THE ANALYTICS CLOUD REVIEW

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1 THE ANALYTICS CLOUD REVIEW A PRIMER FOR B2B MARKETERS

2 90% of the world s data has been created in the last two years 1. At the heart of this data lies a wealth of insights, and a new wave of analytics cloud technology has emerged promising to help us make sense of big data and transform the way we do business. As a B2B marketer, how will you navigate this sea of options? In this guide, we ll explore the current analytics landscape, in an effort to help you finally gain full visibility into marketing s impact on sales.

3 The Rise of Big Data Analytics For B2B marketers, the hype surrounding big data has not yet translated to action. With so much information available, it s hard to believe that more than half of CMOs still rarely or never use big data to make marketing decisions 2. It s an odd truth, considering that most other functions are reaping the benefits of data-driven processes, and have been for some time now. How can marketers get a grip on this data, make smarter decisions, and finally get the total picture of marketing s impact on sales? The answer, of course, lies in analytics. Big data is typically characterized by three qualities: high volume, high velocity, and high variety. Because traditional relational databases aren t capable of processing the mass the data being generated, specialized tools and applications have emerged to automate analysis and do the heavy lifting. Backed by this new breed of analytics technologies, many organizations have already been able to use the insights gained to transform their businesses. While it seems as if Sales, HR, IT, and Operations have their veritable pick of the litter when it comes to analytics options, CMOs still find themselves coming up short. The good news: the solution for marketers exists. But, as a marketer, how can you navigate this sea of new technology to determine which solution will actually help you improve marketing s performance? In this guide, we ll explore why marketing particularly B2B must become increasingly data-driven to succeed. We ll cover why marketing needs its own analytics, provide an overview of the current analytics landscape, and outline the five must-have characteristics of a B2B marketing analytics solution. In doing so, we hope to help you identify the solution that best aligns with your company goals, and finally give you the total picture of marketing s impact on sales. BENEFITS OF PREDICTIVE MARKETING ANALYTICS: Discover performance trends and compare against industry averages Understand campaign influence in complex, multi-touch scenarios. Identify revenue levers, make smarter decisions, and justify your spend Accurately forecast marketing-generated revenue Give boards and executives full visibility into marketing performance Encourage better collaboration between marketing and sales with joint ownership over the revenue cycle 1: IBM: Big Data At The Speed of Business 2: Forrester: The Evolved CMO In

4 Why Marketing Needs Its Own Analytics Today, B2B marketers own more of the funnel than ever before. Because B2B buyers prefer to self-educate prior to engaging sales, marketing now operates at the top, middle, and bottom of funnel and interacts with far more people across more touch points. While a lengthening, complicated sales cycle presents new challenges for B2B marketers, a huge opportunity exists. Marketing s influence on the buying process is bigger than ever and supported by the right tools CMOs have the opportunity to drive substantial revenue and growth. While sales reps deal mostly with a primary buyer, marketing owns all of the contacts that support a sale. A primary buyer is backed by a team of influencers superiors, finance, purchasing, etc. Furthermore, these team members interact with a wide range of touch points website, social, advertising, events, etc. and do so long before and after a deal is closed. All touch points are tracked and, as a result, marketers today have more data than they know what to do with. Between spreadsheets, web analytics, social, marketing automation, and CRM, it s becoming increasingly difficult to connect the dots between disparate data sources, and even more difficult to make sense of it all. Because marketing is dealing with so many people across countless touch points, traditional BI tools are simply not enough. Marketing needs its own analytics. Marketing analytics solutions must be able to attribute campaign performance across complex, multi-touch scenarios. Furthermore, it s critical that a solution be able to connect the dots between data produced by all core marketing technologies (e.g. Salesforce and Marketo). In any instance where data is siloed, it s inevitable that the resulting insights will be skewed, and impossible to answer questions like: 1. What did we say, when, and how did we say it, in which contexts, to get this person to move from discovery to deal? 2. What combination of information dissemination and communication techniques (both online and live) was most helpful in driving this to conclusion? 3. How are these patterns reflected across all leads, opportunities, accounts, customers, renewals? 4. Which dials can we turn to improve the overall yield and quality? 5. And finally, which programs will deliver the leads that turn into deals faster and with better margins for the business? Taking advantage of solutions that allow you to ask and answer these questions is a huge opportunity for marketers, and can provide substantial competitive advantage. 4

5 Overview: Analytics Cloud Landscape The analytics cloud landscape is crowded, and can be difficult to navigate. With so many options available, it s important to understand the unique attributes of each solution when determining which technology is the right fit for you. The following is an overview of the three most common classifications of analytics clouds: Business Intelligence BEST SUITED FOR CIO; COO (Enterprise) EXAMPLES Salesforce Wave, Tableau, SAP, Oracle, GoodData, Jaspersoft, Qlikview, Domo, IBM: Watson Analytics AT A GLANCE Ideal for gaging general business health Likely requires a dedicated data analyst to oversee operations Can require high levels of customization Generally don t offer predictive capabilities Business intelligence, or BI, is a broad category of applications and tools designed to transform raw data into meaningful and useful information. BI technologies are capable of processing large amounts of unstructured data to help identify, develop and otherwise create new strategic opportunities for enterprise business users. The goal of BI is to allow for the easy interpretation of these large volumes of data. BI can be used to support a wide range of business decisions ranging from operational to strategic, and is most often used cross-departmentally to gauge general business health. Due to the cumbersome, IT-heavy nature of traditional BI platforms, many companies hire dedicated IT employees and data analysts to oversee BI operations, modeling, and reporting. Today, a new school of BI tools has emerged characterized by ease of use and elegant UIs that are simple enough for most business users to operate. While it s certainly possible for organizations to derive function-specific insights with BI, this level of custom modeling typically needs to be built from the ground up. When paired with the right people and modeling, BI software can provide data-savvy enterprises with a competitive market advantage and long-term stability. 5

