How to Measure Conversion Lift from AI Follow-Up

How to Measure Conversion Lift from AI Follow-Up

You deployed an AI follow-up agent. Calls are going out, texts are landing, and your CRMCRMThe system of record for leads, contacts, deals, and activity. Thoughtly reads from and writes to your CRM continuously.Read full definition → is filling up with notes. The C-suite question lands in your inbox on schedule: "Is this thing actually working?"

Answering that question with confidence requires more than a vibes check on call volume. You need a measurement framework that isolates the incremental impact of AI-powered follow-up on your pipeline — separating the leads who converted because of the agent from the ones who would have converted anyway.

This guide walks through the complete process: choosing the right baseline, structuring a holdout test, identifying the metrics that matter, and building the reporting layer inside Thoughtly. By the end, you will have a repeatable playbook for proving (or disproving) conversion lift from AI follow-up across voice, SMS, and email.

What You'll Need

• A Thoughtly account with at least one active voice or SMS agent already handling follow-up volume.
• CRM integration (HubSpot, Salesforce, GoHighLevel, Pipedrive, or Zoho) syncing contact records and deal/pipeline data back from Thoughtly. See How to Sync AI Conversations Back to HubSpot or the Salesforce equivalent if you haven't connected yet.
• Automations configured with the On Call Completed trigger writing dispositions, outcomes, and variables to your contacts.
• A minimum of 500 follow-up interactions across your test period. Smaller samples produce noisy results.
• Access to Thoughtly's History and Analytics dashboards plus your CRM's reporting tools.
• A spreadsheet or BI tool (Google Sheets, Looker, Tableau) for the final lift calculation.

Why Conversion Lift Matters More Than Call Volume

Call volume is an activity metric. It tells you the agent is working. Conversion lift is a business metric. It tells you the agent is working on something that matters.

Most teams measure AI follow-up the same way they measure human SDRs: dials, connects, conversations. Those numbers are necessary but insufficient. What you actually need to know is:

• How many additional conversions (appointments booked, quotes requested, applications started) happened because of the AI agent that would not have happened without it?

That delta is your conversion lift. It is the only number that translates directly into revenue attribution and ROI justification.

Step 1: Establish Your Pre-AI Baseline

Before you can measure lift, you need to know what your funnel looked like before the agent showed up. Pull at least 90 days of historical data from your CRM covering the same lead source and follow-up workflowWorkflowAn automated, multi-step process — usually triggered by an event (form fill, new lead) and orchestrating one or more voice / SMS / email actions.Read full definition → the agent now handles.

The baseline metrics you need:

Document these numbers clearly. They become the denominator in every lift calculation you run later.

Step 2: Design a Holdout Test

The most reliable way to measure conversion lift is a holdout test: split your incoming leads into two groups and compare outcomes.

Group A (treatment): receives AI-powered follow-up via Thoughtly — voice calls, SMS, email sequences, or a combination.

Group B (control): receives your existing follow-up process — whether that is manual SDR outreach, a basic autodialer, or no follow-up at all.

Setting up the split in Thoughtly

Thoughtly's Automations support conditional routing that makes holdout testing straightforward:

• Use the Webhook trigger to receive leads from your CRM or form tool. Include a field like test_group: "A" or test_group: "B" in the payload.
• Add a Conditions step (If/Else) immediately after the trigger. Route Group A leads to the Call Contact or Send SMS step. Route Group B leads to an Add Attributes step that tags them as control and exits.
• Ensure both groups get the same disposition tracking. Use the On Call Completed trigger on a separate automation to write outcomes and variables for Group A. For Group B, tag them through your CRM's existing workflow so you can compare apples to apples.

How large should each group be?

Aim for at least 250 leads per group over a two-to-four-week period. For high-volume funnels (insurance, mortgage, home services), you may reach statistical significance in under a week. For lower-volume verticals, extend the test window rather than shrinking the sample.

If pulling 50% of leads out of AI follow-up feels too risky, use a 90/10 or 80/20 split instead. You will need a longer test period, but you protect most of your pipeline.

Step 3: Track the Right Metrics

Not every number in your dashboard matters for a lift calculation. Focus on these five:

For each metric, calculate the value for Group A and Group B separately. The difference is your raw lift.

Step 4: Build the Reporting Layer Inside Thoughtly

Thoughtly provides the raw interaction data. Your job is to structure it for lift analysis.

Use Dispositions for outcome tagging

Thoughtly's Disposition feature (available in Agent Settings and via the Add Disposition automation step) automatically tags calls with labels like "Qualified lead," "No answer," "Left voicemail," or "Request callback" based on transcriptTranscriptThe text record of a voice conversation, used for review, training, compliance audit, and search.Read full definition → content and call outcome. These disposition labels become the primary filter for segmenting successful vs. unsuccessful follow-up attempts.

Write Attributes for persistent tracking

After each call, use an On Call Completed automation to write key variables as contact Attributes. Attributes persist across calls, so they form the longitudinal record you need for conversion tracking.

Recommended attributes to write:

• last_ai_follow_up_at — ISO timestamp of the most recent AI follow-up
• ai_follow_up_outcome — the disposition or key outcome from the last call (e.g., "booked", "callback_requested", "not_interested")
• ai_follow_up_count — running total of AI follow-up touches
• test_group — "A" or "B" (persisted from intake)
• converted — boolean flag set when the lead hits your conversion event

Export and join

Use Thoughtly's History Export to pull filtered call data as a CSV. Join it with your CRM's deal or pipeline export on phone number or contact ID. This merged dataset becomes your lift analysis table.

