For many sales and marketing professionals, it’s a constant struggle to derive any actionable insights from existing data. The crushing volume of incoming data can be intimidating and wholly overwhelming, and attempting to extract the last ounce of the value, or even just the key insights, from that data can be a daunting task.
In the past, the options were few:
- Hire data scientists. The approach of getting professional data analysts to analyze data and come back with answers can be expensive and time-consuming, chewing up weeks or even months, and sometimes still only returning dubious results.
- Trust your gut. History has shown the efficacy of those results can be even more dubious.
- Wait and see what happens. This reactive approach can leave an organization in the miasma of competing with everyone else who’s taken the same approach.
Predictive analytics have cracked the collective consciousness of enterprise sales and marketing professionals, enabling them to develop and fine tune lead scoring models that optimize campaign performance.
Predictive analytics technology has transformed the way enterprises understand, evaluate and engage their current and prospective clients using AI and machine learning, and it’s undergoing a significant evolution in how sales and marketing professionals analyze and extract the value from their data. This has led to further prescriptive analytics developments in the design and deployment of tools that more effectively and more deeply leverage data about an enterprise’s customers and their needs.
Predictive analytics further builds on leveraging machine learning and AI, to quickly assemble customized predictive models. These models enable lead scoring, new-lead generation and enhanced lead data by using an organization’s existing customer and prospect data and forecasting how those leads or customers will engage – all before sales and marketing activity even begins.
The new technology, embedded in solutions such as Microsoft Dynamics 365 and Salesforce CRM, delivers the ability to model customer behaviors in hours via user-friendly processes that are automated and don’t require data scientists. It enables the easy testing of multiple outcomes and advance knowledge of which leads are most likely to buy a company’s product, subscribe to a company newsletter, or convert to a customer in other ways, as well as which leads will likely never buy, no matter how much the deal is sweetened.
This deep behavioral knowledge empowers marketers to optimize the customer experience by leveraging the power of machine learning-based models, and both business and consumer data attributes to get robust, insightful, and predictive lead scoring models. Conversion rates can increase by as much as 250-350 percent, and per-unit order values up by as much as 50 percent.
Predictive, proactive marketing helps a business not only acquire more customers but better customers.
This deep analysis leads to greater understanding of a business or individuals’ likelihood to purchase or engage, while also providing marketers with access to actionable intelligence that ultimately predicts future behaviors. If sales and marketing teams can gain insight into their customers’ current and potential future behavior, they are more likely to present the services and products that will appeal to them. And that means more effective sales and marketing, and ultimately more customers. Chris Matty, CEO and founder of Versium
Predictive analytics enables sales and marketing teams to extract valuable insights from historical customer and CRM data to design predictive models.
Traditionally, Customer Relationship Management (CRM) has been a largely passive, reactive workflow. With the alternatives being spending money and time either on data scientists or on a hunch, being reactive is the least risky approach. Predictive analytics attempts to transform sales and marketing CRM by minimizing the risk and allowing a marketing team to proactively run intelligent sales and marketing campaigns.
Further, predictive analytics enables the generation of predictive lead scores for both B2C and B2B marketing prospects that enable marketing and sales teams to be laser focused on the right customers at precisely the right time, directing them to the right products and right services. These kinds of analytics allow users to generate and augment new, high-conversion prospect lists based on an organization’s existing customer profiles by leveraging a proprietary data set or data warehouse.
Some of the most common use cases of big data analytics have centered around answering the question, What is the customer most likely to buy? Not surprisingly, this has been well-trod ground by BI and analytics tools, by data scientists developing custom algorithms on internal data sets, and more recently, by marketing clouds offered by providers like Adobe, IBM, Oracle, and Salesforce. Over the past year, a new player has emerged with a self-service tool that, under the covers, harnesses machine learning, backed by a proprietary data set with more than one trillion attributes. The company [is] Versium. Tony Baer, Principal Analyst at Ovum
Predictive analytics on consumer behavior is a well-populated field, said Baer. Nonetheless, based on the realization that data is king, he offers that solutions like Versium’s are a compelling alternative because they provide access to a vast repository of consumer and business data with a platform that incorporates machine learning to help marketers predict customer behavior.
About Versium
Versium delivers automated predictive analytics solutions, which provide actionable data intelligence faster, more accurately and at a fraction of the cost of hiring expensive data science teams or professional services organizations.
Versium’s solutions leverage the company’s extensive LifeData® warehouse, which contains more than 1 trillion consumer and business data attributes. LifeData® contains both online and offline behavioral data including social-graphic details, real-time event-based data, purchase interests, financial information, activities and skills, demographics and more. These attributes are matched to an enterprise’s internal data, and used in machine learning models to improve customer acquisition, retention and cross-sell and upsell marketing activities.
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