New Technologies Emerge To Help Unearth Buyer Insight From ...€¦ · Entry Into This Space As vendor offerings fall into three categories -- data aggregators, data enrichers, and

New Technologies Emerge To Help Unearth Buyer Insight From Mountains Of B2B DataDetermine Which Vendors To Work With By Matching Your Process Maturity To Desired Business Outcomes

by Laura RamosJuly 13, 2015

FOR B2B MARKETING PROFESSIONALS

FORRESTER.COM

Key TakeawaysBusiness Decision-Makers Are Investing In Predictive AnalyticsNearly two-thirds of business decision-makers are implementing or upgrading predictive analytics solutions today or plan to do so in the next 12 months. To meet this demand, a hoard of new vendors has entered the market with solutions that potentially unlock key insights from growing mountains of customer data.

B2B Marketers Need To Catch Up To The Market And VendorsTechnological capability is outdistancing the ability to absorb process changes, and most B2B marketers have yet to drive results beyond advanced lead scoring. This view is shortsighted -- the real value of predictive marketing occurs when you apply this approach across the customer life cycle.

Use Marketing Process Maturity To Guide Your Entry Into This SpaceAs vendor offerings fall into three categories -- data aggregators, data enrichers, and predictive modelers -- selecting the right solution means you must weigh the relative sophistication of B2B marketing automation processes and team experience against the right level of big data capability you’re ready to absorb.

Why Read This ReportNew technologies that can anticipate outcomes with a significant probability of accuracy can help B2B marketing professionals entice prospects, keep customers engaged, and justify their accomplishments. Yet most B2B marketers aren’t ready to put the full power of predictive analytics into play. Most will sit on the sidelines or fiddle with the technology until vendors and use cases sort themselves out. The time for waiting is over. This report describes the composition of this emerging market to help B2B marketers get the most out of a rapidly changing vendor landscape that will continue to evolve in the near term.

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Table Of Contents

The Power To Predict Heightens The Allure Of Big Data Analytics

Today’s B2B Marketers Lack The Capacity To Make These Investments Pay Off

Technology Can Shorten The Time To Predictive Success

To Pick The Best Course, Match Your Desired Outcome To Process Maturity

Recommendations

Prepare To Take The Predictive Analytics Plunge

Supplemental Material

Notes & Resources

Forrester interviewed 23 vendors and user companies, including 6sense, Avention, Beyond the Arc, EverString, Fliptop, Infer, InsideView, Leadspace, Mintigo, Radius, and Xactly.

Related Research Documents

From Big Data To Actionable Insight: The Role Of Predictive Analytics In B2B Marketing

Predictive Analytics Can Infuse Your Applications With An “Unfair Advantage”

TechRadar™: Big Data, Q3 2014



by Laura Ramoswith Peter O’Neill, Lori Wizdo, Ryan Trafton, Matthew Camuso, and Matthew Izzi

July 13, 2015

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July 13, 2015

© 2015 Forrester Research, Inc. Unauthorized copying or distributing is a violation of copyright law. [email protected] or +1 866-367-7378

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The Power To Predict Heightens The Allure Of Big Data Analytics

Business decision-makers are falling under the heady spell of big data predictive analytics.1 The ability to predict business outcomes more consistently or accurately reduces the chance that decisions will have poor results.2 For example, predictive marketing analytics can help B2B marketers determine when to engage buyers, how to execute cross-channel campaigns most effectively, or where to spend budget. This promise is driving the popularity of data analytics to new heights across the business generally and within marketing specifically. Forrester’s Global Business Technographics® Data And Analytics Survey, 2015 shows that business buyers are:

› Building more predictive systems. Advances in distributed databases and in-memory computing help businesses close the gap between the growing volume of data and the ability to turn that data into forward-looking business insight.3 So much so that 40% of non-IT decision-makers we surveyed say they are implementing, expanding, or upgrading predictive systems at their firms, and another 24% expect to implement one during the next 12 months.4 Not surprisingly, marketing ranks second behind IT on the list of departments currently tapping the big data potential of these systems (see Figure 1).

