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Show me where company name (minus additions like "Co.", "Inc.", "Corp." or similar common add-ons) largely matches and the record was created on the same day. I would need to see the address and phone information for these records to ensure they are truly a match. Domain is not an option here since we are dealing with brands that have franchises that use the same parent domain.
What are additional ways to find HubSpot companies that are duplicated through a massive list upload. The ways I've found them thus far: Exact match of company name and street address Exact match of company name, phone number, and address is either null or street address matches, or state matches.
Give me a clean count of the total companies we have in HubSpot, along with a sense of how they break out by lifecycle stage and region. It'll help ground the rest of the analysis.
How many deal fields do we even have access to in hubspot rn, and which ones are actually being used? might be worth checking fill rates too — like how many are empty vs populated, esp the ones used in reporting. i want to clean up unused or redundant fields.
What % of our hubspot contacts actually have a lead status filled? and can you also slice by team or source to see where it's missing the most?
Query HubSpot companies and exclude those that have a null phone number field. Group companies by Company Name and Phone Number for the first step of the analysis to determine the number of duplicates that exist with these two parameters.
Please run an analysis on the Company table in HubSpot. I would like to see Companies grouped by Company Name and Street Address. Show me groups with more than one company ranked by descending order (count of companies descending).
How many contacts don't have a phone number filled and does that differ by source (inbound, import, outbound)? also maybe check if there's any correlation between missing phone and low engagement or unqualified status.
Hey can you check how many duplicate contacts we got in hubspot and where they're coming from (imports, forms, manual adds etc)? also how many deals are linked to these dupes before we clean them up.
Can you use the accounts discovery data to pull a list of all titles that are being discarded?
Can you pull me all contacts that came via import and show by import date + owner? flag how many of them ever converted to deals or were touched in last 90 days.
Can you pull all accounts tied to the campaign and check which ones haven't had any follow up activity in the last 10 days? add owner + segment too.
Can you match these emails to any open or closed deals in the system and give it in a table? show deal stage, owner, close date, and value if possible. also flag if the contact is linked to more than one deal (might be overlap or cross-sell).
Can you find out how many dup contacts we got in salesforce, esp across key accounts? and not just count, I'd love to see if there's a pattern (same email, diff accounts, diff owners etc).
Can you create a KD for Opportunity Age using Date Entered Pipeline and Close Date? I want a single, authoritative definition we can reference everywhere going forward.
For this report, I'd prefer we use the standardized Churn Rate KD instead of calculating churn on the fly. Can you swap in the KD version and rerun the plan so the output becomes consistent with the rest of our reporting?
The win-rate formula you used here looks solid. Can you convert that exact logic into a Key Definition so we never redefine it differently in future analyses?
The activities table mixes a lot of different event types. Instead of using the combined table, can you rewrite the plan so email, call, and meeting activity are handled independently? That'll give me more control over how the final numbers are interpreted.
Before executing, flag any steps in the plan that rely on fields with low completeness or inconsistent formatting. Highlight why they're risky and whether there's a safer alternative.
Show me all the different ways these tables could be joined, explain the pros and cons of each path, and then update the plan using the one you think is the most trustworthy.
The segment column you selected might not be the most reliable. Can you swap it out for whichever segment field has the highest fill rate, then regenerate the plan so I can review the impact?
The Campaign Influence ROI formula in this analysis looks useful. Can you convert that formula into a KD and document the inputs so we can reuse it without redefining it each time?
For all Open Opportunities this Quarter, list associated active campaigns and engagement scores. Highlight any open deals with no campaign activity.
Can you pull a report of all deals that were open under definition active pipelines from the period of Jan 1 2022 to Sept 30 2025, and show me the data broken down by pipeline by quarter with sum of total amount.
Using historical trends on the Stage 1 stall opportunity field, are there any trends or key analyses on how we're performing quarter over quarter in our early to late stage progression?
I'd like to see the general differences of opportunities that are flagged as Stage 1 Stall, first group is stage 1 stall = true and still in stage 1, the 2nd group is stage 1 stall = true but the opportunity is no longer in stage 1. I'd like to see what occurred for the ones that are no longer in stage 1 that didn't occur for the ones still in stage one during the period of being stalled in stage 1 that helped them process past stage 1
Analyze all opportunities created in 2025 that remained in Opportunity stage for more than 30 days. For these opportunities, provide a breakdown showing their current pipeline status as of today, categorized as: Still in Opportunity / Closed Won / Closed Lost / Still Open but in a Later Stage. Include both a global summary and breakdown by regions.
I need an analysis of the active pipelines, specifically sales direct. I want to see deals with no activity, no next activity date or no next step in the last week please.
What is the sales cycle length for each closed won opportunity in the last 6 months?
Can you show me all the rows that were filtered out during the analysis and explain why each one was excluded?
Compare the methodology you used here with the one we ran for a similar report last quarter. Point out anything that changed.
Walk me through the pipeline velocity calculation step by step. I want to understand how each filter was applied, which records were included or excluded, and what assumptions were made.
