Enter your support volume
Add monthly ticket volume, average cost per ticket, and how much of that is repetitive. Conservative inputs give you a defensible number.
Calculate monthly support savings, deflection potential, and lead upside before rolling out an AI support chatbot.
Estimated monthly outcome
$202,315
Net of a Growth plan at $149 per month.
Support load removed
448
Conversations deflected per month — saving $8,064 ($96,768/yr).
Lead upside
$194,400
324 captured leads per month, or $2,332,800 annualized.
Recommendation
This looks like an immediate rollout candidate with room to scale.
Recommended starting plan: Growth. Validate these assumptions against live widget traffic once the bot is in production.
Intent
ROI
Capture buyers searching for savings, cost reduction, or chatbot justification.
Output
Monthly model
The page gives a simple economic estimate instead of generic product copy.
Next move
Support rollout
Best handoff is into support deflection or pricing-oriented use cases.
How it works
Enter your ticket volume, cost per ticket, and deflection assumption. You get a monthly savings estimate, lead upside, and the Widgetora plan that fits — in one page, with no signup.
Add monthly ticket volume, average cost per ticket, and how much of that is repetitive. Conservative inputs give you a defensible number.
You'll see deflected tickets, monthly savings, and the sales conversations an assistant can surface alongside support.
Based on your numbers, we suggest the plan that fits — so you can move straight into a trial instead of comparing pricing cold.
FAQs
It is most useful for teams with a steady stream of repetitive support conversations and a clear sense of cost per ticket. If support volume is still low or highly bespoke, use it as a directional model rather than a budgeting tool.
Grounded source content, a realistic deflection assumption, and a clear escalation path matter more than aggressive percentage claims. The calculator is intended to be conservative enough to validate later against production traffic.
The next step is to launch a pilot: index support content, review unresolved conversations, and compare live behavior against the savings and lead assumptions from the model.
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Next step
Create a workspace, index your content, and test real conversations against your support knowledge.