Predictive Sales Analytics: Smarter Insights for Better Sales Decisions

Sales forecasting often feels like a shot in the dark. In fact, CSO Insights reports that 60% of forecasted deals never close. Leaders lack visibility into pipeline health and reps spend precious time chasing the wrong opportunities. But what if you could shine a light on your revenue future? Enter predictive sales analytics.

Predictive analytics uses AI to analyze historical sales data and surface insights that help answer burning questions like:

• Which leads are most likely to convert?
• How likely is this deal to close, and when?
• What‘s the next best product to pitch this customer?
• Is this account at risk of churning?

Armed with these predictions, sales teams can take action to improve key outcomes. But how exactly does this cutting-edge technology work? And what kind of results does it deliver? Keep reading for a deep dive into all things predictive sales analytics.

The Science Behind Predictive Sales Analytics

At its core, predictive analytics leverages machine learning to find patterns in past data that indicate future outcomes. The process involves three key components:

1. Data integration: Predictive models need large volumes of historical data to detect meaningful patterns. This data typically comes from sales tech systems like CRM, marketing automation, and customer success platforms. The first step is integrating and cleansing this data to create a single source of truth.

2. Algorithm training: The integrated data is fed into sophisticated machine learning algorithms that learn what attributes historically correlated with successful (and not so successful) sales outcomes. For example, the algorithm might determine that opportunities in the manufacturing sector with contract values over $100K are 80% more likely to close within 90 days. The models are repeatedly tested and tweaked until they can accurately predict outcomes.

3. Insight application: The trained models are then applied to a company‘s current sales data to make forward-looking predictions and recommendations. Insights can be delivered to sales reps in various forms such as lead scores, close probability percentages, or recommended discount levels.

The below diagram shows how these components fit together in a typical predictive analytics architecture:

Image Source: Medium

Predictive sales analytics is not a new concept. In fact, one could argue it dates back to the first time a sales manager created a forecast in Excel. But the technology has rapidly advanced thanks to the rise of big data, cloud computing, and AI. Some key milestones in the evolution of predictive sales analytics include:

• 1990s: Basic forecasting and trend analysis using Excel spreadsheets became common practice in sales orgs.
• Early 2000s: The advent of SaaS CRM systems like Salesforce made it possible to capture and centralize large volumes of sales data in the cloud. This set the stage for more sophisticated pipeline analytics.
• 2010s: Advances in machine learning, natural language processing (NLP), and cloud data warehousing led to the rise of a new breed of AI-powered sales analytics solutions. These tools made it possible to not only analyze sales data at scale, but to predict future outcomes.
• Today: Predictive sales analytics is becoming table stakes for data-driven sales organizations. Newer entrants are pushing the boundaries with capabilities like real-time scoring and revenue intelligence.

As the pace of innovation continues to accelerate, the future of sales will be written by those who harness data to see what‘s coming next.

Predictive Analytics Improves Core Sales KPIs

Which sales metrics can predictive analytics actually move the needle on? The short answer: pretty much all of them. But let‘s look at some of the core KPIs:

• Lead-to-opportunity conversion rate: Predictive lead scoring ensures reps focus their efforts on accounts most likely to convert. By deprioritizing tire kickers, average lead scores and conversion rates increase. Business software review platform G2 saw a 10% increase in lead quality and 25% boost in conversion rates from predictive scoring.

• Opportunity-to-close rate: Predictive deal scoring helps reps identify at-risk opportunities and take corrective action while there‘s still time. Having early warning enables targeted coaching, more accurate forecasting, and smarter resource allocation. Outsourcing firm TaskUs increased their opportunity-to-close rate from 18% to 23% within one quarter of implementing predictive deal insights.

• Average sales price: Predictive analytics recommends the optimal product mix, bundles, and discount levels for each customer based on historical win/loss data. These insights help maximize deal sizes without sacrificing margin or competitive positioning. Financial software provider Intuit increased revenue per customer by 25% using predictive cross-sell and upsell recommendations.

• Sales cycle length: Predictive models identify which prospect activities indicate sales readiness and deals that are languishing. Reps can trigger automated nurtures or prioritize outreach accordingly to compress sales cycles. IT hardware reseller SHI reduced their average sales cycle from 65 to 43 days after implementing predictive opportunity insights.

• Revenue forecast accuracy: According to Gartner, 55% of sales leaders don‘t have high confidence in their forecasting accuracy. AI-powered forecasting changes that. By analyzing current pipeline against historical patterns, predictive models produce deal-level and rollup forecasts that are 10-20% more accurate than human guesses. InsideSales found that AI forecasts were up to 92.4% accurate.

The below table summarizes how predictive analytics improves key sales metrics:

KPI	Improvement	Real Example
Lead to opportunity conversion	10-25% increase	G2 saw 25% increase
Opportunity to close	5-15 percentage point gain	TaskUs improved close rates 18% to 23%
Average sales price	10-25% increase	Intuit boosted revenue per customer 25%
Sales cycle length	20-30% reduction	SHI reduced cycle from 65 to 43 days
Revenue forecast accuracy	10-20 percentage point gain	AI forecasts up to 92.4% accurate

Sources: G2, Forrester, Gartner, InsideSales

As the above examples show, putting predictive insights into action drives real results. And these are just a few proof points. Predictive analytics yields efficiency and effectiveness gains across the entire sales process from lead prioritization through renewal and expansion.