I would like to do an analysis of "active pipelines" for Q3 2025, any deals that were open within the period, and any activity. Please do not use the activities table, please use emails, calls and meetings separately. I'm looking for key sales KPI trends in quarter (particularly over July), activity, velocity, win rate, execution. I want to see MOM how we won and why along with where we can continue to win.
Can you check if there's any pattern btw activity level + health score. Like are low activity accounts consistently low health or mixed? maybe run a quick correlation if you can.
For Step 3 of this analysis, can you show me the SQL-equivalent logic or underlying operations? I'd like to sanity-check the logic against how I'd think about writing the query manually.
Show conversion rates from early-stage (redacted) to late-stage (redacted) opportunities by quarter for 2024 and Q1-3 of 2025.
Show all early-stage (redacted) opportunities marked Closed Lost this year. Break them down by channel, partner, and territory. Include top 5 loss reasons by count.
Please run an analysis of active pipeline deals that were marked closed lost for "No Show" in Q3 2025. How many were marked that, and how many were reopened after?
Find patterns in slipped deals: common stage at time of slip and any repeated blockers in notes.
Which campaigns this year have influenced the most pipeline? Use First touch.
What is the marketing influence pipeline this month? Use opportunities that have been influenced by campaigns and have a responded member status. Exclude any campaign type equal to email and exclude campaigns that contain in the name.
What trends are you seeing this month for our demo requests? Use the following campaign IDs and use member status updated date to indicate those adding this month.
I am looking for insights on $0 orders (Opportunities with an amount of 0). I'm curious what kind of associations you can make, looking at the Accounts. One metric might be percentage of Account Type where these occur, but I need the values on the Account to be "point-in-time" possibly cross-referencing the Account History object. Suggest any other metrics you think might be interesting.
For these companies, can you pull the latest logged call (who made it, when) + any notes? drop it in a table. if there's been no call in 30+ days, flag it.
Analyze the opportunity conversion for the records with and not done.
Here are the funnel stages we use: Discovery (COMPANY Pipeline), Functional Evaluation (COMPANY Pipeline), and Pricing & ROI (COMPANY). Can you incorporate these stages into the analysis and show stage-level performance and conversion patterns?
I want to do an analysis, specifically for the pipelines: Sales Retail Pipeline, Cross-sell/Upsell and Enterprise Mid Market. Can you please go look up the pipeline IDs and cross them with the names so you know which I'm talking about.
Can you map out different headcount scenarios for the next two quarters based on current AE capacity, attainment, and pipeline coverage?
If we were to reassign territories, can you show the impact on coverage, open pipeline ownership, and expected quota distribution?
Build a few scenarios for how many opps each pod can realistically handle next quarter based on historic activity patterns and win rates.
Can you model what happens to our funnel if demo volume increases 10%, 20%, or 30%? Show downstream impact on SQLs, Opps, and expected revenue.
If we concentrated campaigns only on our top three segments, what shifts would we expect in influenced pipeline and deal size? Show best-case and conservative scenarios.
Help me understand how many opportunities we need to enter pipeline each month to maintain our quarterly target, based on average velocity and stage-to-stage conversion.
Create a territory planning view showing which regions are over- or under-saturated based on pipeline per rep, account volume, and inbound lead flow.
Can you show capacity projections for ADRs if meeting volume grows at the same pace as the last 6 weeks?
Based on recent activity patterns, which accounts should reps prioritize this week? Highlight ones showing risk signals or slipping engagement.
Looking at stalled Stage 1 opportunities, what actions helped similar deals progress? Surface the top patterns reps should repeat.
From the lost-deal analysis, can you recommend the top three process fixes that would have prevented the majority of "No Show" losses?
Identify deals likely to slip this month based on historical behavior and activity velocity, and suggest targeted next steps.
Which campaigns are consistently influencing high-win-rate deals? Recommend how we should redistribute spend next quarter.
Looking across all active renewals, which customers show early churn indicators? Suggest proactive CSM actions.
For accounts with repeated missing data issues, what corrective steps should RevOps take to prevent downstream metric drift?
Recommend which segments or territories reps should focus on next quarter based on pipeline efficiency and recent close rates.
Can you map out different headcount scenarios for the next two quarters based on current AE capacity, attainment, and pipeline coverage?
If we were to reassign territories, can you show the impact on coverage, open pipeline ownership, and expected quota distribution?
Build a few scenarios for how many opps each pod can realistically handle next quarter based on historic activity patterns and win rates.
Can you model what happens to our funnel if demo volume increases 10%, 20%, or 30%? Show downstream impact on SQLs, Opps, and expected revenue.
If we concentrated campaigns only on our top three segments, what shifts would we expect in influenced pipeline and deal size? Show best-case and conservative scenarios.
Help me understand how many opportunities we need to enter pipeline each month to maintain our quarterly target, based on average velocity and stage-to-stage conversion.
Create a territory planning view showing which regions are over- or under-saturated based on pipeline per rep, account volume, and inbound lead flow.
Can you show capacity projections for ADRs if meeting volume grows at the same pace as the last 6 weeks?