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The Predictive Analytics Within Your CRM

The use of Customer Relationship Management (CRM) technology has grown by leaps and bounds over the past five years. The CRM SaaS market is expected to reach $127.5B by 2018 and a Software Advice report found that small businesses (<100 employees) were mostly “very satisfied” or “somewhat satisfied.” The same report noted that 35 percent of large companies (>500 employees) were “very satisfied” with their CRM. However, with the market being so new, there’s plenty of room for growth and advancement. Advancements in predictive modeling, machine learning, and data mining ushered in a new CRM capability: predictive analytics. 

Predicting future behavior by analyzing a trove of data has become a standard feature of CRM software. But using the data to make decisions can be a difficult process. Here’s how to track your data effectively and drive more revenue with predictive analytics.

Track Leads – Who Might Buy 

Tracking the behavior of leads (those visiting your site or trialing your product) is crucial for good analytics. There are a few different ways in which tracking these prospects with a CRM can create useful predictive analytics.

Lead Scoring – Users who download multiple pieces of content or visit the pricing page multiple times are more likely to buy than those who don’t. Lead scoring is the practice of attaching a score to each user so they can be “nurtured” toward a sale accordingly. Companies that excel at lead nurturing generate 50 percent more sales ready leads at a 33 percent lower cost, according to Marketo.

Customer segmentation – Once leads are being scored based on behaviors, CRMs allow for segmentation — i.e., grouping users together based on certain actions. This way, one group can be sent aggressive discount options while the other group is handled differently. This segmentation can be coupled with predictive analytics to identify when certain campaigns should implemented and how they’re likely to perform. 

Track Forecasting – Who Should Buy

Tracking user behavior also enables accurate forecasting. Sales forecasting is defined by the American Marketing Association as “an estimate of sales in dollars, or physical units for a specified future period.” The key word “future” explains where predictive analytics come in. The forecast is calculated by using the historical conversion rate (percentage of opportunities that close) and the current sales pipeline (number of active sales opportunities), then adding in other variables such as average contract value (ACV), average length of sales cycle, etc. 

Microsoft is a great example of how the latest in predictive analytics can improve sales processes. Microsoft built the Azure Machine Learning, which uses regression analysis among other algorithms to compute an forecast called, “opportunity ranking model.” Before the Azure Machine Learning, data such as forecast accuracy, close rate and qualified pipeline coverage ranged between 60 to 70 percent accurate. The early returns of the program are 98 to 99.9 percent accurate. You don’t need to be Microsoft to implement predictive analytics for better forecasting. Customer FX creates help guides and Infor CRM training videos for CRM enablement, such as how to predict future purchases with Infor CRM Sales Intelligence.

Track Customer Behavior – Who Will Keep Buying 

Predictive analytics are essential to people other than sales reps and leaders — customer success practitioners rely on making proactive decisions based on data. Customer success, or anyone focused on post-sale customers, need to track customer behavior throughout the entire customer lifecycle. But they have their own data points and metrics to monitor. All customer success software solutions should include: 

Onboarding – A customer’s initial use of the software and setup process is crucial to a successful relationship. According to research from Oracle Eloqua, the first 90 days of the customer lifecycle are the most volatile.

Usage – Reps need to know exactly who — i.e., which members of the team — are using the product and how vigorously. Low usage is an industry-wide red flag for SaaS users. If data isn’t being delivered in real time, it might be too late. 

Support Tickets/Interaction – Some customers may have low usage and a high amount of support tickets. This information is critical for an account rep before they speak with the “inactive” customer.


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