› Increasing advanced analytic spending. Marketing data analytics systems are no longer emerging, next-generation recommendation engines yet to appear on everyone’s radar.5 Advances in technology that predicts your buyer’s next move are occurring rapidly. And so is investment: Four out of five business decision-makers are prioritizing analytics funding — 45% are increasing spending levels on advanced analytics technologies compared with last year, and 36% are maintaining the same levels.6

› Looking for an edge that can return big results. Predictive models can be powerful and profitable. Models can improve decision-making on both the cost and response sides of the investment equation. In marketing, these results are more immediate. For example, a predictive model that provides a modest 2% increase in consumer direct marketing response rates can yield an almost sixfold increase in profit compared with a similar campaign executed without a predictive model.7



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Figure 1 Marketing’s Use Of Big Data Ranks High Among Organizational Peers

Customer service

Finance

Sales

Operations

Marketing

IT

“What groups or departments are currently using big data/planning to use big data?”

52%

51%

50%

48%

37%

36%

Source: Forrester’s Global Business Technographics® Data And Analytics Survey, 2015

Base: 704 global business decision-makers whose �rms areusing or planning to use big data technologies

(multiple responses accepted)

Today’s B2B Marketers Lack The Capacity To Make These Investments Pay Off

Despite growing enthusiasm for predictive analytics, technological advancements are outpacing the business’s ability to put it to work.8 Realized benefits focus mainly on operational gains, while customer-centered results like shortening buying cycles and increasing per customer purchases rank at the bottom of the list (see Figure 2). B2B marketers are experimenting with analytics, but they haven’t achieved breakthrough advantages because they:

› Have other marketing technology fish to fry. Ten years ago, B2B marketing automation was a novelty. Today, it’s common to find dedicated marketing operations teams knitting together a portfolio of technologies that execute a myriad of digital tactics intended to attract and qualify prospects, convert them to leads, and deliver engaging brand experiences. B2B marketers must prove this investment is paying off, so reporting on results takes priority over predicting where to go next. As a result, data analytics ranks last on the list of top 10 B2B budget spending priorities this year (see Figure 3).9



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› Focus on optimizing sales pipelines. Rooting out waste and inefficiency by automating lead-to-revenue management (L2RM) lets B2B marketers increase pipeline contribution.10 But once they’ve achieved these easy wins, many marketers get tripped up trying to scale suboptimal processes, and they turn to predictive marketing analytics instead.11 This approach is short-sighted: The real value of predictive marketing occurs when marketers apply it across the entire customer life cycle, something our research shows is not yet happening.12

› End up following business technology’s big data lead. While big data technology and science make it possible to predict outcomes more easily, many marketers lack experience with the underlying principles. They depend too much on their technology management counterparts to set the pace. Steven Ramirez, CEO at a top marketing analytics agency, Beyond the Arc, summarized common client situations as follows: “They are investing in setting up Hadoop and aggregating data from the IT side of the house. But then they say, ‘Can you [IT] help us make our marketing better by figuring out what to do with this data lake?’ Marketers aren’t thinking ahead about the hypotheses they want to test or how to use data to advance marketing strategy.”

Figure 2 Reported Benefits Of Data Management Remain Largely Operational

“How has the business bene�ted from the investments in managingdata your �rm has made in the past two years?”

(Top and bottom three responses shown)

24%

21%

7%

6%

Source: Forrester’s Global Business Technographics® Data And Analytics Survey, 2015

Base: 1,200 global business decision-makers

Improved executive decision-making

Improved ef�ciency to meet newbusiness requirements

Increased overall revenues

Brought products and servicesto market faster

Increased the number of purchasesper customer

Shortened purchase time of customers

16%

9% Operationally focusedCustomer focused



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Figure 3 Data Analytics Barely Breaks Into The Top 10 B2B Budget Priority List

