eMarketer Webinar: Predictive Marketing—Using Data Decisively at Every Stage of the Funnel

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Predictive Marketing—Using Data

Decisively at Every Stage of the Funnel

Jillian Ryan

Analyst

June 23, 2016


Today’s Agenda

Predictive analytics and marketing defined

The importance of data in building predictive models

Exploring predictive applications across the customer

funnel

Measuring predictive marketing’s value

The current state of adoption and what’s on the horizon


Defining Predictive


Many B2B

marketers in

the US don’t

understand

the benefits

of predictive

analytics


Predictive analytics delivers more accurate

insights

Predictive analytics uses

predictive models to estimate

which marketing actions are most

likely to pay off in the future

With more accurate insights,

marketers can determine how to

best encourage sales from

existing and new customers


It’s all about the data—and it’s getting more

sophisticated

The way B2B marketers are

using data is evolving

Predictive is rooted in

forward-looking analysis

Rather than relying on

guesswork, predictive is based

on a trusted, analytical fact


Data:

The Engine Powering

Predictive Technology


Marketers are using data, but sophisticated

practitioners are winning

90%+maintained databases on

customers/prospects,

segmented data for

targeting, and measured

campaign results across

channelsSource: Global Alliance of Data-Driven Marketing Associations and

Winterberry Group, Sept 2015

50%+considered themselves

sophisticated

practitioners, while the

remaining portion were

just performing these

data-driven approaches

to some extent


Historical data reveals patterns in behavior

Data tells a story based

on the past experiences

it has recorded

Predictive analyzes data

touchpoints to determine

patterns

It learns from data to

render predictions


The most common

types of data used for

predictive marketing

included:

Website data (47%)

Demographics (44%)

Digital transactions

(41%)

Social (39%)


Data touchpoints

must be managed

properly

Top DQM services

included:

Big data

Master data

management

Data cleansing and

validation


Bigger companies

are more likely to

leverage historical

campaign analytics

for predictive

modeling


Marketers face

several

challenges

leveraging

historical data

for predictive:

Too expensive

Lack of expertise

Vendor tech isn’t

mature enough

Hard to prove value


Machine learning delivers fact-based predictive

insight

The machine

ingests data from

past successes and

failures, then runs a

model to deliver

predictive scores for

various desired

outcomes

68% of respondents

used it for

predictive analytics


Ensuring accuracy in the predictive model

requires two types of data:

Training Data

Used to construct the model

The majority of data points available

Testing Data

Used to verify the predictive model’s precision

A small subset kept separate from the training data


As the machine learns, the model gets smarter

The predictive engine

continues to learn from

data touchpoints

With more experience it

renders more accurate

predictions


Predictive Marketing:

Putting Insights into

Action


Moving from predictive analytics to marketing

Every application of predictive analytics follows the

same two-part structure:

1. Understanding

what is being

predicted

2. Figuring out what

to do with that

prediction


A standard predictive team requires three players*

1. A demand marketing expert

2. A data scientist

3. A technologist

*They all need to communicate well


Putting the pieces of the predictive puzzle in place

Marketers need to know what

type of business problem they

are trying to solve to take

action

Figuring out the business case

will kick off the process


Predictive marketing can be used to improve

the funnel at every stage

43%

of US B2B marketers

used predictive analytics

to get insights about

where prospects are in

the sales funnel

Source: 6sense and OnTarget Consulting & Research, April 2015

26%

of marketing executives

in North America said

that a benefit of

predictive marketing was

better funnel conversions

Source: Forbes Insights, April 2015


Primary objectives for predictive spanned across

the customer funnel

The major objective, at

33%, was customer

acquisition

At 17% each:

– measuring behavior and insights

– ad/campaign effectiveness

– calculating and improving

lifetime value

– retention


Applying predictive marketing at every stage

of the funnel

Finding new prospects

Lead scoring

Lifetime value of current

customers

Personalization

Sales/channel support

Recommendations

Awareness

Consideration

Purchase

Loyalty

Advocacy


Finding new prospects

51%

of US B2B marketers


to find new prospects

Source: 6sense and OnTarget Consulting & Research, April 2015

37%


in North America used

predictive marketing

technologies to find new

prospects

Source: Forbes Insights, April 2015


Kasasa uses predictive marketing for pre-funnel

scoring

“They use predictive modeling to score potential

buyers before they enter the funnel. If a bank

doesn’t have the right combination of attributes like, for

example, specific turnover rates, profit margins or number

of customers, it’s eliminated. But prospects with a

high predictive score are then proactively

marketed to, because the model found they are most

beneficial to talk to and more likely to convert.”

