FloQast EMEA Doubles BDR Productivity Using HighTempo

Working closely with FloQast's sales and marketing leaders, HighTempo began by defining what a great-fit account actually looks like across each European market and product line. This wasn't about applying a single generic ICP. It was about building multiple, tailored profiles that reflected distinct buying motions across the region.

• ICP Development: Creating multiple Ideal Customer Profiles aligned to regional and product-level segmentation, each one reflecting the specific characteristics that made an account a strong fit for FloQast's proposition in that market.
• Signal Definition: Identifying the buying signals that indicate a company is likely to need FloQast, and defining how each signal should be weighted relative to the others.
• Market Understanding: Mapping the nuances of each European market, including regional compliance requirements, typical tech stacks, and competitive landscape differences that would shape targeting decisions.

This strategic groundwork created the clarity needed before any automation or AI-driven enrichment could be effective. It's a step that many organizations try to skip, often at great cost.

With the ICPs and signal definitions locked in, HighTempo configured and trained its AI-driven agents, teaching them what a high-propensity account actually looks like in practice. Three inputs shaped the training:

• The refined ICPs & signal definitions: Ingested directly into the agents as the foundational scoring framework: the rules that defined fit, the signals that indicated intent, and the weightings that determined how each factor mattered.
• Current customers: Analyzed to identify the patterns and attributes that defined FloQast's best-fit accounts, giving the agents a clear picture of what "good" looks like in the real world.
• Closed-lost & previously engaged accounts: Studied to understand why certain fit-looking accounts didn't convert, helping the agents distinguish between accounts that match the ICP on paper and those that genuinely have buying intent.

Multiple cycles of learning, testing, and adjustment followed. Each iteration was reviewed with the FloQast team, surfacing new patterns and refining the propensity ranking model until the outputs matched both the data and the on-the-ground experience of the sales team.

Once the model was validated, HighTempo delivered a Ranking Methodology Document: a transparent breakdown of exactly how accounts would be scored, including point weightings for each ICP attribute and signal. FloQast could review, tweak, and approve the methodology before a single final list was produced, ensuring complete alignment between the scoring logic and their go-to-market priorities.

With the agents fully trained and the ranking model validated, HighTempo ran the final production pass independently. Rather than working from a single static list, the agents drew from three distinct account sources and ranked them all against each other in a single unified universe:

• That region's initial target account list: The accounts FloQast was already focused on, now scored against the refined ICP and propensity model so the team could see which of their existing targets were genuinely high-fit and which were not.
• Closed-lost & previously engaged accounts: Re-evaluated through the new lens to surface high-value re-engagement opportunities. Accounts that were a strong fit but had been lost or gone cold, now with fresh context and timing signals.
• Agent-discovered accounts: Net-new accounts identified by the agents that matched the refined ICP but had never appeared on FloQast's radar, expanding the addressable market beyond what the team had previously considered.

The result was a single ranked universe where every account, regardless of source, was held to the same standard, backed by transparent, auditable evidence including source URLs and scoring breakdowns. Lists were delivered in the exact formats and structures required by FloQast's BDR and marketing teams for immediate execution. No reformatting, no manual processing required.