Post on 14-Aug-2020
transcript
5
Amp Up Your Marketing Automation
With Predictive Analytics!
5 Ways Progressive Companies Are Unlocking The Power of Marketing Automation By Adding Predictive Analytics To Scoring, Nurturing and Data Integration
Presented by Sponsored by
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INTRODUCTION:
Adding Deeper Intelligence To Automation
Marketing automation systems have emerged as
essential engagement tools for many organizations.
By enabling marketers to efficiently organize and deliver
campaigns and also track the impact of those campaigns
across different channels, these systems allow marketing
teams to be more efficient, productive and smarter about
the interests of buyers.
However, today’s marketers also have a growing
opportunity to track buyer behavior across both inbound
and outbound channels, and to tap into a variety of
structured and unstructured data sources to spot
likely buyers.
This convergence of predictive analytics and
marketing is occurring at a time when marketing teams
are being asked to increase their contribution to the sales
pipeline. Given rising expectations for an increased flow
of qualified leads and opportunities, many top companies
are realizing that they need deeper intelligence
in order to keep up with shifting buyer behavior.
In addition, while marketing automation systems provide
great historical insights into the behavior of prospects,
many companies are realizing that this backward-
looking point of view fails to provide useful predictions
about what those prospects are likely to do next.
Illustrating the need for and potential impact of forward-
looking intelligence, Demand Gen Report recently polled
marketing automation users and found:
u 62 percent of marketers identified better predictability
of lead performance as one of their top wish-list items;
and
u 42 percent pointed to the inability to integrate
data across platforms as an issue hindering
their performance.
62%
42%
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As marketing evolves from a cost center to a revenue driver, companies that
have successfully implemented CRM and marketing automation are now looking
to big data analytics as the third pillar in their engagement strategy.
CRM + Marketing Automation + Analytics = The New Triad for Pipeline Optimization
1990s-2000 2000-2010 2010-2020
CRM systems emerged as a must-have for
companies large and small. CRM helped
companies build and manage contact
databases, and the systems also forced
companies to think more about the number
of new leads they were driving for their
sales teams.
Marketing automation systems quickly
became a staple for companies to digitally
engage their databases. The tools provided
new efficiencies and ways for companies to
track the historic behavior of the prospects in
their contact databases.
As progressive companies look for ways to make
their marketing results more predictable by
increasing conversion rates of sales-ready leads,
they adopt big data tools to identify likely buyers.
Using advanced software and algorithms, these
companies now have a deeper intelligence into
which products and services their prospects are
likely to buy next.
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Industry analysts stress that adding a more
predictive layer to marketing automation is becoming
critical as marketing teams look to get closer to prospects
and identify buying triggers and market trends across
different data sources.
“Predictive modeling can be extremely beneficial for
B2B lead gen marketing,” said Jim Lenskold, author of
Marketing ROI: The Path to Campaign, Customer and
Corporate Profitability. ROI improves greatly as more
of the marketing investment is directed to reach those
prospects more likely to convert and more likely to
have high value.
This ebook will offer insights into how marketers can
maximize the power of their marketing automation
systems through the use of predictive modeling and big
data, looking specifically at five key areas:
Lead Scoring
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Lead Nurturing
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Progressive Profiling
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Target Segmentation
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Data Integration
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Tracking The Maturity Curve Of Marketing Automation
While some B2B marketers still view marketing
automation as “cutting edge,” the reality is the
technology has been available for more than a decade.
Eloqua, founded in 1999, is one of the pioneers in the
marketing automation space, and many companies have
been using its solutions for more than five years now.
At this point, the technology is moving to the higher end of the maturity cycle.
Marketing automation by the numbers:
u The Gartner Hype Cycle for CRM Sales, released
in July 2012, categorized Lead Management in the
“Slope of Enlightenment” stage, and predicted the
technology would reach the “Plateau of Productivity”
within two to five years.
u Other research has put the estimates for the installed
base of marketing automation even higher. Forrester’s
The State of Lead-to-Revenue report from July 2012,
reported 45 percent of B2B enterprise marketers had
marketing automation in place, and a recent survey
from Demand Gen Report found 54 percent of
respondents were using automation solutions.
