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B2B Marketing’s Big Data Destinyby Laura Ramos, April 14, 2014

For: CMOs

Key TaKeaways

external Data Gives Buyer Journey Maps a Much Richer Level Of DetailInternal systems contain only a fraction of buyer data that B2B marketers need to understand their customers well. Leading CMOs find that they must kick the bad habit of relying on data found in-house and look outside of their department or firm to track down nonobvious insights about where buyers venture to inform purchase decisions.

existing Customer Behavior Predicts Pathways From Prospect To PurchaseB2B business data analytics work in reverse of the customer life cycle, using the end game to identify the unique attributes that distinguish your best customer successes. Marketers need to apply these insightful patterns to predict where promising prospects will emerge from and invest to intercept their journey.

emerging Technologies will Help expose B2B Data Insights and Put Them To UseAs the volume of interesting buyer data expands, technology becomes essential to separating the insight kernels from the information chaff. New entrants focused on mining structured and unstructured data, inside and outside of the firm, can help automate data analysis approaches without hiring a pack of data scientists.

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wHy ReaD THIs RePORT

Business-to-business (B2B) CMOs who make business decisions based on internal data, with all of its imperfections and gaps, risk the same fate as hikers who head off into the wilderness without a compass or map. A wealth of insights from Internet exploration, smart device usage, and business community social activity reveals the twists and turns that prospects take on their purchase journeys — but only to marketing execs who go beyond basic customer profiling and tap into the abundance of behavioral and firmographic data as well as insights found in unconventional B2B data sources. By analyzing the footprints that their best customers leave behind, B2B CMOs can more accurately map the buyer’s journey and use technology and analytics to predict where the next best business opportunities will show up. This report shows marketing execs how to harness this vast array of information to intercept buyers earlier in the purchase process and help sales engage with the opportunities most likely to close and return long-term business value.

Table of Contents

Marketers struggle To Get an accurate View Of Their Buyers

successful B2B Marketing requires Data-Driven Decision-Making

B2B Marketers Have To Make a Date with Their Big Data Destiny

step 1: Analyze Existing Customers To Define successful Buying Patterns

step 2: Apply Insights To Accelerate sales Interactions

step 3: Use Analytics To Predict Future Prospects

Technology will Help Unearth Insights From Data Mountains

rECoMMEnDATIons

Turn B2B Data Into Profitable Customer engagement

supplemental Material

notes & resources

Forrester interviewed 13 vendor and user companies, including Bizo, BlueKai, Cisco systems, Fliptop, HP, Infer, Lattice, Mintigo, networked Insights, sAP, 6sense, superior Essex, and Zendesk.

related research Documents

Brief: redesign B2B Dashboards Around Customers To Deliver Actionable InsightsFebruary 24, 2014

B2B Marketing Measurement needs An MBAAugust 27, 2013

rethink Marketing In The Buyer’s ContextFebruary 21, 2013

B2B Marketing’s Big Data DestinyHow To Make Business Buyer Data Actionableby Laura ramoswith David M. Cooperstein and Elizabeth Perez

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B2B Marketing’s Big Data Destiny 2

© 2014, Forrester Research, Inc. Reproduction Prohibited April 14, 2014

MaRKeTeRs sTRUGGLe TO GeT an aCCURaTe VIew OF THeIR BUyeRs

As the options that buyers can use to consider and make purchases continue to expand, so does the amount of data being created that could help marketers map them. But overwhelmed with choice, marketers rarely look beyond a limited set of demographic and buyer readiness criteria — like budget, authority, need, and timing (BANT) — when assessing how buyers buy. As a result, the typical marketer’s buyer journey map is fraught with holes, suppositions, and inaccuracies, due to:

■ Messy internal data that is difficult to manage. A wide array of operational systems houses B2B data, making data management challenging and consolidation infeasible. Long sales cycles marked by buyers taking nonlinear paths to explore solutions and the need to engage multiple decision-makers further complicate the customer data hygiene problem. Without the services of a B2B data management specialist, like Extraprise or Infogroup, the process of merging, de-duplicating, standardizing, and augmenting data spins out of control and can cost a large enterprise hundreds of thousands of dollars to get it back on track.1

■ A myriad of complications that stall journey mapping. Projects to build personas and journey maps of buyers can be daunting to complete. Industries served, geographies supported, solution options, and purchase stages make this a task that quickly spins out of control.2 As the head of European marketing at Cisco explained, “At any given time, I have hundreds of thousands of potential buyers at various stages in our pipeline — does this mean I have to map the buyer’s journey for each one?” Taking shortcuts here only reduces the decision-making value of these tools, leaving marketers facing a huge amount of work with few means to expedite it.

■ Organizational silos with separate data and reporting. Marketing tends to organize by channel and isolate data within the specialized tools that serve each channel. The CMO of Lattice explained the challenge this way: “Social looks at TweetDeck or radian6. The website team looks at web analytics. The contracts database is separate from financial systems. Everything is disjointed, leaving marketers with no way to have a 360-degree view of buyers.” Specialized data structures — that make data meaningful only in the context of a specific tool — make it difficult to see the bigger picture and detect key signals like increased purchase intent or likelihood to defect.

■ Technology that helps with “what” but not “why.” Most marketing technology and reporting tools focus on transactions; they don’t explain how to segment an audience, decide where to spend, or help sales present an offer. They require sophisticated integration work to ingest the right customer data from operational systems, external data sources, social sharing behavior, or other marketing applications (e.g., web analytics, ad targeting, customer relationship management [CRM]). The senior vice president (SVP) of marketing at BlueKai sums up this situation by saying, “Unfortunately, siloed marketing automation does little to help marketers execute targeting or decision-making at scale because it relies on first-party data.”

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successful B2B Marketing Requires Data-Driven Decision-Making

The B2B buyers’ path from exploration to engagement has become more complex and nonlinear now that web browsers bring a wider variety of information and new investigative avenues to their fingertips. Marketers who trek through data that charts their customers’ footprints and analyze how purchases actually happen will be better able to engage prospects wherever they may travel.3 B2B CMOs now need to embrace big data and analytics because:

■ Company leadership holds them to a number. Forrester’s 2014 budget survey shows that B2B marketers expect to spend about 4% more on programs this year.4 However, the business expects this money to stretch further — covering new investments in analytics and technology, for example — and to show a more direct business impact. As the SVP of marketing at BlueKai (recently acquired by Oracle) told us, “Every dollar is precious, and marketers must measure an ROI against everything we do.” Numbers, not recognition or awards, now tell the tale of marketing success. When explaining why Cisco Systems ramped up its customer insights efforts, a global marketing segment lead told Forrester, “Understanding our customers’ buying centers, processes, and personas is critical to helping us to tailor our messaging, shape our global marketing plays, and drive greater ROI in our demand-generation activities.”

■ Decision-making needs to depend on facts, not intuition. In research we conducted in 2013 on how marketers manage performance, we were dismayed to find that only 10% of respondents felt that their marketing teams were effective at using data analytics to make decisions (see Figure 1-1).5 When we looked for an explanation, we found that more than half (61%) admitted that most of their data work went into reporting on how they did (see Figure 1-2). Marketers we spoke with for this report recognize that program decisions can no longer rely on historical practice or intuition. A director of integrated marketing at HP put it best when she said, “We just can’t live with educated guesswork anymore.”

■ Customer insight is essential to becoming customer-obsessed. CMOs must enable their teams to obsess over customers from all angles to make sure that marketing activity, content, and frontline service delivers value at each stage in the customer life cycle.6 Those who do this well make data the foundation of their marketing strategy and use it to shift their team’s segmentation and targeting activity from go-to-market to go-to-customer. A senior director of strategy and change management at SAP agreed by saying, “We had to understand how buyers buy to get our mix as tight as possible and direct the right message to different audiences.”

