Strategy & Planning

How to run a pricing analysis as Small RevOps Teams

Strategy & PlanningFor Small RevOps Teams2 apps12 steps~24 min to set up

Pricing analysis for a 2-person RevOps team means pulling deal data from HubSpot or Salesforce, win/loss outcomes from wherever the CRO last dumped them, discount approvals from a Slack thread, and competitor pricing from a mix of public pages and sales rep memory. You spend two hours assembling a spreadsheet that's already stale by the time you present it. Nobody agrees on the segment cuts. The CRO wants it by product line, the AE manager wants it by territory, and you're manually pivoting the same table three times. There's no clean connection between what deals closed at, what was discounted, and what the pipeline looks like at current pricing — so every pricing conversation restarts from scratch.

Build this on Starch → See the apps used

Strategy & PlanningFor Small RevOps Teams2 apps12 steps~24 min to set up

Outcome

What you'll set up

A live pricing analysis app that pulls closed-won and closed-lost deal data from HubSpot or Salesforce, surfaces average discount depth by segment, rep, and product line, and flags where pricing variance is costing you win rate

An automated competitor pricing tracker that scrapes public pricing pages on a schedule and alerts you when a competitor changes a tier or introduces a new plan — no API needed

A consolidated view linking Apollo sequence conversion rates to deal price points, so you can see whether discounted deals from specific outbound sequences are worth the margin hit

The Starch recipe

Apps, data, and prompts

The combination of Starch apps, the data sources they pull from, and the prompts you use to drive them.

Apps used

Sales Agent CRM Growth Analyst

Data sources & config

Starch syncs your HubSpot data on a schedule — contacts, companies, deals, and owners — so deal-level pricing and discount fields are available without a manual export. Connect Apollo from Starch's integration catalog; the agent queries it live when the analysis runs. Connect Gmail from Starch's scheduled sync to pull discount approval threads if your team routes approvals by email. Competitor pricing pages are automated through your browser — no API needed.

Prompts to copy

Build me a pricing analysis app that syncs my HubSpot deals, groups closed-won and closed-lost by product line and deal size bucket, calculates average discount percentage per rep and per segment, and shows me where high-discount deals have a materially different win rate than full-price deals. Flag any rep whose average discount is more than 15% above the team median.

Add a competitor pricing monitor: every Monday morning, go to the public pricing pages for Gong, Salesloft, and Outreach, scrape current plan names and listed prices, and Slack me a summary of anything that changed since last week.

Connect Apollo so I can see, for each outbound sequence, what the average closed ARR is and what the average discount was on deals that originated from that sequence. Surface the sequences where we're closing at lowest price.

Run these in Starch → or paste them into your favorite agent

Walkthrough

Step-by-step

1 Connect HubSpot as a scheduled-sync provider. Starch pulls deals, contacts, companies, and owners on a recurring schedule — including any custom properties you've set up for discount percentage, approved discount reason, and pricing tier.

2 Connect Apollo from Starch's integration catalog. The agent queries sequence and contact data live when your pricing app runs, so you can link outbound source to closed deal price without a separate export.

3 Connect Gmail via Starch's scheduled sync if discount approvals travel through email threads — Starch can read those messages and surface approval patterns alongside deal outcomes.

4 Describe your pricing analysis app in plain language. Tell Starch which HubSpot deal fields to use, which segments matter (product line, territory, deal size), and what 'high discount' means for your business. Starch builds the app.

5 Set up the discount variance view: Starch calculates average discount per rep, per product line, and per segment, and flags statistical outliers. You define the threshold; Starch flags anything outside it automatically.

6 Add the win/loss cut. Tell Starch to group deals by discount band (0–5%, 5–10%, 10–15%, 15%+) and show close rate for each band per segment. This is the table your CRO actually wants and currently takes you 90 minutes to build.

7 Wire the Apollo sequence attribution. For each sequence in Apollo, Starch pulls the deals that originated there and joins them to HubSpot closed-won data — showing average ARR, average discount, and win rate per sequence source.

8 Set up the competitor pricing monitor. Tell Starch which pricing pages to track and how often. Starch automates your browser to visit each page on schedule, extracts plan names and prices, and compares to the prior snapshot.

9 Configure the Slack alert. Any time a competitor changes a price tier or the weekly pricing digest is ready, Starch posts a summary to your RevOps Slack channel — no dashboard to remember to check.

10 Fork the app for the CRO's territory view. Describe the territory cut in natural language ('break the discount analysis down by the six territories in our current quota model, mapped to the region field in HubSpot'). Starch rebuilds the view without you touching the underlying query.

11 Set the analysis to refresh automatically after each week's deals sync. Your pricing deck prep goes from a two-hour manual export and pivot session to reviewing a Starch-generated summary and adjusting the narrative.

12 Publish the app internally so AE managers can pull their own rep-level pricing view on demand, eliminating the 'can you just pull me a list of...' Slack messages about discount data.

