1 mrp Issue 1 1 3 10 The Next Generation of B2B Predictive Analytics Research from Gartner: Predictive Analytics Are Transforming B2B Selling Leveraging Predictive Analytics to Enhance Downstream Tactics: An MRP Case Study 12 About MRP The Next Generation of B2B Predictive Analytics Predictive analytics will undoubtedly continue to transform B2B marketing organizations. There are a wide variety of big data technologies previously unavailable to most modern marketers, providing intelligence from dozens, if not hundreds of data sources. By leveraging predictive analytics with an accountbased marketing strategy we can enable sales and marketing to focus their time and efforts on opportunities with the highest propensity to buy. But how do we ensure these new big data insights are delivered in a concise, meaningful and most importantly, actionable way? MRP has launched the Delta Prelytix to transform the way marketers implement and measure predictive analytics. Delta is built on the kdb+ platform from First Derivatives, MRP s parent company and is used by investment bankers and traders around the world. MRP has adapted this technology, which processes billions of rows of data per day, to create a bleeding edge approach to streaming predictive marketing analytics. Implementing a predictive marketing technology requires marketers to adopt a new way of thinking about how they communicate with their audiences. MRP has determined three fundamental principles to ease the transition of adopting a predictive marketing technology. 1. Data Driven Approach: Predictive marketing is inherently a data driven function. While many vendors provide a plug and play solution, to effectively leverage the power of predictive analytics platforms, marketing must know and have in place the baseline metrics to evaluate and judge the appropriate outcomes. This includes closed loop reporting to provide real ROI metrics and impact on the organization. Further, just as the alignment between sales and marketing is critical, the CMO and CIO must be aligned to ensure the people, process and technology are in place to maximize the value gained by these predictive insights 2. Persona Based Content: Generating predictive intelligence means you want to be proactive in your sales and marketing approach. This intelligence provides marketers with a unique opportunity to provide very specific content to prospects based on their unique solution interest and their stage within the buyer journey. Content is critical, but there is more noise than ever in the marketplace the most saavy marketers provide specific needs based content for early stage prospects and case studies and pricing comparison kits to late stage buyers to help cut through the noise.
2 3. Tactical Alignment: When it comes to prospecting and lead scoring, many organizations mistakenly believe that all predictive intelligence beyond a certain score should go right to sales and marketing s involvement ends. While this often may be the case, it is critical that marketing continues its involvement until the lead is converted to sales qualified opportunity, not just when the lead reaches a certain score. Nurturing prospects with persona based content via email, social media, digital and other mediums is critical in maximizing ROI. These marketing principles ultimately drive a new marketing and organizational philosophy revolving around Account Based Marketing (ABM). ABM is an account first approach, where the organization continuously builds on the intelligence and data collected on the account level and paint a more complete picture of the prospect; everything tracks back to the account. We then can identify and target the appropriate decision maker to drive the messaging and content. The path to transformation becomes much more apparent when you evolve the core marketing principles into an ABM approach. The continuous learning and intelligence gathered on an account makes the sales cycle more targeted and efficient. It requires alignment, communication and feedback at every step of the process. Predictive analytics is not about marketing simply sending leads to sales- when used correctly, it provides the bridge to fill the gap between marketing and sales, driving coordinated downstream tactics in both organizations and increasing ROI throughout. The Delta Marketing Cloud s end-to-end capabilities provide this bridge by leveraging streaming analytics to create a dynamic account based profile and lead scoring algorithm. The scored profile is created from a broad range of data sets designed to uncover buying intent. This intent data relies not just on internally available first party data, but also integrates the research that B2B prospects are doing on the internet. No other platform has access to the broad range of unique intelligence provided within Delta. The dataset and score is customizable based on specific client needs and its accuracy improves over time with machine learning