IDC Maturity Model of Data Driven Marketing — beyond marketing automation -dec2013

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Published on July 4, 2014

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Filing Information: December 2013, IDC #244993 CMO Advisory Service: Industry Developments and Models I N D U S T R Y D E V E L O P M E N T S A N D M O D E L S I D C M a t u r i t y M o d e l : D a t a - D r i v e n M a r k e t i n g — B e y o n d M a r k e t i n g A u t o m a t i o n Gerry Murray Kathleen Schaub I D C O P I N I O N Marketing must be reinvented around customer data. Customers now expect personalized concierge-level services that deliver useful insights every step of the way along their buyer's journey. To deliver this experience, marketers need detailed, real-time information on each customer and their context. However, a number of barriers prevent this vision from being realized. Information required for deep insight is typically stored across a broad array of internal and external sources with varying degrees of availability to marketing. Each source contains a just a few pieces of the customer puzzle that must be assembled to optimize each interaction. Assembly time is measured in the nanoseconds between when a user clicks on a link and the content displays on their device. IDC's Data-Driven Marketing Maturity Model provides a stage-by-stage guide for overcoming these and other barriers. It directs marketing executives on the organizational structure, processes, and technology necessary to use data to transform marketing and realize the opportunity inherent in customer data. Companies that make these changes will get to the best customers the fastest (and the cheapest) and will enjoy significant competitive advantage over their peers. The key findings of the IDC Data-Driven Marketing Maturity Model include:  Strategic focus: Marketing must be measured by customer outcomes as much as it is by the usual operational KPIs.  Organizational orientation: Marketing can no longer be a collection of specialists working in silos. Marketers must be as connected or more so as the customers they serve.  Process changes: Flow is an important process concept for both deliverables and data. Processes must be oriented to customer outcomes and deliver content that is intelligent about the customer's context.  Technology road map: Technology cannot be a collection of point solutions. Data cannot be locked in disparate repositories. To be most effective, marketing not only needs to transform from within but provide a road map for how other functions like sales, finance, fulfillment, support, and services participate in making marketing more effective.  Data mastery: Data and analytics are critical to understanding everything from customer experience to content effectiveness. Marketers must have the skills and tools to master data, and marketing must adopt a data-driven culture. GlobalHeadquarters:5SpeenStreetFramingham,MA01701USAP.508.872.8200F.508.935.4015www.idc.com

©2013 IDC #244993 1 I N T H I S S T U D Y M e t h o d o l o g y This study describes IDC's Data-Driven Marketing Maturity Model framework, which identifies the stages, critical measures, outcomes, and actions required for companies to effectively develop data-driven marketing competency. It is the result of comprehensive interviews IDC conducted with senior marketing professionals in large, mostly high-tech, mostly B2B enterprises. IDC's Data-Driven Marketing Maturity Model enables an organization to assess its competency and maturity with respect to using enterprise data to drive marketing strategy and tactics; define short- and long- term goals and plan for improvements; prioritize technology, staffing, and program investment decisions; uncover maturity gaps among business units and between business and IT groups; and benchmark maturity against industry peers. Organizations will use IDC's Data-Driven Marketing Maturity Model to maximize return on investments in marketing programs, platforms, and related technology, people, and processes. Additionally, organizations will be able to use the maturity model as a tool to encourage and improve intra- and interdepartmental collaboration in defining and executing the data-driven strategy. S I T U A T I O N O V E R V I E W T h e C a s e f o r D a t a - D r i v e n M a r k e t i n g Market share, revenue, and margin growth depend on getting to the best customers faster and cheaper than competitors. That means identifying potential customers very early in their buying process, delivering the most compelling content to the right people at the right times, and doing so instantaneously at scale. The data requirements are enormous as are the organizational, process, and technology changes necessary to make it happen. It is a long-term strategic commitment. But being first means being many weeks or months ahead of your key competitors in terms of finding and developing key customer relationships. Today, the typical company is unprepared. Marketing is largely organized around a set of silos based on specialized program functions such as events, email, Web site, video production, and technical writing. Each of these functions has its own database or audience source. These functions may be further fragmented when replicated across business units and geographies. On a grander scale, the whole enterprise is organized around silos of customer activity across marketing, sales, finance, fulfillment, service, and support. There are too many disjointed points of customer contact, too many disparate sources of customer data, and too many disconnects across departments. To truly optimize customer creation and revenue generation, each of these functions must learn how it can participate in making marketing more effective at delivering a quality customer experience. As a result, marketing must be reinvented around data and lead the charge to reinvent the whole enterprise around customer experience.

