Predictive Data Analytics is the process of using historical and current data combined with machine learning to forecast certain outcomes. In the marketing world, predictive analytics uses monitoring and reporting to accurately plan strategies and campaigns. For nearly a decade, this type of marketing research has been changing the landscape of how organizations reach and impact their audiences.
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Using the historical data of both a certain company and the industry a company is in, certain factors pertaining to sales leads can be found using Predictive Analytics. For instance, a financial advisory firm may find that individuals between the ages of 52 and 58 who exhibit certain behaviors on social media are significantly more likely to become clients.
Such indicators can be used in a number of ways including:
Many companies employ the use of a customer relationship management software (CRM). These tools typically include a way to score leads. Scoring is simply a numbering system to alert the marketing and sales team when a lead is close to making a decision. When this data is combined with machine learning and artificial intelligence, identifying sales-qualified leads becomes easier over time. Moreover, predictive analytics can be used to shorten the sales cycle by better predicting a leads behavior when in the funnel.
When the process of identifying sales-qualified leads (SQLs) is done manually, there are many mistakes that can occur. For instance, if a lead downloads a certain resource it could trigger the marketing team to send that lead to sales. However, predictive analytics may tell you that a lead may have downloaded it too quickly and is not ready for a sales conversation.
Try as they might to understand one another, the marketing team and sales team have very different roles. More often than not, this results in a breakdown in communication that can cost a company revenue. The nature of predictive analytics is to improve over time. Data from both the sales and marketing team can improve multiple factors including:
Many organizations rely on customer retention and add-on sales over the course of time. Retail banks, software-as-a-service companies, financial advisors and many others rely on customers sticking around for a long time. Predictive analytics helps to understand not only leads and new customers, but also the behaviors of existing clients. These factors influence marketing in a number of ways.
Perhaps one of the most impactful ways predictive analytics will reshape the marketing world will be through automation. Once the behaviors of lucrative prospects are identified, sophisticated programs can interact with leads almost immediately.
Here are a few examples:
Improved understanding of who your buyers are, where you can find them and the resources to use to garner interest can all dramatically decrease ad spend waste. Overtime, predictive analytics can alert the marketing team to platforms (i.e., Facebook, Adwords) that are less effective as well as methods (i.e., video, cold email) that are not as likely to work. Conversely, the same predictions can be used to increase spending where efforts are likely to achieve the desired results.
Predictive data analytics is quickly becoming the driving force behind modern marketing. From drastically improving lead qualification to better aligning sales and marketing initiatives and making targeted marketing automation more in-tune with customers’ needs in-the-moment, predictive data analytics amplifies the ability to cater to individual customers – and that’s the magic formula for success in the modern marketing landscape.
The Intelligent Engagement Platform gives you a 360-degree view of every customer. Harnessing customer data, this feature-rich CDP combines real-time scoring with decision-making capabilities to predict and guide the next best interaction and exactly the right time.