techniques. Delta is also able to take these scored account profiles and drive persona based content to the appropriate decision maker within the account, driving the right content at the right time across multiple tactics via the platform. All of this is managed in a customizable, closed loop dashboard which provides real time results and feedback throughout every step in the process. Over the last 14 years, MRP has quietly built a global marketing organization, building data driven, account based marketing programs for some of the largest companies in the world. It is this domain expertise that allowed us to design the Delta Market Cloud with not only the latest technology and machine learning techniques, but also to do so with the end result always in mind. Gartner, in their white paper Predictive Analytics are transforming B2B selling, gives a clear overview of this emerging sector and some tips on how to work with potential vendors. MRP s Delta Marketing Cloud takes a Predictive Analytics a step further by integrating the scored probable outcome with tactics designed to generate maximum ROI. Source: MRP
3 Research from Gartner Predictive Analytics Are Transforming B2B Selling Predictive analytics is promising for improving B2B sales processes, especially lead and opportunity conversion levels. Gartner is encouraging IT leaders charged with supporting the sales organization to analyze their business purposes and product capabilities before proceeding with these solutions. Impacts Predictive analytics solutions have had a positive impact on B2B lead and opportunity conversion rates. Predictive analytics solutions support sales effectiveness by putting sophisticated machinelearning capabilities into the hands of sales users. Predictive analytics solutions require large datasets to be most effective. Proofs of concept can demonstrate the accuracy of vendors predictive analytics capabilities before solutions are purchased. Recommendations IT leaders supporting sales should: Consult with their sales partners on how predictive analytics solutions can affect their sales processes. Be aware that predictive solutions don t actually predict the future; they only calculate probabilities of what may happen. Reinforce CRM data quality programs to ensure that the source data is clean, accurate and complete. Conduct proof-of-concepts comparisons before purchasing solutions. Analysis During the past four years, predictive analytics technologies have emerged as viable options for IT leaders and B2B sales leaders. These solutions enable them to augment their sales processes with solutions that reduce the uncertainties inherent in B2B sales cycles, without the need to invest in the statistical tools common to advanced analytics solutions. Predictive analytics produces insights that traditional approaches to business intelligence (BI) such as query and reporting are unlikely to discover. The solutions in this space are exclusively delivered as SaaS applications. Applications are either considered point solutions that address only a single point in the sales funnel or more complete suites that address different use cases across the different parts of the typical B2B customer life cycle. They include: Prospecting Lead scoring Opportunity scoring Pricing Customer renewal/upsell/cross-sell Churn analysis Predictive solutions are attractive to sales leaders, because they are turnkey solutions that give sales users sophisticated insights and recommendations without the need to integrate with an internal data warehouse or to rely on corporate-standard BI platforms. In addition, they offer rapid time-tovalue: Instead of needing to learn how to build statistical models, end users only have to consume the outputs of the model. Predictive analytics is not a new technology; it has existed in advanced analytics platforms and packaged analytics applications for many years. In Gartner s Hype Cycle, predictive analytics has reached the Plateau of Productivity.
4 Gartner positions predictive analytics on a continuum of analytical capabilities common to analytical solutions (see Figure 1). To be considered predictive solutions, the analytics described in this research must provide predictive capabilities, and many also provide prescriptive functionality. Although predictive analytics for B2B sales is relatively new, Gartner has seen a significant uptick in client interest during the past year. Venture capital investment in these vendors has grown steadily, and vendors often raise more than $20 million at a time. Gartner is tracking more than 20 different vendors that are offering solutions for B2B salespeople, nearly all of which are SaaS-based applications. Traditional CRM lead management, sales force automation (SFA), and configure, price and quote (CPQ) solutions offer some functionality to help marketers and salespeople make more-effective and efficient decisions; however, they are based primarily on predefined rules, as well as diagnostic