2 #244993 ©2013 IDC Defining Data-Driven Marketing IDC defines data-driven marketing as follows: Data-driven marketing is the practice of using data to continually optimize everything that happens in marketing. These practices enable the enterprise to most efficiently sense and respond to opportunities and threats involved in customer creation; to account for the impact of marketing activities on customer creation; and to continually optimize those capabilities. Marketing without data is like watching fireworks with your eyes closed. There's an enormous amount of noise, but you have no idea what's really going on. Key Driver: Buyer "Fit" Data and Associated Challenges IDC's research into IT buyer experience shows that in the earliest stage of the buyer's journey, the buyer's main concern is establishing "fit" between an urgent business issue and a solution. The faster buyers establish fit, the sooner they move into the next phase of the customer creation process. As buyers progress through their buyers' journey, they continually seek more fit information, bringing the prospective solution closer and closer to their need. Establishing fit requires a great deal of customer insight and customizable content so that everything about every interaction can be as relevant to the customer as possible. These insights are scattered everywhere in the company. For example, marketing may plan an upselling campaign for existing customers. However, marketing may want to exclude customers that are delinquent in paying their bills, or who have major support issues, or have larger sales opportunities open. Without insight into finance, support, and sales data, marketing will not be able to make those differentiations in its targeting process. As a corollary, what if a marketing campaign could make special offers to qualified customers that pay on time, use very little support, or haven't bought anything in a while? What if you could do that as a standard operating procedure instead of having to pull it all together every time a new campaign rolls out? The following questions are just a sample of the set of "fit" data marketing may need to know in order to appropriately personalize a buyer's journey and associated campaign. Questions that marketing is likely able to answer about fit are:  Is this a new contact or a return customer?  What industry does the buyer work in?  What site/function are they in?  What segment are they in?  What region are they in?

©2013 IDC #244993 3  What device are they using?  How did they find us and get to us?  What search terms did they use? Questions that marketing needs access to sales data to answer are:  Does the buyer work for a named account?  Who's the right sales rep or partner for this account?  Are there open opportunities at this account?  What decision makers are they connected with? Questions that marketing needs finance to answer are:  How much business do they do with us?  What products do they already have?  Do they pay on time? Questions that marketing needs support/services to answer are:  Are they a happy customer?  Are there any happy customers we can contact that are similar to this buyer? Key Driver: Engagement Data In addition to the fit data needed to personalize buyer interactions, marketing will need detailed information gained from sensing buyer behavior to progress prospects down the buying path. It is this behavioral data that when combined with the personalization capability of fit data enables the right content to be delivered. This response is sometimes called "the next best offer." The goal of this sense and respond is to motivate the buyer to take the next step in the journey. Engagement data is derived from a growing multitude of digital systems. Today, buyers interact with multiple systems simultaneously. To get an accurate view of customer behavior, marketers must unlock the databases frozen in each media- related silo. Web, social, advertising, email, mobile, and other programs across the organization must contribute to a continuous flow of real-time interaction data. It is important to understand fit data as being about a specific customer and engagement data as being about customer journeys through your enterprise customer-facing systems. The true value of engagement data comes from backtracking from transactions to points of contact and assessing the contribution and importance of each. Representative insights that require engagement data include:  Which leads converted the fastest?

