CS Tools & Strategy

The CS Platform Evaluation Checklist: 12 Questions to Ask

Most CS leaders evaluate platforms on features and price. The 12 questions below get to what actually matters - implementation, AI depth, adoption, and what happens when things go wrong.

Lucas Bennett
Lucas Bennett
4 min read
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The CS platform evaluation checklist — 12 questions to ask before you buy any CS software

The CS Platform Evaluation Checklist: 12 Questions to Ask

The RFP process misses the questions that actually matter. These twelve get to what's real.

Why Standard Evaluations Miss the Point

Most CS platform evaluations start with a list of features. The team builds a requirements document. The vendors respond. Everyone gets a score on a spreadsheet. The one with the highest feature coverage wins.

This process produces the wrong outcome with surprising consistency. It optimises for feature breadth (what can the platform do in theory) rather than value delivery (what will it do for your team in practice, in 90 days, given your actual resources).

The 12 questions below are the ones that reveal the difference.

Implementation Questions

Q1: What is your median implementation time — and your 90th percentile?

Not the best case. The median and the tail. Ask for verified customer data.

Q2: What does the customer provide during implementation — and what do you provide?

This question surfaces how much work lands on your team. Data cleaning, integration configuration, health score calibration, training — what's on your plate vs theirs?

Q3: Who from your team will own the platform configuration after go-live?

If the answer is "the platform is self-configuring" — evaluate that claim carefully. If the answer is "your CS Ops team" — do you have one?

AI and Signal Questions

Q4: What does your AI require to work — specifically?

Not "AI-powered." What data does it need? What configuration? What integration? What time before it delivers useful output?

Q5: How does your platform catch signals it hasn't been configured to catch?

This question distinguishes rule-based automation from genuine AI pattern recognition. If the answer is "you configure the rules," that's automation. If the answer is "the AI learns your account patterns," ask how.

Q6: How long until the health score is accurate for our specific business?

Default health scores are almost always wrong. Ask: when does it get calibrated, who does the calibration, and how do you know when it's right?

Adoption Questions

Q7: What is your average CSM adoption rate at 90 days?

Ask for data. Not a case study. Average across your customer base.

Q8: What happens when a CSM doesn't adopt the platform?

This question surfaces whether the platform has thought about the human side of implementation or just the technical side.

Q9: What does a CSM's daily workflow look like with your platform?

Ask them to walk through a typical Monday morning for a CSM using their platform. If the workflow requires significant platform navigation before getting value, that's an adoption risk.

Commercial Questions

Q10: What is the total cost of ownership in year one — including implementation?

Licence fee + implementation services + training + professional services + CS Ops time. Get a number.

Q11: What does your customer churn rate look like?

Vendors will resist this question. Push for it anyway. A vendor whose customers churn is a vendor whose product isn't delivering value.

Q12: What does success look like at 12 months — specifically?

Ask for the metrics they would expect your team to hit at 12 months post-implementation. If they can't answer specifically, that's important information.

How Clynto AI Answers These Questions

For reference — how Clynto AI answers the questions above:

Q1: 40 minutes to first insight. No months of implementation.

Q2: You connect integrations. Larry runs the 8-topic interview. First signal surfaces.

Q3: No dedicated admin required.

Q4: Connect HubSpot, Freshdesk, Stripe, Mixpanel, Calendar. Complete the Larry interview.

Q5: Larry reads signal combinations — patterns you didn't configure — calibrated to your business through the interview.

Q6: The Larry interview calibrates health interpretation to your specific CS motion before monitoring begins.

Q7: Data pending (pre-launch). Target: 80%+ adoption at 90 days through daily action prioritisation.

Q8: Larry's daily priority list means the platform gives CSMs value every morning without requiring them to navigate to it.

Q9: Larry's CSM Feed surfaces 3 priority accounts every morning. The CSM acts on what Larry found. No dashboard required.

Q10: Subscription only. No implementation fee. No professional services.

Q11: Data pending (pre-launch). Transparency is the product.

Q12: Signal coverage across 100% of portfolio. CSM prep time reduced. First churn save within 90 days.

Lucas Bennett

Clynto AI

Customer Success practitioner with over 10 years building CS teams from scratch across US, Canada, Singapore as a CSM, team lead, CS leader, and consultant.

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