and descriptive analytics. Advanced analytics platforms also provide predictive analytics; however, these tools rely on sophisticated quantitative methods for example, statistics, descriptive and predictive data mining, simulation and optimization that are not commonly available to business users. Predictive lead-scoring solutions are commonly purchased by demand generation/marketing operations leaders (with buy-in from IT and other sales and marketing leaders) when their current two-dimensional (demographic/firmographic and behavioral data) lead-scoring definitions with CRM lead management systems prove to be insufficient. Applications that address the other parts of the sales funnel are typically purchased by sales operations or sales management (with support from IT). They are designed to use datadriven insights, which are unavailable in SFA and CPQ systems, to help their representatives improve prioritization, address challenges more quickly, and increase close rates and margins. Occasionally, decisions will be made by both marketing and sales leadership, when there is a desire to purchase solutions that address the entire funnel. As you evaluate predictive analytics solutions, consider the impacts and Gartner s recommendations (see Figure 2). FIGURE 1 Analytics Capabilities Framework Source: Gartner (May 2015)
5 FIGURE 2 Impacts and Top Recommendations for Predictive Analytics Are Transforming B2B Selling (and, in some cases, prescriptive) analytics are being used with existing customers across the different stages of the sales funnel (see Figure 3). Source: Gartner (May 2015) Impacts and Recommendations Predictive analytics solutions have had a positive impact on B2B lead and opportunity conversion rates Because they promise significant ROI from increased conversions and higher revenue and profitability, 1 enterprises are adopting predictive solutions to improve sales effectiveness. Predictive The models typically use a combination of internal (e.g., transactional data from SFA, ERP, marketing automation and order management systems) and external (e.g., social, public and proprietary databases) data. As you move further down the funnel in Figure 3, and learn more about the prospect or customer, your reliance on external data will lessen. Many vendors provide suites of applications that address different points in the funnel, either through separate applications or by adjusting the models for different use cases. For example, most of the vendors that provide opportunity scoring solutions also have models for upselling and cross-selling. However, vendors also specialize in prospecting, pricing and renewals. It s common for Gartner clients to have solutions from three or even four different predictive analytics providers. FIGURE 3 Predictive Applications Across the Sales Funnel and Beyond Source: Gartner (May 2015)
6 The most attractive quality of predictive analytics solutions is that they enable users to remove the uncertainty inherent in sales processes. For example, companies struggling with forecast accuracy, can use opportunity scoring to reduce the level of educated guessing that underlies the forecasts submitted by representatives. Companies with lead-scoring processes, can apply lead-scoring optimization to eliminate some of the imprecision, such as the inadequate weightings and misapplied attributes that are common to complex leadscoring models. If you don t use predictive analytics to increase the effectiveness of sales processes, it can often be difficult to pick a starting point, because nearly all of them could be improved. Gartner recommends starting where the pain is greatest, which is usually tied to where decision making has the highest degree of uncertainty (see Table 1). Nearly all of the vendors have tight integrations with Salesforce, and several integrate with Microsoft Dynamics, Infor, SalesLogix or other SFA applications. However, the SFA vendors are not generally providing predictive analytics capabilities. IT application leaders supporting sales should evaluate many different point solutions, unless all they need is to find a solution for a single use case. Although the major SFA vendors have not incorporated predictive capabilities into their offerings, Gartner expects this to change during the next year. Acquisitions remain possible, especially for vendors other than Salesforce (which has a large ecosystem to support with AppExchange partners, including those with native Force.com applications); however, the morelikely scenario would be the limited addition of functionality. Future plans for SFA vendors should not affect decisions to invest in these solutions, especially because they are SaaS-based and have low switching costs. Recommendations: Companies considering predictive analytics should: Be prepared to evaluate many different predictive analytics point solutions to address different sales processes, even though vendors offer solutions for more than one process (many Gartner clients use three or even four different vendors for different processes). Prioritize the sales processes with the greatest amounts of uncertainty, because the ROI will be larger and faster and the buy-in from salespeople more likely, even though predictive analytics can improve many sales processes. Watch for major SFA vendors to offer predictive analytics capabilities in a limited fashion in the near future, but don t expect them to match the functionality offered by the pure-play vendors. Table 1. Sales Process Uncertainty and Applicable Predictive Solution Uncertainty Trouble identifying which companies or contacts are most similar to current customers Low confidence in lead qualification correlating with a propensity to buy and/ or difficulty prioritizing the leads for sales follow-up Lack of visibility into which opportunities are most likely to close by their targeted close date and/or difficulty producing accurate forecasts Highly elastic market in which pricing too high will cause a deal to be lost, but pricing too low will affect margins Inability to determine which customers are likely to buy premium or complementary solutions Uncertainty in determining the likelihood of a particular customer to churn Functionality Prospecting Lead scoring Opportunity scoring Price optimization Upselling Renewal (churn analysis) Source: Gartner (May 2015)
7 Predictive analytics solutions support sales effectiveness by putting sophisticated machinelearning capabilities into the hands of sales users Gartner advises prospects to become familiar with the technical underpinnings of these solutions. Predictive analytics uses a combination of heuristics, statistical models and machine-learning functionality. The core of these solutions features algorithms that produce self-adjusting statistical models. The models adjust as more transactional data is collected. Because these solutions are turnkey, buyers have limited ability to directly define and adjust the models. Users also lack the time to determine which statistical techniques should be employed, or rejected, to produce the strongest models. Stated differently, most systems are black boxes that leave clients with little control over the performance of the system. Some systems take a static approach to which attributes are included in the models. This restricts the upper limits of their machine-learning capabilities. Most vendors employ multiple different statistical techniques in their models, and many vendors (e.g., DxContinuum, Infer and Lattice Engines) run multiple different models serially and in parallel. This latter capability is important, because these systems compare predicted outcomes with actual outcomes to determine which model produces the closest fit. Nonetheless, this opacity and these limits have not been issues with clients, although Gartner still cautions buyers to understand the risks of turning over business decisions to algorithms that they don t directly control. Gartner has analyzed the approach of companies such as Lattice Engines and Mintigo. Their algorithms are black box, but they also expose the algorithms modeling assumptions to users, allowing them to define some of the attributes and constraints that contribute to the model. Similarly, users of C9 can see which cues contributed to each opportunity propensity score. Carefully evaluate vendor claims about prescriptive capabilities. Gartner finds that vendor rhetoric about prescriptive capabilities is greater than actual practice. Vendors may claim that they can provide prescriptive functionality, which will come in the form of next best action, recommended pricing ranges or best time to call; however, they may provide only enhanced predictive capabilities. Their recommendations may be derived from regression analysis, where they expose which attributes such as types of sales activities, lead attributes or products are missing from the opportunity or lead at hand. Therefore, if prescriptive capabilities are important to your sales processes, then be aware that vendors that have demonstrated expertise-building models in your industry will be a better match, and will be better able to deliver a full set of prescriptive capabilities in the near term. Recommendations: Companies considering predictive analytics should: Be aware that predictive capabilities deal in probabilities, not certainties. Require vendors to demonstrate the degree of algorithm transparency and model performance monitoring that their solutions provide. Expect vendors to designate a technical expert (i.e., assign someone who will monitor model performance on their behalf), because most clients don t directly control the statistical models. Require vendors to demonstrate how the accuracy of their models has improved for specific clients and across all clients, in addition to showing the success metrics enjoyed by their clients. Select vendors based on their demonstrated domain and industry expertise.
