Going Beyond The Obvious With Predictive Analytics How Progressive Marketing Organizations Are Looking Beyond Behavioral Triggers To Truly Understand Their Best Potential Customers
In the world of B2B marketing and sales, some things never change: It s all about the leads. The sales team is always looking for hot leads that are likely to convert, and marketing is continually searching for new channels to identify qualified prospects. For marketers, however, there continues to be a lack of clarity around who the Ideal Customers are and the most effective and productive ways to approach them. Ideal Customer Profiles The first step to finding prospects is developing Ideal Customer Profiles (ICPs). This can be done by examining a subset of current customers and analyzing their activities on social networks and other websites, as well as commonalities such as job functions and technologies used. Deep insights into the ICPs come from coordinated inputs from both the marketing and sales teams. Sophisticated predictive analytics are one part of the equation, but there is also subjective input. For example, sales leadership may have strong opinions based on personal experience about the qualities of the leads that would be most valuable and likely to convert. Once Ideal Customer Profiles have been created, marketers can start discovering new leads and enhancing existing leads to focus their demand generation activities. Data distilled from Big Social Data and multiple contact databases drive this process. Prospects are vetted based on how closely their online presence matches the Ideal Customer Profiles. Real-time data gathering and matching ensures that the most current information is being used to determine the best prospects. Going Beyond The Obvious With Predictive Analytics 1
This demand generation challenge has intensified, as B2B buyers are becoming harder to reach as the level of noise from traditional channels, such as email, online advertising and content marketing, has skyrocketed. Marketers are struggling to just get basic demand gen done the right way, says Carlos Hidalgo, CEO of ANNUITAS. Moreover, marketers have continued to face scrutiny in terms of proving the return on investment (ROI) from their efforts. In its 2014 B2B Enterprise Demand Generation Study, ANNUITAS found that only 2.8% of B2B marketers reported that they were effective in accomplishing their goals. One of the big disconnects ANNUITAS identified in the report was a foundational lack of intelligence around the buyer. of B2B buyers now source social media to make buying decisions, according to research from LinkedIn. You have to be able to understand who your buyers are, Hidalgo explains. Buyer insight is key, both at an account level and at an individual level. Marketers have to start getting better at predicting who their buyers will be. A critical part of understanding buyers is knowing where they are looking for information, and today s B2B buyers are increasingly reliant on the web and social media for research and gathering recommendations on potential solutions. Highlighting this shift, 83% of B2B buyers now source social media to make buying decisions, according to research from LinkedIn. Building on that trend, the Earnest Agency reported that 53% of B2B buyers share useful vendor content information via LinkedIn, 39% via Twitter and 28% via Facebook. Going Beyond The Obvious With Predictive Analytics 2
Progressive companies, such as SAVO and Five9, have already started to adopt this trend. They are taking a new approach to demand generation by using tools to scour this often-unstructured data primarily from the social web to understand their buyers and find prospects that align with their Ideal Customer Profiles (ICPs). SAVO Boosts Conversion Rates 67% With Heat Maps By focusing on Ideal Customer Profiles, marketing efforts become more targeted and effective. You might be targeting a smaller audience, but at the same time, the total volume of opens is so much greater, says Eric Marcy, VP of Marketing Operations, SAVO. It s been huge for us. This white paper will outline: The history and limitations of traditional lead scoring; Why predictive analytics practices have evolved; and How social indicators are increasingly used to understand your true potential buyers. Forward-thinking B2B marketers, such as SAVO, a leader in sales enablement technologies and services, use predictive analytics to launch highly targeted campaigns aimed at well-defined audiences. The company strives to develop a steady supply of quality leads and a predictable lead engine. [Predictive analytics] allow us to create a heat map that takes our eyes right to the people we can help most, says Eric Marcy, VP of Marketing Operations at SAVO. The results: Conversion rates improved by 67% Email open rates are up 25% to 30%; and Buyer insight is key, both at an account level and at an individual level. Marketers have to start getting better at predicting who their buyers will be. Click-through rates are as high as 15%. Easy access to quality data improves marketing and sales performance and results in more closed deals. We are just blowing out traditional numbers, says Marcy. Carlos Hidalgo, CEO, ANNUITAS Going Beyond The Obvious With Predictive Analytics 3
