Hubspot recently published an AI email marketing case study showing how they increased conversion by 82% with generative AI.

We’ve anticipated for months that businesses could achieve huge conversion gains with AI-powered email personalization. Thanks to Hubspot’s case study, we now have a solid case study and a better understanding of how to build out email personalization at scale.

The Hubspot case study is a crucial advancement in AI marketing because it details their process for improving email conversion with AI personalization.

Choosing AI Use Cases

The Hubspot marketing team started by prioritizing tasks and workflows that could be accelerated with AI. They identified several marketing problems for consideration–all common issues for marketing teams.

They choose AI use cases to tackle by:

• Building a dedicated AI solutions team to identify marketing AI use cases.
• Brainstorming a list of 100+ marketing AI use cases.
• Evaluating each potential AI use case based on its:
  • Breadth of impact
  • Efficiency and speed

This process helped them prioritize AI use cases. Ultimately, they chose to implement AI email personalization because of its likely impact on important metrics and applicability across many marketing initiatives.

The Problem

The HubSpot team focused on visitors who download educational resources from their website. Their goal was to increase engagement with their marketing emails and push their website visitors closer to a sale.

Hubspot’s email CRO efforts had historically only produced small, incremental gains. The team hoped to use AI to achieve a more significant lift in conversion.

The Goal

The two goals of the Hubspot marketing team relevant to this case study were to:

1. Learn how to accelerate marketing workflows and results with AI
2. Improve email conversion rates to better nurture leads to a purchase

Metrics

The primary metrics the Hubspot team targeted for improvement were email open rate and click-through rate.

Methodology

The Hubspot team made a critical decision: they paired an AI expert with an email marketing and brand persona expert. This ensured that brand voice and email marketing best practices were applied at every step of the AI implementation.

The team designed their AI personalization as follows:

1. When website visitors complete a download form, they submit their name, company website URL, phone number, and email.
2. Hubspot scrapes their company’s website to learn more about what they do. They combine this information with other profile data on this visitor–e.g., pages visited, previous downloads, webinar attendance, etc.
3. This information allows the AI to infer what the visitor is trying to accomplish, which led them to download the resource.
4. Using the “jobs to be done” framework, AI writes a persona that details the visitor’s journey until this point and what they are looking to accomplish today.
5. AI predicts the perfect website resources (whether or not they exist) that would help the visitor accomplish their goals.
6. AI performs a semantic search in a vector database to find website resources similar to the ideal resources.
7. Finally, AI writes a personalized email to the visitor, including the selected website resources, explaining how they could help them accomplish their goals.

Technology Used

To achieve their solution, Hubspot utilized the following tools:

1) GPT4 – writes an individualized persona for the lead using the “jobs to be done” framework and predicts the ideal website resources for this individual (whether the resources exist or not).

2) Vector database – uses semantic search to identify existing website resources relevant to the ideal resources generated by the LLM’s prediction.

3) GPT4 – writes a personalized email using the individualized persona and the relevant resources.

Personalized Persona Example

Hubspot released the following example of a persona written by GPT4 for a website visitor who runs a coffee shop.

[Name], associated with [Company], a coffee online retailer, has demonstrated a consistent interest in enhancing her marketing strategies. Initially engaging with influencer marketing resources, she has recently shifted her focus towards content organization and planning. As the winter season unfolds, her interest in content calendars suggests a drive to streamline marketing efforts, possibly in preparation for seasonal promotions or new product launches. [Name] ‘s journey from influencer marketing to content planning reflects a strategic approach to brand growth and audience engagement.

Results

Hubspot’s email conversion rate increased by 82%!

They haven’t mentioned any evidence that this increase in conversion leads to sales. But, more engagement with their content likely moves website visitors closer to a purchase.

Learnings

This case study provides critical lessons learned that can be applied to nearly every marketing team implementing AI for email personalization.

Here are some lessons learned from the case study:

1) Effective prioritization

The Hubspot team measured the productivity and marketing impact of each potential AI use case. This helped the team prioritize 100+ AI ideas and deploy their resources where they would have the most impact.

2) Redesign entire workflows

Hubspot focused on AI projects aligned with critical metrics and have a significant business impact. Many marketers are experimenting with AI for individual tasks and miss how AI could help them redesign entire workflows. Hubspot developed the AI email personalization workflow rather than using AI to automate individual tasks in the existing segmentation workflow.

3) Collect the right data

Hubspot combines the download form, website behavioral data, and information scraped from the web to build the visitor’s profile. The visitor’s role and website are most useful for building the personalized visitor persona.

4) Predictive, not generative AI

The Hubspot team uses an LLM to do prediction when it infers the most helpful resources for each visitor. Many marketers don’t realize that LLMs can be used for prediction. I’ve used LLMs for prediction and classification tasks like categorizing contacts.

I’ve been thinking about generative AI consulting. 

My head has been spinning with AI use cases for marketers, agencies, and business operations.

