Descriptive analytics: what happened
Descriptive analytics answers questions about the past. It rolls up closed deals, win rates, average deal size, sales cycle length, and pipeline value into dashboards and reports. This is the most common form of sales analytics because it is the easiest to build. A quarterly business review is descriptive analytics. So is a pipeline report or a quota attainment chart.
The limit is that describing history does not predict the outcome. Knowing you closed 25% of deals last quarter says nothing about whether you hit target this quarter. Descriptive analytics reports the score after the game.
Predictive analytics: what will happen
Predictive analytics uses historical patterns to model future results. Instead of reporting last quarter's win rate, it estimates each open deal's probability of closing and its likely close date. That takes a model trained on your own sales history, not a fixed rule applied to every company.
Forecasting is the flagship predictive application. A real forecast breaks the quarter into three revenue sources:
- Carry-over deals already in the pipeline on day one that are expected to close this quarter. - In-quarter deals that do not exist yet but will be created, qualified, and closed inside the quarter. - Pull-forward deals from future periods that may close early, usually at a discount.
Pipeline coverage, the familiar 3x to 5x rule, is an input to this, not the answer. A team can carry 4x coverage and still miss badly. High coverage hides the composition of the quarter: pipeline that has aged out, or a number that leans on two or three large deals which usually close for less than their recorded value.
Where forecasting sits on the curve
Analytics maturity usually runs through four stages, from describing the past to prescribing the next move.
| Stage | Question it answers | Example |
|---|---|---|
| Descriptive | What happened? | Win rate, pipeline value, quota attainment |
| Diagnostic | Why did it happen? | Loss-reason analysis, stage conversion drop-off |
| Predictive | What will happen? | Deal-level close probability, revenue forecast |
| Prescriptive | What should we do? | Deal prioritization, next-best-action |
Frequently Asked Questions
What is the difference between descriptive and predictive sales analytics?
Descriptive analytics reports what already happened, like last quarter's win rate and quota attainment. Predictive analytics uses that history to model what happens next, like the probability that each open deal closes and when. Descriptive tells you the score of the last game. Predictive estimates the score of the next one.
Is sales forecasting descriptive or predictive analytics?
Forecasting is predictive analytics. It uses historical sales patterns to estimate future revenue instead of summarizing past results. A report that shows closed revenue is descriptive. A forecast that projects what will close this quarter, and splits it into carry-over, in-quarter, and pull-forward deals, is predictive.
What are the four types of sales analytics?
They map to a maturity curve: descriptive (what happened), diagnostic (why it happened), predictive (what will happen), and prescriptive (what to do about it). Most teams live in descriptive reporting. The largest gains come from moving into predictive forecasting, where you can still act on the quarter before it closes.
Can you forecast accurately with messy CRM data?
Yes. Imperfect data is nearly universal, and it does not have to produce bad forecasts. Consistency is what matters. If your data is captured the same flawed way every period, a model can learn the pattern and correct for it. Waiting for perfect data is the more expensive mistake.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like sales analytics into prescriptive action for your team.
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