disparate platforms and the explosion of data 01

Marketing Intelligencethe guide to

Over the past few decades, marketing has evolved from a hunch-based and immeasurable practice to one driven by data, analytics, and software. At the beginning of the century, Eloqua and Marketo led the convergence of email marketing, online lead nurturing, and inbound lead capture to bring the first marketing automation solutions to market. Thanks to marketing automation, today’s marketers can test, score, and nurture leads without having to be engineers. However, today’s marketers face new challenges. Marketers now work with advanced soft-ware and myriad channels to identify new prospects and engage customers. Marketers once againface technical challenges for which the solutions require technical knowledge and engineering background. So what’s next? As the volume and complexity of data available to CMOs and senior marketers grows, which new technology will help them make data-driven de-cisions to engage and convert prospects?

i n t r o d u c t i o n

section i:

section ii:

t a b l e o f c o n t e n t s

disparate platforms and the explosion of dataThe Explosion of Data Frustrating and Disparate Platforms

the emergence of marketing intelligenceFaulty Processes and Solutions Helpless Marketers Select Half-Baked Solutions

marketing intelligence at a glanceInsight to Matched RecordsDetailed Segmentation New Targets Redeployment

section iii:

conclusion

disparate platforms and the explosion of data 01

Disparate Platforms and the Explosion of Data

s e c t i o n i

disparate platforms and the explosion of data 02

In March 2014, Forrester Analyst Laura Ramos released a report that explores new ways forB2B marketers to evaluate large quantities of data to understand customers and identify new prospects:

The concept is attractive in theory, but idealistic in practice; most marketing teams don’t have the capability to merge and analyze the increasingly large amounts of data they now collect. The field of data science has emerged to help organiza-tions formulate insights from these mountains of data to better understand prospects and customers, but data science is still inaccessible and misunderstood by most marketers.

The Explosion of Data

volume

Data at restTerabytes to exabytes of existing data to process

Streaming data, milliseconds to seconds to respond

Structured, unstructured, text and multimedia

Uncertainty due to data inconsistentency, and incompleteness, ambigu-ous, latency, deception and model approximations

Data in many formsData in motion Data in doubt

velocity variety veracity

IBM’s data scientists help us understand the 4 dimensions that define big data – popularly known in the data science community as the 4 Vs.

Data Science refers to the practice of creat-ing and testing hypotheses that explore new ways to interpret the world through data. Data scientists blend their theories from a variety of scientific fields.

Some of those fields include statistics, mathematics, astrophysics, data engineering, pattern recognition, machine learning, and more. They use that data to extrapolate unique and revolutionary insights from large sets of data.

Making sense of big data is a challenge not only because of its magnitude, but also because of its complexity. The value of big data doesn’t stem from the volume of data you aggregate, but rather, from the relationships between the data.

A wealth of insights is available to B2B marketers if they are willing to dig in. Internet exploration, search, smart device usage, content browsing, and business community social activity reveals the twists and turns customers take throughout their lifetime.

fig. 1

disparate platforms and the explosion of data 03

To extract insights from sets of data, data scientists have to take the volume, velocity, variety, and veracity of data into account; they don’t just experiment on data, they merge, organize, clean, and test data as well.

A data scientist’s job starts with the organization and management of architectures to normalize disparate data sets. Then they must merge, de-duplicate, and scrub data for inaccuracies. Data scientists often refer to data preparation as data janitorial work, and once it’s complete, data scientists then model and experiment to recognize trends and relationships between otherwise disparate data.

400,000

140,000data science jobs

but only

professionals

by 2015 there will be

fill themthat are qualified to

Finally, data scientists present results in ways that influence action, such as through data visualizations or predictive analytics tools.

On their own, none of these steps constitute data science; data science is the combination of activities that transforms raw data into action-able insights, and unless you’re dealing with relatively small data sets and unchanging busi-ness goals, there’s simply too much work for a single data scientist to effectively formulate hypotheses from your data.

Some organizations advocate for every marketing team to hire a data scientist, but this is neither feasible nor scalable.

The Harvard Business Review crowned Data Scientist the sexiest job of the 21st century, but according to a 2013 McKinsey report, “The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.”