Data analytics

Public relations

Digital events: webinars, virtualevents, video

Other

Agency fees

Direct marketing

Website

Content marketing

Digital advertising/marketing

In-person trade shows,conferences, events

10%

9%

9%

9%

7%

7%

4%

4%

4%

Base: 132 B2B senior marketers*

14%

“Approximately what percentage of your 2015 budget will be spent on the following?”(Top 10 responses shown)

Note: “I don’t know” responses are excluded.Source: Forrester/DMN 2014 North American Digital Marketing Online Survey;Forrester’s Q4 2014 North American B2B Marketing Budget Online Survey

*Responses shown are combined from two surveys with identical questions deployed separately todifferent populations of marketers.



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Technology Can Shorten The Time To Predictive Success

Breakthrough advances in data analytics and predictive marketing use cases won’t happen without new technology investments. A surplus of new entrants — and established vendors with upgraded portfolios — are crowding the market to help B2B marketers get started (see Figure 4).13 Be cautious: You must balance the desire to jump into data analytics against the value of setting a strategy that may necessitate wholesale change to your sales and marketing processes. While an early-stage market makes it difficult to determine which capabilities will help you today, needs and vendor solutions are starting to coalesce around:

› Data aggregators. These solutions collect, append, and organize business data so businesspeople can use it to better inform business decisions. Firms like Avention, ReachForce, and ZoomInfo gather data from a myriad of external sources — including via manual research — and aggregate it into company and contact profiles. Others, like Dun & Bradstreet and Infogroup, add research-intensive editorial perspectives to firms or contacts of interest. Most also offer data management services to clean and enrich internal data with their lead/contact/company research. Data aggregation is ideal for firms entering a new market, cleaning up internal data, or supplementing gaps in their customer data architecture.

› Data enrichers. This second group focuses on appending and enriching data used specifically in marketing and sales activity, rather than applying general company/contact information in areas like finance, service, or support. Providers like InsideView and Leadspace crawl the Web, collect feeds, tap dynamic sources like browsing and clickstream data, and extract insight from social and crowdsourced activity. Most offerings haven’t yet crossed into the realm of predictive model building, but they are likely to head in that direction. Consider data enrichers like Demandbase and RainKing when you need to improve marketing segmentation or targeting for sales; create deeper insight into buyer needs and background; personalize messages; or interact with individual buyers in a more business-relevant manner.

› Predictive modelers. The last group is chock-full of new market entrants, none of which have reached critical mass of mind share and market share. Technology innovation and sophistication abound as providers collect thousands of potential buying signals and apply advanced mathematical algorithms to find patterns and match patterns to best-fit criteria. Many add buyer intent and fit information to the interest data collected via L2RM systems (e.g., Act-on, Marketo, and Oracle Eloqua) to predict which firms or contacts represent the best business potential. Since drawbacks include rapidly changing technology, varying degrees of “black box” analysis, and short track records, these solutions best support marketing automation experts looking to close gaps in their demand management capability and to extend it across the entire buyer life cycle.



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Figure 4 An Abundance Of Providers Complicates B2B Data Analytics Decisions

Analyze data

Aggregate data

Acquire data

Vendor’s primary data analytics

approach

Novice Expert

Marketing team’s process maturity

Oracle*Salesforce*

ZoomInfo

ReachForce

Avention*

InsideView

Dun & Bradstreet*

Infogroup

Demandbase

RainKing

Right On Interactive

Fliptop

Infer

Leadspace

LatticeMintigo

Radius

SalesPredict

6sense

EverString

Data aggregators

Data enrichers

Predictive modelers

Madison Logic

Bombora*

*Note: Oracle includes the BlueKai acquisition. Salesforce includes Data.com, which was formerly Jigsaw.Dun & Bradstreet includes the NetProspex acquisition. Avention was formerly Onesource. Bombora wasformerly Madison Logic Data.

