Learn how to use Apollo.io AI  Research to prioritize accounts at scale and personalize outreach without stitching together Clay, Zapier, or extra tools. We build a targeted list with lookalikes, craft a web-enabled prompt, create a Product Signal Score custom field, and turn that into a clean sentence you can drop into Apollo sequences. Then we automate it with weekly workflows that research companies, score fit, and queue verified contacts so your team spends less time digging and more time starting conversations. If you want help implementing this in your Apollo instance, reach out and I can set this up for you.

00:00 Intro and goals of the tutorial
00:12 Apollo.io AI Research inside Apollo (no Clay or Zapier)
00:19 Research goals: pick accounts, segment, lifecycle, personalize email
00:45 Client example and target list (user testing company)
01:02 Lookalikes for connected fitness (Whoop, Fitbit, Zwift)
01:20 Filters vs AI for hard-to-find signals
01:31 SDR manual research vs AI at scale in Apollo
01:58 Overview of the Apollo.io AI Research workflow
02:06 Stage 1 – build and run a custom AI prompt (use ChatGPT to design it)
03:02 Model choice in Apollo (Perplexity Sonar with web access)
03:16 Long-prompt details: scan site, collect signals, score 1–5
03:44 What to count as good vs bad signals (beta programs, roadmap, changelog; avoid Glassdoor)
04:09 Create the “Product Signal Score” custom field
04:34 Test on 10 rows and review outputs
05:07 Iterating and refining the prompt for better signals
05:29 Stage 2 – generate the explanation field
05:47 Stage 3 – generate a product-signal sentence for emails (style rules)
06:15 Example: Zwift scored 5 and referenced in the email
07:01 Using variables in the Apollo sequence
07:24 Contact example (Claire McGowan at Zwift) and FutureWorks reference
07:51 Tips before scaling: trial and error, tweak first
08:03 Automation overview
08:17 Workflow 1 – company-level weekly research run
09:09 Weekly cadence, 500 companies, credit usage
09:28 Workflow 2 – person-level sequencing after scoring
10:20 Verify emails and drop into the personalized sequence
10:41 Throughput pacing (about 300 per week)
10:49 Measure what works and iterate
11:10 Wrap-up and CTA to reach out for help

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Apollo.io AI Research for Personalized Outreach and Account Prioritization

Introduction

In this tutorial, I’ll walk through how to use Apollo’s AI Power-Ups to research accounts at scale. Instead of stitching together tools like Clay, Zapier, or other integrations, we can now research directly inside Apollo with AI.

The goal is to:

• Identify which accounts are worth pursuing
• Segment accounts by fit and stage
• Craft personalized emails
• Automate workflows, all inside Apollo

For this example, I’ll show how I worked with a client in the user testing and feedback space, using AI to prioritize accounts and personalize outreach.

Building the Initial List

I started with a tight list of 43 connected fitness companies—wearables like Whoop, Fitbit, and Zift.

Sometimes, though, you’ll have much bigger lists—2,000+ accounts. Apollo already has strong filters and segments, but AI adds another layer of granularity that’s typically hard to automate.

Traditional vs. AI Research

Traditionally, an SDR or AE would:

1. Visit each company’s website
2. Look for signals like beta programs, change logs, or public roadmaps
3. Decide whether the account is a good fit
4. Draft a personalized message

Now, Apollo’s AI Power-Ups do this work at scale, freeing your sales team to focus on higher-value activities.

Step 1: Running Custom AI Prompts

My workflow usually involves two tools side by side:

• Apollo (for running the Power-Ups)
• ChatGPT (to refine prompts and scoring logic)

I feed GPT the context: ICPs, personas, target accounts, and messaging. Then I ask it:

• What signals should I look for?
• Which are good vs. bad signals?
• Where should AI look on a website to find them?

The AI then generates a strong starting prompt that I run inside Apollo using Perplexity Sonar (the model with web access).

This prompt instructs Apollo’s AI to:

• Visit the company’s website
• Look for relevant signals (e.g., beta programs, roadmaps, changelogs)
• Avoid irrelevant sources like Glassdoor
• Score the account 1–5 (or “unsure”)

I call this field the Product Signal Score and store it at the account level.

Step 2: Scoring Example (Zift)

When I ran this on Zift, Apollo scored it a 5.

• Why? Zift had a specific beta program called Future Works for testing and collecting user feedback.
• The reasoning is now included in Apollo’s AI output (this used to be inconsistent, but it’s gotten better).

This score is incredibly valuable:

• 1 = No product testing
• 5 = Heavy user testing and feedback loops

Now we can prioritize high-signal accounts and deprioritize low-signal ones.

Step 3: Iterating the Prompts

Getting accurate signals takes iteration.

• Early prompts may return irrelevant data.
• I refine by telling AI what was useful vs. not useful.
• Over time, the scoring logic improves significantly.

This back-and-forth ensures the final signal score is reliable enough to automate.

Step 4: Creating Explanations and Personalized Sentences

Once accounts are scored, I run two more prompts:

1. Explanation Prompt
   • Expands on why the account received its score.
2. Personalization Prompt (using Claude)
   • Generates a clean, usable sentence for outreach.
   • Example for Zift:
   “I noticed Zift is running its Future Works platform to collect user feedback.”

This sentence is polished, specific, and can be inserted directly into email sequences.

Step 5: Automating in Apollo

After refining the workflow, you can set it to run automatically:

Company-Level Workflow

• If Product Signal Score = Unknown, run the AI prompt.
• Exclude current clients, pipeline accounts, and do-not-prospect lists.
• Assign a score (1–5) and save the explanation.
• Run this weekly against 500 companies (to manage credits).

Contact-Level Workflow

• Pull the right personas (e.g., product managers, directors).
• Verify email addresses.
• Drop them into a sequence that includes the personalized sentence.
• Limit new additions (e.g., 300 contacts per week) to control pacing.

Why This Workflow Works

This system transforms outbound outreach by making it:

• Targeted — Only engage accounts with strong product testing signals.
• Personalized — Every email references a real, relevant signal.
• Scalable — Automations run weekly without manual effort.

Plus, the personalization feels authentic, not generic.

Ongoing Optimization

This isn’t a one-and-done setup. To keep results strong:

• Review outputs regularly
• Adjust prompts when AI misinterprets signals
• Monitor which sequences generate the best replies

The more you refine, the more effective and scalable the process becomes.

Conclusion

Using Apollo’s AI Power-Ups, you can automate what used to take hours of manual SDR research. By scoring accounts, generating explanations, and producing personalized sentences, you build highly targeted and scalable outbound campaigns.

I’ve set this up for multiple companies, and the results are consistently strong. If you’d like help configuring Apollo for your own team—AI Power-Ups, workflows, automations, or outbound strategy—reach out anytime.

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