The best practices & pitfalls avoided we've spotted amongst teams building our their account-based marketing strategies this year.
We've spotted a trend amongst SaaS teams in how they approach lead qualification; pre-qualifying leads with data and syncing to sales in real-time.
One of the most common questions our customers ask is "how are other teams using Hull?". Here's the top five use cases
We've spotted sales teams are using real-time data to enable their sales teams. Using triggers from inside their product, website, review sites, social & more to automate personalized sales outreach.
Multi-touch attribution is very similar to other data integration problems. Here's how teams like Pollfish, Drift & Lengow are building attribution models.
Sales and marketing don't easily work together. One of our largest Spotted posts addresses the best practices we've seen
Lead scoring is a widely used practice, but arbitrary scores have limited utility. Here’s how we’ve spotted teams using lead scoring & signals.
Almost every team we work with at Hull uses data enrichment to qualify leads, personalize messages, and fill in their profiles. Here’s the best practices we’ve spotted.
Your website has the widest reach, but is the trickiest to personalize. Here’s the web personalization trends we noticed B2B teams using.
Scaling SaaS startups generate leads from more channels than ever. Here’s how they’re tracking, unifying and taking action on all of their lead data.
"Personal email" more than a plain text template. Here's the best practices how data-driven teams are delivering 1:1 email personalization at scale.
We noticed teams using Hull to nurture leads across channels with personalized content. Here’s how they did it, and the results they saw.
Integrations aren’t the only way to integrate customer data. As startups-turn-scale-up, we see most startups move beyond integrations to these five tactics.
We’re seeing more B2B SaaS teams using account-based retargeting ads (vs. traditional retargeting ads).
With advanced ABM, “not enough data" is rarely the underlying problem. Your problem is tying all this data together.