4 #244993 ©2013 IDC  How deeply can we micro segment our customer base?  Which steps are most important to advancing at each stage?  What affinities exist between buying patterns for different products?  Which titles/roles are best at facilitating/inhibiting the buying process?  What content is most effective for which types of buyer journeys?  What are the best practices for coordinating the digital and interpersonal dialogue for different buyer journeys?  What media channels are more effective for which products, customers, and regions? Each of these questions can be applied at different levels of segmentation by sector, size, region, product lines, competition, sales channel, and so forth. The Benefits Imagine the first impression customers will have — they won't have to dig through 10,000 pieces of content to find what's most relevant; it comes to them. It positions your brand as a source of insight specific to their business needs. It saves them time and helps them clarify their challenges and define the solution. It's a huge advantage in identifying the best customers the fastest. It sets an important first impression about the level of expertise and service they can expect from your company. To realize these benefits, organizations must take on a significant strategic transformation as outlined in IDC's Data-Driven Marketing Maturity Model in the section that follows. I D C ' s D a t a - D r i v e n M a r k e t i n g M a t u r i t y M o d e l I n t r o d u c t i o n The five fundamentals of data-driven marketing are:  Strategy: Transition from focusing on marketing outcomes to a focus on customer outcomes.  Organization: Transition from organizational silos to systems that reconnect the specialist roles with a network orientation around collaborative workspaces and end-to-end process ownership and data stewardship.  Process: Establish a hierarchy of metrics and a series of workflows that facilitate a collaborative, networked organization and connect the operational reporting structure to the business. Differentiating and then linking between execution- level, operational, and corporate metrics are critical to measuring marketing contribution to business objectives. Develop workflows (involving both humans and machines) that link the data used in the various customer creation tasks and data life cycle.

©2013 IDC #244993 5  Technology: Develop an integrated marketing technology infrastructure that not only ties together the scores of systems and tools used by marketing but makes the marketing infrastructure an integral part of the overall enterprise IT environment.  Data: Create an enterprise customer data value chain to bring relevant customer data into marketing from all customer-facing activities throughout the business. These five fundamentals make up the core evolutionary work streams of IDC's Data- Driven Marketing Maturity Model (refer to Table 1). At each level of maturity, the work stream should progressively improve in consistency, completeness, quality, and alignment. Note that different work streams may mature at different rates. For example, a company may be very mature in lead management, connecting, using, and formally managing customer relationship management (CRM), yet be very immature linking inbound activities. The choice of where to start depends a lot on how well attuned senior leadership is to data-driven decision making. With senior leadership support, a methodical approach will normally take place. People will be assigned to process ownership and optimization, and the technology deployment and integration will commence in conjunction with data management activities. Another company may use technology as a catalyst for transformation and push that work stream ahead to slowly build the case for data-driven marketing at large. IDC's Data-Driven Marketing Maturity Model As shown in Table 1, IDC's Data-Driven Marketing Maturity Model describes five stages of maturity across five critical measures.

6 #244993 ©2013 IDC T A B L E 1 I D C ' s D a t a - D r i v e n M a r k e t i n g M a t u r i t y M o d e l D e t a i l e d D e s c r i p t i o n b y S t a g e Stage 1: Ad Hoc Stage 2: Opportunistic Stage 3: Repeatable Stage 4: Managed Stage 5: Optimized Strategy No data used; decisions made with gut instincts Getting started with marketing technology and data — typically siloed in one/few work groups Formalizing use of marketing technology and data including key points of cross- silo integration Adding management and governance structures to the use and life cycle of marketing technology and data Connecting and formally managing marketing technology and data across the enterprise Organization No dedicated resources Marketing operations role added to staff Marketing operations capability expanded to include data roles Functional leaders establish customer data-driven council; analytics becomes a shared service Global executive team to coordinate customer data- driven operations Process No defined processes Processes beginning to be identified and defined Collaborative workflows established; optimization based exclusively on available data Group-level adoption of collaborative workflow approach for on- and offline marketers Global adoption of collaborative workflow approach for on- and offline marketers Technology Fragmented systems for outbound activities Campaign management integrated with Web and CRM Marketing technology deployed but not integrated for content marketing, outbound/inbound channels, and consumption trend analysis Integrated marketing platform with group- level customer data infrastructure Integrated enterprise marketing platform Data No/limited, poorly managed partner data Inconsistent customer data in multiple email/transaction al systems Consistent customer data with governance and stewardship functions in place Group-level customer data value chain; unified data structure and cooperative governance in all customer-facing functions Enterprise customer data value chain; unified data structure and cooperative governance in all customer-facing functions Source: IDC, 2013

