the predictive power of three: discovery, scoring and ... · discovery, scoring and enrichment in...

The Predictive Power of Three:

Discovery, Scoring and Enrichment in B2B Sales and MarketingHow three key elements in predictive analytics drive demand and lead generation

Sponsored by Leadspace

Today’s Sales, Marketing, and Demand Generation processes leave much to be desired. In particular, a system of record to reconcile and coordinate marketing and sales efforts is missing. Input sources live in separate silos, and they get manually channeled to different (equally siloed) executions. This has two major negative impacts on your business:

• It leaves money on the table, as opportunities fall between the cracks.• It drives up the cost of customer acquisition, as the team has to work

that much harder to connect with the customer.

This one-two punch can slow a business’s growth substantially.

Many teams rely on marketing automation to fill the system-of-record gap, yet capturing all your data sources and aligning them with marketing outputs remains a challenge. All the data sources at your disposal, including:

• inputs from sales prospecting, • list creation, • web forms, • event lead captures, • and syndicated content,

just to name a few, can overwhelm the limited logical capabilities of marketing automation software to translate analytics to action.

Analysis paralysis ensues.Today’s business-to-business (B2B) sales and marketing teams find themselves with excess manual work and few actionable insights from their software investments. Poor results follow from missing context around B2B leads, caused by data silos and ineffective external data use, whether from the web or third parties. A survey by the research consultancy Bluewolf found that just 7 percent of companies are extremely satisfied with their marketing automation systems. 1

Why B2B Sales and Marketing are hindering (your) growth.

Discovery, Scoring and Enrichment in B2B Sales and Marketing 2

Bluewolf found that just 7 percent of companies are

extremely satisfied with their marketing

automation systems. 2

Predictive analytics for B2B sales and marketing teams fills the gap in the marketing stack. Predictive analytics, with its ability to forecast based on existing data, provides an intelligence layer to reconcile inputs and outputs, and it incorporates rich, contextually-relevant lead information from the open and social web. B2B marketing spearheads adoption of predictive analytics, which Forrester Research reports is being implemented or adopted by nearly two-thirds of business decision-makers across all lines of business.3

Predictive Analytics to the Rescue


PredicitiveAnalyticsPlatform

ENGAGEMENTCHANNELS

DATA SOURCES

WEB & SOCIAL

Figure 1. Predictive analytics fills an essential gap in the marketing stack.

“…[P]redictive marketing analytics

can help B2B marketers determine

when to engage buyers, how to

execute cross-channel campaigns most

effectively or where to spend budget.” 4

—Forrester Research

But not all predictive analytics is created equal. In B2B sales and marketing, three key elements — lead discovery, scoring, and enrichment — need to work in synchrony to equip teams with the insights they need to maximize results. Discovery, scoring, and enrichment are symbiotic processes that generate a whole greater than the sum of their parts.

Yet many sales and marketing professionals have an uncertain grasp of what predictive analytics can do for performance, and what’s required to put the technology to work. Do we need a team of Ph.D. data scientists, a 100-node Hadoop grid, or a 500 teraflop supercomputer? Thankfully, no.

With intuitive interfaces that insulate users from the analytics’ complexity, today’s best predictive analytics platforms put the power of machine learning and statistical modeling directly in the hands of non-technical sales and marketing teams. The right solution also improves sales and marketing collaboration with a common foundation that inside sales teams can use for finding new prospects, and marketing teams can use for creating targeted lists and for ranking and enriching leads. Predictive analytics brings a uniform understanding of the customer, which is the basis for an effective discussion between sales and marketing.

This ebook demystifies predictive analytics technology and outlines how and why interlocking lead discovery, scoring, and enrichment are crucial ingredients in the recipe for sales and marketing success.


Discovery, scoring, and enrichment are

symbiotic processes that generate a whole

greater than the sum of their parts.

Spaghetti junctions between disparate sales and marketing applications make it difficult for teams to address top challenges in lead and demand generation. The tangle between inputs (data sources such as inbound leads, lists, prospecting, and events) and outputs (engagement channels such as automated marketing and email, sales force automation, advertising, and content management) is a key culprit in lackluster sales and marketing performance.

Today’s typical sales and marketing environment is an “accidental architecture” that evolved over time. In the 1990s, relatively simple databases were rolled out for sales teams. Those basic sales enablement platforms spawned customer relationship management (CRM) software, with richer functionality and workflows. Then, marketing adopted CRM-like marketing automation applications.

