Sales & Marketing Automation
One dashboard. One number. No more Monday morning debates.
The most expensive meeting in most B2B companies is the weekly pipeline review, where the sales manager, the CEO, and the CFO all bring different numbers for the same pipeline. Marketing says "we generated 200 leads." Sales says "we have 40 active opportunities." Finance says "we see ₹2Cr in the forecast." The number is different because the source is different. A properly built CRM reporting layer means every function reads from the same system, and the debate goes away.
The reporting problems we fix in every engagement.
CRM reporting is broken in specific, consistent ways. Here is what we find in every audit.
The pipeline review is a manual Excel exercise.
Someone exports the CRM every Friday, copies it into a spreadsheet, applies filters, and builds the pipeline summary manually. By the time it is used on Monday, it is already out of date. Two hours of work per week, every week, producing data that is immediately stale.
There is no single agreed definition of "pipeline."
Marketing counts every MQL as pipeline. Sales counts every discovery call as pipeline. Finance counts only opportunities past the proposal stage as pipeline. Three definitions, three numbers, one confused leadership meeting. The definition of every metric must be agreed and enforced in the CRM before reporting can be meaningful.
Nobody knows why the close rate is what it is.
The team knows the aggregate close rate, say, 22%. But nobody knows the close rate by rep, by source channel, by deal size, or by time of year. The aggregate number is useless for improvement. The segmented number shows where the problem is.
Marketing cannot show its contribution to revenue.
The marketing team tracks leads and email opens. The sales team tracks deal stages. Nobody has connected the two in a way that shows which marketing activity produced which closed deal. Marketing is defending its budget with activity metrics because the revenue attribution data does not exist.
The reports were built by whoever needed a number last quarter.
The report library has 200 reports. "New Pipeline Q1 2024 FINAL," "Test - do not use," "Old pipeline - compare with master." Nobody knows which one is current, which one the CEO looks at, or which one marketing and sales are supposed to align on.
The reporting layer we build.
Clean, automated, role-specific dashboards built in your CRM, so every stakeholder sees the number that is relevant to them, in real time, from the same source of truth.
Metric definition & data audit
- Metric definition workshop, every KPI agreed in writing with the exact CRM field it pulls from
- CRM data audit, data completeness and accuracy assessed for every metric being reported
- Data quality fixes, missing source fields, incomplete lifecycle stages, and orphaned records corrected
- Report library audit, existing reports reviewed, duplicates removed, current reports identified
- Reporting requirements per role, what each stakeholder (CEO, CRO, CMO, rep manager, rep) needs to see
Dashboard build
- Executive dashboard, pipeline value by stage, close rate, revenue vs. target, and CAC by channel
- Sales manager dashboard, rep performance, pipeline velocity, deal aging, and win/loss by rep
- Sales rep dashboard, personal pipeline, activity target vs. actual, and deals at risk
- Marketing dashboard, MQL volume, MQL-to-SQL rate, pipeline influenced, and CAC by campaign
- Revenue operations dashboard, pipeline accuracy, forecast vs. actual, and source attribution
- Customer success dashboard, health scores, churn rate, NPS, and expansion revenue (if in scope)
Attribution model
- UTM framework, standardised UTM taxonomy for all acquisition channels
- Lead source field mapping, every lead tagged with acquisition source in CRM
- Campaign-to-deal attribution, marketing campaign linked to deal records via CRM association
- Multi-touch attribution model, first-touch, last-touch, and linear attribution available per report
- CAC by channel, marketing and sales spend allocated to deals closed per channel
- LTV by cohort, customer lifetime value by acquisition source and month of close
Automation & training
- Automated email reports, weekly pipeline summary and monthly revenue report sent to stakeholders automatically
- Data quality alerts, automated alert when data completeness drops below defined threshold
- Looker integration, external BI dashboard connected to CRM data for advanced analysis (if required)
- Training, each role trained on their specific dashboard and how to interpret every metric
- Reporting governance document, which report is official for each metric, maintained by whom
What is included in a CRM reporting engagement.
Data Foundation
- Metric definition workshop
- CRM data audit
- Data quality fixes
- Report library cleanup
- UTM framework
- Source field mapping
Dashboards
- Executive pipeline dashboard
- Sales manager dashboard
- Rep performance dashboard
- Marketing attribution dashboard
- RevOps forecast dashboard
- Custom role dashboards
Attribution
- Campaign-to-deal attribution
- First / last / multi-touch models
- CAC by channel
- LTV by acquisition cohort
- Pipeline influenced by marketing
- Source ROI comparison
Automation
- Weekly pipeline email report
- Monthly revenue summary
- Data quality alerts
- Looker integration (optional)
- Forecast accuracy tracking
- Governance documentation
Why work with us.
Metrics agreed before dashboards built.
We run a metric definition workshop before building a single report. Every number is agreed in writing, its exact formula, the CRM field it pulls from, and who is responsible for its accuracy. No ambiguity, no debate.
Role-specific views.
The CEO does not need to see rep-level activity. The rep does not need to see the board-level revenue forecast. Every dashboard is built for the specific decisions the viewer needs to make.
Attribution that closes the loop.
We connect marketing campaigns to deal records so the CMO can show the CFO exactly which campaigns drove which revenue. Not leads. Revenue.
This is right for you if:
- B2B companies where the pipeline review involves reconciling numbers from multiple sources
- Marketing teams that cannot attribute their spend to closed revenue
- Sales leaders who are building their pipeline report manually in Excel every week
- Companies preparing for a Series A or B where investor data room reporting needs to be rigorous
- CRMs that have been live for 12+ months but reporting has never been properly configured
Not the right fit if:
- Companies with fewer than 50 deals in the CRM, the data volume does not yet justify a reporting architecture project
- Businesses whose primary reporting need is financial accounting, that is a finance and ERP project, not a CRM project
Frequently asked questions.
Do we need Looker or can we use the native CRM reports?
For most growth-stage companies, native CRM dashboards (HubSpot, Zoho, or Salesforce) are sufficient for the pipeline and sales reporting we build. Looker or another BI tool becomes necessary when you need to join CRM data with data from other systems, finance, product analytics, or ad platforms, to produce cross-system reports like blended CAC or LTV by acquisition cohort.
How long does it take to clean the data in a messy CRM?
Dependent on the volume and the nature of the issues. For a CRM with 2,000–10,000 contacts and the typical data quality problems (missing sources, duplicate records, incomplete stages), the data cleanup phase takes 5–8 business days. For larger data sets or more severe issues, we will scope the cleanup as a separate workstream.
What is the single most important report for a B2B sales team?
Pipeline velocity, the average time a deal spends in each stage, segmented by deal size and source channel. It tells you where deals are getting stuck (which stage is too slow), which channels produce fast-moving deals, and whether the overall pipeline is accelerating or decelerating. Most companies do not have this report despite it being available in every major CRM.
Can you build attribution reporting if our marketing team uses a separate tool from the CRM?
Yes, using UTM parameters and CRM lead source fields. If a lead comes from a LinkedIn ad, the UTM captures the campaign and ad set, passes it through the form, and is stored in the CRM lead source field. When that lead converts to a deal, the source is in the deal record. We build the bridge between the marketing platform and the CRM so attribution is available without merging the two tools.
Ready to end the pipeline debate and start reading from one number?
Book a 30-minute call. We will review your current CRM data and reporting and show you exactly what it will take to build a dashboard every stakeholder trusts.
Book a call