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Want better insights to close deals fast, or up-to-date pipeline numbers to feed accurate forecasts? Sales data to the rescue.
Kristen Page
Writers were aided by AI to draft these FAQ questions.
Every quarter, my clients’ data footprint continues to expand. Whether it’s customer response rates, leads in pipe, or quota attainment, there is always another metric to track.
But we ignore these metrics at our peril. Sales pros are nearly unanimous in the importance of sales data. Ninety-six percent agree that real-time data is essential to keep up with customer expectations, according to the Trends in Data and Analytics for Sales Report .
That same report also reveals concerns. Sixty-three percent say their company’s data is not properly set up for generative AI, and only 42% are completely confident in their data accuracy.
“Show me the sales data!” has become the new refrain. Sales leaders and sales reps are realizing that to focus on the customer, they need to focus on the data underneath their engagement efforts. They’re working to pull sales data in from trusted sources, analyze it with sales analytics software to make better decisions, and run AI on top to become more productive. Below, we share how you can join the movement.
Discover how Sales Cloud uses data and AI to help you build relationships and close deals fast.
Sales data falls into two big buckets. The first is external data: any information collected about prospects, including demographics, interest, behavior, engagement, and activity as they move through the sales funnel. internal sales data, which includes deal attributes like product type and pricing; and sales rep performance metrics. Together, this external and internal data is used to inform deal actions, gauge progress toward sales targets or other key performance indicators (KPIs), and fuel time-saving tools like AI.
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Sales data gives you a way to measure all of the activities related to your sales efforts. This allows you to determine performance benchmarks and set targets to guide sellers toward growth. It also reveals risks in your pipeline, allowing you to address them before they snowball, and helps you identify opportunities that you can make the most of.
For example, a sales team can analyze sales data — whether manually or with analytics like AI — and discover a deal that’s stalled and needs extra attention, an objection in a sales call that went unanswered, or a deal whose dollar amount has changed, putting the sales forecast at risk.
Those are warning signs. But sales data shows you green lights, too. For example, you can use sales data to predict when a customer might be ready for an upsell, or organize a leaderboard that fosters healthy competition among your reps.
Another reason why sales data is so important: taking advantage of generative AI . Nearly four in five sales leaders (78%) are concerned their company is missing out on generative AI according to our latest data report , which can write emails, create sales pitches, and give real-time guidance for next steps in the sales process.
Part of the problem is that generative AI outputs are only as good as its data outputs, and 63% of sales pros say they company’s data is not properly set up for generative AI. It’s why sales teams are focused on cleaning up and collecting their sales data — the basis for insights and the fuel for AI.
Sales data can be categorized into data on individual customers and companies, like demographic and buying behaviors; and internal sales performance data, including data collected during the sales process. Here’s a close look at these:
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Sales data that helps you sell better? Sign us up. But how exactly do you find and collect this information? Invest in a CRM that serves as your single source of truth and tracks all customer engagement, automate data collection to keep your information up to date, and carefully incorporate external data while prioritizing security and privacy.
Once you’ve collected your data, it’s time to interpret and take action on that data. First, identify specific business goals or targets. Then, identify KPIs that will help you achieve those goals. Last, map your sales data to those KPIs within your CRM, so you can track progress toward your goals. Make it easier by creating dashboards to make complex data more digestible. Here’s how it all fits together:
Global consulting firm Korn Ferry uses sales data to increase their efficiency. Recently, they were looking to improve win rates and accelerate deal velocity. With CRM analytics, tracking KPIs like win rate and velocity was easy, but understanding how to impact those KPIs required turning data into insights and actions. What attributes were having the greatest impact on win rate and sales cycles? They started by looking at the data to find the answers:
First, Korn Ferry began capturing more data on their opportunities. They combined deal data captured in Sales Cloud with deal data captured by Korn Ferry Sell, their sales methodology application powered by Miller Heiman, built natively on the Salesforce AgentExchange. By combining both, Korn Ferry gets a more complete view of their deals, including qualitative information about sales cycles that would historically get captured off-platform in client conversations, meetings, and the minds of sellers.
Next, they dove into analysis. Combining Korn Ferry Sell with Einstein and Sales Cloud Analytics, Korn Ferry uncovered how certain deal attributes — like the relationship with a key buying influence — correlated to success, allowing them to identify trends and adjust both sales strategy and enablement to move the needle on their KPIs.
Korn Ferry also began leveraging AI-driven “opportunity scores” to track the health of their deals after making strategy and enablement shifts. These scores are calculated using a combination of standard deal health metrics, like activities completed and close rates, and qualitative metrics from their sales methodology, like a buyer’s feelings about Korn Ferry. This allows Korn Ferry to spot pipeline quality issues when scores drop below benchmark and take corrective action. It also allows them to allocate resources more efficiently, improve forecast accuracy, and lead more productive pipeline review meetings.
You don’t need a heavy tool belt to manage data-driven sales. In fact, you only need two main tools: an intuitive CRM with built-in AI and security, and a sales analytics tool. Often, these are part of the same platform.
Customer relationship management (CRM) software: A CRM that serves as your single source of truth ensures every customer interaction, from initial contact to final purchase, can be stored in the same place. Automation-equipped CRMs do more than just provide a space to manually store data, however. They store it for you by pulling in relevant details from customer engagement sources like emails, phone calls, and video meetings and dropping them into deal records automatically. A big must-have here: a security layer that masks sensitive data so it isn’t visible to anyone on the outside, and a zero-retention feature that ensures no data is retained by your CRM.
Sales analytics and reporting tools: At the very least, onboard an analytics tool that allows you to see the status of your progress toward business goals and KPIs in real-time. Even better, find one with intuitive, customizable sales dashboards, making complex data easily understandable. If you really want to stay on top of the competition, make sure your analytics platform includes AI functionality that delivers recommendations for deal actions and strategy changes based on real-time updates to your sales data. This allows you to stay on top of evolving customer needs and market trends without falling behind — and potentially falling short of your goals.
Sales data does more than provide information you can turn into trackable metrics. it lights the path toward action. To make the most of your sales data, prioritize regular reviews of CRM-surfaced insights, adjusting your sales and enablement strategies to close more deals. Then focus on delivering value that keeps customers coming back.
By centralizing, organizing, and analyzing sales data, CRM systems help sales teams improve their sales processes and decision-making. These systems automate data collection, track customer interactions, and provide a clear view of the sales pipeline.
Analyzing sales data helps business leaders identify market trends, refine sales strategies, and tailor customer experiences. This results in increased revenue and a stronger competitive edge. Dashboards offer real-time insights into pipeline status and deal progress while AI can use historical data to forecast future sales trends.
Reducing manual errors is a reliable way to improve your sales data quality — and tools like Sales Cloud make it easy to log interactions automatically. Other steps include regular data cleaning to remove duplicates, update outdated information, and verify accuracy. Use AI to identify patterns and deliver real-time insights. Finally, regularly review dashboards and reports to spot and resolve data quality issues.
Sales data is the raw information you collect about sales activities and customers. Sales analytics involves analyzing that data to gain insights, detect patterns, and make predictions to boost business performance.
Make informed decisions and adjust strategies on the fly by tracking sales data. With a better understanding of customer behavior and preferences, you can strengthen relationships by tailoring interactions. Forecasting improves when you have access to accurate, real-time data. Automating data tracking reduces manual work, enabling you to focus on your greatest strength: selling.
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