While these industry analysts agree that automation
software has increased the productivity and efficiency
of marketing teams, they point out that many users have
struggled to see the full impact of these tools on their
pipeline. That’s because many companies rely on their
marketing automation tools’ outbound capabilities to
reach large audiences with email messages, but they still
fall short in their ability to track and learn more about the
behavior and preferences of their prospects.
Illustrating this point, SiriusDecisions reports that
85 percent of marketing automation users are not
using the technology to its full potential.
As marketing automation technology reaches a tipping point in adoption and maturity, many companies now push harder on their marketing teams to increase their pipeline contributions.
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automation systems provide these organizations with
useful historical reporting on their prospects’ past
behavior, predictive analytics allow companies to identify
the triggers that indicate what and when a prospect is
likely to buy next.
“CRM and marketing automation systems are effective for
reporting, but marketers now need to track performance
and purchase trends,” said Jon Russo, CEO of the
consulting firm B2B Fusion Group. “It comes down to
people and information. Ideally, you want to find the path
that your best customers walked, and then replicate it
through predictive analytics.”
As marketing automation technology reaches a tipping
point in adoption and maturity, many companies now
push harder on their marketing teams to increase their
pipeline contributions. Despite the fact that many
companies have had marketing automation in place for
several years, marketing’s average contribution to the
sales pipeline has remained stagnant between
20 percent and 30 percent.
According to a March 2013 report from research firm
CSO Insights, marketing supplies only 30 percent
of sales leads for B2B companies. In addition, the
research found more than 47 percent of those
surveyed believe the quantity and quality of those
leads still need improvement.
In order to drive higher conversion rates of leads
into revenue and move the needle on marketing’s
contribution to the pipeline, progressive companies are
supplementing their marketing automation systems with
predictive analytics solutions. While CRM and marketing
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5
Taking Marketing Automation To The Next Level
According to research from Demand Gen Report,
only about one-third of users say they are effectively
using advanced tools, such as lead scoring and lead
nurturing. In reality, a key reason that companies have
struggled to succeed with these approaches is that they
currently have limited intelligence and visibility into
buyers’ changing needs.
In this section, we’ll look at five core areas where
progressive companies are unlocking the power of
marketing automation by adding predictive analytics.
Marketing automation has driven efficiency for marketing teams, increasing their ability to reach large audiences with outbound campaigns, but many users have struggled to realize the full promise of these systems.
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The concept of lead scoring — identifying and ranking
the best prospects for sales to engage — is one of the
most appealing features of a marketing automation
system, as this process promises efficiencies for both
marketing and sales.
However, lead scoring is often one of the most
challenging parts of marketing automation to
successfully implement. A key part of the problem
is that most companies work from assumptions —
not from hard facts — concerning their prospects’
behaviors and intentions.
For example, a marketing team will often arbitrarily give a
certain amount of points or weighting to prospects based
on either their profile (title/company/role) or activity
(watching a demo or visiting certain pages on the
company web site). This approach to scoring requires
guesswork and assumptions, and marketers struggle
to identify real signals.
By adding predictive analytics, companies can use the
power of big data to actually know which prospects are
most likely to buy and target those accounts. Predictive
analytics also allow these companies to pinpoint which
products or services customers are likely to buy next.
These predictive analytics tools — which work in a
similar fashion to the recommendation engines used
by e-commerce giants such as Amazon and Netflix —
allow B2B marketers to prioritize prospects based
on precise buying triggers.
By adding predictive analytics, B2B marketers can prioritize prospects based on their buying triggers.‘
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Adding Real Buying Signals to Lead Scoring1
Predictive Scoring IN PRACTICE Historically, a scoring system for an office supply company would have prioritized prospects based on broad profiles such as “CIOs” or “office managers.” However, by adding deeper algorithms that incorporate internal and external data sources, one large office supply company and Lattice customer can now set more detailed priorities, such as “companies that have announced plans for expansion or a new round of funding.”
Using data from social media and other external sources, this Lattice customer also identified companies that are likely buyers for office furniture, laptops, wireless networking equipment and other items.