Marketers want to be data-driven, but increas-ingly find the time-consuming and laborious work required to scale existing processes has become untenable in an age of seemingly end-less data.

sources

collect

process

decide

Before data scientists and marketers can extract value from large data sets, data aggre-gation and engineering teams must merge and organize data to reflect a uniform format.

fig. 2

Both data scientists and marketers categorize data in two large buckets: internal and external. Internal data includes attributes on leads, accounts, and activities collected by web forms, uploaded in static lists, or input by sales reps and stored in CRMs and marketing automation systems. External data includes additional signals about prospects, such as revenues, headcount, online advertising activity, social media presence, web technologies, and more.

External data can offer undeniably valuable clues about buyer behavior and preferences, but due to the volume,velocity, and inconsistent veracity of the data, most marketers simply ignore external data that they deem themselves unequipped in skills and tools to manage.

Frustrating and Disparate Platforms

“Marketing departments with data scientists tend to get awarded an ‘A’ by their employees for using data versus their peers who grade their departments at a ‘C’ or lower.”source: 2013 the team solution to the data scientist shortage by accenture

According to BIZO’s survey of over 800 B2B mar-keting leaders, less than 20 percent believe they are using data well. And for marketers targeting SMBs, the data problem is even worse.

Only 8 percent of marketers have a 360-degree view of their SMB customers. Given the universal challenges that marketers face today, specific to big data and customer insight, this is not necessarily surprising.

And similar to overall big data issues, while mar-keters believe they have good access to transac-tional and customer support data (50 percent), 21 percent admit their customer data is in silos across the organization, while 39 percent only have basic contact details without rich profiles or background information on customers or markets. (CMO Council)

disparate platforms and the explosion of data 04

How effectively does your marketing organization use data?

have nailed itare doing wellare on the right pathare strugglinghaven’t started

4% 38% 40% 18% 2%

disparate platforms and the explosion of data 05

With billions of data points at their fingertips, marketers often prioritize data that’s easilyaccessible and ignore data that requires added investment, time, or processes to access. Be-cause of the complex processes required to fuse internal and external data, most marketers sim-ply ignore the rich insights available in matching their internal data with external data.

As the second funnel above demonstrates, the utopian case is infinite segmentation based on the product, service, customer, and even age of the lead. However, even top tier engineering

targets

segmentinbound and outbound leads

interested

considering

decision

buyer

Yelp ratings, Facebook, Twitter, Accepts Credit Cards, Shopping Cart, 500K-1MM

Credit Score, HealthRevenue, Growing

Using Competitor like existing customers

Learn to resegment next prospect

IndustryDecision MakerRetarget Audience

organizations like Facebook, Google, and Apple – organizations that have internal data science teams – face enormous challenges with keeping data fresh and usable for marketing segmenta-tion purposes. The complex relationships between data don’t easily map to a sales funnel, nor do they fit neatly into ownership buckets within the sales, marketing, and IT departments.

The most forward-thinking marketers have begun to adopt brand new software and other tools to enable advanced customer segmentation and lead generation.

Gartner Analyst Laura McLellan predicts that by 2017, the market-ing department will spend more on technology than the IT depart-ment. Given the expansion of the marketing stack, this prediction isn’t even remotely far fetched.

Traditionally, marketers segment prospective buyers based on buying stage. Marketers can use segmentation technology to predict which prospects will fall into each buying stage. Segmentation opens up the marketing funnel so marketers can explore how new insights impact each stage of the buying process.

fig. 3

As of Q1 2014, there are 947 different marketing technologies available to marketers. Just two years ago this number was only 350, and three years ago, as few as 100 technology tools were available to marketers.source: chiefmartec.com

disparate platforms and the explosion of data 06

evolution of the marketing stack

Before Saleforce brought customer relationship management to the cloud, marketers managed spreadsheets and homegrown software systems.

Before Content Management solutions like-Wordpress made it possible for non-technical individuals to build websites, marketers had to outsource web production and online publishing to expensive external agencies.

Before systems like Marketo and Hubspot were available, marketers bought email-marketing tools like MailChimp and developed custom lead forms with the help of expensive agencies.

The development of every software application that we use to manage the marketing process

today has been prefaced by expensive, complex, and inaccessible solutions.