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Figure 1 B2B Marketers Struggle To Use Data Analytics Effectively

Source: Forrester Research, Inc.110363

Source: Forrester/ITSMA/VEM May 2013 Global Marketing Executive Performance ManagementOnline Survey

“How effective would you say marketing is at using data analytics?”

Base: 174 marketing leaders

Ninety percent of marketers say that they are not effective at putting data analytics to work1-1

57%

30%

10%

3%

NeutralEffective Noteffective

Don’tknow

More to report past performance

Both about equally

61%

19%

13%

3%

3%

We don’t use data analytics

More as a predictive tool

Don’t know

“Do you use data analytics as a predictive toolor to report past performance?”

Base: 174 marketing leaders(percentages may not total 100 because of rounding)

Reporting doesn’t show marketing where to go next1-2

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B2B MaRKeTeRs HaVe TO MaKe a DaTe wITH THeIR BIG DaTa DesTIny

Marketers must put customer data to work. They need to source that data from a wide array of data sources to look for the signals that indicate changes in purchase intent. And they need to map the data against the customer life cycle.7 This process starts with identifying which attributes distinguish the best customers and working in reverse to match these characteristics to patterns found among unqualified leads or undifferentiated traffic that predict those most likely to convert (see Figure 2). To accomplish this, CMOs need analytic systems and practices that (see Figure 3):

1. Analyze their most-valuable accounts to determine who influences the journey at each stage, what information they rely on to make decisions, and where they go to get it.

2. Apply insights about what distinguishes the better customers in their portfolio to marketing messages and sales interactions to drive shorter and better close rates.

3. Use data insights to better identify and target early market opportunities and better allocate market spending and sales efforts on audiences that best match these patterns.

Figure 2 Data Analytics Help Deconstruct The Customer Life Cycle


Apply insights toenable sales and

increase conversionrates

Use data to discovernew market opportunities

and predict outcomes

Use patterns toengage with early-stage prospects

Pattern-match to identifystrong complementary

opportunities

Profile existingcustomers to discover

unique value andsatis�ers

3

1

EXPLORE

USE

ASK

ENGAGE DISCOVER

BUY

2

4

5

Data analytics cycle

Customer life cycle

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Figure 3 Deep Data Analysis Delivers Actionable Insights


Which individuals represent theaccount?

What content shouldwe deliver toprospects?

• Brand af�nity• Pro�tability• Total lifetime value• References• Referrals• Depth of relationship

• Link individuals toaccount

• Score collectively• Pattern-match to

prioritize

Identify:• Key decision-makers• In�uencers• Sources of information• Types of content• Engagement points

Varies by:• Role relationships• Engagement level• Content interactions• Clarity of business

need

Gather(inputs)

What is the new or best opportunity?

• Collect signals thatpredict best �t

• Pattern-match“ideal” to “anonymous”

• Prioritize prospects using patterns

• Use patterns to tunemessage

Analyzeexisting

customers

Apply insightsto accelerate

sales

Predictfuture

prospects

What makes customers valuable?

How do theybecome customers?

Analyze(patterns, signals,pro�les, graphs)

Where do we�nd them? (Pattern-match to identify . . . )• Sources of information

buyers explore• Needs expressed• Types of content

researched• Intracompany

connections

How do weincrease intent?

• Pain points• Motivations• Sentiment• Key triggers• Expressed needs• Risk pro�le

• Create customizedcontent

• Deliver sales-readytalking points

• Present next-bestoffer/call to action

What attributesdistinguish them?

How do we spendtime/$ to engage?