“Append-to” data



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To Pick The Best Course, Match Your Desired Outcome To Process Maturity

Selecting the right solution means you must weigh the relative sophistication of your automation processes and team experience against the level of big data capability you are ready to absorb. Depending on the maturity of your lead-to-revenue initiatives, data analytic technologies can help you:

› Fill in the data blanks. If you simply need to get sales working with specific contacts at target accounts, size a market, grow an account, or enrich contact profiles, data alone can be the answer. When UK-based Fujitsu sales teams wanted a more comprehensive view of cross-sell and upsell situations, they joined up with Dun & Bradstreet to integrate D&B business data with five other data inputs to round out potential customer data sets.14 The result? Fujitsu’s market intelligence team was able to configure specific sales reports detailing both financial and market information so reps could engage leads in more relevant, business-oriented discussions.

› Make demand generation more projectable. Despite progress in L2R process maturity and technology innovation, sales and marketing continue to wrangle over the value of lead generation programs. Basing lead scores on behavior alone, as a proxy for interest, is not always the best predictor of readiness to buy (see Figure 5). To move from guesswork to science, leading B2B marketers are using analytics to make lead scoring more predictive. Goodness of fit (determined by matching known criteria to unknown opportunities) and buyer intent (determined by looking for predictive patterns in behavior happening external to brand-managed properties) are the two analytic dimensions new vendors are bringing to the science of improving demand management.

› Increase the precision and relevance of sales interactions. Data analytics supports marketing’s sales enablement charter by focusing messages and conversations on customers and their goals.15 By coordinating display advertising across touchpoints, CSC’s marketing team uses account-based targeting from Demandbase to establish its credibility with individual buyers and start a two-way dialogue that account managers seamlessly integrate into their sales conversations at scale.16 And customer success managers at Marketo use InsideView’s account management software to stay apprised of key account activity such as personnel changes, acquisitions, new funding, and financial performance to make it easier to manage the hundreds of accounts each person oversees.17

› Break into new market opportunities. Moving into a new industry or downstream from enterprise to small and medium-sized businesses (SMBs) can trouble marketers who lack insight about or experience with these new buyers. When FiveStars, a customer loyalty network provider, wanted to better align its marketing execution around the small businesses it serves, it used real-time predictive marketing software from Radius to select market segments with the highest propensity to convert. By concentrating on those segments and avoiding the cost of broadly scoped campaigns, FiveStars engaged its addressable market and achieved an initial twentyfold increase in marketing-driven revenue.



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› Predict the next best action to take with any customer. Businesses that sell directly to consumers collect vast amounts of purchase data, and leading marketers use this data to predict the next best offer to put in front of shoppers. Until recently, it was difficult for B2B marketers to get the volume of data and experience needed to replicate this. Scott Broomfield, CMO at incentive compensation solution provider Xactly, overcame this by using real-time behavioral intent scorings from 6sense to distinguish prospective opportunities that are active in the purchase cycle from those just researching or investigating options. This allows Xactly to target more directly, increase close rates, and shorten sales cycles, which collectively lower customer acquisition costs.

› Optimize customer interactions across their lifetime. To put big data to work converting demand, start by identifying ideal customer attributes and matching those to patterns found among unqualified leads or undifferentiated traffic.18 Predictive marketing analytics newcomer EverString is helping customers like 8x8, Apttus, Infusionsoft, and Zenefits analyze their CRM and marketing automation databases to determine what distinguishes their best customers from the rest. These vendors then apply both explicit rules and discovered patterns to predictably identify demand across all stages of the purchase process, both in and out of the funnel.



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Figure 5 Analytics Better Aligns The Buying Stage With Lead Scores

Explore

Use

Ask

Engage Discover

Buy

Explore

Discover

Buy

Interest Fit Intent

What it showsabout buyerquali�cation

Action with leadscoring only

Action with predictivemarketing analytics

Not an opportunity Continue to track,but don’t yet engage

Accelerate nurturingor send to sales as“quali�ed”

Early discovery Target with industry — or segment-based online ads to judge interest and engage

Ignore as "not yetready" for nurturing

Late explore Personalized email with focus oncase studies for comparison tools

Standard nurtureemail

Discovery toearly explore

Telesales outreach via email thenfollow-up phone call to offerassistance

Ignore as “not yetready” for nurturing

Buy — looking todecide soon

Route to sales as quali�ed/ready to close

Standard nurtureemail

Availability ofinformation aboutthe buyer’s . . .