Most recently, we’ve seen the expansion of marketing automation. Prior to marketing automation, the common complaint of senior marketers was, “How do I keep all these tools operating smoothly? They keep breaking and they’re a disaster to maintain.” Marketing automation has more or less solved the head-aches of managing inbound leads and nurturing those inbound leads. For the marketer looking to embrace Marketing Intelligence, we’re seeing the same behavior. So what’s in the marketing intelligence toolbox?

marketing automation

cloud crm

bi

marketing intelligence

Homegrown On-Site

Data Science IT Disconnect Tools

Creative Teams IT Agencies

Excel MSQL Analyst

The marketing stack is constantly evolving. Just as Cloud CRM, Marketing Automation, and Business Intelligence evolved out of collections of complex tools that solved smaller pieces of a bigger problem, Marketing Intelligence has emerged to stream-line the modern marketing workflow.

fig. 4

disparate platforms and the explosion of data 07

Today’s marketers accumulate collections of technology just to manage data in the same way marketers managed disparate solutions to run campaigns before marketing automation brought campaign management into a single, reliable, and easy to use cloud-based solution.

Maintaining this assemblage of systems presents obstacles that most marketers are not equipped to solve. Many of these solutions require machine learning, vast amounts of fragmented external data, and advanced software. Data science is crucial to marketing departments grappling with the challenges big data presents.

If hiring a data science team was originally a marketer’s solution to building a marketing intelligence platform internally, they ought to

be prepared to build a top tier recruiting organization to deal with today’s

shortage of qualified data scientists.

So how can marketers take advantage of the wealth

of data signals available to them to segment markets, find new prospects, and con-vert more customers without becoming data scientists?

Before it’s presentable as actionable insights within a Mar-

keting Intelligence platform, data goes through a series of complex

processes to aggregate, scrub, orga-nize, merge, and design into user-friendly applications. Data science is at work throughout all these processes.

Marketers use 7 data-driven solutions under the umbrella of Marketing Intelligence:

Lead Scoring Segmentation Marketing Ops Web Personalization Analytics Customer Success Sales Intelligence

fig. 5

disparate platforms and the explosion of data 01

The Emergence of Marketing Intelligence

s e c t i o n i i

the emergence of marketing intelligence 09

This process is unfortunately painful and yields mediocre results. Typically, marketers point to data quality as the fault behind ineffective cam-paigns with dismal conversion rates. However, because this process relies on so many outside applications that require so much outsourced labor, it’s unclear why certain campaigns succeed where others fail. Marketers know this is the case, but consistently tracking and analyzing results correctly is difficult, and in many cases impossible. With more access to data and more channels to reach new audiences than ever

Developing effective marketing intelligence solutions isn’t a matter of fusing disparate solutions into one all-encompassing platform; rather, it’s a matter of rethinking the processes we use to find and convert new customers.

Faulty Processes and Solutions

before, marketers spend more money to bom-bard as many potential prospects as possible without fine tuning messages or remarketing to the right prospects.

A number of B2B marketing and sales organi-zations, such as Groupon or Livingsocial, build massive teams to gain sales traction and convert leads. As prospects grow increasingly inundated with marketing and sales messages, this strategy delivers decreasing rates of success.

buy lists cut hire quality

Dedupe

InfoUSANetProspexInfoFree

segment clean run campaigns

In an effort to market intelligently, most marketers buy lead lists before segmenting prospects and planning campaigns. Dismal campaign success rates may not stem from poor quality data, but rather, from a faulty process that should begin with segmentation, not with lead lists.

fig. 6

Redpoint Ventures partner Scott Raney advocates the construction of a marketing intelligence platform that can use data science to build “an enormous model of a company’s past leads to close deal conversion, and [tie] those deals to all kinds of outside data they can track down. Then… use that model to predict which incoming leads are most likely to convert, and thus, where to allocate salespeople’s time.” Source

The ability to identify which KPIs best affect your historical success rate so you can prioritize current leads has manifested into predictive lead scoring. Predictive lead scoring goes a long way towards constructing an intelligent mar-keting platform, but the problem isn’t just about salespeople researching and calling the wrong leads. We don’t just need to make the sales and marketing funnel more efficient; we need to rethink the way we enter and sort the data that enters the funnel.

