www.brightfunnel.com [email protected]
90% of the world’s data has been
created in the last two years
1
.
At the heart of this data lies a wealth of insights,
and a new wave of analytics cloud technology has emerged
promising to help us make sense of big data and transform the
way we do business. As a B2B marketer, how will you navigate
this sea of options? In this guide, we’ll explore the current
analytics landscape, in an effort to help you finally gain full visibility
For B2B marketers, the hype surrounding big
data has not yet translated to action. With so
much information available, it’s hard to believe
that more than half of CMOs still rarely
or never use big data to make marketing
decisions
2. It’s an odd truth, considering that
most other functions are reaping the benefits
of data-driven processes, and have been for
some time now. How can marketers get a
grip on this data, make smarter decisions,
and finally get the total picture of marketing’s
impact on sales? The answer, of course, lies
in analytics.
Big data is typically characterized by three
qualities: high volume, high velocity, and
high variety. Because traditional relational
databases aren’t capable of processing the
mass the data being generated, specialized
tools and applications have emerged to
automate analysis and do the heavy lifting.
Backed by this new breed of analytics
technologies, many organizations have
already been able to use the insights gained
to transform their businesses.
While it seems as if Sales, HR, IT, and
Operations have their veritable pick of the
litter when it comes to analytics options,
CMOs still find themselves coming up short.
The good news: the solution for marketers
exists. But, as a marketer, how can you
navigate this sea of new technology to
determine which solution will actually help
you improve marketing’s performance?
In this guide, we’ll explore why marketing—
particularly B2B—must become increasingly
data-driven to succeed. We’ll cover why
marketing needs its own analytics, provide an
overview of the current analytics landscape,
and outline the five must-have characteristics
of a B2B marketing analytics solution. In
doing so, we hope to help you identify the
solution that best aligns with your company
goals, and finally give you the total picture of
marketing’s impact on sales.
BENEFITS OF PREDICTIVE
MARKETING ANALYTICS:
▶
Discover performance trends and
compare against industry averages
▶
Understand campaign influence in
complex, multi-touch scenarios.
▶
Identify revenue levers, make smarter
decisions, and justify your spend
▶
Accurately forecast marketing-generated
revenue
▶
Give boards and executives full visibility
into marketing performance
▶
Encourage better collaboration
between marketing and sales with joint
ownership over the revenue cycle
The Rise of Big Data Analytics
1: IBM: “Big Data At The Speed of Business” 2: Forrester: “The Evolved CMO In 2014”
Today, B2B marketers own more of the
funnel than ever before. Because B2B buyers
prefer to self-educate prior to engaging sales,
marketing now operates at the top, middle,
and bottom of funnel and interacts with far
more people across more touch points.
While a lengthening, complicated sales cycle
presents new challenges for B2B marketers,
a huge opportunity exists. Marketing’s
influence on the buying process is bigger
than ever and—supported by the right
tools—CMOs have the opportunity to drive
substantial revenue and growth.
While sales reps deal mostly with a primary
buyer, marketing owns all of the contacts that
support a sale. A primary buyer is backed
by a team of influencers—superiors, finance,
purchasing, etc. Furthermore, these team
members interact with a wide range of touch
points—website, social, advertising, events,
etc.—and do so long before and after a deal
is closed.
All touch points are tracked and, as a
result, marketers today have more data
than they know what to do with. Between
spreadsheets, web analytics, social,
marketing automation, and CRM, it’s
becoming increasingly difficult to connect
the dots between disparate data sources,
and even more difficult to make sense of it
all. Because marketing is dealing with so
many people across countless touch points,
traditional BI tools are simply not enough.
Marketing needs its own analytics.
Marketing analytics solutions must be able
to attribute campaign performance across
complex, multi-touch scenarios.
Furthermore, it’s critical that a solution be
able to connect the dots between data
produced by all core marketing technologies
(e.g. Salesforce and Marketo). In any instance
where data is siloed, it’s inevitable that
the resulting insights will be skewed, and
impossible to answer questions like:
1.
What did we say, when, and how did we
say it, in which contexts, to get this person
to move from discovery to deal?
2.
What combination of information
dissemination and communication
techniques (both online and live) was most
helpful in driving this to conclusion?
3.
How are these patterns reflected across all
leads, opportunities, accounts, customers,
renewals?
4.
Which dials can we turn to improve the
overall yield and quality?
5.
And finally, which programs will deliver the
leads that turn into deals faster and with
better margins for the business?
Taking advantage of solutions that allow you
to ask and answer these questions is a huge
opportunity for marketers, and can provide
substantial competitive advantage.