8 Predictive analytics solutions require large datasets to be most effective To be effective, predictive analytics solutions demand a significant amount of granular transactional data. The data needed will vary by business process; however, it should include at least one of the following categories of information: Sales activities, such as telephone calls and emails Opportunity updates Lead updates Website interactions Product market basket analysis The definition of significant will vary by industry and business process; however, in general, predictive analytics require a volume of transactions that is statistically valid. One vendor has noted that its solution requires at least 2,000 historical, closed opportunities to be effective. Similarly, pricing optimization systems work best in SKU-intensive, high-order volume industries, such as automotive parts distribution. Zilliant typically needs at least 100,000 rows of historical transactions to build a statistically valid dataset for a pricing and product churn optimization models. Not all processes (e.g., opportunity management) produce large amounts of transactional data. Hence, some vendors, in addition to internal data, collect external data sources to augment their propensity models. For example, Lattice Engines buys or collects syndicated data from 35 different news sources to produce a curated database of company information, tracking such details as recent job postings, social media usage, revenue trends and software installations. FirstRain, a sales enablement service for prospecting, works with internal and external data. Its solution sits atop representatives SFA systems. For each account or contact in its system, FirstRain curates social postings, news feeds, and topical events, producing a highly customized, userspecific information service. Its machine-learning algorithms continuously adjust the feeds, based on the news items that best match users needs. If your lead, opportunity and pricing processes rely on manual data entry, then develop data quality programs. Validation rules, mandatory fields, triggers and workflows solve some of this, but users need to be prepared to deliver two additional data processes. First, update these attributes from source systems, then consider adding mobile applications that make it easy to enter sales information from the mobile interface. For example, Clari addresses this issue by providing its clients a custom mobile app that eliminates the inaccurate data entry common to most SFA deployments. Although its unnecessary to initiate a big data project to deliver a statistically valid dataset to the tools, be careful about data quality. Recommendations: Companies selecting a predictive analytics solution should: Ensure that their companies have clean, accurate and deduplicated account, contact and opportunity data in their SFA systems before implementation. Work with the vendor continuously to prevent model performance degradation. Implement data quality programs to keep lead, opportunity and pricing detail data clean, accurate and deduplicated. Be prepared to backfill historical opportunities with additional data that aligns with the most important attributes used by the statistical models, if there s no data warehouse that stores historical opportunities. Ensure that they implement the same model quality-monitoring recommendations described in the previous section if they select a predictive analytics package that uses external data sources. Only the vendor will be able to determine whether their service is missing relevant data signals that can improve model accuracy.
9 Proofs of concept can demonstrate the accuracy of vendors predictive analytics capabilities before solutions are purchased Predictive sales analytics solutions are effective only when both sales managers and individual salespeople buy in to the required process changes. Many salespeople have been selling for more than a decade and can be resistant to change, especially if they re consistently hitting their quotas. They may be especially skeptical of analytic models that run counter to their instincts and gut feel decision making. One of the best ways to help achieve buy in is to use a proof of concept (POC) for the vendors that make the shortlist. Most of the vendors that Gartner follows will offer POCs that use historical data on transactions they ve won or lost. The data is fed into their predictive models, and the projected outcome (based on the models) can then be compared with the actual outcomes. Assuming you ve provided an accurate and robust dataset, you should expect a high degree of accuracy during the POC. The models will be less accurate than when they are tuned for specific customers and before the benefits of machine learning take hold; however, they should still perform well enough to give you confidence in their ultimate accuracy. Include sales leaders and a few salespeople in your POC processes. This will ensure buy-in from the leadership, and the individual salespeople that were involved can act as evangelists to their colleagues. During the POC, you might see some variations in model accuracy among the different vendors. Although accuracy is a critical priority for any solution you implement, it shouldn t be the only criteria. Most vendors use similar data science techniques, so there should be relatively similar results in POCs. However, even when there is a more substantial difference, some of it will be erased through tuning and machine learning. Look at domain expertise, data sources, customer references and other criteria to make decisions regarding vendor selection, especially when model accuracy is close during a POC. Recommendations: Companies considering predictive analytics should: Start with a POC based on historical transaction data to evaluate the accuracy of vendors models and increase the likelihood of sales buy-in. Involve sales leaders and key individual salespeople in the POC process, not just sales operations. This will improve the likelihood of sales buy-in. Avoid using accuracy as the sole factor for vendor selection, even though there should be a baseline in model accuracy (generally around 80% with a large dataset); they should expect vendors to meet in a POC. Realize that domain expertise, external data sources, customer references, price, APIs and tools (especially when accessing ERP system data) are equally important reasons to select a vendor, especially because model accuracy is likely to improve based on machine learning. Consult with two or three references during the POC stage to determine how their models will perform in the long term. Evidence Gartner conducted primary and secondary research with software vendors and end users of the solutions described in this research. 1 Big Data, Analytics and the Future of Marketing and Sales Source: Gartner RAS Core Research Note G00274067, Tad Travis, Todd Berkowitz, 15 May 2015