Helpless Marketers Select Half-baked Solutions

As higher volumes of partially qualified leads enter the funnel, marketers depend increasingly on technology that ensures only the leads most likely to convert into paying customers trickle down the funnel.

The explosion of data has revolutionized everything we think we know about our custom-ers, but unless we have the tools to determine which data matter, we will continue to add more data to our funnels to increase our chances of conversion.

The past decade has seen broad adoption of inbound marketing across the B2B landscape.

Inbound leads, however, don’t enter the funnel pre-qualified and ready to convert. High quanti-ties of inbound leads tend to bloat the top of the funnel, and lead scoring and external data be-come crucial to the movement of leads through the funnel. A bottleneck emerges between lead acquisition and conversion. As higher volumes of partially qualified leads enter the funnel, mar-keters depend increasingly on technology that ensures only the leads most likely to convert into paying customers trickle down the funnel.

“B2B CMOs need to think about big data, not as a data quality and technology problem but as the way to transition their teams from list managers and campaign number crunchers to custodians of customer insight.” - forrester research

the emergence of marketing intelligence 10

the guide to marketing intelligence | understanding what is important for true marketing intelligence 08

Where predictive lead scoring identifies which prospects in your CRM and marketing auto-mation may convert, this model only applies to warm leads and inbound leads. In addition to Predictive Lead Scoring, marketers still need to buy lists and supplement their best inbound with external data. Predictive Lead Scoring doesn’t allow marketers to break the flawed process, it only aids a portion of the concern: the bottle-neck between lead acquisition and conversion.

Without these components, it’s difficult to know which pieces of your marketing process perform and which do not. Gartner shows that to manage a truly predictive marketing process, marketers need to answer not only which warm leads will convert, but also in which segments their message will resonate best.

What would the solution that answers “What will happen?” and“How can we make it happen?” look like? In the next section, we’ll go through theideal platform that makes marketing intelligence possible.old funnel new funnel

The old marketing funnel is bloated with unqualified leads that slowly trickle through the bottleneck of scoring and nurturing. The new funnel is enriched with qualified leads at every stage of the buying cycle.

To fully reduce the pains of modern marketing and build an intelligent solution, marketers must find a platform that: Matches external data to existing leads and customer records Offers an abundance of methods for segmenting records Provides new leads in top performing segments Identifies old segments and old leads that need to be re-engaged Offers sales reps insights to win more business, which increases overall marketing ROI Is productized so any marketer can use it

fig. 7

the emergence of marketing intelligence 11

disparate platforms and the explosion of data 01

Marketing Intelligenceat a Glance

s e c t i o n i i i

A Marketing Intelligence platform does four things:

Insight into matched customer records Detailed Segmentation New Targets/Leads Redeployment to stale leads and segments

marketing intelligence at a glance 13

Marketing Intelligence PlatformsMarketing intelligence is the layer connected to other platforms like CRM, Marketing Automation, and Web Content Management Systems that leverages data and machine learning to deliver insights on custom-ers and market opportunities, and reduces operational overhead.

Marketing Automation

CRM

Marketing Intelligence

Marketing Intelligence is comprised of seven main functions. While a num-ber of players in the B2B marketing space have developed core competen-cies around one of these seven capabilities, there’s no single platform that delivers an effective solution for all seven areas of marketing intelligence.

Marketo, Demandbase

web personalization

Infer, Fliptop, Lattice Engines

lead scoringRadius, 6Sensesegmentation

NetProspexmarketing ops

InsideView, C9, InsideSales

sales intelligence

Tableau, IBMpredictive analytics

Gainsight, Bluenose, Totango

customer success

New data signals often reveal more about your prospects than traditional firmographics. Matching your CRM data to an external database can reveal which signals are most likely to predict purchase behavior.

Old Record: Email, Phone, Address, City, State, Postal Code

New Record: Primary Contact,Secondary Contact , Primary Industry, Facebook, Twitter, Online Reviews, Website

Effective Marketing Intelligence provides detailed cohort analysis based on past success. Rather than requiring marketers to import new data into their CRM systems, a Marketing Intel-ligence platform will link to your CRM system, analyze the data, and reveal deep insights and patterns based on past success and failure. Mar-keting Intelligence can surface data signals that impact your entire dataset of customer account status, but it’s even more powerful when applied to specific cohorts.