©2013 IDC #244993 7 Strategy — Focusing on Customer Outcomes Most marketing people are myopically focused on operational metrics that pertain to their particular specialty. Campaign managers still look at open rates, click-throughs, and downloads. Web managers are looking at page views, keywords, and navigation pathways. Social marketers obsess over sentiment, retweets, likes, and so forth. Senior-level marketers are looking at lead quotas, funnel performance, and program budgets. Who's measuring whether any of this is benefitting the customer? Marketers need to measure their success in terms of customer outcomes. What does this mean? Throw out your lead waterfall. Replace your MQL and SAL models with progression definitions that are based on buyers' journeys. You can still call them what you will. But instead of exclusively using arbitrary scoring methodologies based on digital activity, begin to define leads based on which journey the buyer is on. Establish metrics for customer outcomes based on:  Types of journeys. This includes ecommerce for low-margin transactions, early sales engagement for complex enterprise opportunities, and many variations in between.  Stage of journey. Buyers have specific goals that change dramatically as they move through the exploration, evaluation, and purchase stages (see Figure 1). At each stage, there are many resources marketing can deliver or enhance to enable sales to support the customer (see Figure 1 for a few examples). F I G U R E 1 B u y e r G o a l s a n d S a l e s E n a b l e m e n t E x a m p l e s b y S t a g e Source: IDC, 2013

8 #244993 ©2013 IDC Organization — Creating a Data-Driven Marketing Organization The journey along IDC's Data-Driven Marketing Maturity Model is one of evolving from operating in silos to collaborating in systems. Systems thinking is crucial for managing the interconnectedness of everything marketing does and how marketing works. Systems thinking is a big challenge for an organization that is traditionally designed around highly specialized roles working in a deadline-driven, project- dominated work environment. Despite its reputation as a freewheeling, creative, and dynamic function, corporate marketing is, in reality, a deeply entrenched, hierarchical, fragmented organization. Marketers think of themselves as innovative and self-reliant, but the downside is that they are also often ad hoc. Specialists typically function in separate domains. Specialists move from project to project, often without looking back. Work gets thrown over a series of organizational walls so that connections between producers and consumers of marketing content are either broken or never formed. The need to be highly responsive to changes in direction has created a culture adverse to structured workflows. As shown in Figures 2A and 2B, there are several forces that are starting to change this. The advent of marketing automation systems redefined campaign management into an explicit business process that has slowly extended from marketing into sales and beyond. As marketing automation continues to evolve into an enterprise system, with the various point solutions consolidating, leading CMOs are beginning to redesign their organizations around workflows instead of program or media silos. Rather than having social, Web, advertising, content, analytics, systems admin, and so forth in separate organizational buckets, these roles are being reoriented into communities that remain connected to their deliverables, their audiences, and the overall process in which they work. Analytics is a good example. Analytics is a feature found in almost every marketing system, from social to campaign management to ad/SEO to the Web site. As a result, many companies have pigeonholed analytics and do not have a unified global dashboard for marketing. A systems approach adds a shared services capability for analytics that applies a holistic perspective on how each activity relates to the optimal performance of the overall customer creation process.

