scratch. Eric had previously run product for Sprinkler and helped bring to market a big data analytics platform (Docus Group) that analyzed social data to help brands optimize their paid, owned, and earned media tactics. When Sprinkler acquired Docus Group, the team had already built a map of roughly 50,000 companies and brands across geographies with deep benchmarking of their social performance. Trendkite took this DNA and focused it squarely on earned media—the high-credibility endorsements that come from third parties—which CMOs view as a distinct lever alongside paid, owned, and shared media.

Finding the First Customers

With a product-market fit thesis already validated, Eric spent all of 2014 doing something he recommends every founder prioritize: figuring out unit economics. He wasn't chasing vanity metrics. Instead, he obsessed over the customer acquisition payback period, understanding both the revenue and cash basis of that metric (which can differ significantly depending on payment terms). This disciplined focus on unit economics guided all subsequent decisions about how to scale the sales engine. The company served large enterprise customers like Nike, who used Trendkite to understand how their brand image varied across geographies based on sports popularity, endorsers, and product trends in each region.

What Worked (and What Didn't)

By the time of this podcast interview (2017), Trendkite had nailed a highly efficient direct sales playbook. With 200+ employees, roughly 75 of them were quota-carrying sales reps (BDRs, SDRs, inbound qualifiers, and prospectors). Eric implemented a "dynamic CAC allocation" system that tuned account allocations and sales support bi-weekly based on rep efficiency. Minimum contract values started at five figures (as low as $10K) and climbed into the six figures, with most customers paying "five figures to low six figures annually" or "conservatively $50K all the way up to well over $100K." The company achieved an 8-month payback period on a gross profit basis and maintained net retention rates in the north of 90%, though they were just beginning to crack negative net revenue churn. Expansion revenue became a serious lever in the last 12 months before this interview, driven by identifying geographic, brand, and business unit expansion opportunities within their install base, as well as launching two major products during that year.

Where They Are Now

By 2017, Trendkite had raised approximately $37M in total funding and grown to serve between 1,000 and 10,000 customers across virtually every vertical and geography—from two-person PR agencies to Fortune 500 companies like Coca-Cola. The company was headquartered in Austin with major offices in San Francisco and London. With a minimum ACV of $10K and a large customer base leaning toward six-figure deals, the company was running at well north of $10M ARR with triple-digit year-over-year growth rates. Eric was confident they'd maintain triple-digit growth through 2017 and 2018 before the "law of gravity" kicked in with 3x, 3x, 3x, then 2x, 2x, 2x progression toward an eventual IPO exit.

Why It Worked
  • The founding team's deep expertise in media analytics and prior success building similar products meant they could focus on product-market fit validation rather than discovering what customers needed, enabling them to move faster to unit economics optimization.
  • By obsessing over customer acquisition payback period and cash-basis economics rather than vanity metrics, Eric built a repeatable sales model that could scale predictably without burning capital on inefficient channels.
  • The combination of enterprise-direct-sales traction with high contract values ($50K-$100K+) and strong net retention created a sustainable unit economics foundation that justified investing heavily in a 75-person sales organization.
  • Implementing dynamic CAC allocation tuned bi-weekly based on rep efficiency allowed the company to systematically improve sales productivity rather than assuming a static playbook would work across all customer segments.
How to Replicate
  • 1.Before scaling sales, spend dedicated time calculating your true customer acquisition payback period on both a revenue and cash basis, accounting for actual payment terms, and use this metric—not vanity metrics—to guide all subsequent hiring and channel decisions.
  • 2.Hire a leadership team with direct prior experience building similar products or solving related problems, so you can validate product-market fit quickly and avoid months of customer discovery that competitors without that expertise will need.
  • 3.Target enterprise customers with minimum contract values in the five-figure range who have a measurable, recurring need for your solution, and build your entire sales organization around closing deals of that size rather than trying to serve both SMBs and enterprises.
  • 4.Create a systematic process to measure individual sales rep efficiency weekly or bi-weekly, and reallocate accounts, support, and resources to the most productive reps rather than assuming all salespeople will perform equally across different customer types.

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