With the steady incorporation of add-ons and custom integrations, we find today the spaghetti junctions that bedevil sales and marketing performance. Even as speed and precision grow more important in our highly digitized, mobile, and social world of rising expectations and attenuated attention spans among prospects and customers, our sales and marketing software suites plod and grind ever more slowly.

Fragmentation makes it difficult to coordinate across channels and processes, which inevitably leads to discrepancies in your data. You have your website, email, and advertising campaigns, conferences, and so on. Each channel tends to have its own organizational group who take responsibility for that channel. These groups try to convert incoming leads into customers via different execution channels, but without a common platform and visibility, results are often underwhelming.

Why B2B Sales and Marketing Needs Predictive Analytics


Only 2.8% of B2B market-ers feel they are effective in

achieving goals. 5

80% of leads are lost, ignored or discarded when marketing hands them to

sales. 6

72% of marketers send unqualified leads to sales. 7

Only 52% of marketers are confident in their data’s

accuracy. 8

2.8%

80%

72%

52%

Teams are left ill-equipped to address the top challenges in lead and demand generation:

• Understanding leads well enough to know what will get their attention 9 • Providing relevant and valuable content to leads 10

• Maintaining a productive workforce to generate and pursue leads 11

• Measuring and forecasting the quality of leads generated• Handing off the right leads from marketing to sales 12

Spaghetti junctions were commonplace in many early technologies—transistor radios, early computers, and telephone-switching systems of decades ago. The advent of the integrated chip cleaned up the mess in those and other markets. The integrated circuit chip has catalyzed innovation across every aspect of enterprise and consumer technology, from server farms and cloud computing to smartphones, tablets, and wearable smart devices.

In today’s B2B sales and marketing environments, predictive analytics is the integrated circuit chip. It can eliminate fragile, high-maintenance connections across fragmented applications to elevate sales and marketing performance. The logic behind the “chip” of predictive analytics in sales and marketing is encapsulated in three key elements—discovery, scoring and enrichment.


Every B2B business today engages in discovery, scoring, and enrichment of some form. Leads are found, they are prioritized in some manner, and they are researched for valuable information that impacts lead qualification, contact, messaging, routing, and so on. The issue that hinders the growth of a business is that these activities are done largely manually, and, more importantly, in a siloed fashion. The most effective demand generation comes from integrated discovery, scoring, and enrichment.

Demand generation becomes “breakthrough,” however, when it capitalizes on predictive analytics. Although predictive analytics is a hot topic in B2B sales and marketing, it’s important to recognize that many vendor platforms billed as “predictive analytics” are only partial solutions. Some may provide data enrichment, but not predictive modeling. Some may offer data aggregation, but not scoring.

The ABCs of Discovery, Scoring, and Enrichment


A comprehensive approach bundles the

three core functions of discovery, scoring,

and enrichment into a single platform.

Figure 2. Discovery, scoring and enrichment provide the foundation for effective predictive analytics in B2B sales and marketing.

A comprehensive approach bundles the three core functions of discovery, scoring, and enrichment into a single platform. This gives the marketer a unified environment that takes in leads from all sources, analyzes them, and makes a plan to turn each lead into a customer. These activities are the heart of demand generation.

What is DiscoveryThe essence of discovery is understanding and identifying your ideal buyer. Predictive analytics minimizes guesswork through intelligence-gathering that zeroes in on potential customers across a broad range of dimensions. The best solutions will give you both prospect modeling based on your best existing customers while supplying B2B prospect lists updated dynamically based on data scoured from social media, blogs, online communities, conferences, and other web sources.

Predictive analytics turns your customer database inside-out to identify key attributes of your best customers and generate an ideal customer profile. It enhances those profiles with external data and builds a model matched against rich prospect lists to identify look-alikes. Discovery goes far beyond basic demographic and firmographic data, such as job title and company revenue, to uncover buying-readiness signals. It also helps you narrow down top company prospects in a given industry and identify prospects within a company more promising than ones you have.

What is ScoringScoring prioritizes leads against a predictive model to estimate which leads are most likely to become customers. The best model incorporates everything you can offer it—your data and your insights (and special knowledge) about your customers, as well as external data from firmographic data providers and an array of web sources. Note that traditional B2C scoring methods fall short in B2B applications, as B2B data sets are substantially smaller than those of B2C. So an approach crafted for B2B is required.