Starting Out: A Traditional Rules- Based Approach With more than half of the B2B buyer s research being completed online even before they speak to a sales representative, according to the Corporate Executive Board, digital body language has typically provided clear signals of buying intent. The classic use of lead scoring was for marketing to determine which leads were ready to be handed off to sales. David Raab, Principal, Raab Associates To help prioritize sales leads based on buyers online activities, many marketers start by using the lead scoring models available in marketing automation systems. The classic use of lead scoring was for marketing to determine which leads were ready to be handed off to sales, says David Raab, Principal of Raab Associates, a marketing consultancy. Usually scores were created in two dimensions whether the company is a likely buyer based on company size and industry; and then how probable they are to make a purchase soon based on their online behavior. This traditional approach became the norm, as data accessibility was limited. However, the biggest limitation of these models is that they base their scoring on a select number of activities and simplistic rules such as webinar attendance and asset downloads. Richer data sets and greater insight are needed to identify a prospect s true intent to buy. Going Beyond The Obvious With Predictive Analytics 4
Next Step: Predictive Analytics Leveraging Prospect Behavior In the world of marketing, predictive analytics practices have evolved as ROI results of traditional models were falling flat. This change came in parallel with the big data explosion, as marketers have been able to collect a broader set of data on their customers and prospects. Today, predictive analytics is being used to score prospects using richer data sets overlaying statistical models on top of behavioral triggers to identify prospects that are most likely to convert. of companies rate their use of predictive analytics as mature. Many progressive companies are beginning to layer predictive analytics on top of their existing efforts, and apply statistical and analytical procedures to behavioral data. They also go beyond the marketing automation platform (MAP) to gather and analyze data from other sources, including behavior on other websites and interactions with digital advertising channels, to come up with a predictive lead score. Bluewolf, a global business consulting firm, recently published their annual State of Salesforce report in which they highlight that only 19% of companies rate their use of predictive analytics as mature. While the market for predictive analytics may still be in its nascent stages in terms of adoption, marketing teams have already seen striking results. Click here for the full State of Salesforce report. Going Beyond The Obvious With Predictive Analytics 5
It s a very exciting time and the space seems to be gaining momentum rapidly, says Kerry Cunningham, Research Director at SiriusDecisions. A lot of companies are looking to go to the next step with their lead scoring efforts. A recent Marketo blog post points out that a traditional lead score can be thought of as the activity score, with the lead s activities suggesting their interest in becoming a customer, while the predictive lead score focuses on prospect understanding, becoming a fingerprint to match the fingerprints of existing customers. A lead may have high activity but a low predictive score, or vice versa. Leads that exhibit high levels of activity, but have a low predictive score, have a far lower propensity to purchase from you, notes the Marketo blog. Leads with a high predictive score, regardless of their activity score, still have a higher propensity to purchase from you. David Lewis, CEO of DemandGen International, a global marketing consultancy, adds: I like to say that predictive lead scoring is almost a CIA approach. You collect all of this information and, based on predictive algorithms, you see certain patterns within the information in relation to your business. Then you can rank the likelihood of this person to buy relative to your customer base. In its Field Guide For Predictive Lead Scoring 2014, SiriusDecisions points out a variety of statistics and key benefits to predictive lead scoring, including: Providing more valuable leads to sales; Helping marketing effectively target the most indicative attributes of prospects who sign deals; and Strengthening the relationship between marketing and sales by establishing a common definition of a sales-ready lead. The guide also highlights some data around companies that have already adopted predictive lead scoring practices:...of users agree that predictive lead scoring provides more value than traditional (map and point-based attribute and activity) lead scoring approaches....of respondents agree that they are receiving value from their predictive lead scoring platform. Going Beyond The Obvious With Predictive Analytics 6
