by Trivas.aiMost brands pick an analytics platform the same way they pick a new pair of shoes: they see something shiny on a demo call, check the pricing page, and sign. No structured comparison, no written record of what was promised, no way to hold a vendor to a claim six months later when the dashboard doesn't match reality.
An RFP fixes that. It forces every vendor you're considering to answer the exact same questions, in writing, side by side. That's the whole point. A sales call lets a rep steer the conversation toward their strengths. A written RFP doesn't let anyone dodge the question about data latency or attribution reconciliation just because it's inconvenient.
This is written for DTC brands running Shopify, Amazon, or both, who are evaluating tools like Triple Whale, Northbeam, Polar Analytics, or similar platforms and want something more rigorous than a vibe check. Below you'll find the reasoning behind each section of a solid ecommerce analytics RFP template, plus a downloadable version you can send to vendors this week. For more frameworks like this one, our guides and reports library has a few others worth pulling from.
The most common mistake: judging tools on how pretty the dashboard looks instead of what's actually feeding it. A slick UI built on modeled, blended data will look better in a demo than a raw feed that hasn't been polished yet. But raw data is what you need when you're trying to reconcile numbers, not a smoothed-over chart.
Attribution is the second landmine. Teams rarely press vendors on their methodology during the sales process. Then three months in, the platform's reported ROAS doesn't match GA4 or Meta's own numbers, and nobody can explain why. That conversation should happen before the contract, not after.
Integration depth gets assumed rather than verified. A vendor's website says "Amazon, Shopify, Meta, Google integrations" and everyone nods. Nobody asks whether that Amazon connection covers Seller Central and Vendor Central, or whether it's read-only, or whether it updates hourly or once a day.
And almost nobody asks the unglamorous questions: how far back does historical data go, what's the latency between an ad platform update and it showing in your dashboard, and what happens to your reporting continuity during a migration. Those are the questions that actually determine whether the tool works for you in month six, not just in the demo.
A good RFP isn't a novel. It's six sections, each forcing specificity.
Data sources and integrations. List every platform you actually run: Amazon, Shopify, Meta, Google, GA4, TikTok, whatever else. Ask which are native integrations built and maintained by the vendor, versus third-party connectors that could break without notice.
Reporting and BI capability. Does the tool ship prebuilt dashboards only, or can you build custom reports against the underlying data? Ask whether it's backed by a proper data warehouse (Redshift is one common answer) or whether everything lives inside a closed dashboard layer. This distinction matters more than it sounds. Warehouse-backed BI and reporting means you're not stuck with whatever chart the vendor decided to build for you.
Attribution and forecasting. What model powers their attribution, and how does it reconcile against platform-reported spend? Separately, ask about forecasting: is it a static projection based on last quarter's trend line, or something closer to real forecasting and simulation that adjusts as conditions change?
AI/automation layer. Does the tool flag anomalies and surface insights on its own, or do you have to go digging every morning to find the one metric that moved?
Pricing structure and contract terms. Per-order fees, SKU or revenue tier limits, minimum commitment length. Get this in writing, not verbally on a call.
Support and onboarding. Dedicated CSM or a generic ticket queue? Average time to a working first dashboard, not "it depends."
Once you've got answers back, you need a way to score them that isn't just gut feel. Here's a starting framework, weighted by how much each factor tends to actually matter once you're a live customer rather than a prospect.
Criteria
What to check
Suggested weight
Integration breadth
Native connectors relevant to your actual stack, not their total marketed count
20%
Data ownership
Can you export raw data, or are you locked into their dashboard views only
20%
Implementation time
Guided setup with a dedicated resource vs. self-serve config that drags for weeks
15%
Forecasting/simulation depth
Static historical reporting vs. forward-looking, AI-driven projections
15%
Support model
Response time SLAs, access to product team vs. offshore tier
15%
Total cost at scale
How pricing shifts as order volume or ad spend grows
15%
The one people underweight most is data ownership. If a vendor won't let you export raw, unblended data, you're not really building an analytics stack, you're renting a view of one. That's worth digging into hard before you sign anything longer than a month-to-month term.
Triple Whale, Northbeam, Polar Analytics, and Peel each lean into a different strength. Some are built primarily around paid media attribution and ad spend tracking. Others aim for full-funnel reporting that pulls in Amazon and GA4 alongside the ad platforms, backed by a warehouse rather than a closed dashboard.
Rather than assert who wins where (that's a moving target, and vendors update their feature sets constantly), it's more useful to look at detailed breakdowns and test the differences against your own stack. We've put together side-by-side comparisons for Triple Whale, Polar, and Trivas, Northbeam, Polar, and Trivas, and Polar, Peel, and Trivas if you want specifics.
Use the RFP scorecard above on each one. Marketing pages will tell you a tool "integrates with everything." The scorecard tells you whether that's true for your specific mix of channels.
Copy these directly if you want a fast start:
Send the same six questions to every vendor you're evaluating. Don't paraphrase them differently per vendor. The value of an RFP comes from comparing identical answers, not summaries of different conversations.
The full template includes the scorecard above in a fillable format, the complete question bank, and a weighting guide you can adjust based on what matters most for your business (a brand scaling fast on Amazon will weight integration breadth differently than a Shopify-only brand focused on forecasting accuracy).
Worth including Trivas in your evaluation set while you're at it. Its reporting runs on Redshift rather than a closed dashboard layer, and the forecasting engine is built to project forward, not just summarize what already happened.
If you'd rather skip the back-and-forth and just talk through your stack with a real person, our team is happy to walk through it directly. And if you're building out a broader research process, it's worth subscribing to see future breakdowns as new tools enter the space.
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