Scoring can be based both on behavior, measuring ever-changing multichannel engagement, such as website interactions and campaign response, and more stable attributes that match an ideal customer profile. Scoring is a critical element in effective B2B sales, as it enables sales reps to concentrate on your most promising individual (or company) leads, while less-promising leads can be targeted with lower-cost outreach such as email nurturing.


“To move from guesswork to

science, leading B2B marketers are using

analytics to make lead scoring more

predictive.” 13


What is EnrichmentEnrichment fills in data gaps to provide insights into a lead’s needs, interests, and intent and plays a crucial role in scoring. Ideally, enrichment blends data from both third-party firmographic-information providers as well as unstructured information culled from the open and social web. Incorporating rich sets of attributes on your prospects and customers enhances the ability of a scoring engine to perform meaningful segmentation, pinpointing your best sales opportunities.

To be effective, enrichment must bring back data on actionable leads. To achieve this, the most up-to-date information is required, which must obviously include free-form data (a.k.a. “unstructured” data) from the web. Hence, the best predictive analytics platforms feature advanced technology to transform unstructured data from the web into a usable format, and use matching and aggregation algorithms to generate the most accurate profile information. Enrichment is best done in real time and on a continuous basis, given rapidly changing indicators from web sources that can affect scoring models. With a sound foundation for ongoing lead enrichment, sales and marketing teams are equipped to identify and pursue optimal leads based on a holistic view that accounts for a wide range of variables.


The best predictive analytics platforms

feature advanced technology

to transform unstructured data

from the web into a usable format.

Discovery, scoring and enrichment each provides value in its own right. When the three work in concert as key cogs in a predictive analytics engine, that value is multiplied exponentially, for both inbound-marketing-oriented organizations as well as outbound-oriented ones.

Why Discovery and Scoring are Better Together Discovering a large number of new leads risks overwhelming your personnel resources who try to pursue all those leads. So you must score, or prioritize, the leads you discover to ensure you increase your pipe. Also, scoring tells you about leads’ fit and how to treat specific leads based on their value.

When you score your leads, you effectively prioritize them. As a result, you’ll find some leads in your pipeline are poor fits and unlikely to buy. You must turn to discovery to replace those leads with better ones, or else you either reduce your pipe or waste your sales resources chasing unclosable deals. The distribution of lead scores in your pipe also informs how you should balance resources between inbound and outbound lead generation.

Why Scoring Needs Enrichment and Vice-VersaScoring your leads requires you feed what data you have about these leads into a model, and the model determines whether these leads are a good fit for your business and are likely to buy. If you don’t have much information about your leads, however, your scoring model may not have enough data to accurately prioritize your leads. Your scoring is only as good as the data you have. So you must enrich your lead data with additional attributes to ensure your scores are as accurate as possible. The more accurate your lead scores, the more likely they are to prioritize your pipe in such a way as to increase conversion rate.

The score alone usually isn’t enough to inform a complete lead routing solution, however. The purpose of scoring is to help make decisions about

How Discovery, Scoring and Enrichment Work in Concert to Drive Results


Your scoring is only as good as the data you

have.

your leads. While the lead’s score is important, you need additional inputs to make decisions. For instance, nurturing tracks, messaging, and territory routing require enriched lead data like industry, job function, and geographic location.

Beyond powering accurate scoring and robust routing, enrichment, when applied to existing lead data, also lets you send new messages and re-engage with your existing leads. In essence, enriched data creates new potential activities for your team, providing new contact opportunities, market segmentations, campaigns, and so on. If you want to act on this enriched information efficiently, however, you must prioritize leads for the finite engagement resources at hand. So you have yet another reason to turn to a scoring model to ensure these activities lead to increased conversion rate.

Why Discovery and Enrichment are SymbioticWhen you discover new leads, you want as much info as you can get on them. This helps you engage them more effectively, as you can access them via multiple contact channels, send them relevant messages, and ultimately increase your pipe. Enrichment lets you properly segment your discovered leads and act on them. After all, the sales and marketing game doesn’t stop at acquiring leads, it’s about engaging with them.

When you enrich your existing lead data, you’ll discover some of the leads in your pipe you thought were good candidates have moved companies or changed roles, making them unqualified to buy. You must turn to discovery to replace these leads with better ones, or else you either reduce your pipe or waste your sales resources chasing unclosable deals. Enrichment also reveals trends in the types of leads you already have, and the types you may be missing and need to augment.


In essence, enriched data creates new

potential activities for your team.

