Intro to the Switch Readiness Score framework
Read the full story: The autonomous email engine behind 200% conversion growth, featuring Adam Cain of ElectricityRates.com, published on the AI Lab by ActiveCampaign.

Get the playbook
This one-pager distills the intent-scoring framework Adam Cain built at ElectricityRates.com into a reference you can scan in 30 seconds. The framework is transferable to any business where customers have a predictable conversion window and you can connect external market signals to internal customer data.
The big idea
Most email programs segment by demographics or past behavior. The Switch Readiness Score segments by current intent — a real-time signal that combines where a customer is in their lifecycle, how engaged they are, and whether external conditions make a conversion valuable right now. The result: automated campaigns that fire at the right moment without anyone manually scheduling them.
The framework
1. Score inputs: what you measure
| Signal type | ElectricityRates.com example | Your equivalent |
| Lifecycle timing | Contract end date <60 days away | Renewal date, trial expiry, subscription anniversary |
| Engagement history | Email opens, clicks, site visits | Login frequency, feature usage, support activity |
| Value threshold | Available savings ≥ 7% | Discount size, deal value, inventory discount |
| External market signal | Utility rate change in the customer’s area | Price drop, competitor news, policy change, new inventory |
2. Scoring tiers: how you act on it
| Score | What it means | Campaign response |
|---|---|---|
| 85–100 | High intent + high value: ready to act now | Up to 14 emails/week; aggressive cadence |
| 70–84 | High intent, lower value: approaching readiness | Active nurture sequence; moderate frequency |
| 0–69 | Low intent: not ready yet | Low-frequency drip; re-score as conditions change |
3. Trigger
Filter and send automation loop
External event detected (e.g., rate change, price drop)
↓
Filter affected customers by Switch Readiness Score
↓
Generate personalized email via AI (referencing the specific event)
↓
Send at optimal time per recipient (predictive sending)
↓
Adjust follow-up cadence based on opens and clicks
No one drafts the campaign. No one schedules the send. No one decides who receives it.
When to use this
- Your customers have a natural conversion window you can identify in advance (contract end dates, renewal cycles, trial periods)
- You operate in a market where external conditions change frequently and affect conversion value (pricing, inventory, regulations, competitor moves)
- You need to send highly relevant, timely messages at volume without growing headcount
- You’re a small team managing a list that would otherwise require 4–6 people to operate manually
Common mistakes
- Asking AI for specific things too early
- Start with open-ended analysis: “Look at this data and tell me what you see.” Let the model surface patterns before you constrain it.
- Building one score threshold
- Three tiers (high/mid/low) map to three different campaign behaviors. One threshold creates a binary on/off that misses nuance.
- Treating the score as static
- Scores should update whenever signals change. An external trigger (rate change, price drop) can move a customer from 55 to 90 overnight.
- Connecting triggers before validating the score
- Test your scoring formula against historical conversion data first. Automating an inaccurate model at scale amplifies the error.
Quick-start
Upload 500–1,000 customer records to Gemini, ChatGPT, or Claude and ask: “Which customers are most likely to [your conversion action] and why?” Review what the AI surfaces, then add two constraints — a timing rule and a value threshold — to produce three actionable tiers. Create a custom field in ActiveCampaign to store the score, set up a sync to keep it current, and build one trigger-based campaign for your highest-scoring segment. The rest of the infrastructure follows from there.
Ready for the full story?
Read The autonomous email engine behind 200% conversion growth, featuring Adam Cain of ElectricityRates.com, published on the AI Lab by ActiveCampaign.
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