See this running on Starch

Connect your tools, describe what you want, and the agent builds it. Closed beta is free.

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Worked example

Q1 2026 Pricing Review — Mid-Market Segment

Sample numbers from a real run

Closed-won deals, full price (0–5% discount)	142
Closed-won deals, moderate discount (5–15%)	89
Closed-won deals, heavy discount (15%+)	34
Average ARR, full-price deals	28,400
Average ARR, heavy-discount deals	21,800
Win rate, full-price deals (%)	31
Win rate, heavy-discount deals (%)	29

Going into the Q1 pricing review, your CRO wants to know whether the discount policy is actually buying win rate or just giving away margin. You open the Starch pricing analysis app. It shows 265 closed mid-market deals from HubSpot: 142 at full price, 89 with moderate discounts, and 34 with discounts above 15%. The win rate on full-price deals is 31%; on heavy-discount deals it's 29% — meaning two points of win rate difference doesn't justify the $6,600 average ARR haircut on the 34 heavy-discount deals. The app then breaks this down by rep: two reps account for 26 of the 34 heavy-discount deals, both on outbound sequences sourced from a specific Apollo cadence called 'SMB-to-MM expansion.' You pull the Apollo attribution view and confirm: that sequence closes at $21,800 average ARR with a 29% win rate, while inbound demo-request deals from the same segment close at $28,400 at 31%. The recommendation writes itself — the expansion sequence needs a pricing floor, not a discount lever. You export the Starch summary, drop the CRO a Slack with the three numbers that matter, and spend the rest of the morning on something else.

Measurement

How you'll know it's working

Average discount percentage by rep, segment, and product line

Win rate by discount band (full price vs. 5–15% vs. 15%+)

Average closed ARR by deal source (inbound vs. outbound sequence, with sequence-level detail from Apollo)

Competitor pricing tier changes per month (tracked via browser automation)

Time to produce pricing analysis for weekly forecast call (target: under 15 minutes)

Comparison

What this replaces

The other ways teams handle this today, and how the Starch version compares.

HubSpot native reporting + Google Sheets manual export

You get the data but you're rebuilding the pivot every week; no live Apollo attribution, no competitor tracking, and the CRO's territory cut means starting over each time the quota model changes.

Salesforce + Tableau or Looker

Powerful for a dedicated analyst who owns the data model, but a 2-person RevOps team will spend more time maintaining the dashboard than running the analysis — and competitor pricing still lives outside the BI tool entirely.

Gong Analyze (deal intelligence)

Good at surfacing discount patterns from call recordings, but it doesn't join to Apollo sequence data, doesn't track competitor pricing externally, and produces its own silo that still needs to be reconciled with HubSpot deal fields.

Clari or Bowtie for revenue analytics

Strong forecast and pipeline tooling, but purpose-built for forecast accuracy, not pricing variance analysis — and adding another point tool means another data sync for the RevOps team to maintain.

On Starch RECOMMENDED

One platform — sales agent crm, growth analyst all running on connected data. Setup in plain English; numbers stay current via scheduled syncs and live agent queries.

Try it on Starch →

FAQ

Frequently asked questions

We use Salesforce, not HubSpot. Can Starch still pull our deal and discount data?

Yes. Connect Salesforce from Starch's integration catalog; the agent queries your deal objects, opportunity fields, and custom discount fields live when the analysis runs. HubSpot is a scheduled-sync provider with deeper native integration, but Salesforce is fully reachable from the catalog.

Our discount approval process is in Slack, not in the CRM. Can Starch read that?

Starch syncs Slack channels and users on a schedule. If discount approvals follow a consistent pattern in a specific channel, you can describe what to look for and Starch will surface approval threads alongside deal outcomes in your pricing app.

Will the competitor pricing scraper break if a competitor redesigns their pricing page?

It might need a quick re-description — if the page layout changes significantly, you'd tell Starch what to look for on the new layout. It's not a set-and-forget integration you never touch; it's a browser automation that behaves like a person navigating the page, so structural changes to the site may require a refresh.

Does Starch store our deal pricing data, or does it query it fresh each time?

HubSpot data syncs on a schedule and is stored in Starch's database, so your pricing analysis can run against a consistent snapshot. Apollo data is queried live from the integration catalog each time the app runs — it's not stored in Starch. For most RevOps use cases this is fine; if you need a historical archive of Apollo data over time, that's worth knowing upfront.

We're not SOC 2 certified as a company yet — is Starch compliant enough for our deal data?

Starch is not SOC 2 Type II certified today. If your security review requires a certified vendor for CRM data, that's a real constraint worth knowing before you start — we'd rather you make an informed call than discover it mid-implementation.

Can AE managers pull their own rep-level pricing view, or does everything have to go through RevOps?

Once you've built the pricing analysis app, you can publish it internally so managers have their own view. You control what fields are visible — you can expose rep-level discount data to managers without exposing the full cross-team analysis. This is how you stop fielding 'can you pull me a list of my reps' discounts' requests every Monday.