How Predictive Analytics Addresses Top B2B Challenges


14x growth in the use of predictive lead scoring in

three years. 15

90% of users say predictive lead scoring is more

effective than traditional approaches. 16

88% of respondents report they are deriving

value from predictive lead scoring. 17

The past several years have seen rapid growth in predictive analytics adoption across B2B sales and marketing organizations. For instance, the B2B research and advisory firm SiriusDecisions reports a 14x increase in the number of B2B organizations with predictive lead scoring between 2011 and 2014. 14 Of companies surveyed, 90 percent of users agreed predictive lead scoring provides more value than traditional approaches.

Predictive analytics equips both B2B marketing and sales teams with breakthrough capabilities to address top challenges:

Understanding leads well enough to know what will get their attention. Enrichment attaches deep attributes to leads, using demographic, firmographic and behavioral data. Having context on a lead lets you speak to their needs, pain-points, position in the buyer’s journey, and likely next moves.

Providing relevant and valuable content to leads. Once a lead’s job role, industry, behavior and predicted intent to buy is known, content can be properly personalized and targeted for maximum effect.

Maintaining a productive workforce to generate and pursue leads. Predictive analytics powers sales and marketing efficiencies and minimizes manual research work, empowering teams to prioritize leads and use resources effectively.

Measuring and forecasting the quality of leads generated. Predictive scoring tools assess leads against one-another, or against an ideal, while closed-loop analytics feed back to the scoring model. This aids in resource allocation, lead routing decisions, and forecasting of impending pipeline and revenue.

Handing off the right leads from marketing to sales. Predictive analytics helps ensure that marketing routes only the best leads to sales, positioning the organization for the best returns and minimizing fruitless work.

14x

90%

88%

The ideal predictive analytics platform relies on the fundamentals of a virtual database, semantic classification, a universal model, and a scoring model (a.k.a. an “ideal customer profile”). These technologies power the interlocking processes of discovery, scoring, and enrichment.

Semantic ClassificationSemantic classification uses natural language processing and text analytics to extract data points from unstructured web data (e.g., social activity, titles, executive bios, etc.). This is critical for constructing and making comparisons to ideal customer profiles. The advanced capabilities of sophisticated semantic classification provide far greater speed and reach than lead researchers traditionally charged with the task.

Figuring out who’s the decision-maker in an organization requires understanding how roles relate to one another in a business, and how this can vary by industry or by company size. Semantic classification assembles data into meaningful, actionable hierarchies that map the rank and role of leads, as well as relationships between individuals within an organization.

Virtual DatabaseThe “virtual database” serves as a dynamic repository for lead data, updated in real time from structured data sources and open and social web sources, including press releases, blog posts, online forums, news articles, and more. This powerful data repository is called a “virtual” database because it’s not a physical, persistent database. Any physical database containing such a broad swatch of information would be too large to keep updated and accessible in real time. This real-time data source only acts like a database for the user, as it must respond on-demand to user queries.

The Technology Components of a Predictive Analytics Platform


“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.” 18


From those many sources, the virtual database generates an intelligent composite of lead information at the individual and company level. The best solution is distinguished by continuous monitoring of the web to update records nearly instantaneously, ensuring vital information is as fresh as possible.

Universal ModelTo be effective for businesses, semantic classification must be industry-sensitive. To succeed in predictively modeling leads in a specific industry, a machine learning system must understand the terminology relevant to said industry. To do this it needs an industry-specific semantic model, a granular way of understanding vertical-specific lead attributes and terms, most importantly those related to job roles, technologies, and professional expertise.

For a software system to be useful across industries and keep up with evolving terminology in high-velocity domains, it must be able to manage many industry-specific semantic classifications. This is a “universal model,” a dynamic collection of industry-specific semantic models that cover every industry to which the technology has been exposed. A universal model absorbs new domains on-demand and improves over time, as each business that provides data to the model allows it to learn and identify new terminologies.

Figure 3. The virtual database generates ideal customer profiles from a multitude of internal and external data sources.


Scoring Model (Ideal Customer Profile)The lynchpin of a predictive analytics platform is its scoring model, or “ideal customer profile,” which represents your best customers and is used for finding new leads in marketing and sales. This unique model uses activity-based rules, statistical analysis, intent modeling, and subjective input to generate a clear picture of your ideal buyer. With it, prospects are matched against your best customers to identify those most likely to buy.

Ideal customer profiles are created automatically when a predictive analytics platform analyzes your customer base and identifies similar individuals online through social networks, blogs, corporate websites and press releases, as well as third-party data. Ideal customer profiles can be optimized with input from sales reps, and can be tuned for better accuracy, targeted campaigns, or new product launches.


