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Why You Need a Strong Sales Data Foundation

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Why You Need a Strong Sales Data Foundation

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Sell smarter by combining AI that’s tuned for your business with complete and accurate sales data.

If you’re a sales leader, you understand the pain of poor quality sales data. Consider this situation: EOQ is approaching and you need to know if your top deals are going to close.

Sure, you could go back to ask the salesperson for their opinion. Or you could skim through dozens of call recordings.

But do you really have time for that?

Next, say you’re looking to boost sales rep performance with a new coaching or sales enablement program. 

Which sellers should you focus on? And do you know what kinds of behaviors to recommend?

Whether you’re forecasting, coaching, or otherwise running your sales team — you need to trust your sales data quality and completeness.

All of this requires a strong foundation of CRM sales data.

Sales Data is a Challenge for Sales and Revenue Operations Too

Now, if you’re a sales operations or revenue operations leader, the challenge is similar. You want to provide your sales leaders and sellers with the B2B sales data they need to make better strategic and tactical decisions. Imagine the two situations above from an operations perspective.

First, you’ve got your CRO on Slack trying to get the latest data on sales performance analysis. It’s urgently needed for the board meeting. They want to know whether critical deals will close or not. But if you haven’t provided that foundation of sales data, they won’t be prepped. And you’ll hear about it. 

You might hear ‘which dashboard do I use?’ or more likely, ‘can I trust this data?’ and ‘how can I interpret the sales data?’. You need to let them self-serve and ask the follow-up questions to get the answers they urgently need. 

Second, you’re getting ready for your Sales Kickoff and have compiled a set or reports on how well reps are hitting activity targets and advancing deals past key milestones. But if you’re up on stage, presenting that data, and it’s not accurate — you’ve just lost critical goodwill with the sales organization. 

How to Address Your Sales Data Issue

A key problem with sales data, particularly sales activity data, is the incompleteness of the information that you capture. You likely have a mass of sales engagement data that is locked away in individual silos. Emails will be stored in users’ Gmail or Outlook accounts. Call transcripts are hidden in Chorus, Gong, or Zoom. Sales engagement communications are in Salesloft and Outreach. Completeness matters or else you’re just getting a partial perspective on rep and deal activity.

You’ll want to ensure that the data from these various tools are captured and centralized — in a sales data lake or a single source of truth like your CRM (hello, Salesforce). A related challenge is that many of these tools do offer a sales data capture function. But because each does it in a different way, your central repository of data is going to be virtually unusable because the tools write back data in very different ways and with varying levels of accuracy.

Automate the Collection of Sales Activity Data

In addition to the range of quality that you have for capturing data from your various tools, there’s also a challenge of automation. Many sales leaders simply expect their sellers to document every call and email manually. That makes the quality of activity capture even more variable. One rep might be particularly thorough in writing notes about their calls. Another not at all. Another might be thorough, but only for some calls. This inconsistency is a major challenge for data quality.

The optimal approach, then, is to automate the collection of data with an agnostic solution that is able to pull the data from across your full GTM stack. This approach ensures that the collection of data is standardized and can be organized the way you want. The result is a firmer foundation of data for things like pipeline analysis

Armed with this foundation of sales activity data — every email, call, and calendar interaction with prospects and customers — is a treasure trove for sales organizations. New sellers can get up to speed on deals fast and move them to close faster. Sales leaders can spot challenges and adjust their forecast – and offer better coaching.

A Quick Aside about Syncing Sales Data to Salesforce

You want your CRM sales data to be current. So, now you’re automating sales data collection. But the challenge remains that every tool is going to do Salesforce data sync in a different way. Wrong fields, wrong data standards, inconsistent formats. It all means that your CRM is out of date, making things like Salesforce data migrations that much more difficult. 

An effective data solution is going to map activity data intelligently and consistently to the right fields. The result is a vibrant, useful CRM that your team will want to use.

From Sales Data to Sales “Signals”

A great foundation of sales activity data is a very good start. But to truly leverage your sales data means going beyond cataloging sales activities to understanding what’s actually happening and which activities matter for deal health and sales performance management

An effective approach to getting sales data insights will analyze the various emails, calls, transcripts, and calendars to identify the persona that is communicating with your team. It will look at which activities are most effective by correlating behaviors and personae with real momentum. That allows your team to truly understand which deals will close and which need help. And that’s all based on a solid catalog of sales data.

Getting Ready for Sales AI

Another reason to have a great foundation of sales activity data is AI. Sales AI is positioned to revolutionize the way sales is conducted, beyond just ChatGPT for sales or sales chatbots. It can help sellers understand their deals instantly. Do your meeting prep in a snap. Determine winning behaviors. And know what to do next to advance deals. 

The challenge for any AI sales tool, however, is the quality of the data that it operates on. AI can act as a 24/7 AI revenue analyst. But if your data is poorly structured, incomplete, and inaccurate, then your teams can’t trust the results. A well executed data strategy, though, means that AI or machine learning solutions for sales are operating with the right data. The result is your teams have instant access to the answers and insights they need to sell smarter.

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