Buy — workingwith competitor

Personalized email with emphasison competitive takeaways orcomparison points

Ignore as “not yetready”

Analytics better aligns the buying stage with lead scores5-1

Data analysis should consider interest, �t, and intent5-2

Behavior-based leadscores don’t surfaceprospect �t or intent.



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Recommendations

Prepare To Take The Predictive Analytics Plunge

B2B marketers are just beginning to write the first chapter in the predictive marketing analytics book. The biggest business impact will happen when marketers learn how to use data analytics to simultaneously target markets efficiently, streamline pipeline conversion, retain customers, grow lifetime account value, and turn loyal customers into advocates. To prepare for this eventuality, B2B marketers must:

› Model ideal account and lead opportunities. Most B2B marketers would be embarrassed to admit how little they know about how customers really use their products and services.19 To kick off predictive marketing analytics correctly, sit down with sales, support, and service delivery to talk about how ideal customers differ based on their key characteristics (fit), the way they look for solutions (intent), and their interest in what you offer (interest). Concentrate on fit characteristics, since company/buyer facts tend to change less frequently during the purchase process.

› Anticipate what success looks like. To use advanced analytics well, you don’t need to start with a hypothesis, but you do need to have an objective to inductively format the question — such as, “Which customer is most likely to respond to this offer?” — and let the models and algorithms uncover the patterns in the data that answer the question.20 Starting with overall company objectives, B2B marketers should look beyond lead scoring to prioritize the list of business questions to tackle with new data analytic approaches.

› Work with technology management to size architectural requirements. B2B marketers can’t build advanced analytic systems alone. Developing the business discipline and technology needed to harness insights and consistently turn customer data into action — what Forrester calls systems of insight — requires close collaboration with your technology management counterparts.21 Marketing’s job is to define where and how to embed insights discovery in visualization tools or marketing applications — for example, to help industry, product, or content marketing managers determine which message or campaign element will connect better with which audience to drive interest, preference, and purchase.

› Use insight to create more valuable sales conversations. Customers expect salespeople to be experts on their businesses and the market conditions in which they operate. Having insight into account specific issues, and empathy for buyers’ challenges, makes sales conversations more business relevant and welcome.22 To get there, focus your initial analytics endeavors on helping sales not only pursue the most promising opportunities, but also apply this deeper understanding of their unique client needs to what your brand can promise.



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Supplemental Material

Survey Methodology

Forrester’s Global Business Technographics® Data And Analytics Survey, 2015 was fielded to 3,005 business and technology decision-makers located in Australia, Brazil, Canada, China, France, Germany, India, the UK, and the US from SMB and enterprise companies with 100 or more employees. This survey is part of Forrester’s Business Technographics and was fielded from January 2015 to March 2015.

Each calendar year, Forrester’s Business Technographics fields business-to-business technology studies in 10 countries spanning North America, Latin America, Europe, and Asia Pacific. For quality control, we carefully screen respondents according to job title and function. Forrester’s Business Technographics ensures that the final survey population contains only those with significant involvement in the planning, funding, and purchasing of business and technology products and services. Additionally, we set quotas for company size (number of employees) and industry as a means of controlling the data distribution and establishing alignment with IT spend calculated by Forrester analysts. Business Technographics uses only superior data sources and advanced data-cleaning techniques to ensure the highest data quality.

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Forrester’s Global Business Technographics Data And Analytics Survey, 2015 includes many additional questions and parameters by which you can analyze the data contained in this report. If you wish to subscribe to Forrester’s Business Technographics services, please contact your account manager or [email protected]. If you are an existing Technographics client, please contact your data advisor at [email protected]. We can provide additional insights about global data and analytics decision-makers’ investment, process, and change management plans related to advanced analytics, big data, and other data-driven initiatives.

