How to Use Time Series Sales Forecasting to Drive Predictable Revenue

If you’re in sales, marketing, operations, customer success, you’ve probably asked yourself this question many, many times: where are we going to land at the end of the quarter?

So here’s the basic concept: In order to predict where you’re going, you have to know the steps that got you to where you are.

In B2B, the number of signals that go into a successful month, quarter or year, is staggering. Think about all won and lost deals, all the activity that went into them (emails, meetings, phone calls, etc...), all the sales stages and forecast categories they went through, every change in close date or opportunity amount throughout the sales cycle. Each of these activities, positive or negative, contributed to the outcome of the quarter.

And if you’re able to capture all of that data (including how it changed over time) — and build the AI engine to ingest and analyze it — very powerful insights can come out of it.

What is time series sales forecasting?

Time series sales forecasting would give you the ability to inspect every change across your pipeline instead of playing a guessing game or wasting time during your 1:1s or forecast calls reviewing data. You would not only have visibility into deal slippage and conversion rates, but also the ability to predict outcomes and forecast accurately based on historic patterns.

There’s just one problem.

Most traditional databases were built to store and query the current state of the data. They were not designed to capture changes and run queries across time.

Time series databases can handle scale for recording data whenever it’s updated, tracking change through time. We know what you’re thinking: No, CRM can’t do this either.

Time series database vs. CRM

While CRM has become an essential system of record for sales contact and transaction information, it wasn’t designed for the most critical goal of modern sales organizations: predictable revenue.

Because CRM doesn’t maintain a complete history of the business, it doesn’t have enough data for sophisticated AI and machine learning techniques to model the business. This is not to mention CRM data is only as good as the diligence of the reps tasked with manually inputting it. And even if your reps are best in class at inputting their data, some CRMs put a cap on the number of fields you can history track, limiting your ability to capture the full picture.

A time series database that not only automatically captures sales activity and customer engagement data, but also snapshots it over time solves for this problem.

Introducing Clari’s Time Series Data Hub

The minute you connect your Salesforce account to Clari, we start ingesting all of your CRM and activity signals and snapshotting them in real time. Our AI and analytics engine then automatically illuminates insights in a user-friendly interface across both web and mobile, perfectly integrated with your sales forecasting process. This means that you always have key insights at your fingertips when running your 1:1s, forecast calls, pipelines calls and all other critical moments in revenue operations.

We call it our Time Series Data Hub.

Here are a few use cases:

  • Is my deal at risk? Clari analyzes past won and lost deals and provides an Opportunity Score that indicates the overall health of the deal. It uses historical deal data, like the amount of time an opportunity stays in a specific stage or whether the deal size increased or decreased, to assess how likely it will close. This gives your sales team instant visibility into where a deal stands.
  • How is the quarter looking? Clari’s AI-powered forecast projection predicts where you’ll land at the end of the quarter. As your quarter progresses, you can compare the AI projection (the machine call) to what your team is calling (the human call) to see where you might have risk so you can come up with a plan to mitigate it.
  • How much pipeline do I need? Snapshotting data means Clari is tracking your historic conversion rates by sales stage or forecast category and uses that to predict exactly how much pipeline you need to meet your quota next quarter. The entire team gets visibility into what needs to be done in terms of pipe generation and pipe coverage to hit the number.

We can can continue to geek out over the ins and outs of our Time Series Data Hub (and we certainly will. Look out for an additional blog covering more of the technical aspects). As you can see we’re very passionate about leveraging it to make revenue operations more connected, efficient and predictable.

Request a demo and let’s talk about how it may fit your organization.

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