If you use Zapier, Make, or a direct webhook, you can also push Thoughtly call outcomes directly into a Google Sheet or BI tool in real time using a Send Webhook step in your On Call Completed automation.

Step 5: Calculate Conversion Lift

With data from both groups in hand, the lift formula is straightforward:

Conversion lift = (Group A conversion rate − Group B conversion rate) ÷ Group B conversion rate × 100

Example: if Group A (AI follow-up) converted at 18% and Group B (manual follow-up) converted at 11%, your lift is:

(18% − 11%) ÷ 11% × 100 = 63.6% conversion lift

Sanity-check your results

Before presenting the number to leadership, verify:

• Sample sizes are sufficient. A 63% lift from 30 leads per group is noise. From 500+ per group, it starts to mean something.
• Lead sources are comparable. If Group A got all the high-intent leads and Group B got the recycled list, the test is invalid.
• The time period is consistent. Both groups should cover the same calendar window to control for seasonality.
• No other variables changed. If your sales team also launched a new pitch deck during the test window, you cannot cleanly attribute lift to the AI agent alone.

Step 6: Translate Lift Into Revenue

Conversion lift is compelling. Revenue impact closes the deal with finance.

Use this framework:

Adjust the numbers for your vertical. Insurance and mortgage leads typically carry higher per-conversion value. Home services and education enrollment may convert at higher rates but with lower per-deal revenue. The framework stays the same.

Common Mistakes

• Measuring call volume instead of conversion outcomes. High dial counts feel productive but tell you nothing about pipeline impact. Always tie measurement back to a downstream conversion event — an appointment booked, a quote requested, an application started.
• Comparing AI follow-up to no follow-up and calling it lift. If the alternative is zero outreach, any activity will show a lift. The more honest (and useful) comparison is AI follow-up vs. your existing human or automated follow-up process.
• Running the test for too short a period. A three-day test during a slow week will produce misleading results. Run for at least two weeks, preferably four, covering both peak and off-peak periods.
• Ignoring multi-touch attribution. A lead who received an AI voice call, then an SMS, then converted on a human callback was influenced by all three touches. If you only credit the final touch, you will undercount AI contribution. Use first-touch or multi-touch attribution depending on your CRM setup.
• Forgetting to track the control group's outcomes. The control group needs the same outcome tracking as the treatment group. If you only measure conversions for AI-touched leads, you have no denominator for the lift calculation.

Measuring Success

Once you have your initial lift measurement, here is what to track on an ongoing basis to ensure the agent continues delivering value:

Review these in a standing weekly or bi-weekly ops review. Thoughtly's Analytics dashboard gives you the activity-level view; your CRM reporting gives you the pipeline-level view. The combination is where lift measurement lives.

Frequently Asked Questions

How many leads do I need before I can measure conversion lift?

Aim for at least 250 leads per group (treatment and control) over a two-to-four-week window. For high-volume verticals like insurance or mortgage where you process thousands of leads per month, you may see statistically meaningful results within a week. For lower-volume funnels, extend the window rather than lowering the sample size.

Can I measure lift without running a holdout test?

You can compare your pre-AI baseline metrics (from CRM historical data) to your post-AI metrics. This is called a before/after comparison. It is simpler to set up but less rigorous because it does not control for seasonal trends, changes in lead quality, or other variables that may have shifted between the two periods. A holdout test is always more defensible.

What if the AI agent and human reps both touch the same lead?

This is a multi-touch attribution challenge. Use Thoughtly's contact Attributes to tag every AI interaction, and ensure your CRM logs every human touch. When a lead converts, check the full timeline. Many teams credit the AI agent for the first-touch (it re-engaged the lead or booked the callback) and credit the human for the close. The right model depends on your sales motion, but the data needs to be captured either way.

Does Thoughtly have built-in A/B testing?

Thoughtly's Automations include a Random Outcome step that can split traffic for experiments — for example, routing 80% of leads through one agent flow and 20% through another. You can use this for A/B testing different scripts, channels, or follow-up cadences. For holdout testing specifically, use the Conditions (If/Else) step with a test_group field passed in via webhook.

How often should I re-run the lift measurement?

Run a formal holdout test quarterly, especially if you change agent scripts, add new channels, or shift lead sources. Between formal tests, monitor the ongoing metrics (contact rateContact rateThe percentage of inbound leads your team actually reaches by phone. Most B2C teams hover around 25%; Thoughtly typically delivers 90%+.Read full definition →, conversion rate, speed-to-lead) weekly using Thoughtly Analytics and your CRM dashboards. If those leading indicators shift significantly, trigger an off-cycle test.

Sources and Further Reading

• Thoughtly Automations Documentation — Build workflows that trigger follow-up and capture outcomes.
• Thoughtly Analytics — Monitor agent performance, call volumes, and usage trends.
• Thoughtly History and Call Statuses — Filter, review, and export call data for analysis.
• Attributes vs. Metadata in Thoughtly — Know what data persists on contacts vs. per-call context.
• How to Build a Speed-to-Lead AI Agent with Thoughtly — Related implementation guide for inbound follow-up.
• How to Automate Form-Fill Follow-Up Calls with Thoughtly — Set up the follow-up agent whose lift you are measuring.
• Thoughtly Solutions — Speed to Lead — Product positioning for speed-to-lead use cases.
• InsideSales/XANT Lead Response Study — Research on speed-to-lead impact on conversion rates.