Insight into Matched RecordsWith Marketing Intelligence, you can pinpoint which insights most impact each cohort. A Marketing Intelligence platform can surface in-sights as detailed as, “my best performing cus-tomer segment is businesses between $500k and $1MM in revenue that are active on Twitter and maintain a shopping cart on their website through PayPal.” Gleaning this level knowledge allows marketers to focus on the segments most likely convert into customers.

marketing intelligence at a glance 14

disparate platforms and the explosion of data 01

Once you’ve identified which segments convert the best, you can discover new segments that go beyond your current success history. This is a crucial component of planning go to market strategies for new product launches or new sales territories. For instance, if you know that you perform well with businesses that maintain Facebook pages, you can focus on segments that

Detailed Segmentationare likely to contain high concentrations of Face-book friendly businesses, as opposed to guess-ing which industries are most likely to convert. Testing new industries or attributes that overlap with existing successful segments allows mar-keters to expand reach without wasting dollars on prospects that don’t make sense.

Segmentation allows you to:

Build a customer-centric organization Expand established markets Find niches to compete with bigger companies Identify specific wants and needs of groups Re-position existing products Determine best marketing mix for campaigns Inform content marketing topics

marketing intelligence at a glance 15

“A well-constructed lead list can lower cost-per-lead by 60%. “source: brian carroll, b2b lead roundtable blog

After identifying your best segments and rec-ommending new markets, a Marketing Intelli-gence platform can provide new leads. Most lead generation programs are built around the practice of uploading new leads into the top of the funnel – either through inbound channels or static lead lists – which can result in tons of duplicate data entering your CRM or marketing automation system. A Marketing Intelligence solution is designed to simplify the lead acqui-

Lead Acquisitionsition process so you can find new leads that resemble specific segments. Marketing Intelli-gence essentially renders the practice of static lead list acquisition completely obsolete. If your Marketing Intelligence platform already knows which leads are in your CRM and which types of leads perform well, it can provide a new set of leads that are guaranteed net new; you’ll never enter duplicate data again.

marketing intelligence at a glance 16

Timing is often the single most influential factor in any B2B buying decision, and for today’s informed marketers, may be the biggest contrib-uting factor to a growing collection of endlessly open accounts.

Redeployment

“We had opportunities that were highly qualified to buy Salesforce, and they might have come through the sales process four or five times over a two year period before buying.” gregfiorindo, vp sales, radius, formerly salesforce.com

A Marketing Intelligence platform identifies which opportunities that are queued for re-nur-ture programs are actually perfect potential customers within top performing segments. Why invest in new data and new leads when hidden opportunities are already in your CRM?

marketing intelligence at a glance 17

“All of this (data, technology, and channels) makes marketing enor-mously complex and confusing and difficult to manage, which also makes it that much more of a competitive weapon. Most companies will screw it up, but those that nail it can create massively outsized advantages for themselves.” - Pat Grady, Partner at Sequoia Capital

The era of Marketing Intelligence is upon us. The nexus of data, recommended segments, abundant segmentation options, and integration with CRMs allows marketers to be truly thoughtful about the ways they communicate with customers. The ques-tion ultimately is not, how will all these disparate solutions fit together with large quantities of data, but rather, how can I take advantage of an end-to-end solution to engage with my best segments and my best prospects.

c o n c l u s i o n

disparate platforms and the explosion of data 01

Radius is the marketing platform that empowers B2B marketers with data and insights to reach the right customers. Unlike fragmented data providers and consultative lead scoring solutions, Radius provides vertically-integrated software that incorporates billions of business data points, predictive analytics, and seamless integration into CRMs and marketing automation solutions.

With deep roots in data science, our predictive marketing software is built on the Radius Index, the most comprehensive living-and-breathing data source of U.S. businesses. Our machine-gen-erated insights and intuitive user interface allows marketers to better understand their customers, find more look-a-likes, identify the top opportuni-ties in their CRM, and track & measure success.

radius.com114 Sansome St. STE 1000 San Francisco, CA 94104 info@radius.com @radius

About Us