Free ebook: How Predictive Fuels Lifetime Customer Value

87% of B2B marketing leaders said they had already implemented or were planning to implement predictive analytics in the coming 12 months. Adoption has increased and benefits are known and substantial. However, for those who have not yet implemented predictive analytics, it might be helpful to understand top use cases for driving efficiencies and results in marketing and sales activities. Download the eBook and learn more about use cases such as: Filling the pipeline for fast-growing, high-performing sales teams. -Optimizing go-to-market activities. -Lowering your customer acquisition cost. -Prioritizing outbound efforts. -Determining the best upsell and cross-sell opportunities within your current customer base.

First 3 Pages

How Predictive Fuels Lifetime Customer ValueFeature Using Predictive Analytics To Drive Sales & Marketing SuccessPredictive analytics is fast becoming a critical part of the marketing stack. In a survey that Radius commissioned with Forrester Consulting earlier this year, 87% of B2B marketing leaders said they had already implemented or were planning to implement predictive analytics in the coming 12 months. Adoption has increased and benefits are known and substantial. However, for those who have not yet implemented predictive analytics, it might be helpful to understand top use cases for driving efficiencies and results in marketing and sales activities. 1. Fill the pipeline for fast-growing,high-performing sales teams. Keeping pipeline filled is the first step in driving sales momentum. Predictive analytics can provide your sales organization with comprehensive prospect data, focusing teams on the right accounts and equipping reps with valuable insights for converting more customers. 2. Optimize go-to-market activities. Predictive analytics helps you visualize past performance and gain a comprehensive understanding of the market segments you should target. Market opportunities are backed by rich data that helps you build the right teams and campaigns to drive maximum success. This gives you a data-backed sense of the market opportunity so that you can build the appropriate teams and campaigns to drive maximum success. 3. Lower your customer acquisition cost. By analyzing the attributes of your best customers, you can launch look-alike campaigns and tailor messaging to those prospects. This enables you to target prospects that resemble your best customers and have a higher likelihood of converting. 4. Prioritize outbound efforts. Assessing the performance of previous marketing efforts enables you to determine the profiles of prospects that demonstrate a higher likelihood of converting. It allows you to run campaigns that have a better chance of success given similarity to these previous efforts, which is ultimately a better and more efficient use of marketing resources. 5. Determine the best upsell and cross-sell opportunities within your current customer base. To enhance customer “stickiness” and maintain lasting relationships, you want to approach your current customers with additional products and offers that best match their needs. With a flexible predictive analytics platform, you can understand how different segments have performed on very specific campaigns. The Bottom Line By modeling the profiles of your best customers based on prior conversion data, predictive analytics helps you target the right prospects to quickly and successfully grow your business. The New Rules Of Predictive Analytics: CMO Edition Now that we’ve established the role of predictive in sales and marketing success, it time to lay the groundwork for what CMOs need to know about this emerging tool in the marketing stack. In a Forrester report titled “New Technologies Emerge To Help Unearth Buyer Insight From Mountains Of B2B Data,” Principal Analyst Laura Ramos cautions CMOs against investing in overly complex solutions. Predictive analytics helps you target the right prospects to quickly and successfully grow your business. The key to success with predictive analytics is knowing what your marketing organization needs.As a deluge of new solutions, with varying levels of data capabilities and resource requirements, make their way into CMOs’ inboxes, it can be challenging for non-technical marketers to pinpoint the best tools and technologies for their organizations. Here are three simple rules to guide the exploration of solutions that exist, regardless of familiarity with data science or statistical expertise. Rule #1: Don’t Let Complexity Become A Deterrent For marketers without advanced statistics backgrounds, data science will always be complex and perhaps daunting. While predictive solutions provide the answers to many Big Data woes, machine learning algorithms come with many complexities and moving parts. To predict real world scenarios, models often incorporate hundreds of variables, a multitude of algorithms, and dozens of first- and third-party data sources. Given the complexity inherent in Big Data solutions and the resources required to generate usable outputs, how do you exert positive influence over such advanced deep learning and processing? With unlimited computing possibilities, what objectives and capabilities should be executed on first? And what tools should marketers select to enable the outcomes they need? Recommendation: Seek out partners who have productized and developed superior front-end processes for what marketers are doing already: customer analytics, market segmentation, campaign planning and sales enablement. Rule #2: Think Beyond Lead Optimizations As Ramos points out, marketers are looking for an edge that can return big results. However, existing solutions — even if combined with vast data sources and other tools — can’t deliver the speed and insight required by marketers to achieve explosive growth. Unfettered by the confines of existing solutions, predictive models better support decision- making and have the potential to vastly improve on both cost and revenue. Even subtle adjustments can multiply reach and profits. Radius, for example, enables companies to discover new customers and markets with predictable and actionable success, providing insights that enable vastly greater outcomes than simply repeating past marketing efforts. Recommendation: Keep high-level goals front of mind when assessing solutions for next-stage growth. Framing the analysis solely in the context of current solutions or marginal upgrades limits the outcomes. Ramos calls this mindset “short-sighted,” saying, “The real value of predictive marketing occurs when marketers apply it across the entire customer life cycle.” Rule #3: Always Outsmart The Algorithm Predictive analytics is a means to an end: mechanisms to help grow the business and achieve marketing goals. For that reason, marketers need to first assess what success looks like for marketing initiatives to programmatically bring about the desired outcomes. While it isn’t necessary to have full-fledged hypotheses for exploratory campaigns, marketers should have a clear understanding of the questions they’re trying to answer and why they’re relevant to the businesses.