©2013 IDC #244993 9 F I G U R E 2 A C o n n e c t e d n e s s i n t h e N e x t - G e n e r a t i o n M a r k e t i n g T e a m — F r o m a S i l o M o d e l Source: IDC, 2013 F I G U R E 2 B C o n n e c t e d n e s s i n t h e N e x t - G e n e r a t i o n M a r k e t i n g T e a m — T o a S y s t e m s M o d e l Source: IDC, 2013

10 #244993 ©2013 IDC Process — Creating Data-Driven Marketing Processes Metrics and the overall reporting process will be critical to successfully managing the transition from silos to systems. A few new metrics should be established to encourage and reward the alignment of previously disconnected specialists. These metrics should be created at the operations level and include measures such as:  Productivity: Peer assessment on collaboration and contribution  Performance: Time to market; data compliance; marketing asset inventory control; channel coverage, content effectiveness and cost; number of effective analytic models Marketers must be diligent to differentiate business, operations, and executive-level data. IDC's Hierarchy of Marketing Metrics uses the following definitions:  Execution: Execution metrics are used almost exclusively by marketers themselves for improving marketing results. Rarely should execution metrics be reported to senior management (see Figure 3).  Operations: Operational-level metrics are used to make sense of marketing. The CMO's team uses operational metrics to manage resources and assets, forecast performance results, and diagnose the "red" areas on the QBR charts.  Corporate: These are measures that capture marketing contribution to strategic goals such as revenue, share, and margin. For more on IDC's Hierarchy of Marketing Metrics, see Reporting Done Right: IDC's Hierarchy of Marketing Metrics (IDC #242636, August 2013).

©2013 IDC #244993 11 F I G U R E 3 I D C ' s H i e r a r c h y o f M a r k e t i n g M e t r i c s Note: For more details, see Reporting Done Right: IDC's Hierarchy of Marketing Metrics (IDC #242636, August 2013). Source: IDC, 2013 Technology — Road Map for Data-Driven Marketing Many large enterprises have numerous "point of pain" solutions in marketing. Some technology marketing organizations have 50 (or more) marketing systems assigned to various activities across business units and geographies. The good news is that as more marketing activity is automated, supported, or at least tracked with digital systems, the more manageable marketing can be. The bad news is that we are in a state of early adoption in which many of these systems have been implemented independently and have therefore resulted in highly fragmented environments that reinforce existing silos. Because of the proliferation and specialization of solutions, models for marketing technology can get busy to the point of confusion. In general, most marketing technologies will fall into one of the three categories or the platform space depicted in Figure 4. In detail:  Content life-cycle technologies manage the requisitioning, production, storage, compliance, upkeep, and retirement of marketing assets. Categories in this area include but are not limited to content management systems (CMS), Web content management (WCM), digital asset management (DAM), content production tools, primary market research, intelligence portals for customer, competitor, and market data.  Channel management technologies manage inbound and outbound communications for digital and physical formats. Categories of solutions in this area include but are not limited to ad management, ecommerce, events

12 #244993 ©2013 IDC management, PR/AR, communities, Web design, social marketing, and mobile marketing.  Consumption or data life-cycle technologies manage the aggregation, analysis, and application of data that is accumulated in the content and channel areas. While many solutions in these areas also have analytical capabilities, data life-cycle technologies provide the horizontal perspective that connects how all the content and channels interact. Categories of solutions in this area include but are not limited to customer data warehouse, social listening, Web analytics, marketing resource management (MRM), BI, predictive analytics, and personalization.  Platforms are systems that manage the orchestration of content life-cycle technologies, channel management technologies, and consumption or data life- cycle technologies. There are multiple platforms emerging and a great deal of consolidation going on as major providers pull more of the satellite functionality into the core offering. Examples of solutions in this area include but are not limited to marketing cloud, customer experience cloud, brand management, agency management, marketing automation, and enterprise social networks. Clearly, there is room for consolidation between platforms. F I G U R E 4 I n t e g r a t e d M a r k e t i n g I T I n f r a s t r u c t u r e Source: IDC, 2013 Recent M&A activity from major providers such as Adobe, IBM, Oracle, and salesforce.com suggests there is a lot of supply-side investment going into the platform. However, it will take several years before these offerings evolve into