In the past six weeks, I have hosted a dozen AI training events and witnessed firsthand how our generative AI technology has increased productivity at my agency by 30%.

Yet, agencies and consultants are failing to sell their generative AI consulting services. I’ve heard from several agency owners struggling to sell AI consulting services.

Why?

They believe businesses are hesitant because the competition for AI services is fierce. But that’s not the problem. 

The problem?

Businesses aren’t ready to buy generative AI consulting.

Let me explain.

My AI workshop attendees were most interested in exploring generative AI to extract insights from data, create document drafts, streamline report generation, and automate data entry.

But despite the potential, businesses are hesitant to purchase AI consulting services.

Why Businesses Aren’t Buying 

Businesses are hesitant to buy for several reasons. Every business is different. But here’s what I’ve

1. Still Exploring – Many businesses are still in the early stages of exploring how AI can benefit their operations. They don’t yet have a clear understanding of use cases or potential benefits. They may have a wait-and-see attitude or be satisfied with their internal efforts.

3. Lack of Business Case – AI consultants struggle to articulate a convincing business case for generative AI. ROI is difficult for you to quantify BEFORE thoroughly assessing a business’s current processes and objectives. This can feel like a chicken-or-the-egg situation for consultants. What comes first? The business case or the consulting engagement that clearly defines the business case?

2. The DIY Option – Some business leaders think implementing AI will be straightforward and prefer a DIY approach before hiring outside experts. This often leads to frustration. But this approach feels safer than going “all-in” with an expensive consultant.

4. Overhyped Technology – After years of AI hype without many tangible use cases, some business leaders have AI fatigue and are hesitant to invest in more consulting to prove its value. Convincing case studies on rival businesses may be required to get some to act.

5. How to Choose – Businesses have no way to evaluate consultants pitching AI services. Every proposal looks vague and similar. Generative AI is now so they have few contacts who can refer them to a trustworthy AI consultant.

6. Cultural Resistance – Change management is hard, even for well-managed organizations. Resistance may come from competing business priorities, employees fearing job loss, or managers preferring “low-risk” incremental change.

Do Businesses Want Generative AI?

Some individual contributors are racing ahead with generative AI, while leadership is more cautious about the risks.

Individual contributors want training on how to automate their tedious day-to-day work. If they have a training budget, they will spend it to learn more about generative AI. 

At the leadership level, however, the perspective shifts towards a more strategic approach. 

Business leaders are intrigued by the potential of generative AI to streamline their organizations, yet they are approaching AI adoption with extreme caution. 

Business leaders view AI implementation as a challenging exercise in change management. Risks include employee resistance, data privacy concerns, cultural inertia, unforeseen technical hurdles, and uncertain ROI. Before they make the leap, they need proof points and case studies from similar organizations showing real-world gains from generative AI.

Selling Generative AI Consulting is Hard

Businesses have been burned in the past by consultants with flashy presentations and promises of results.

Consultants who sell AI’s sizzle without the steak of proven results will struggle to convince business leaders to invest anything beyond a small, exploratory budget. Cautious executives are hard-pressed to pay top dollar for speculative projects.

Additionally, the market is flooded with consultants focusing only on generative AI training and strategy. Businesses want not only strategic advice but also the tangible, practical implementation of AI solutions. Offering a comprehensive service—from initial assessment to deploying custom AI solutions—becomes a key differentiator. 

Still, without proof points, AI sounds theoretical and intangible to skeptical business leaders. They want to know how AI will impact productivity, efficiency, costs, and other important business metrics.

Agencies are Well-Positioned

Businesses will hire generative AI consultants from firms they trust, not a cold email. 

As an agency or consultant, you are a trusted advisor to your clients. Many consultants overstate their capabilities, making promises they can’t deliver to win business. So businesses are wary.

If you can’t secure business within those trusted relationships, it’s a strong indication that you need to refine the offering, target different use cases, or work on your case studies.

How Will This Market Develop? 

In the near term, businesses will look to trusted partners to implement exploratory generative AI projects. They will be persuaded only by proven use cases backed by industry case studies. 

Over time, competitive pressure will build for businesses to deploy AI as case studies emerge showing its benefits. As word of AI success stories circulates, the demand for AI consulting will surge.

Go-to-Market Strategy  

Sell generative AI consulting by ditching the speculative pitch and first building AI champions internally. AI training leads to organic buy-in and upsell opportunities.

By offering existing clients AI training or no-cost assessments, agencies can establish credibility, demonstrate tangible benefits, and organically build demand for targeted AI solutions, leading to larger consulting deals.

Here’s my recommended go-to-market strategy:

1. Offer AI training to your existing clients. 

2. Upsell them on AI consulting for targeted solutions for specific use cases.  

3. Write case studies based on your work 

4. Win new clients by pitching case studies showing tangible benefits for specific use cases 

This approach will allow you to build demand for your AI consulting while building the business cases required to win future contracts.