10 Leveraging Predictive Analytics to Enhance Downstream Tactics: An MRP Case Study Frustrated with the lack of ROI from their latest marketing efforts, an MRP client was looking for a better way to identify and monitor the buying cycle of their prospects to more effectively communicate the right content via the right tactic at the right time. Understanding their client s needs and the difficulty they were facing in trying to predict the buying cycle, MRP developed an account-based marketing strategy that determined their target accounts through predictive analytics and leveraged these findings to refine their current communications strategy and lead qualification process. Implementing a Predictive Analytics Plan The first thing MRP recommended was to run a predictive analytics campaign via Delta Prelytix to prioritize their target market, solution and identify verticals in which success was eminent. MRP worked with their client to customize intent data based on their target market, topics and keywords related to their solution. The output from Prelytix identified real time organizational needs and intent that allowed MRP to assign a score and buying stage to each of the client s target accounts. Building an Informed Integrated Engagement Strategy Prior to partnering with MRP the client had built a content library but was struggling to implement nurture programs via their existing marketing automation tool. Armed with actionable data and insights from Prelytix, MRP worked with the client to enhance their downstream tactics beginning with their content delivery strategy. Leveraging the client s existing content library, MRP mapped marketing assets to early, mid and late stages of the buyer journey and determined which content and messaging to deliver to each target account. This focused integrated engagement strategy allowed MRP to address the client s pain point and provide them with highly qualified prospects ready for sales engagements. Flushed with highly engaged prospects, MRP s client was unable to handle the follow-up due to a lack of bandwidth across their sales organization. MRP recommended engaging with the Delta Voice team to take over monitoring, prioritizing and further qualifying late stage prospects. MRP s client was able to view progress, feedback and route sales-ready leads easily through the Delta Marketing Cloud dashboard ensuring that the right opportunities were delivered to the right sales reps. Results: An End-to End Sales and Marketing Strategy By partnering with MRP, the client was able to implement an account-based sales and marketing strategy that provided the following: A deep understanding of their target accounts via Delta Prelytix, which created efficiencies in prioritizing prospects across the buyer journey. They were able to create a range of new content designed to serve the needs of a target account and implement an integrated marketing engagement strategy that delivered the right content at the right time. By utilizing the Delta Marketing Cloud dashboard, the client was able to monitor all incoming sales leads and measure the success of their newly developed engagement strategy. The Delta Marketing Cloud s powerful integration of predictive analytics, integrated marketing services and real-time reporting provides companies with an edge in identifying, engaging and driving sales opportunities. (see Figure 1).
11 FIGURE 1 Delta Marketing Cloud Closed-Loop Deliver System Source: MRP Source: MRP
12 About MRP MRP is a global provider of marketing intelligence, software, and services. Since 2002, clients have relied on MRP to drive pipeline and deliver the insights needed to more effectively sell to their key target markets. MRP s Delta Marketing Cloud combines predictive intelligence with integrated marketing tactics to deliver closed-loop marketing programs generating industry leading ROI and conversion. MRP has 12 offices, covers over 100 countries and is a wholly owned subsidiary of the FD Group, PLC (LSE: FDP). mrp The Next Generation of B2B Predictive Analytics is published by MRP. Editorial content supplied by MRP is independent of Gartner analysis. All Gartner research is used with Gartner s permission, and was originally published as part of Gartner s syndicated research service available to all entitled Gartner clients. 2016 Gartner, Inc. and/or its affiliates. All rights reserved. The use of Gartner research in this publication does not indicate Gartner s endorsement of MRP s products and/or strategies. Reproduction or distribution of this publication in any form without Gartner s prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. The opinions expressed herein are subject to change without notice. Although Gartner research may include a discussion of related legal issues, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner is a public company, and its shareholders may include firms and funds that have financial interests in entities covered in Gartner research. Gartner s Board of Directors may include senior managers of these firms or funds. Gartner research is produced independently by its research organization without input or influence from these firms, funds or their managers. For further information on the independence and integrity of Gartner research, see Guiding Principles on Independence and Objectivity on its website.