As of 13 August 2026, AI can choose the best time to send emails to UK customers.

What to actually do

1. Use a tool built for this

   The route this page recommends

2. Hand it to a person

   Second choice

   A person who owns the outcome does this end to end, worth it when the failure is dear.

3. Do it yourself

   The distant third

   A chat interface, power-user skill, and roughly 1 hour until you can act on the result.

   How to actually do it

   1. Open your email platform and export campaign-level results with send date, send time, time zone, audience segment, audience size, delivery rate, open rate, click rate, conversion rate if available and unsubscribe rate.
   2. Gather the business constraints that affect timing, including customer locations, fulfilment hours, support coverage, campaign objective, audience segments and the send windows your platform allows.
   3. Remove or label test campaigns, unusual one-off events, incomplete records and campaigns with missing metrics, then save the cleaned export as a table.
   4. Paste the business context, constraints and cleaned campaign table into the prompt, replacing every bracketed slot with your own information.
   5. Ask the chatbot to produce a primary window, a backup window, the evidence behind each choice and a controlled comparison test rather than treating historical performance as proof.
   6. Compare the recommendation against the original export, your platform's available scheduling options and your customer-support coverage before scheduling anything.
   7. Send the test groups through your email platform, record the same outcome metrics for each group and use the result to confirm or change the next campaign's send window.

   Prompt

   You are helping choose an email send time for UK customers. Use only the campaign data and business context I provide, and do not invent benchmarks or claim that one time is universally best.
   
   Business context:
   - Business and product: [describe]
   - Customer locations and time zones: [describe]
   - Main customer segments: [describe]
   - Campaign objective: [sales, registrations, bookings, or other]
   - Email platform and available send windows: [describe]
   - Constraints such as staffing, fulfilment or customer support coverage: [describe]
   
   Campaign history:
   [paste a table or CSV containing campaign name, audience segment, send date, send time with time zone, audience size, delivery rate, open rate, click rate, conversion rate if available, unsubscribe rate and any other relevant result]
   
   Analyse the data by segment and objective. Account for different UK customer time zones, campaign type, audience size and any obvious data-quality problems. Separate what the data shows from what you are inferring. Recommend a primary send window and a backup window, explain the evidence for each, identify cases where the data is too weak to support a recommendation, and propose a controlled test that compares send windows while keeping the audience, message and other conditions as consistent as possible. State which metrics I should compare and how long I should wait before judging the result. Do not use outside statistics, invent missing figures or recommend sending at a time that conflicts with the constraints I supplied.

   Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.