Getting Started with Predictive Sales Analytics

Ready to bring predictive insights to your sales organization? Follow these steps to build an implementation plan:

1. Define clear use cases: Clarify which sales activities you want to enhance with predictive insights. Do you need more accurate forecasting? Higher lead conversions? Lower churn rates? Define target metrics and align predictive use cases to them.

2. Evaluate predictive solutions: Not all predictive sales analytics tools are created equal. Look for solutions that offer:

   • Seamless integration with your CRM and other sales tech
   • Robust predictive models that cover a range of sales use cases
   • Explainable AI that shows the ‘why‘ behind each recommendation
   • Actionable insights delivered in reps‘ existing workflows
   • Reporting and analytics to track results and ROI

   The below table compares some of the leading predictive sales analytics solutions:

   Vendor	Use Cases	Key Differentiators	Target Customer
   Salesforce	Forecasting, lead/opportunity scoring, rep performance	Native to Salesforce CRM, Einstein AI engine	Enterprise Salesforce customers
   Infer	Predictive lead scoring, sales intelligence	Provides ‘fit‘ and ‘behavior‘ scores, data enrichment	SMB and mid-market B2B
   Clari	Forecasting, pipeline inspection, revenue intelligence	Real-time insights, forecast collaboration	Large and enterprise B2B
   InsideSales	Forecasting, lead scoring, sales coaching	Sales rep-focused insights, sales acceleration tools	SMB and mid-market B2B
   People.ai	Sales rep activity capture, engagement insights, revenue intelligence	Automated sales activity capture, sales process maps	Enterprise sales teams

3. Assemble your data: The foundation of an effective predictive analytics implementation is clean, complete historical sales data. Audit the data in your CRM and other key sales systems. How far back does it go? Are there gaps or inconsistencies in how fields are used? Consider enriching first-party data with third-party firmographic and technographic data for more accurate predictive models.

4. Roll out in phases: Trying to boil the predictive ocean is a recipe for failure. Start with a proof of concept focused on one or two high-impact use cases. Engage a cross-functional team of sales ops, IT, and data science in the planning. Make sure to define success criteria upfront and track rigorously. Use early wins to secure broader investment and adoption.

5. Enable your team: Predictive analytics is only as effective as the sales reps who use it. Make sure your team understands what the AI recommendations mean and how to take action on them. Consider gamifying the experience through dashboards and competitions to drive adoption. Over time, predictive insights should become a core part of your sales process.

As Benj Cohen, founder of predictive sales platform Proton.ai put it: "Predictive analytics is becoming table stakes for sales teams. If you‘re not leveraging AI to drive revenue, you can bet your competitors are."

Innovations Shaping the Future of Predictive Sales Analytics

As powerful as predictive sales analytics is today, we‘re only scratching the surface of what‘s possible. Some of the emerging innovations that will shape the category include:

• Real-time scoring: The current state-of-the-art for predictive lead and opportunity scores is daily batch updates based on the previous day‘s activities. The future is real-time scoring that updates based on prospect engagement as it happens. Imagine a sales rep getting an alert the second a hot lead fills out a high-value form or an open opportunity‘s engagement score drops below a key threshold.

• Natural language processing: NLP will enable predictive models to analyze unstructured data like call transcripts, emails, and meeting notes to detect buyer sentiment and noteworthy behavior. This will unlock a whole new level of insight into deals and relationships.

• Automated sales motions: We‘ll see AI recommendations grow more prescriptive and trigger automated workflows in sales engagement and CRM platforms. For example, a target account being flagged as ‘surging‘ could automatically kick off a multi-channel outreach sequence from the appropriate sales rep.

• Convergence with other technologies: Predictive sales analytics will increasingly overlap with other emerging technologies:

  • 5G networks will enable real-time sharing of rich sales interaction data from the field
  • IoT devices will provide usage and diagnostics data for more proactive customer engagement and predictive maintenance
  • Blockchain smart contracts will surface leading indicators of future sales from partner ecosystems

Taken together, these developments paint an exciting future where sales organizations have perfect foresight and can deliver perfectly timed, relevant engagement to every revenue opportunity.

According to Gartner, by 2025, 75% of B2B sales organizations will deploy AI-guided selling solutions. Will yours be one of them?

Bringing It All Together

B2B selling has never been more challenging. Buyers expect experiences on par with the hyper-personalized engagement they enjoy as consumers. Sales teams must navigate larger buying committees, longer deal cycles, and a growing tide of data. Amidst this increasing complexity, sales leaders are on the hook to deliver predictable growth.

Predictive analytics is quickly becoming an indispensable advantage for sales organizations. It enables teams to cut through the noise and focus on the opportunities and actions that make a difference. Leaders gain newfound visibility and confidence in their forecasts and pipeline health. Managers can monitor team performance and ensure coaching is tailored to each rep‘s needs. And sellers can engage buyers with the right message at the right time. Every time.

If you‘re not already harnessing the power of predictive insights in your sales org, it‘s time to start. Define your use cases. Evaluate providers. Assemble your data. Roll out iteratively. Enable your team. The only real risk is inaction.

Because as the old business adage goes, "If you don‘t know where you‘re going, any road will get you there." With predictive sales analytics, you‘ll always know the fastest path to revenue – and have the foresight to swerve around any bumps along the way.