Visualizing the Buyer’s Journey with Intent

Much of the buyer’s journey happens behind closed doors. So how do you find the buying behaviours that are traceable, and make them work in your favour?

Intent can’t tell you everything, but layering buying behaviours, from multiple sources, lets you know when someone’s in-market, how close they are to buying, and how you can make sure you’re top of mind while they’re looking. To test this theory, we mapped the buyer’s journey. Here’s how:

Using technographics, we found any company that installed HubSpot, Stripe, or Shopify, over a 90-day period. From there, we analyzed nearly 1,000 companies to find the relevant intent signals each company showed in the 90-days leading up to installation. Those intent signals include engaging with competitors, custom keywords, hiring relevant roles, or attending events. From there, we used the timeline from first sign of intent to install and the different intent signal types to visualize the buying process.

How does buying intent change from beginning to end? How does what you’re selling impact buying behaviour? Are competitive signals further in the buyer’s journey? And what can marketers do to turn data like this into a competitive advantage?

On that note, we broke the buyer’s journey into 4 stages. Each stage makes up 25% of the buying cycles. In other words, if the buyer’s journey that’s visible with intent is 100 days, Stage 1 is days 1-25, Stage 2 is 25-50, and so on. 

The Buyer’s Journey as Seen by Intent

Across nearly 1,000 companies a few trends were universal:

• • Action-based intent increases 60% from Stage 1 to 2, 36% from stage 2 to 3, then drops heavily in the final stage of buying.
  • Competitive engagements peak approximately 80% of the way through the buying cycle.
  • Most signals peak then fall, except competitive signals which continuously grow.
  • Hiring is prioritized about 60% of the way through the buying cycle.

Key Findings and What to Do About Them

What to do with this data:

• • If a buyer’s been showing intent for a while, focus on more direct channels like hyper-targeted ads and outbound sales.
  • Prioritize competitive and value-based messaging with social proof and comparison charts.
  • Pay closer attention to first-party channels if third-party signals decrease to check if buyer’s are still looking.

What to do with this data:

• • Use product-led marketing to help buyers visualize their workflow in your tech before buying, helping you stand out and set expectations for once they’re set up.
  • Complex implementation and competing with big brands like HubSpot? Focus messaging on features you have that they don’t. Use intent signals to see what your buyers care about specifically and let that shine.

What to do with this data:

• • Create marketing automation to engage warm accounts as they start showing intent. If you have the granularity to see the types of intent evolve, tailor your messaging to what buyers specifically engage with.
  • Automate the creation of ad audiences so as soon as buyers enter the market, they start seeing your name pop up. Use targeted search ads to be first on the list as they do research.
  • Build intent into your lead scoring model to pass leads to sales as they are warmed up between engaging with other content and competitors, and starting to see your ads in their feeds.

What to do with this data:

• • Create hiring-based content. An onboarding checklist, guides to setting and tracking goals, a compiled list of relevant people to follow on LinkedIn. These build awareness, get your name circulated internally, and place you top of mind once they’ve hired.
  • Monitor when fit companies are hiring for relevant roles. Factor this into lead scoring, use it as personalization, and even create marketing drip campaigns tailored to companies actively hiring.