6 Overview: Analytics Cloud Landscape (continued) Business Intelligence Spotlight: Salesforce Analytics Cloud (project wave) Salesforce s recently-announced analytics cloud promises to be analytics for the rest of us, and make it easier to access and interpret SFDC reports. With an elegant user interface and the improved mobile app, it ll be exciting to see how the new tool fares relative to other BI tools when Wave launches on a TBA date. While a native SFDC analytics integration is certainly appealing, be aware that a lack of predictive capabilities and connectivity boundaries between platforms may potentially hinder the utility of the tool for marketers. Predictive Lead Scoring Also commonly referred to as predictive analytics, predictive lead scoring is primarily used by salespeople to better understand customers and prospective customers. It can help better prioritize sales leads, determine which products a prospect would be most likely to buy, nurture contacts who aren t yet ready to buy, and develop more reliable sales forecasting. BEST SUITED FOR VP of Sales; Sales Reps EXAMPLES Lattice Engines, 6Sense, Predixion, Wise.io, Fliptop, Infer, Mintigo AT A GLANCE Ideal for sales leaders and reps Supplements native data with external signals Improves upon automation lead scoring Often available as CRM add-ons These vendors start with a company s native sales data, and then add in signals from public sources such as number of employees, revenue and income, credit history, social media activity, press releases, job openings, patents, etc. With this intersection of internal and external data, they re able to identify common characteristics of the accounts that were won by sales, and score leads so that sales can better anticipate the likelihood of closing each prospect. Predictive lead scoring can offer huge benefits to sales organizations, and may work well as a supplement to BI tools. 6

7 Overview: Analytics Cloud Landscape (continued) Marketing Analytics BEST SUITED FOR CMO; Demand Gen Managers EXAMPLES BrightFunnel, FullCircle CRM, Allocadia AT A GLANCE Specialize in multi-touch attribution Connect and analyze all core marketing data sources Benchmark performance trends and compare against industry averages Predict marketing-generated revenue (not all are predictive) Also referred to as marketing intelligence, marketing analytics solutions help CMOs and their teams gain better visibility into marketing s impact on revenue. Similar to BI tools, marketing analytics solutions are able to process large amounts of data structured and unstructured but specialize in the analysis of data between core marketing technologies CRM, automation, web, social, and more. With blended data analysis that incorporates all prospect touch points, marketers can identify opportunities and better attribute, plan, and forecast. Furthermore, marketing analytics solutions generally specialize in multi-touch attribution. With so many touch points inherent in the B2B buyers journey, effective attribution is necessary to help marketers understand campaign influence in complex, multi-touch scenarios. Multi-touch attribution lets marketers pinpoint which campaigns performed well, understand why, and determine whether success is repeatable. Finally, a critical differentiating factor between marketing analytics solutions and other options is the ability to predict. Based on historical performance and machine-learning, predictive solutions can prescribe how investments will most likely translate to sales. With these insights, marketers can identify revenue levers (e.g. What will happen if we change X? ), and develop strategies that drive towards organizational objectives. 7

8 Marketing Analytics: Five Critical Components While all analytics solutions come with unique strengths, B2B marketers must identify the solutions that provide the capabilities and insights that align with core objectives. The following checklist contains five core tenants that should exist in a marketer s analytics solution, along with some questions to ask when evaluating your options: EASE OF USE Does it provide easy-to-understand visual representations of my data? Do I need to rely on IT or Operations to derive insights? Does it require extensive data clean up to integrate? How much customization is required? MARKETING-SPECIFIC Is it built for marketing? Is it built for B2B? Industry-specific? ATTRIBUTION-FOCUSED Does it specialize in multi-touch attribution scenarios? What kinds of attribution models are available out of the box? PREDICTIVE CAPABILITIES Does it offer prediction as part of its core offering? Can it accurately forecast marketinggenerated revenue? Can I adjust spend assignments? CONNECTIVITY Will I be able to integrate with all of my core marketing vendors? Are there any limitations to connecting data between technologies? 8

9 The Bottom Line Today, B2B marketers own most of the revenue cycle, and are therefore more accountable for revenue generation than they have been traditionally. While sales forecasts may provide accurate visibility into the coming month or quarter, predictive marketing can provide executives and boards with visibility into the coming year and beyond. The data exists to facilitate smarter marketing decisions, and supported by predictive analytics technology CMOs can stop relying on their gut and start making sense of their data. While the analytics landscape is crowded, marketers that invest the right technology today, will reap the benefits tomorrow armed with an unprecedented understanding of revenue levers, and the newfound ability to develop strategies that they can be confident will impact the bottom line. The time for predictive marketing is now. About BrightFunnel BrightFunnel is the industry s only predictive analytics cloud for B2B marketers. For the first time, CMOs and their teams have a complete picture of marketing s impact on revenue. Through multi-touch attribution and intelligent forecasting, B2B marketers can now understand the revenue impact of every decision, and align marketing plans with business priorities. BrightFunnel s clients are data-driven B2B marketing leaders such as HootSuite, Nimble Storage and ServiceMax. BrightFunnel s Predictive Analytics Cloud for B2B Marketers is available now. To get started or learn more about predictive marketing analytics, contact BrightFunnel today.

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