Where traditional lead scoring would have only targeted companies based on demographics, this company’s marketing and sales organizations can now tap into intelligence gleaned from big data to identify businesses that are likely to purchase specific products and services in the future.This predictive ability eliminates the guesswork from scoring and focuses the customer’s efforts on prospects with immediate budget and buying needs.
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Moving Lead Nurturing Beyond ‘Set and Forget’
Lead nurturing is another critical marketing automation
function that many marketers struggle to use effectively.
While all professional marketers see the value in
keeping in touch with buyers and educating them
throughout the buying cycle, most nurturing campaigns
have been limited to sending out a series of static
messages or offers to prospective buyers based on
lead stage.
The “if/then” workflow settings within marketing
automation systems can make a series of offers
appear more relevant based on past interest, but this
approach cannot actually identify a prospect’s
changing buying behaviors and needs. The reality
is many marketers using automation systems fall into
the “set and forget” trap, where they set up a nurture
program to send a new content offer every two weeks,
without recognizing or understanding a prospect’s
changing business needs.
By tapping into the power of big data, combined with
predictive analytics, marketing teams can send targeted
messages based on a buyer’s latest hot-button issues.
Rather than simply sending a series of generic content
assets or offers, marketers can send dynamic messages
based on recent events in a prospect’s business life —
such as an acquisition, expansion or relocation.
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Dynamic Nurturing IN PRACTICE
Security breaches are a serious matter for
companies of all sizes, and they immedi-
ately create a heightened need for updated
and often upgraded security solutions. One
information management provider and Lattice
customer is now tapping into big data with
predictive analytics to identify and reach out
to companies that have had a security breach
within hours of the event. By offering a free trial
on the latest cutting-edge security solutions
while it is top of mind for the targeted
company, one company has dramatically
increased its open, conversion and
close rates.
One recent example illustrates the power of big data
and predictive analytics. The marketing team for a
network equipment company used this approach to
identify the predictive triggers associated with buyers
of the company’s routers and switches. After using
predictive analytics to determine that a recent real
estate transaction was an indicator of a future purchase,
the provider sent a personalized message to targeted
prospects directly referencing their need for new
equipment. This intelligence made the company’s
nurture programs more dynamic and tied them to
a specific need and triggering event for each prospect.
The resulting customer data illustrates how powerful
this approach can be: company relocations triggered
a purchase of networking equipment in 90 percent of
the cases studied.
The power of big data enables B2B marketers to identify a buyer’s latest hot-button issues. ‘
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Big Data and Progressive Profiling:
A Smarter Approach for Marketers
The process for progressing a prospect to a qualified
sales lead within a marketing automation system typically
calls for asking that prospect to continually respond to a
series of offers. In the process, the prospect gives a little
more information with each offer.
Most automation systems accomplish this with a feature
called progressive profiling. Progressive profiling
enables marketers to automatically populate or hide some
of the data fields in a form and then ask for a little more
information over time. This helps improve conversion
rates on qualified leads by limiting the number of
questions a prospect is required to answer on each form.
However, many marketers have found that progressive
profiling is really only a patch to the problem of collecting
accurate data on a prospect over time, rather than asking
a lot of questions at once.
The reality is that a bigger problem still exists — one
which marketing automation systems can solve — and
that is the lack of forward-looking intelligence to
support marketing and sales activities. Buyers “tune out”
companies that ask too many questions without really
learning anything useful about their needs, and marketers
are frustrated when that dissatisfaction results in lower
conversion rates and fewer qualified leads.
Big data analytics enables marketers to reduce their reliance on progressive profiling by drawing upon a wealth of sources to gather detailed and up-to-date intelligence on their prospects.
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Big data analytics offers a way out of this dilemma by
essentially allowing marketers to reduce their reliance
on progressive profiling by drawing upon a wealth of
sources to gather detailed and up-to-date intelligence
on their prospects.
By adding predictive analytics, marketers are able to
access “knowable” information on a prospect’s
industry, location, annual revenues, number of
employees, installed base of hardware, etc.
There is a growing expectation that companies should
already know the basic information, so asking for basic
information only frustrates prospects and customers.
