amp up your marketing automation with predictive...

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

Amp Up Your Marketing Automation With Predictive Analytics!

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%


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.


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


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.

‘

’


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


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.

‘

’

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.‘

’

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.



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. ‘

’



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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Name

Address

City, State, ZIP


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.

70%

40%

10%


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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

“

”

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


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.

‘’

5


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:


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. ‘

’


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.

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