The Forrester/DMN 2014 North American Digital Marketing Online Survey collected data from 1,133 marketing professionals from the Direct Marketing News (DMN) membership list. In conjunction, Forrester launched its Q4 2014 North American B2B Marketing Budget Online Survey and collected responses from 103 marketing professionals. For this report, Forrester screened responses to include only those respondents from firms that sell primarily or in part to other businesses, who work at companies with 100 employees or more, and who have budget or management responsibility at their firms. For quality assurance, we required respondents to answer basic questions about their firm’s 2014 revenues, industry, and job title/role.

The surveys were fielded in October and November 2014. Respondent incentives included a complimentary copy of this report. Exact sample sizes are provided in this report on a question-by-question basis. Survey responses are not guaranteed to be representative of the population at large. Unless otherwise noted, statistical data is intended to be used for descriptive and not inferential purposes.

Companies Interviewed For This Report

6sense

Avention

Beyond the Arc

Bombora

Data.com (now part of Salesforce)

Demandbase

Dun & Bradstreet

EverString

Fliptop

Infer

Infogroup

InsideView

Lattice

Leadspace

Madison Logic

Mintigo

Radius

RainKing

ReachForce

Right On Interactive

SalesPredict

Xactly

ZoomInfo



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Endnotes1 Forrester defines predictive analytics as “Techniques, tools, and technologies that use data to find models — models

that can anticipate outcomes with a significant probability of accuracy.” See the “Predictive Analytics Can Infuse Your Applications With An ‘Unfair Advantage’” Forrester report.

2 Predictive analytics helps business decision makers make better decisions, avoid risks, and create differentiated and more individualized customer experiences. Predictive models lie in your data but must be ferreted out. That’s what predictive analytics is all about, and every B2B marketer should know what predictive analytics is about. See the “Predictive Analytics Can Infuse Your Applications With An ‘Unfair Advantage’” Forrester report.

3 When it comes to big data, an exclusive focus on Hadoop misses other ways of satisfying businesses demands to get more insight from more data more quickly. Forrester’s TechRadar™ methodology tracks the maturity and trajectory of 20 different technologies across the entire data life cycle and can give B2B marketers insight into the breadth of options available to discuss with their technology management counterparts. See the “TechRadar™: Big Data, Q3 2014” Forrester report.

4 Source: Forrester’s Global Business Technographics Data And Analytics Survey, 2015.

5 While recommendation technologies have been around for a while, the influx of big data opens up the potential for a wealth of additional insight about prospects and customers that can make recommendations even smarter. In 2013, Forrester began tracking what we called a “new breed of recommendation engine” geared at helping marketers tap into new data sources to identify their best prospects and engage them in the right context. Mintigo and Leadspace, firms that appear in this report, were included among those reviewed at the time. See the “Emerging Technologies For CMOs To Watch: Q3 2012” Forrester report.

6 Source: Forrester’s Global Business Technographics Data And Analytics Survey, 2015.

7 Using predictive models, our comparison shows that direct marketers can reduce mailing distribution costs by three quarters while tripling response rates. The big drop in production costs more than compensates for the 25% reduction in net buyers, and increases the gross profit by 575%. See the “Predictive Analytics Can Infuse Your Applications With An ‘Unfair Advantage’” Forrester report.

8 In our recent complementary research, we found few early adopters making progress in predicting business opportunity beyond the customer acquisition stage. While improved lead analysis and scoring shortens sales cycles and eliminates time wasted on opportunities not yet ready to close, we believe the real value of predictive marketing occurs when marketers apply it across the entire customer life cycle. See the “From Big Data To Actionable Insight: The Role Of Predictive Analytics In B2B Marketing” Forrester report.

9 At 4% of expected 2015 budgets, data analytics still occupies a relatively small share of spending, but at a much greater level than 2014’s mere 1% share. See the “Direct Your 2015 B2B Budget Gains Toward Creating Exceptional Customer Experiences” Forrester report.