Rather than burning a valuable touch with a contact
by asking for basic profile information, progressive
organizations are using each outreach to address triggers
that have shown a propensity to buy. This intelligence
has often changed the types of questions marketers are
including on gated forms, such as: are you planning to
relocate offices in the next 90 days? or are you planning
to add new employees over the next quarter?
For example, rather than asking the standard profiling
question of “number of employees,” the marketing
department could ask questions around plans for office
expansion. This intelligence would more likely point to a
trigger or purchase intent, because new office space
often correlates for a need for new equipment.
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Modeling analytics identify the profiles of those leads that successfully convert — taking into account the types of buyers that are receptive to the solutions offered and the marketing and sales messages being delivered.
— Jim Lenskold
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Another major selling point for marketing automation
systems is their ability to allow companies with longer,
complex sales cycles to track shifts in behavior or interest
among their customers and prospects.
Unfortunately, while these systems can track buyer
behavior over time, they still fail to help marketing and
sales teams identify “trigger events” that indicate a
high likelihood to purchase. In fact, research from CSO
Insights found that 48 percent of all companies need to
improve their ability to recognize these trigger events.
Trigger events can include a prospect changing jobs, a
company or office relocation, or the deployment of new
technology. For companies that have long lead-to-close
cycles, the ability to identify these events quickly and accurately is critical.
Because most trigger events are dynamic, however,
they will not show up in the historical view of a prospect’s
behavior within a marketing automation system. That’s
why leading companies are using big data tools
to identify trigger events and identify buying patterns that were not visible to them previously.
This practice is especially useful because it gives
companies the ability to develop what are referred to
as look-alike prospect models. By tying together, for
example, the predictive attributes of companies that
have expanded their office space, added employees or
deployed a new cloud computing solution, big data
applications can identify other prospects with the
same attributes — and the same likelihood to make
future purchases.
Gartner analyst Adam Sarner recently predicted this
ability will emerge as a significant competitive differentiator among companies. “Marketers that do this
right (fewer than 20 percent of marketing organizations
today) will see their marketing messages receive, at
minimum, five times the response rate of non-targeted
push messages,” Sarner wrote. “Event-triggered
marketing enables relevant offers to customers rather
than traditional outbound blast campaigns, which
causes major customer contact fatigue.”
Author Jim Lenskold has also emphasized that
look-alike modeling will significantly improve
campaign results. “There is a great opportunity to
improve marketing effectiveness using look-alike
modeling. Modeling analytics identify the profiles of
those leads that successfully convert — taking into
account the types of buyers that are receptive to
the solutions offered and the marketing and sales
messages being delivered.”
Identifying Look-Alike Buyers
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Integrating Internal and External Data Sources
Marketing automation systems were designed to collect
and track internal data sources from contact databases
and web visits. However, with the explosion of social
media and other unstructured data sources, there is
a growing need to integrate intelligence from external
sources that can identify short-term changes in prospect
purchase behavior. Extracting value from these
unstructured data sources is a key element of a big
data strategy, which is why many top brands in financial
services, technology and other verticals are using
analytics tools that combine external and internal
data sources.
External data mining, for example, can gather
intelligence from sources such as news stories and
job postings, and then integrate those sources with
internal data to provide a holistic view of customers and
prospects. This approach gives marketers a wealth of
additional insights and cues into what a prospect is
likely to do next and how likely they are to buy a product
or solution.
The ability to integrate multiple data sources also
improves internal sales and marketing alignment.
Working with disconnected data sources in the past,
marketing and sales teams often had no visibility into
the last action taken by the customer. For example,
marketing could send an offer about upgrading to a new
solution on the same day the customer called in to the
help desk with a service issue. With big data analytics
in place, marketing and sales have a 360-degree view
of the prospect and can better anticipate their needs
and potential purchase patterns.
Integration of multiple data sources provides insights into a prospect’s likely next move and improves internal marketing and sales alignment.
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Marketing automation: These platforms generate streams of data on the historic behavior of the contacts in their databases. This includes historic views of opens, clicks, site visits, form fills, downloads, etc.
CRM: Most enterprises are now integrating data between their CRM and marketing automation platforms for a shared view of prospect behavior. As companies look for deeper reporting on pipeline trends and conversion rates, there is a heightened need for integration and analysis of data.