—Phil Winters, Author, “Customer Impact Agenda: Doing Business

from the Customer’s Perspective”


Lead scoring

B2B marketers can use

predictive models to score

leads to determine which

individuals have a higher

propensity to convert

Experts note this is the

most common use case

Still, only 7% of B2Bs used

predictive lead scoring


Getty Images scores leads with predictive to

prioritize sales efforts

“Using custom-built predictive indicators, we can take a set

of 10,000 new leads that we’ve identified and then sort

them in priority order based on the data that is

available to us, and then use a predictive

model to understand which leads are more

likely to convert. … Sales can understand which leads

have the highest propensity to buy. That information

allows them to prioritize their efforts.”

—Jason Widup, Senior Director, Demand Generation and Marketing

Operations, Getty Images


Increasing customer lifetime value

32%

of data management

professionals worldwide


to predict lifetime value

of each customerSource: Experian Data Quality and Dynamic Markets Ltd.,

Dec 2014

14%


used predictive

marketing to forecast

and minimize churn

within current accountsSource: Forbes Insights, April 2015


Major US wireless carrier uses predictive to

understand the value of its B2B customers

Predictive models help the

carrier acquire companies

In the funnel, it determines

which customers will have

the highest lifetime value

– add more lines

– upgrade to higher data buckets

– add more premium devices


Getty Images predicts churn within its current

accounts to minimize cancellations

When the predictive engine

detects a decline in usage

or another attribute

associated with churn, the

machine flags that account

This triggers a sales or

marketing action to prevent

the loss


Predictive techniques for personalization still

have a way to go

While 41% used

predictive to accelerate

personalization, only 12%

said predictive analysis

was an essential criterion

for personalization

success

Personalization might be

the end goal, but it is a

strategy of predictive that

needs refinement


A shortcoming to predictive personalization is

the lack of a human touch

“Most marketers want to hit the ‘easy’ button, but

marketers need to tailor the message and be

more empathetic. The problem is that empathy is

not something that predictive analytics—the way

it’s being sold today—is doing at all. It doesn’t provide you

with empathy to understand the situation and the

disposition of who you are talking to.”

—Tim Hayden, Vice President, Marketing, Zignal Labs


HP employs predictive personalization through

recommendations in partner channels

Partner channel Sales Central uses predictive to tailor

solutions to each third-party channel partner or reseller

The hub delivers personalized suggestions for more

successful campaigns and sales tactics for each partner

Then, personalized recommendations provide a tailored

campaign or piece of content

This isn’t a direct-to-customer approach, but overlaps with

two other essential uses of predictive marketing

technology: sales support and recommendations


Measuring Predictive

Marketing’s Value


Predictive marketing is seen as moderately

valuable

Extent to which investments in intelligent software, like predictive, are

delivering expect business value for worldwide executives:

Exceeding: 16%

Expected value: 33%

Some value: 39%

Underperforming: 8%

Source: Economist Intelligence Unit (EIU), sponsored by Accenture and Pegasystems, April 2015


How impactful is predictive marketing?

Just under half of B2B

marketing decision-makers

said predictive analytics

had a very high impact on

their business

Over one-third (36%) said

there was considerable

impact

13% cited some impact

Only 5% claimed to see

little or no impact


Benchmarking predictive vs. nonpredictive

determines effectiveness

55% of US B2B

marketers who used

predictive said their

strategy was effective,

compared with 18%

who didn’t use

predictive

Dell uses A/B tests and

compares the results

to see predictive’s

effect


The most common

metrics used to

measure the success

of predictive

marketing included:

customer retention rates

customer value

cost per lead

projected ROI

total conversions


Predictive is not perfect … but it can still work

Data-derived predictions

will never be 100% accurate

all of the time

This isn’t science fiction or

a crystal ball

Predictive equips marketers

with information that they

need to be smarter and

more efficient


Current State of B2B

Predictive Marketing Adoption


Predictive tech firms say the tipping point is now

While only 31% currently used a predictive analytics

tool, 54% were likely to invest in predictive analytics in

the next 12 to 36 months. Source: 6sense, April 2015

38% planned to significantly increase the role of

predictive analytics in the next year. Source: Lattice, Oct 2015

68% believed predictive marketing would be key to the

marketing stack. 47% were investigating how to use

predictive, and 25% were currently using it. Source: Everstring, Sept 2015


Sources other than vendors tell a different story

Tactics that occupied vs. will occupy time and resources in 2015 and 2016

2015 2016

Predictive modeling 44.4% 44.4%

Source: IAB Data Center of Excellence and Winterberry Group, January 2016


Why such a discrepancy?