The Critical Missing Element: Really Knowing Your Customer Whether it is traditional or predictive lead scoring, companies often lack a laser-sharp focus on the key component that matters. That is, the entire social footprint of current customers and how those characteristics map to potential prospects. But until now, leveraging social, unstructured data has mystified marketers. While the data is rich with useful attributes and information, marketers have struggled to make it actionable. With so many B2B buyers turning to the web and peer reviews during the decision-making process particularly sharing information with colleagues on blogs or on sites such as LinkedIn and Quora their social footprint provides a rich source of data. Social provides three core insights for marketers, including: A real understanding of existing customers and potential buyers; A broader representation of a buyer s behavioral score, i.e. what they tweet or what groups they join; and The earliest indicator of potential buying signals. Three out of four B2B buyers and eight out of 10 executive buyers within that group use social media to make purchasing decisions. 3 out of 4 B2B buyers use social media to make purchasing decisions. Social Buying Meets Social Selling: How Trusted Networks Improve the Purchase Experience, IDC Going Beyond The Obvious With Predictive Analytics 7
The difference in conversion performance can be upwards of 2X to 3X versus just analyzing behavioral activity through traditional scoring methods. Companies like SAVO have already seen conversion rates increase by 67% leveraging a buyer s social footprint. In a 2014 IDC study, Social Buying Meets Social Selling: How Trusted Networks Improve the Purchase Experience, researchers found that three out of four B2B buyers and eight out of 10 executive buyers within that group use social media to make purchasing decisions. Understanding how they use social media, and which channels they prefer, provides a clear path to finding Ideal Customers. 8 out of 10 executive buyers use social media to make purchasing decisions. Five9 Doubles Lead Productivity Virtual call center solution leader Five9 added social signals and essential online indicators to enhance its predictive capabilities. The company needed to hone in on historical sales success and predictive sales data to help build their Ideal Customer Profiles. Then, social signals and other outside data helped create a Pandora-like predictive scoring system. Five9 prioritized and ranked their prospects with better accuracy, while subjectively adjusting different characteristics. Finally, leads were enriched in real time with continuous feedback. Every predictive update added to in-depth knowledge about prospects. In just a few months, the company doubled its lead productivity. Five9 has experienced improved sales efficiency, improved return on inbound marketing programs, and improved enterprise sales productivity, says Divya Jumani, Senior Manager of Marketing Operations. By using the Ideal Customer Profiles, we have definitely been able to increase inbound leads and identify them earlier in the buying cycle. Going Beyond The Obvious With Predictive Analytics 8
Conclusion: Staying Ahead Of The Curve Predictive analytics is reshaping the way B2B marketers operate. However, it s important to understand the keys to future success in this next wave of demand generation best practices. Supported by industry research, there is a clear case for leveraging predictive analytics alongside building out Ideal Customer Profiles: Enrich the profiles of existing customers and leads to truly understand the buyer; Enhance lead scoring with an expanded number of indicators and advanced statistical models; and Discover new prospects using a broader approach with a clear definition of the buyer. With limited resources and budgets, taking on these next steps can be overwhelming for marketers. The good news? Technologies exist today not just consulting services that can automate the work involved, and integrate with existing marketing automation platforms and other systems. This new evolution of marketing will finally deliver the promised lead conversion and bottom-line ROI that has been eluding marketing teams. Using predictive analytics, SAVO launched highly targeted campaigns at well-defined audiences and boosted conversion rates by 67%. Going Beyond The Obvious With Predictive Analytics 9
About Leadspace 47 Kearny Street, Suite 600 San Francisco, CA 94108 P: 1-855-LEADSPACE Leadspace is the leading B2B demand-gen platform, enabling companies to tap into social, internal and external data in real time to discover, enrich and understand prospects, and drive increased marketing and sales performance. info@leadspace.com About Demand Gen Report 411 State Route 17 South, Suite 410 Hasbrouck Heights, NJ 07604 P: 201.257.8528 F: 201.426.0181 info@demandgenreport.com Demand Gen Report is a targeted e-media publication spotlighting the strategies and solutions that help companies better align their sales and marketing organizations, and ultimately, drive growth. A key component of the publication s editorial coverage focuses on the sales and marketing automation tools that enable companies to better measure and manage their multi-channel demand generation efforts. Going Beyond The Obvious With Predictive Analytics 10