Proving ROI with Low-Code Chatbot Builders and Experimentation

Hook: ROI conversations determine funding

Bain & Company notes that 78% of executives now require quantified ROI before approving conversational AI budgets, up from 44% just two years ago.¹ Low-code chatbot builders make experimentation faster, but without a rigorous measurement stack, the wins stay anecdotal. Optimly helps you convert those experiments into proof that protects and expands your automation investments.

Problem: ROI stories break down without unified data

Teams often struggle to answer simple questions:

• Which automations drive revenue versus cost savings?
• How do new flows impact customer sentiment or lifetime value?
• Did the last release improve containment, or did we just shift volume to a different channel?

PwC research shows that fewer than one-third of organizations have instrumentation to tie conversational AI to business outcomes.² Low-code tools accelerate releases, but measuring ROI requires standardized metrics, disciplined experimentation, and stakeholder-ready storytelling.

Solution: Operationalize ROI measurement with Optimly

Step 1: Define ROI formulas that blend efficiency and growth

• Track cost-to-serve by comparing self-service completion rates to assisted interactions using Optimly's containment dashboards.
• Quantify revenue influenced by tagging conversations tied to upsell, cross-sell, or conversion events and linking to CRM outcomes.
• Measure customer experience lift through sentiment scores, CSAT, and effort metrics streamed from the low-code builder.

Step 2: Run structured experiments

• Use the builder's visual branching to set up control and treatment experiences.
• Capture experiment metadata (variant, traffic split, hypothesis) within Optimly so analysts can run significance tests quickly.
• Automate guardrails that pause experiments when key KPIs regress beyond agreed thresholds.

Step 3: Tell the story with executive dashboards

• Build Optimly scorecards that show baseline vs. uplift across cost, revenue, and CX metrics.
• Layer qualitative insights by linking transcripts and agent feedback to each metric movement.
• Embed the Optimly low-code video in stakeholder briefings to demonstrate how experimentation loops back into builder workflows.³

Step 4: Institutionalize learning

• Host monthly ROI reviews where product, finance, and operations teams compare performance across bots.
• Publish experiment retrospectives that outline hypothesis, results, and next steps.
• Cross-link to the evaluation guide, implementation blueprint, support playbook, and governance framework from this series to drive holistic program maturity.

With consistent measurement and storytelling, low-code chatbot builders evolve from shiny tools into compounding assets. Optimly keeps every experiment observable, every success repeatable, and every investment defensible.

Footnotes

1. Bain & Company, Conversational Commerce Maturity Study, 2024. ↩

2. PwC, 2024 AI Business Survey. ↩

3. Demonstration of Optimly's low-code experimentation workflow. ↩