• Marketing mixrecommendations

• Channel options• Content/message

choices• Test/execute/revise• Measure against

patterns

Create(outputs/activity)

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step 1: analyze existing Customers To Define successful Buying Patterns

B2B CMOs must understand the roles played, actions taken, and sources of information consumed by buyers, from problem identification through purchase, adoption, and ongoing use. Scientific techniques based on statistics, modeling, machine learning, and data mining help discover the hidden, nonobvious, and statistically significant characteristics that distinguish high-value customers. In tapping this data, B2B CMOs will need to:

■ Focus on the account as a whole. Market maturity and solution complexity can cause buyers’ purchase paths to vary widely. Rather than detail each individual path, marketers must start at the account level to identify which factors make an organization a good customer target. Firms like BlueKai, Bizo, Demandbase, and Lattice have the data to help marketers identify anonymous buyers and associate them to the accounts they represent. They use a combination of statistical plus cookie-enabled methodologies, including both first- and third-party cookies, to uncover unique signals at both the individual level (like site visits and news topic reads) and the account level (like news of a new round of funding or the recent hiring of a chief financial officer) and use these to reliably predict which accounts will be more likely to buy.

■ Then identify which buying centers have a stake in their offerings. Within each account, marketers need to understand which individuals dominate decision-making at each stage of the customer life cycle and how their influence waxes and wanes during discovery, exploration, and decision-making. Cisco solved this challenge by associating personas to related buying centers and studying the path these personas take in aggregate to make and implement a purchase. This allows Cisco marketers to sharpen their targeting and to apply nuanced language when addressing the chief information officer versus the network manager or a prospect from a business line.

■ Look beyond marketing and sales data to see the full picture. Patterns that signal changes in buying intent come from many places inside and outside of the firm. In addition to CRM, sales automation, as well as your email, website, and digital advertising platforms, call center systems, finance departments, legal departments, and operational interactions offer additional sources of insight. Atypical insights come to light when data analysis crosses organizational boundaries to tap into contract language, service delivery reviews, purchase histories, channel-partner agreements, and other information squirreled away in data warehouses or business intelligence systems. The chief executive officer of Bizo, a leading B2B data management and targeting provider, told us: “The ability to synthesize information from every functional area will be what drives outsized returns for businesses that sell to other businesses.”

step 2: apply Insights To accelerate sales Interactions

Marketers that put this collected insight to use can intercept decision-makers as they explore solutions and give them more targeted information that helps accelerate the purchase process. To improve time-to-close and conversion rates in the middle and later stages of the pipeline, data-savvy B2B CMOs need to:

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■ Arm sales with commercial insights proven to grab buyers’ attention. Buyers initiate a purchase process when a business disruption causes them to recognize a problem with serious consequences that requires active resolution. Knowing how current customers identified these disruptions helps marketing uncover the pressure points and “ah ha” moments that move prospects to action. The VP of marketing at a global wire, cable, and accessory manufacturer, Superior Essex International, converts insights into call guides, whiteboard scripts, and point-of-view cue cards that help sales persuade qualified opportunities to take the next step in the process of signing a deal.

■ Use math to predict likely business outcomes. New, highly scalable data processing technologies like in-memory databases and Hadoop make it easier and less costly for B2B CMOs to use heavy statistical modeling techniques to overcome data gaps and inconsistencies prevalent in past B2B data sets. A worldwide marketing project manager at HP, who plays a data scientist role for his global team, explained, “Scientific models always fit a bell curve. Predictive modeling helps us get beyond the exceptions and holes in our data to see buying patterns two-thirds of prospects fit into and take action.” Using data analytics to predict outcomes can also help marketers adjust program execution and trade off spending without waiting weeks for campaigns to complete or months for results to come in.

