How to improve sales forecasting accuracy with unified data

Sales forecasting estimates future revenue, guiding sales and finance teams while they set future budgets and quotas. But the quality of those forecasts hinges on the data they use. Some only incorporate CRM pipeline data, missing renewal and expansion revenue from billing and product usage records.

Sales and finance teams get a complete forecast when pipeline, billing, product, and marketing data reach one warehouse on a regular schedule. Learn how to create a reliable sales forecasting workflow with a data integration tool like Fivetran, so your teams can make better-informed decisions with more accurate forecasts.

What is sales forecasting?

Sales forecasting is a planning process that estimates how much revenue a company expects to generate in a future period, such as the next quarter or fiscal year. Finance teams use these forecasts to set budgets and revenue targets. Sales and operations leaders use the same numbers to assign quotas and make resource allocation decisions about team size and spending.

Common sales forecasting techniques include:

• Historical forecasting: Historical forecasting projects future revenue from sales during previous quarters.
• Length-of-sales-cycle forecasting: Each open deal’s age, measured against the average sales cycle for its deal type, indicates the deal’s chance of closing.
• Multivariable analysis: Statistical models can combine pipeline stage, deal size, rep history, and seasonality into one revenue estimate.

These methods produce inaccurate results when sales reps don’t update close dates or apply inconsistent criteria when moving deals between stages. Teams can use a weekly pipeline review to catch missed updates and inconsistencies in stage definitions.

How integrated data improves sales forecasting accuracy

Integrating data puts current pipeline, billing, product, and marketing records in one warehouse, which can ultimately improve sales forecasting accuracy. Built-in CRM sales forecasting tools only read the fields sales reps enter, and teams get more value from Salesforce data when their Salesforce records are connected to revenue data from other systems.

Brings sales and revenue signals together

CRM data shows the deals that sales reps are working on and their expected close dates, but it doesn’t predict the future likelihood that existing customers renew or expand. A more accurate forecast combines CRM data (like opportunity amounts, deal stages, close dates, and owner changes) with signals from other business systems:

• Billing and finance data: Invoice and renewal records from an ERP or billing platform confirm which booked deals turn into collected revenue and when contracts come up for renewal.
• Product and usage data: Login counts and seat usage from your product database point to accounts that are likely to expand and accounts at risk of churning at renewal.
• Marketing and engagement data: Campaign responses and support ticket trends reveal buyer intent and account health that never appear on an opportunity record.

When analysts join these sources on a shared account ID, they get a 360-degree customer view.

Keeps forecasting data current and consistent

A warehouse full of outdated records or inconsistent data produces an inaccurate forecast. Some of the common data quality problems that affect forecasting include:

• Stale data: A forecast built from an outdated pipeline export misses recent stage changes and counts deals a buyer has already declined.
• Inconsistent definitions: When sales and finance count a committed deal differently, the two teams report different revenue totals, and analysts can’t reliably compare forecasts across quarters.
• Missing or duplicate records: Duplicate deals inflate the pipeline, while missing accounts lower expected revenue.

Automated syncs keep pipeline and billing records up to date without manual exports. Analytics engineers can also write deal definitions and deduplication rules in SQL and apply them to every refresh. That way, every quarter’s forecast uses one definition of a committed deal and counts each deal only once.

Measures forecast performance against actual results

A revenue operations team can compare each quarter’s forecast with closed revenue by segment and sales rep to find recurring misses. For instance, if enterprise deals close below forecast for four quarters in a row, the win-rate assumptions used for that segment’s pipeline stages might be too high.

To compare a forecast with closed revenue, analysts also need a record of how the pipeline looked on the day your team made that forecast. CRMs overwrite fields like close date and amount, so analysts often rely on historical Salesforce analysis that retains every past value and shows which forecast assumptions proved inaccurate.

How to build an integrated sales forecasting workflow

Your forecasting software won’t correct a close date that sales reps haven’t updated in months. Here’s how to improve sales forecasting accuracy at the data level, whether your forecasts run in a SQL model or dedicated software:

1. ‍ Define the decisions the forecast supports: A quarterly bookings forecast for the board needs different inputs than a monthly renewal forecast for finance. Agree on the period and revenue type before anyone pulls data, and keep new business and renewals in separate models if they behave differently.‍
2. Map each source and field: List every field the forecast uses along with its source system and the key that joins it to an account or opportunity. A company that sells through Salesforce and bills through NetSuite, for example, needs a NetSuite and Salesforce integration that matches each opportunity to its invoice.‍
3. Centralize and standardize the data: Load every source into one warehouse with automated data integration so analysts don’t have to rebuild spreadsheet exports every week. Managed connectors from a platform like Fivetran keep each source current, while SQL models apply shared definitions for stages, bookings, and duplicate handling.
4. ‍Compare forecasts with actual results: Save a copy of each forecast with its date, then let new syncs fill in closed revenue next to them. Review the variance monthly with sales and finance leaders, and adjust stage probabilities or seasonality factors when the same miss repeats.

Once the workflow runs on a regular schedule, sales and finance leaders can focus forecast reviews on deal risk instead of reconciling spreadsheets.

How Fivetran supports sales forecasting data integration

With Salesforce and billing records in one warehouse, custom pipelines from each source can break when an application adds or renames a field. Fivetran automates the movement of Salesforce and billing data into your warehouse, so your sales forecasting software or SQL models work from updated records. Fivetran pulls changed records on your chosen schedule, and your team can run dbt models after each sync to update forecast tables.

With more than 750 prebuilt connectors, analysts can join billing, ERP, product, and marketing data to Salesforce records on one account ID. The Fivetran Salesforce connector includes a history mode that saves every past close date and deal amount, so analysts can compare old forecasts with closed revenue.

OUTFRONT, a media company, used to load its revenue data once a day, so sales teams often waited until the next day to see how revenue was tracking. With Fivetran and dbt, OUTFRONT refreshes centralized revenue data every 15 minutes. Sales teams now track revenue performance in near real time and update their forecasts faster.

Start a free trial to try the Fivetran Salesforce connector, centralizing business data for more accurate forecasts.

FAQ

What data is used for sales forecasting?

Most sales forecasts start with closed-won historical data, open pipeline value, stage-level win rates, and average sales cycle length for each customer segment. Analysts adjust the resulting forecast for seasonality, planned price changes, market trends, and economic indicators to match expected conditions in the coming quarter.

How can you improve sales forecasting accuracy?

Clear exit criteria for each pipeline stage, like a confirmed budget before a deal reaches proposal, make stage-based probabilities more dependable. Scenario planning helps, too. A base case published next to low-end and high-end cases shows leadership the extent to which a few large deals impact revenue.

How do you create a sales forecast?

A basic forecast multiplies each open deal’s value by the historical win rate for its stage, then totals the results by expected close month. Analysts adjust the monthly totals for seasonal patterns and known renewals, then compare the adjusted forecast with the revenue your sales managers commit to closing.

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