©2013 IDC #244993 13 seamless solutions. In the meantime, customers will be forced to exert disproportionate manual effort to run all the different point solutions. A key to doing so successfully is customer data. Data — The Enterprise Data Value Chain Delivering content personalized for each customer based on the collective knowledge of the company and outside sources in a nanosecond requires an enterprise perspective on customer data. Marketing can make great strides on its own to bring its many disparate systems and sources together. But it's an additive process, and as more information becomes available from other departments, the more powerful marketing becomes in its ability to engage the best customers the fastest. Ultimately, customer data should be managed as an enterprise service and every source of customer data should be linked together as an enterprise value chain. But customer data is only one type of data that marketing must master:  Customer data: This comprises the customer-specific information necessary to help establish fit: basic contact information, personal demographics, business firmographics, and so forth; relationship information such as new or returning customer, purchase history, payment status, contact center/support activity, and so forth; and a litany of new dimensions such as social graph and influence, devices, and interaction patterns. It spans external and internal sources.  Execution data: This is "engagement" information about the execution of all marketing activity and how it is performing. Examples include number of campaigns, volume of activity on different channels, and response rates (email, social, Web, events, and so forth). Execution data can be viewed at the specific tactical level or grouped for analysis. This data can be very helpful for marketing specialists to optimize their performance. However, care must be taken with respect to reporting this data up the management structure as it naturally tends to reinforce the fragmentation of work.  Operational data: Operational data is information on the productivity of processes and people. This data is used by managers to optimize core processes and resources. Examples of operational data might include time to market for solutions, the level of engagement from partners, and lead conversion metrics. It can also include employee engagement, customer satisfaction, partner performance metrics, and brand equity trends.  Financial data: This information is the planning, budgeting, and expense reporting data that senior managers need to understand how marketing activities are performing from an investment point of view. This is typically aggregated at the product, regional, and/or business unit levels. It includes data such as program spend by channel, digital versus traditional media mix, staff allocation by function, and revenue performance by campaign, region, and product.  Corporate data: This information is the aggregate of the financial data and connects it to strategic corporate goals. How did marketing influence overall revenue, within key segments, key accounts? How did marketing support market

14 #244993 ©2013 IDC share gains in key regions? How did marketing improve margin on specific product lines? Customer and execution data tends to run horizontally across functions and systems — in marketing and beyond marketing as well. Operational, financial, and corporate data tends to flow vertically up and down the organizational structure as shown in Figure 5. F I G U R E 5 U n d e r s t a n d i n g M a r k e t i n g D a t a F l o w Source: IDC, 2013 As Figure 5 makes exceedingly clear, the practice of data-driven marketing will affect everyone at every level in marketing. Therefore, the culture of the marketing organization must become oriented around data. No one is exempt from understanding how data is structured and used and how data flows through the specialized areas into the bigger picture. To accomplish this, CMOs need to clearly and consistently communicate the vision of data-driven marketing and the changes needed to evolve the four pillars. F U T U R E O U T L O O K B r i n g i n g t h e F u l l P o w e r o f D a t a t o M a r k e t i n g Almost all companies today chop up their customer interactions into marketing, sales, fulfillment, finance, service, and support functions or some limited variations thereof. While this makes good sense from managerial and operational perspectives, it has led to the deconstruction of the customer experience. In large enterprises, many