■ Turn analytical revelations about buyers into confident marketing mix decisions. Analytics from marketing mix models help marketers more accurately estimate and compare the impact of disparate program options, like buying online ads versus sponsoring a trade show. Explaining how data insight gave her the confidence to stand firm against demands for marketing programs that included tactics like airport displays or ads in high-cost print media, a director of integrated marketing at HP explained, “I am very brave in taking a controversial point of view when I have data to back me up. My goal is to be able to measure how different media or channel choices perform with different audiences and use that to inform our media strategies. Today, our program reviews focus as much on numbers as creative. We are becoming a bunch of marketing engineers.”

step 3: Use analytics To Predict Future Prospects

Digital tools and social networks allow early-stage buyers to self-educate anonymously and avoid sales contact until after they’ve made up their minds. Marketers need to sift through undifferentiated top-of-the-funnel activity — on owned digital properties and elsewhere — to spot and cultivate the new leads. Analytics help marketing teams:

■ Put buyers on the map before they identify themselves. Real-time analysis that updates continuously will replace survey-based demand-side analysis that is outdated almost as soon as it is published. Predictive models that study traffic, examine behavior, and graph interpersonal connections will help marketers better understand available opportunities before prospects officially raise their hand by filling out a form or submitting their email address. Technology

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entrants like 6Sense and Mintigo are beginning to deliver solutions that let marketers develop these models without hiring a herd of data scientists. Savvy B2B marketing executives should look into solutions like these to help their teams spot which buyers are most likely to buy specific products, even though this action may not happen for three to six months.

■ Tune spending in response to emerging buyer demand. By incorporating data-driven insights into developing smarter, more relevant cross-channel programs, marketers create a virtuous cycle characterized by tighter segmentation that pinpoints which buyers have needs that best fit a firm’s offerings and more efficient spending that matches messages and content to the exact issues a buyer’s investigative activity reveals. High-growth companies like Zendesk turn to data analytics to help determine which of the huge volumes of their daily site visitors need sales attention immediately and which ones they should put into nurturing programs, thereby avoiding spending marketing money and sales effort on opportunities that aren’t yet ready to buy.

■ Use data to align the marketing message across the firm. Journey mapping helps Cisco create a consolidated, consistent “One Cisco” approach to corporate-level planning and messaging that helps keep messages from mutating in the field. By running a few centralized plays that target key personas in the buying centers that the analysis shows are most likely to purchase its products and services, Cisco corporate marketing gives local business units the freedom to localize different elements of the play while maintaining consistent messages across product lines and geographies.

TeCHnOLOGy wILL HeLP UneaRTH InsIGHTs FROM DaTa MOUnTaIns

Data analytics can help promote marketing from creative center to customer steward when CMOs embrace the practice of using data to drive strategy and planning. Tools that collect data and run regressions against a wider variety of internal information and external signals, clues, and patterns aid this transformation (see Figure 4).8 To progress from warehousing data to delivering actionable insights, CMOs should look out for new technologies and approaches that will help:

■ Sift through information to identify reliable buying signals. New highly-scalable data processing technologies combined with predictive modeling applications simplify the process of comparing heaps of attitudinal, informational, need-based, and role-based information against the various stages of the buyer’s journey. Solutions from companies like Fliptop and Infer scour structured and unstructured data, from inside and outside of the organization, to reveal behavioral, preference, and hidden insights — found by combing through quirky places such as job descriptions, patent applications, embedded tags, or scripting code found on company websites. This new breed of technology can help expand data analytics beyond the limited view that standard demographics and customer qualifiers provide.

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■ Aggregate signals into patterns against which marketers can take action. Like the Federal Bureau of Investigation profilers who use models and probabilities to classify criminals based on historical activity, B2B marketers need tools to enhance their investigative skills. Ultimately, these systems should help marketers answer the question: “Which interest signals add up to the right fit for our business and indicate where we should spend time and money cultivating opportunities?” Traditional data warehouse stalwarts like Informatica, MicroStrategy, SAS, and Teradata have long helped marketers gain insight and draw inferences about the buyer’s journey. Next-generation solutions will have to deliver this information in a form that marketing can take action against. For example, they will choose whether to present a message and which message to present to which buyer at what time, in which context, and with what type of content.

