Key takeaways
- Descriptive analytics says what happened, diagnostic says why, predictive says what is likely next.
- Most sales data analysis starts in the CRM, so its gaps become the analysis's gaps.
- Sales and marketing analytics work best on one shared funnel with agreed definitions.
- Every analysis should end in a decision someone will make.
Descriptive, diagnostic and predictive sales analytics
Sales analytics comes in three kinds that build on each other: descriptive, diagnostic and predictive. Most teams spend most of their time on the first, and the later kinds are only as good as it is.
- Descriptive: what happened, such as revenue by month, deals won by segment or pipeline created.
- Diagnostic: why it happened, such as which stage deals were lost at and for what reasons.
- Predictive: what is likely next, such as which deals will close this quarter.
The data sales data analysis needs
Sales data analysis needs complete, consistent records of deals and the activity around them. Missing close dates, stale stages and lost deals left open are the usual reasons an analysis cannot be trusted.
- Deals with amount, stage, owner, created and close dates.
- Won and lost reasons recorded in set categories rather than free text.
- Activity such as meetings and emails linked to the right deal.
- Where each lead came from, for marketing analysis.
- Billing data for renewals and expansion.
Sales and marketing analytics on one funnel
Sales and marketing analytics work best when both teams measure one funnel with agreed definitions for each stage. If marketing counts a qualified lead differently from sales, each team's figures look fine while the handover between them fails.
A shared view answers the questions both care about: which channels produce pipeline that closes, how long leads wait for follow-up and where they drop out.
Who does sales operations analytics
Sales operations analytics is usually done by sales operations or revenue operations, sometimes with a dedicated sales analyst. They keep the data clean, build the reports and dashboards and work through the questions leaders ask.
In a small company the founder or head of sales often does it from the CRM and a spreadsheet. The method matters less than asking a clear question and keeping the definitions the same from one period to the next.
Why a sales analysis goes wrong
A sales analysis usually goes wrong for one of two reasons: the data underneath is poor, or nobody decided what the analysis was for.
- Trusting figures from records nobody keeps up to date.
- Tracking dozens of metrics that nobody acts on.
- Averages that hide large differences between segments.
- Changing definitions between periods, so trends are not real.
- Reports with no owner and no decision attached.
Where Pebbles Ai fits
- CapabilityAI workspacePebbles Ai is not a sales analytics tool. Its only analytics is the Team Productivity board for admins on Team and above, covering speed, quality and contribution, with no revenue reporting.
- CapabilityProspecting & customer recordsFor pulling a set of deals together by hand, the Pebbles Ai CRM, which is in Beta, offers saved views and filters over each deal's stage, amount, close date and probability.
Common questions
What is the difference between sales analytics and sales reporting?
Reporting presents figures on a schedule, such as weekly revenue. Analytics digs into the figures to explain them and decide what to change.
Do small sales teams need sales analytics?
They need a little: a few figures reviewed every week, such as pipeline created, deals won and lost reasons. A CRM with clean records and a spreadsheet is often enough.
What tools are used for sales analytics?
Most teams start with their CRM's reports and a spreadsheet, then add business intelligence tools or revenue intelligence software as their data and questions grow.
About this page. Written by the Pebbles Ai team for people who run sales, marketing and growth. Last updated .