©2013 IDC #244993 15 business units don't even know they are targeting the same customers with competing offerings. This fragmentation not only wastes the customer's valuable time but makes it nearly impossible to know if the best offer is being prioritized relative to corporate goals. The solution to reassembling the customer experience is to manage customer creation as an end-to-end process in which every customer-facing function and system participates. Data and data-driven marketing will be fundamental to this effort. Maturing your data-driven marketing capabilities will require active contributions from CIOs as well as leaders of sales, finance, service, support, and so forth. These resources should form a customer experience leadership council to:  Continually monitor and assess weak and missing links in the customer data collection process.  Evaluate every customer interaction for its potential to capture data — including all sales and support interactions.  Endorse new modes or channels for certain interactions to optimize data capture (e.g., analog media increasingly features ways to connect to digital media; unstructured interactions [social/voice] should be recorded, transcribed, and subjected to semantic analysis).  Establish policies to make all appropriate customer datasets as accessible as possible in compliance with privacy laws.  Apply quality control KPIs to everyone that produces customer data.  Make customer data and analytics part of hiring, onboarding, and training programs. Everyone should understand the company's customer data structure, who owns what data, and how their role produces and consumes data. E S S E N T I A L G U I D A N C E Leading companies have already established the enterprise data governance and infrastructures needed to support data-driven decision making in all areas of the business, including marketing. Formalized customer data value chains are the next level of maturity beyond the governance. Marketing is more dependent on information outside of the marketing department for insights into the revenue generation process than most other customer-facing areas. Therefore, it has the most to gain and the most work to do. CMOs must be at the forefront of the effort to make customer data an enterprise resource. IDC recommends that to compete, the following actions (at a minimum) be taken over the next five years:  Now (assess the following):  What is the current state of customer data — the number of databases, consistency, freshness, accuracy, and so forth?  How effectively has customer data governance and stewardship been implemented across departments?

16 #244993 ©2013 IDC  How is customer data produced and consumed by different functional areas (marketing, sales, finance, service, support, etc.)?  How much redundant data aggregation, cleansing, normalization, and analysis is being performed?  How is customer data being managed in the partner organization?  How much is all this costing the company in terms of productivity, redundancy, and opportunity cost?  What is the state of marketing automation adoption and optimization? Is there enough visible executive support, training programs, budget, technical admin and support staff, regional coverage, and so forth to create an integrated marketing infrastructure?  Is the necessary level of executive sponsorship in place to execute on building an enterprise customer data value chain?  Is every customer touch point optimized for data capture? If not, can it be linked to one that is? How efficiently does it get into the customer data value chain?  Next budget cycle and over the next one to two years:  Concentrate on aligning customer-facing functions with a single line of business/BU.  Create a cultural orientation and expectation for data-driven decision making. In addition:  Outline metrics for measuring how different areas contribute to customer data.  Socialize the success of data-driven marketing initiatives no matter how small in scope.  Raise executive awareness of the business impact data-driven marketing has had and how much it depends on an enterprise approach.  Facilitate communication and coordination between functional areas and BUs on how customer data is defined, produced, and consumed.  Drive adoption of key systems to the 90% mark.  Build out the analytics function and provide examples of how the scope of data availability affects the level of insight that can be used to improve operations.  Define key roles, responsibilities, and skill sets around data at every level of the marketing organization.

©2013 IDC #244993 17  Deploy mandatory training on data and analytics for all marketing roles.  Over the next three to five years:  Adopt a data-driven approach to everything related to customer engagement. You should be producing insights that both reinforce and challenge current thinking on what drives revenue. Everyone that interacts with customers should understand how they participate in the customer experience process. Metrics around customer data contribution should be embedded into all customer-facing roles. Link customer data to strategic planning and start to coordinate how multiple lines of business interact with the customer — prioritize customer engagement around the most lucrative opportunities from an enterprise perspective. L E A R N M O R E R e l a t e d R e s e a r c h  Capturing the Buyer's Voice: Summary Findings from the CMO Advisory Marketing Leadership Meeting, September 25, 2013 (IDC #244061, October 2013)  Next-Gen Web: Summary Findings from the CMO Advisory Marketing Leadership Meeting, September 25, 2013 (IDC #244058, October 2013)  Marketing Planning Practices: Summary Findings from the CMO Advisory Marketing Leadership Meeting, September 25, 2013 (IDC #244056, October 2013)  Building a Successful CMO + CIO Relationship: Summary Discussion from the CMO Advisory Marketing Leadership Meeting, September 25, 2013 (IDC #244055, October 2013)  IDC's 2013 IT Buyer Experience Survey: Create and Close Customers up to 40% Faster (IDC #242608, September 2013)  Solution Marketing: Overcoming the Muscle Memory of Products (IDC #WC20130815, August 2013)  Reporting Done Right: IDC's Hierarchy of Marketing Metrics (IDC #242636, August 2013)  The DNA for Success Is in the Data: The Case for a New Approach to Channel Enablement (IDC #WC20130718, July 2013)  IDC 2013 Tech Marketing Barometer Study Special Insight: Cloud Software Versus 2nd Platform Marketing Trends (IDC #241215, May 2013)  The Collaboration of Marketing and IT: An Interview with Motorola Solutions' Eduardo Conrado (IDC #240768, April 2013)