Business intelligence, or BI, is a broad category of applications and tools designed to transform raw data into meaningful and useful information. BI technologies are capable of processing large amounts of unstructured data to help identify, develop and otherwise create new strategic opportunities for enterprise business users. The goal of BI is to allow for the easy interpretation of these large volumes of data.
BI can be used to support a wide range of business decisions ranging from operational to strategic, and is most often used cross-departmentally to gauge general business health. Due to the cumbersome, IT-heavy nature of traditional BI platforms, many companies hire dedicated IT employees and data analysts to oversee BI operations, modeling, and reporting.
Today, a new school of BI tools has emerged— characterized by ease of use and elegant UIs that are simple enough for most business users to operate. While it’s certainly possible for organizations to derive function-specific insights with BI, this level of custom modeling typically needs to be built from the ground up. When paired with the right people and modeling, BI software can provide data-savvy enterprises with a competitive market advantage and long-term stability.
Overview: Analytics Cloud Landscape
BEST SUITED FOR
CIO; COO (Enterprise)
EXAMPLES
Salesforce Wave, Tableau, SAP, Oracle, GoodData, Jaspersoft, Qlikview, Domo, IBM: Watson Analytics
AT A GLANCE
ȗ
Ideal for gaging general business healthȗ
Likely requires a dedicated data analyst to oversee operationsȗ
Can require high levels of customizationȗ
Generally don’t offer predictive capabilitiesThe analytics cloud landscape is crowded, and can be difficult to navigate. With so many
options available, it’s important to understand the unique attributes of each solution when
determining which technology is the right fit for you. The following is an overview of the three
most common classifications of analytics clouds:
Business Intelligence
Also commonly referred to as “predictive analytics,” predictive lead scoring is primarily used by
salespeople to better understand customers and prospective customers. It can help better prioritize sales leads, determine which products a prospect would be most likely to buy, nurture contacts who aren’t yet ready to buy, and develop more reliable sales forecasting.
These vendors start with a company’s native sales data, and then add in signals from public sources such as number of employees, revenue and income, credit history, social media activity, press releases, job openings, patents, etc. With this intersection of internal and external data, they’re able to identify common characteristics of the accounts that were won by sales, and score leads so that sales can better anticipate the likelihood of closing each prospect.
Predictive lead scoring can offer huge benefits to sales organizations, and may work well as a supplement to BI tools.
Overview: Analytics Cloud Landscape
(continued)BEST SUITED FOR
VP of Sales; Sales Reps
EXAMPLES
Lattice Engines, 6Sense, Predixion, Wise.io, Fliptop, Infer, Mintigo
AT A GLANCE
ȗ
Ideal for sales leaders and repsȗ
Supplements native data with external signalsȗ
Improves upon automation lead scoringȗ
Often available as CRM add-onsBusiness Intelligence Spotlight: Salesforce Analytics Cloud (project wave)
Salesforce’s recently-announced analytics cloud promises to be “analytics for the rest of us,” and make it easier to access and interpret SFDC reports. With an elegant user interface and the improved mobile app, it’ll be exciting to see how the new tool fares relative to other BI tools when Wave launches on a TBA date.
While a native SFDC analytics integration is certainly appealing, be aware that a lack of predictive capabilities and connectivity boundaries between platforms may potentially hinder the utility of the tool for marketers.
Also referred to as “marketing intelligence,”
marketing analytics solutions help CMOs and their teams gain better visibility into marketing’s impact on revenue. Similar to BI tools, marketing analytics solutions are able to process large amounts of data—structured and unstructured—but specialize in the analysis of data between core marketing technologies—CRM, automation, web, social, and more. With blended data analysis that incorporates all prospect touch points, marketers can identify opportunities and better attribute, plan, and forecast.
Furthermore, marketing analytics solutions generally specialize in multi-touch attribution. With so many touch points inherent in the B2B buyers’ journey, effective attribution is necessary to help marketers understand campaign influence in complex, multi-touch scenarios. Multi-touch attribution lets marketers pinpoint which campaigns performed well, understand why, and determine whether success is repeatable.
Finally, a critical differentiating factor between marketing analytics solutions and other options is the ability to predict. Based on historical performance and machine-learning, predictive solutions can prescribe how investments will most likely translate to sales. With these insights, marketers can identify revenue levers (e.g. “What will happen if we change X?”), and develop strategies that drive towards organizational objectives.
Overview: Analytics Cloud Landscape
(continued)BEST SUITED FOR
CMO; Demand Gen Managers
EXAMPLES
BrightFunnel, FullCircle CRM, Allocadia
AT A GLANCE