Yes, predictive vendors might have a self-serving

reason to be biased …

… but the more likely scenario is that predictive

techniques can be bundled into larger marketing

automation tools

Thus, predictive marketing’s adoption might be much

larger than we think, because the capability might be

folded into other marketing tech products


Predictive undercover in Google Analytics

Google Analytics offers predictive capabilities without

actually labeling them as such

The Smart Goals feature uses machine learning to

identify which website visits are most high-value and

have a higher propensity to convert

Google doesn’t call Smart Goals predictive because

many marketers using it might be turned off or

intimidated by words like “predictive” or “machinery”


49%

expected growth of

predictive intelligence,

according to marketing

leaders in the US

Source: Salesforce, Jan 2016

38%of marketing executives

worldwide said predictive

analytics will have the

biggest impact on

marketing companies by

2020

Source: Economist Intelligence Unit (EIU), April 2016

Predictive is booming today, and likely tomorrow


Predictive will be here for a while and has a long

way to go

“As investors continue to pump funds into

predictive vendors and technology, the

capabilities will mature very rapidly. We

are in the early stages right now, but

predictive marketing isn’t going

anywhere just yet.”

—David Rabb, Principal, Marketing Technology and

Analytics, Raab Associates


Key Takeaways

Predictive models, built from past customer touchpoint data,

guide marketers by estimating which actions are most likely to

pay off in the future

Using this and other data allows for more efficiency at all

points of the marketing funnel

Measuring ROI can be a challenge, but benchmarking past

performance will indicate if the method is adding value

B2B companies are slowly adopting predictive marketing and

using it within their marketing technology stack

It remains uncertain whether this is the year predictive

marketing goes mainstream

©2016 EverString

Predictive marketing starts with Audience Selection

Audience Selectionis the process of using data

to identify the most relevant

prospects…

So you can market and

sell to the right prospects,

even if they are not in

your funnel

©2016 EverString

Focus of predictive marketing to date

Build Pipelinewith Predictive Demand

Generation

Increase Conversion with Predictive Scoring

Total Potential Market (all companies)

Predictive Scoring prioritizes your

existing funnel

customer

model

A

B

C

DTotal Captured Marketwithin your CRM

& marketing automation

Predictive Demand Generation delivers net new, high value accounts

©2016 EverString

A breakthrough in predictive marketing

EverString Audience Platform

powered by the

EverString Company Graph

Uses semantic similarity to map

the connections & similarities

between companies

Like the Google knowledge graph

and Facebook people graph,

but for B2B company data

The EverString Company Graph maps the

known universe of 11M B2B Accounts

Total Potential Market (TPM)

©2016 EverString

An evolution in predictive marketing

Data science helps identify and expand

segments beyond the boundaries of your

funnel, creating…

Predictive Segments

…which use semantic similarity to find

look-alike accounts that are similar on

specified dimensions

These segments can be mapped to your

products, territories, teams, events,

campaigns and more

EverString B2B Graph: known universe of accounts

Total Potential Market

Total Captured Market (TCM)

Segment

Segmentsegment

Segment

©2016 EverString

An evolution in predictive marketing

EverString B2B Graph: known universe of accounts

Total Potential Market

Total Captured Market (TCM)

Segment

Segmentsegment

Segment

Use cases to consider1. Funnel Prioritization – use scoring to prioritize

the prospects in your funnel

2. Segmented nurture – uses segments to optimize

the nurture paths for your existing database

3. Multi-channel Programs – create and use a

segment to market a campaign, program or

event

4. New market expansion – identify a wholly new

market to expand into and get accounts today

5. Territory Planning – create a segment for an

individual territory for a rep or team

6. Account Based Marketing – identify target

accounts and preform more directed campaigns


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Jillian Ryan

Predictive Marketing—Using

Data Decisively at Every Stage

of the Funnel

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eMarketer Webinar: Predictive Marketing—Using Data Decisively at Every Stage of the Funnel

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