Web analytics: Whether companies are using Google Analytics, Omniture, Webtrends or other tools to track web traffic patterns, there is a lot of great intelligence from these sources that marketers can mine to identify trends, patterns and shifts in behavior.
Social media: Tools such as Radian6, Vocus and other social media monitoring platforms help companies monitor relevant conversations on the web. For this data to be meaningful, however, it needs to be analyzed and integrated with data from other sources.
Customer feedback: There are a variety of tools available to track customer satisfaction and net promoter scores. These platforms excel at showing shifts in customer preference and needs, but again these tools must be integrated with other data sources in order to yield actionable insights for marketing and sales teams.
The reality is that the tools listed above, as well as other data sources that currently exist within most organizations, are often managed in separate
silos — making it nearly impossible to analyze information across them. Progressive companies can adopt predictive analytics tools to span these silos and create an integrated view of their data sources.
The concept of accessing and analyzing big data can be intimidating for marketers, but the reality is most
marketing teams are already working with a variety of data sources on a daily basis. Big data, which involves
intelligence gathered from a variety of sources — structured and unstructured, internal and external —
is really the next step in this process.
Breaking Down Data Silos to Gain Intelligence on Buyers
DATA CURRENTLY BEING COLLECTED BY MARKETING TEAMS:
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CONCLUSION:
Changing Buyer Behavior Requires Rich Marketing Data
There is a growing need for marketers to strike the
correct balance between “right brain” and “left brain,”
where organizations are realizing that data and insights
into behaviors are equally critical to the creative
messaging. The ability to utilize technology and data
is part of a modern marketer’s core job description.
In fact, Gartner has predicted that by 2017, CMOs
will spend more on technology than CIOs.
The driver behind this trend is the need to keep pace
with connected customers and more agile B2B
buyers. Just as CRM and sales intelligence tools
have helped sales teams keep pace with this change,
marketing automation systems have given marketers
a platform to track and record the past behaviors of
customers and prospects.
But the stakes keep getting raised, and marketers
need to keep pace by combining their traditional,
historical views of prospects with more forward-looking,
predictive intelligence.
As we have explained here, the ability to gather this
predictive intelligence requires the ability to gather data
from a wide range of internal and external sources.
Industry experts predict the next generation of automation
will not only focus on lead management capabilities,
but will use the power of big data to predict the needs
and buying patterns of prospects — turning marketing
into a precision science that has a direct and quantifiable
impact on revenue.
“In the near future, I think marketing can reach a point
where it will be able to track and predict its ROI,” said
Jon Russo of B2B Fusion Group. “But the learning curve
will need to be accelerated and it will take a lot of
engagement and effort.”
The modern marketer depends on technology and data along with creative messaging. ‘
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About Lattice Lattice delivers data-driven business applications that help companies of all sizes market and sell more
intelligently. Inspired by today’s consumer applications, Lattice brings every relevant buying signal into the
world’s most comprehensive, cloud-based applications. With Lattice, companies stop guessing and start
relying on modern data science that anyone can use to find, prioritize and close their next customer.
Its rapidly growing customer base, including Dell, SunTrust and VMware, rely on Lattice’s open and
secure applications to generate 75 percent more pipeline, triple conversion rates, and double win rates.
Lattice is privately held and backed by NEA and Sequoia Capital with headquarters in San Mateo, CA.
Learn more at www.lattice-engines.com and follow @Lattice_Engines.
About Demand Gen ReportDemand Gen Report is a targeted e-media publication spotlighting the strategies and solutions that
help companies better align their sales and marketing organizations, and ultimately, drive growth.
A key component of our coverage focuses on the sales and marketing automation tools that enable
companies to better measure and manage their multi-channel demand generation efforts. For more
information, visit www.demandgenreport.com.
Lattice Headquarters1825 South Grant Street, Suite 510San Mateo, CA 94402
1.877.460.0010www.lattice-engines.com
411 State RT 17 S, Suite 410Hasbrouck Heights, NJ 07604
1.888.603.3626E-mail: info@demandgenreport.comwww.demandgenreport.com