18 #244993 ©2013 IDC  2013 CMO Tech Marketing Barometer Study: Trends, Forecast, and Essential Guidance for Tech Marketing Executives (IDC #240712, April 2013)  Paid Social Media: A Look into How Top Brands Are Utilizing Paid Social Campaigns — Insights from Adobe, Salesforce.com, and HubSpot (IDC #240390, March 2013)  Marketing Operations Expands: A Study of the State of the Marketing Operations Profession (IDC #240453, March 2013)  IDC Chief Marketing Officer ROI Matrix — Lessons from the 2013 Marketing Leaders (IDC #239651, March 2013)  Worldwide Chief Marketing Officer 2013 Top 10 Predictions: Today's CMO Becomes Master of Data (IDC #238987, January 2013)  CMO Advisory Service Best Practice Series: Predictive Analytics for Marketing (IDC #238417, January 2013)  Take the Buyer's Journey — On the Shoulders of the Buyer (IDC #238171, December 2012)  Marketing Investment Planner, 2013: Benchmarks, Key Performance Indicators, and CMO Priorities (IDC #237813, November 2012)  The 2012 IT Buyer Experience Survey: Accelerating the New Buyer's Journey (IDC #237207, October 2012)  CMO Advisory Service Best Practices Series: Realizing the Vision of 21st Century Lead Management (IDC #236396, August 2012)  Emerging Issues: The New CMO + CIO Dialog: Building a Productive Relationship (IDC #236397, August 2012)  Data-Driven Marketing: A Survey of Marketing Automation Maturity in Global High-Tech Companies (IDC #236289, August 2012) S y n o p s i s This IDC study describes IDC's Data-Driven Marketing Maturity Model framework, which identifies the stages, critical measures, outcomes, and actions required for companies to effectively develop data-driven marketing competency. This study details the progressive steps necessary to institute a best-in-class data-driven marketing initiative. It contains guidance on how to assess the current stage of development in five key areas — strategy, organization, process, technology, and data. It also provides insight into the competitive advantages of progressing through the model and specific actions needed to advance practices from a departmental initiative to an enterprise process. "Data is the fuel for the modern marketing engine," said Gerry Murray, research manager, IDC CMO Advisory program. "Companies at the high end of the maturity

©2013 IDC #244993 19 model have discovered the transformative power that data and analytics can have on marketing. However, data-driven marketing is in fact an enterprise process that requires the active support and participation of every customer-facing activity. Companies that elevate data-driven marketing to the enterprise level will be able to make the most compelling offers to the best customers the fastest." C o p y r i g h t N o t i c e This IDC research document was published as part of an IDC continuous intelligence service, providing written research, analyst interactions, telebriefings, and conferences. Visit www.idc.com to learn more about IDC subscription and consulting services. To view a list of IDC offices worldwide, visit www.idc.com/offices. Please contact the IDC Hotline at 800.343.4952, ext. 7988 (or +1.508.988.7988) or sales@idc.com for information on applying the price of this document toward the purchase of an IDC service or for information on additional copies or Web rights. Copyright 2013 IDC. Reproduction is forbidden unless authorized. All rights reserved.

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