10 Firms that have implemented lead-to-revenue management contribute 44% of the sales pipeline, while firms that have not adopted L2RM contribute only 34%. See the “The Forrester Wave™: Lead-To-Revenue Management Platform Vendors, Q1 2014” Forrester report.

11 After bagging impressive early wins, L2RM pioneers find it hard to sustain ongoing improvement. The root causes are numerous, but the primary one is the failure to re-engineer customer engagement strategies, which requires the simultaneous re-engineering of the end-to-end marketing and sales process. See the “Lead-To-Revenue Pioneers Discover It’s Not Demand Generation On Steroids” Forrester report.




















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12 Using customer insight to retain and serve current customers can have a big impact on the business and on marketing’s standing as the steward of lifelong customer experiences. B2B marketers can use predictive analytics to help their companies better understand buyers and enhance their longer and more valuable journey from customer to advocate. See the “From Big Data To Actionable Insight: The Role Of Predictive Analytics In B2B Marketing” Forrester report.

13 Availability of new data sources and delivery models provides unprecedented insights into customer and partner behavior and improves your capacity to understand and optimize business processes and operations. This new data economy also brings disruptive change to firms mired in an obsolete view of how data flows through an organization. The key is to inform a new data strategy by incorporating external data into your business intelligence and decision-making. See the “Navigating The New Data Market Landscape” Forrester report.

14 Source: “Fujitsu Easily Delivers Sales Intelligence Reports with Insight from Dun & Bradstreet,” Dun & Bradstreet (http://www.dnb.com/lc/sales-marketing-education/fujitsu-delivers-sales-reports-using-dnb-D-U-N-S-number.html#.VXTZvc9VhBc).

15 With buyers completing a majority of purchase decision-making before they contact a rep, effective sales enablement must help get the company’s brand promise and value proposition in front of buyers earlier — regardless of whether they come from marketing, sales, or another department. To do this, B2B marketers must work with their sales executive counterparts to rethink sales enablement around the customer life cycle. See the “From Priming The Pipeline To Engaging Buyers: The B2B CMO’s New Role In Sales Enablement” Forrester report.

16 Source: “CSC Shares Best Practices for Target Account Engagement and Conversions,” Demandbase (https://www.demandbase.com/case-study/csc-shares-best-practices-for-target-account-engagement-and-conversions/).

17 Source: “Marketo Grows Accounts and Prevents Churn,” InsideView (http://learnmore.insideview.com/rs/insideview/images/CaseStudy-Marketo.pdf).

18 By profiling fit and analyzing the digital footprints that their best customers leave behind, B2B marketers can more accurately map the buyer’s journey and use technology and analytics to predict where the next best business opportunities will show up. See the “B2B Marketing’s Big Data Destiny” Forrester report.

19 If you look at a couple dozen B2B websites or so, as we did, you’ll find most talk more about their products and services than about what customers want and need and what they do with their products. As a result, 26 out of 30 B2B websites we reviewed failed to pass our basic customer engagement assessment. See the “Brief: B2B Content Fails The Customer Engagement Test” Forrester report.

20 Demanding customers and competitive pressures require firms to treat insights — not just data — as a business asset. Top firms are building systems of insight that combine business discipline and technology to harness insights and consistently turn data into action to better manage these assets. See the “Digital Insights Are The New Currency Of Business” Forrester report.

21 The holistic process of creating systems of insight lets firms systematically implement, embed, and test digital insights in the context of tangible business actions. By adding developers and business practitioners to the insights team, B2B marketers can be in a better position to insert tested insights into their everyday work to improve customer engagement. See the “Digital Insights Are The New Currency Of Business” Forrester report.

22 The primary idea behind sales enablement is relatively simple: Create the content, develop the skills, and provide the tools required by your sales force to have valuable conversations with increasingly demanding, more informed, higher-altitude buyers. Forrester has identified six principle business goals that increase the effectiveness of these reciprocal interactions across their various sales channels. See the “The Sales Enablement Execution Landscape” Forrester report.












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