■ Scale through automation and self-learning. Most marketers can manually map the journey of a buying center against one specific purchase decision without tools. Expanding this process across hundreds of offerings, in different geographies and industries, means sifting through mountains of data to find a few key nuggets of insight. To keep this from turning into a science experiment, CMOs should pick solutions that minimize the effort required to build and refine models by processing feedback about how customers interact with marketing activity and grading the results of their program spend. These tools should also incorporate macro factors, like economic, competitive, and market shifts — and micro business strategy changes, like mergers, spinoffs, and product launches — seamlessly into model maintenance and testing.

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Figure 4 B2B Analytics Must Leverage More Unconventional Data Types And Sources


Demographics

Quali�cation criteria

• Company size• Industry• Geography• IT spend

• Budget• Authority• Need• Timing

Unstructured

• Keywords/JavaScript• Press releases• Web content• Blog posts• Videos• Social media• Consumer-generated

content• Job descriptions• Patent �lings

Traditional customer pro�le

Internal

• Contracts• Purchase history• Service records• Call center logs• Licenses deployed• Competitive wins

Structured

• Legal entity maps• Financial statements• Credit rating• Media channels• Property records• Tax �ling• Geolocation• Sensor data• Business

relationships

Behavioral

• Communityparticipation

• Content accessed• Website visits• Registration• Attendance• Cross-channel

exposure• Sentiment• Activity patterns

Expanded data requirements

R e c o m m e n d at i o n s

TURn B2B DaTa InTO PROFITaBLe CUsTOMeR enGaGeMenT

Data analytics let B2B marketers we talked with use facts and statistics to measurably improve returns when they deploy it correctly. Although investing in big data will help CMOs drive business more predictably, they can receive shorter-term gains when they use analytics to:

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■ Prepare sales for battle. B2B CMOs put data analytics to immediate use when they apply purchase propensity patterns to help assess the quality of the forecast and determine which offer to present to which opportunity. Backed by quantitative analysis about how customers pay attention and when, data analytics let marketing deliver content tailored to late-stage deals and help sales spend less time talking about what they sell and more time negotiating or closing business. When analysis uncovers unforecast opportunities around specific regions, buyer needs, or industries, CMOs can use this insight to help sales managers get smarter about where they may need to focus quarterly sales spiffs or to rebalance territories.

■ Eliminate rogue programs and one-offs. As much as they may hate to admit it, every marketer is guilty of signing up for a trade show booth from a show attended last year or sponsoring a charity golf outing because the local sales team begged for it. To eliminate the wasted time and effort associated with these pet projects, CMOs should require their teams to use data to assess the relative value of these spontaneous requests and demonstrate that integrating single activities into an overall program delivers better longer-term results. Investing in systems and processes that aggregate internal, external, behavioral, and unstructured information to understand how buyers buy let SAP reduce one-off tactics in favor of multitouch campaigns that integrate digital and offline tactics to better target prospects, nurture leads, and accelerate deals.

■ Put partner data to work. Second-party data on loan from resellers and distributors for project-specific use can be an invaluable source of buyer journey information, especially when partners act as the frontline for solution delivery and account management. To mine this private network of intelligence about customer endpoints for key insights, CMOs should bring together a dozen or so of their top channel partners — along with their head of partner management — to explore nonthreatening ways to share account and service information to unlock insight that benefits everyone. CMOs who lead here are beginning to work with partners to mine this data and use it to inform how they spend marketing development funds or invest in co-marketing programs — like bundling partner solutions to serve a specific industry — that go beyond simple content repurposing and lead sharing.

sUPPLeMenTaL MaTeRIaL

Methodology

As part of a joint study, Forrester Research, the ITSMA, and VisionEdge Marketing surveyed 424 business professionals from each respective customer base or membership list. For this report, Forrester screened responses to include only those respondents from firms that sell primarily or in part to other businesses, who hold marketing titles or role-level responsibility, and who have

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budget or management responsibility at their firms. For quality assurance, we required respondents to provide contact information and to answer basic questions about their firm’s 2012 revenues, industry, and job title/role.

