2 Steps to Using Predictive Sales Analytics for More Accurate Forecasting

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Data Quality + Data Science = Faster Time to Revenue

Did you read our recent blog about improving your time to revenue? If not, we strongly recommend you stop here and read it to give this article about predictive sales analytics more context. Understanding the importance of creating a faster and more predictable time to revenue is the critical first step in your path to revenue success.

Being able to trust your data is ultimately the backbone of any successful forecasting, automation or analytics strategy. Gartner says, “It’s important to trust the quality of the data you collect, use and share to match your business context and requirements. Separately, organizations must trust their data sources.”

If your data is messy and unclean, it is virtually impossible to derive from it any sort of logical or practical insight. You can’t budget or forecast with confidence if you can’t trust your data quality foundation. 

At RevOptics, we sit at the intersection of data integration, data quality, and data intelligence. This unique intersection has allowed us to combine a trusted data foundation with data science to create a predictable funnel and a faster time to revenue. The objective isn’t as much about finding leads; it’s getting the right leads through the funnel faster.

Related: Top 5 Marketing Analytics Metrics for the Revenue-Driven Team

If you are a RevOps Manager, Marketing Ops leader, or a Sales Ops professional, we have valuable information that can help you do what you do better and provide greater value to the business. 

Predicting Which Opportunities are Most Likely to Close & Stay

According to a 2018 Forbes article, sales forecasting is one of the most critical factors to determine long-term business growth. They go on to say that 74% of large B2B companies engage in forecasting on a weekly basis. Clearly, forecasting – and forecasting correctly- is vital. But, they acknowledge that sales forecasting tends to be an imprecise art.

We could not agree more. 

RevOptics set out to make sales forecasting more predictable and reliable so it can inform marketing as well as sales. By combining trusted data with data science, there is no reason why 69% of B2B companies should have ineffective forecasting. 

With the launch of Predictive Opportunity Analytics, CEOs, CROs, and B2B Revenue teams across the globe can now trust their forecasting capabilities. Here is how predictive sales analytics works. 

Step 1: Assess what you have

Is your HubSpotMarketoSalesforce, or NetSuite database clean? If your answer is maybe, that is not good enough. Start by taking a quick look at your database to see if it can be trusted. This could be a labor-intensive process, but we offer a quick tool that gets results within a couple of hours. Start by inserting your data in the free data quality assessment

If your data is already clean, then go ahead and skip to Step 2. 

It is highly likely, however, that through this assessment you will learn your data quality is less than ideal. You may think it’s clean, but until you thoroughly evaluate it, are you absolutely certain? Are you comfortable with basing critical decisions on the data?

If your data needs some help or you aren’t sure, we can put a quick action plan together that will involve our data automation platform to clean and append the bad data. Automation makes this a simple step. Within a few days, we can ensure that your data quality is top-notch.

Step 2: Leverage sophisticated technology

With a trusted database as our foundation, we will then incorporate machine learning. Our proprietary algorithms look at twelve months’ worth of your historical clean data, along with more than 30 other data points. Our platform then starts to understand the behaviors from the CRM and marketing automation platform together that objectively lead to someone becoming a customer. 

The key is how we automate the customer data journey. Instead of trying to manually collect customer data points, such as customer and prospect activities and purchasing habits, our solution integrates the data automatically, then cleanses and presents it in a way you can actually use to measure marketing impact.

Related: Marketing Metrics: Impact versus Attribution

As sophisticated as our AI is, we believe that the best sales reps also have great instincts. We take that instinct and combine it with our science to deliver a score that reps can trust from day one. This score represents the opportunities within your pipeline that are most likely to close and close quickly. 

But it does not stop there. Once we identify which opportunities you should be prioritizing based on science, we then help the sales rep know the next best action to take for each opportunity within the pipeline. This is predictive sales analytics at its finest. So, if your sales rep is not sure whether to send an email, pick up the phone, or send a promo item, Predictive Opportunity Analytics can help.

What sales rep do you know that isn’t interested in moving deals through the funnel faster and winning a higher percentage of those deals? RevOptics can mean more money in their pocket and more accolades from an appreciative VP of Sales. 

It’s Not a Lead Score

The platform doesn’t give you a lead score. Lead scoring ranks leads in order to determine sales-readiness. While it’s always good to understand where your prospects are in the funnel, lead scoring has its drawbacks.

First, it takes a lot of resources to collect and analyze the data necessary to predict customer behavior and attitudes and then prescribe the next best action. Not all leads are ready to move to sales, but that doesn’t mean they aren’t going to get there with the right nurturing. How many resources and how much time will you need to dedicate to gathering all of this information and analyzing the proper touchpoints and timing?

Beyond resource limitations, there are many pitfalls in using marketing-based lead scores. The most obvious is they are typically subjective at best. When humans assign scores to leads, subjectivity is always a risk. 

On the other hand, you can leverage automated reporting and prescriptive analytics to accelerate the scoring process with complete objectivity – all with no resources. Instead of dedicating resource time to hunting down data and trying to figure out what to do with it, your resources can focus on strategy execution based on science.

Now is the perfect opportunity to ditch weighted pipeline forecasting that’s little more than assumptions based on questionable data and jump into the next generation of revenue science. Predictive sales analytics is the future. Click here, contact Cortney today and explore ways in which we can help predict your future revenue with greater precision.

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About Matt Klepac

CEO & CoFounder at Vertify