By Erin Fraboni
Unless you’re an expert in palm reading or tarot cards, you probably don’t have the ability to see into the future. Even though you may not have the technology (or superpowers) to know exactly what the future holds, you do have the next best thing: data.
Data fuels predictive marketing, a strategy that helps in understanding which leads are most likely to become sales by identifying trends and patterns in customer behavior. Predictive marketing, or predictive analytics, involves a combination of data mining and statistical analytics to predict future results. No crystal balls, palm reading, or tarot cards required.
The Impact of Predictive Analytics
According to Radius’ 2016 B2B Demand Generation Benchmark Study, B2B marketers met their objectives 55% of the time when they applied predictive analytics to demand generation, while those who did not use predictive analytics only met their objectives 30% of the time.
How does predictive analytics do this? Through a few different ways.
1. Targeting the right leads.
By analyzing the past research behavior of prospective buyers, you can predict with a good amount of accuracy which of these prospects are likely to buy and which are less likely to become customers. Getting these qualified leads to sales (instead of those who are unlikely to buy) significantly increases conversion rates.
2. Improving data segmentation and nurture campaigns.
Predicting buyer behavior allows for better customer segmentation, giving you the opportunity to deliver the right message to your best leads. The more you know your buyer personas based on their historical data, the better you can tailor communication to each customer segment. Personalized messaging results in more effective nurturing of your customers.
3. Reducing marketing spend.
Predictive analytics helps marketers focus their efforts on quality leads that are likely to buy, resulting in a higher return on investment (ROI). By filtering out the leads that have a low probability of becoming sales, you can greatly reduce costs that would have otherwise been put into targeting all leads – including those who may not be interested at all.
More and more companies are seeing the value in predictive marketing.
Source: Forrester Consulting
Predictive Marketing Is Not a New Concept
Despite its recent popularity among marketers, predictive marketing is a strategy that has existed for years. Analyzing historical data, customer behavior, and patterns to predict probability is not a new concept, but big data has changed the way marketers use predictive analytics, providing more insight into customer behavior and intention than ever before.
Data: The Make or Break for Predictive Analytics
Marketers now have a wealth of data available to them, so why not make the most of it with predictive analytics? With big data, marketers have access to information that can help them predict which leads are likely to become customers based on historical data. However, you won’t get the predictive marketing results you want without quality data.
When it comes to the data you’re using to fuel your predictive marketing, your data should be:
- Complete: It is much more difficult – if not, impossible – to segment customers when your data is missing critical information.
- Consistent: Inconsistencies or duplicate entries often result in wasted efforts targeting the wrong contact or the same contact more than once.
- Clean: Data hygiene is essential for targeting qualified leads. Verified data, as opposed to free or crowdsourced data, ensures you don’t waste time pursuing uninterested or inaccurate leads.
If your data doesn’t fit into these prerequisites, it will be very difficult to see the results you’re hoping to achieve with a predictive marketing strategy. ReachForce SmartForms captures real-time, verified data that helps marketers better segment their data and target qualified leads, helping get your predictive analytics on the right track.
Request a demo to discuss your predictive marketing goals and learn how SmartForms can help.