We fielded the global survey online in June 2013. Respondent incentives included an executive summary of the June 2013 “Increasing Marketing’s Relevance to the Business” ITSMA/VEM/Forrester report and a complimentary copy of this Forrester report.

We provide exact sample sizes in this report on a question-by-question basis. Survey responses are not guaranteed to be representative of the population. Unless otherwise noted, statistical data is intended to be used for descriptive and not inferential purposes.

Companies Interviewed For This Report

6Sense

Bizo

BlueKai

Cisco Systems

Fliptop

HP

Infer

Lattice

Mintigo

Networked Insights

SAP

Superior Essex

Zendesk

enDnOTes1 To deal with customer data management complexity and governance, Forrester has long advised B2B

marketers to seek help from marketing database marketing services providers, especially those like Allant, Extraprise, Harte-Hanks, and Infogroup that maintain a separate focus on B2B. See the June 27, 2008,

“Database Marketing Fundamentals For B2B Marketers” report and see the January 12, 2011, “The Forrester Wave™: US Database Marketing Service Providers, Q1 2011” report.

2 As a hypothetical case, imagine an average $1-billion-plus firm serving customers in 15 industries, across 12 regions, with five product lines, and in four primary stages of the customer life cycle. Multiplying this out, its CMO would face 3,600 variations on buyer journeys to consider in this fairly conservative mix of go-to-market options.

3 Forrester believes that marketers now have the experience and technology needed to put big data to good use delivering actionable insight. To manage the complexity of continuously evolving marketing environments, we believe CMOs must combine the art of marketing — channel coordination based on audience needs and behaviors across the customer life cycle — with the science of marketing— making investment decisions based on desired business outcomes and informed by data insights. See the December 30, 2013, “Predictions 2014: Marketing Leaders Put Insights To Use” report.





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4 In our most recent budget survey, we found B2B marketing execs expect budgets to increase by 6% on average in 2014. While this is good news, their peers expect CMOs to stretch these dollars across a broader set of initiatives that extend beyond campaigns and programs. Technology, data analytics, innovation, and IT support top the list of priorities CMOs must figure out how to fund. See the January 21, 2014, “Focus B2B Marketing Budget Gains On Business Outcomes To Succeed In 2014” report.

5 Despite advances in performance management, many marketers still fall victim to operational myopia when they focus on past performance to defend net-new budget requests or plan future programs. B2B CMOs must move away from reporting outputs and move toward predicting outcomes by leveraging advances in analytics, predictive modeling, and dashboards. See the August 27, 2014, “B2B Marketing Measurement Needs An MBA” report.

6 In 2014, Forrester expects data to form the basis of nearly all successful marketing executions. As the year progresses, CMOs will invest in tying data to planning and to demonstrating marketing’s impact on pipeline health and customer relationship depth. See the December 19, 2013, “Predictions 2014: B2B CMOs Will Juggle Data, Brand, And Organizational Investments” report.

7 Empowered customers educate themselves through professional and peer-created content available online. This newfound control over decision-making forces B2B marketers to put the buyer’s journey at the center of their marketing efforts. Forrester offers a framework for understanding the buyer’s journey and providing a road map for navigating that journey before, during, and after the sale. See the February 21, 2013,

“Rethink Marketing In The Buyer’s Context” report.

8 Traditional customer data management integrates various customer data sources from packaged applications, application logs, and data warehouses to support a 360-degree view of the customer. In practice, these approaches deliver limited insight into their behavior, sentiment, and experiences across channels and touchpoints. To gain competitive advantage, CMOs must invest in multidimensional platforms that use all available information about buyers — including information pertaining to psychographics, social networks, smart devices, geolocation, and Internet usage — to deliver individualized and contextual experiences. See the March 6, 2013, “The Future Of Customer Data Management” report.









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