Your scoring model should represent your best customers, which

is achieved using activity-based rules,

statistical analysis, intent modeling, and

subjective input.

Predictive analytics can drive more effective sales and marketing. When you add predictive analytics to your sales and marketing stack, you gain the ability to rapidly identify, objectively evaluate, and confidently pursue new market opportunities. When the predictive analytics platform features interlocking discovery, scoring, and enrichment, your teams are equipped to unleash the power of big data and analytics to transform B2B sales and marketing:

Putting Predictive Analytics to Work


Individuals or companies can each be treated as leads

Every lead can be enriched

Every lead is scored based

on a meaningful benchmark or

archetype

Multiple benchmarks and archetypes are available for lead

scoring

Sales can use prospecting tools

for on-demand lead discovery

Marketing can use targeted list tools for ad-hoc discovery of

campaign audiences

Predictive analytics is most appropriate for mature, data-driven B2B sales and marketing teams. When implemented properly, predictive analytics accelerates sales efficiency, grows pipeline, and increases conversions. When implemented incorrectly or incompletely, predictive analytics can compound confusion, generate incorrect estimates based on bad data, and waste resources without demonstrating lift.

Several elements indicate whether your organization is at a level to benefit from predictive analytics. Naturally, your team must collect and track data on past marketing and sales performance. The basics, such as tracking the full lead lifecycle, identifying trends in the attributes of your target audience and buyer, and reporting pipeline metrics (including Lead-to-MQL conversion rate, MQL-to-Opportunity conversion rate, and Opportunity-to-Customer conversion rate), should be well-integrated into your sales and marketing disciplines. A predictive system needs this foundation of trustworthy data from which to “learn” in order to construct a predictive scoring model.

In addition, core tools such as marketing automation and CRM software should be installed and used consistently across the sales and marketing organizations. Scalable engagement and tracking systems must be in place to capture and capitalize upon the predictive system’s deliverables, which can include leads scores, enriched lead data, and net-new leads.

If you see the above elements at-play in your organization, and your team shares a growth-driven mindset, you may be ready to incorporate predictive analytics into your sales and marketing stack.

Are You Ready for Predictive?


47 KEARNY STREET SUITE 600, SAN FRANCISCO, CA 94108 | 1-855-LEADSPACE | [email protected] | LEADSPACE.COM LDS-WP02-10252015

About Leadspace

This guide is intended to drive clarity for organizations considering or engaging in their first predictive sales and marketing endeavors. The technical and theoretical elements that must come together for a predictive platform to drive B2B sales and marketing results can be dense, and applying the lessons shared in this guide to your business may require assistance from those with experience in the field.

Leadspace is the only end-to-end predictive analytics platform that offers integrated discovery, scoring, and enrichment, leading to real actionability and dramatic improvements in B2B sales and marketing effectiveness. Built from the individual up, the Leadspace platform combines extensive social, web, intent, and structured data to enrich, discover and score in real time both companies and individuals who have the greatest intent to buy.

Leadspace is trusted by over 100 of the leading B2B brands such as Oracle, Autodesk, Microsoft and Five9. The company is based in San Francisco and Tel Aviv and backed by Battery Ventures, JVP and Vertex.

Visit us at www.leadspace.com.

1 The State of Salesforce (Bluewolf, October 2014).2 Ibid.3 New Technologies Emerge to Help Unearth Buyer Insight from Mountains of B2B Data (Forrester Research, July 2015).4 Ibid.5 Enterprise B2B Demand Generation Study (ANNUITAS, November 2014).6 The Demand Gen Marketer’s Guide to the Galaxy (Leadspace, May 2014).7 Ibid.8 The State of Salesforce (Bluewolf, October 2014).9 Top 10 Lead Generation Challenges (RainToday and ITSMA, 2010).10 Ibid.11 Ibid.12 Top 5 Challenges for B2B Demand Generation Marketers (Marketo, October 2007).13 New Technologies Emerge to Help Unearth Buyer Insight from Mountains of B2B Data (Forrester Research, July 2015).14 The SiriusDecisions Predictive Lead Scoring Study Infographic (SiriusDecisions, October 2014).15 Ibid.16 Ibid.17 Ibid.18 New Technologies Emerge to Help Unearth Buyer Insight from Mountains of B2B Data (Forrester Research, July 2015).

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