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CONTENTS

SO WHAT IS PREDICTIVE LEAD SCORING? 3

Predictive Lead Scoring: What You Need To Know 4

See The Infer Difference 5

Hear From Our Customers 7

Continued Learning 7

PATH TO PREDICTIVE INTELLIGENCE 8

Map Your Journey 9

Level 1: Group Contact Management 9

Level 2: Sales Automation 10

Level 3: Marketing Automation 10

Level 4: Predictive Intelligence 11

Level 5: Data Scientists 12

Continued Learning 12

CREATING YOUR PREDICTIVE MODEL 13

Step 1: Start With Your Existing Data 14

Step 2: Add Thousands Of External Signals 14

Step 3: Determine Which Signals Are Predictive 15

Step 4: Create The Optimal Model 15

Step 5: Test The Accuracy Of The Model 16

Step 6: Push It Live Into Production 16

Step 7: Measure The Results 17

Continued Learning 17

GETTING BUY-IN ON PREDICTIVE 18

Keep Pace With Or Surpass The Competition 19

Raise The Batting Average For All Your Reps 19

No More Manual Lead Scoring 19

Don’t Let Marketing Automation Slow You Down 19

A Home Run Initiative 20

Amazing Customer Stories 21

Continued Learning 21

PREDICTIVE PLAYBOOKS 22

Use Case #1: Filtering 23

Use Case #2: Prioritization 24

Use Case #3: Nurture 25

Additional Use Cases 28

Continued Learning 28

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30-Second Summary

• Predictive lead scoring is an automated,

data-driven way for businesses to de-

termine which prospects are most likely

to convert, and which are going to have

the biggest revenue impact.

• Traditional lead scoring relies on man-

ually-defined point values. This method

breaks down as more data points are

added. Unlike traditional lead scoring,

nothing with Infer is done manually.

• Infer uses the most advanced predictive

intelligence and machine learning algo-

rithms available to deliver lead scoring

that is statistically proven to be accurate.

• Infer gets results quickly. Your sales

team will see an increase in average

deal size and conversion rate. And your

marketing team will increase their ability

to target the best opportunities, improve

the pace of innovation and drive down

the cost per good lead.

So What Is Predictive Lead Scoring?

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Predictive lead scoring is an automated, data-driven way for

businesses to determine which prospects are most likely to

convert, and which are going to have the biggest revenue im-

pact. It’s always-on optimization. Scores can be used to filter

out bad leads, prioritize follow-up efforts, measure marketing

effectiveness and extract more value out of your nurture da-

tabase.

Predictive Lead Scoring: What You Need To Know

Marketing automation systems like Eloqua, Marketo, and Pardot support

lead scoring. In fact, most Infer customers are already using one of those

three applications. So how is Infer’s predictive lead scoring different?

1. External Signals

2. Deep Data Science

3. Automated Execution

4. Automated Optimization

With nothing more than an email address, Infer grabs thousands of sig-

nals about the individual and the organization they work for. Things like

relevant job postings, employee count, patent filings, social presence,

website traffic and even the technology vendors they use.

With traditional lead scoring, like that found in marketing automation

systems, you manually define point values. Obviously, this breaks down

when you’re talking about hundreds or thousands of attributes.

With the Infer approach, nothing is done manually. We use the most

advanced predictive intelligence and machine learning algorithms avail-

able. You can be confident in your lead scoring, which is statistically

proven to be accurate.

Because predictive lead scoring taps into thousands of external signals,

the lead is scored the instant it’s created. This allows you to take action

on the best leads, getting a jump on your competition. Also, you can

re-score leads even if they aren’t active on your website, which enables

you to light up the best leads in your nurture database.

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Infer is able to automatically adjust to new signals and changing busi-

ness dynamics. The model retrains itself, learning from new closed deals

and improving its accuracy. For example, you might introduce new prod-

ucts, change your pricing or see new competitive pressures. Infer is

constantly monitoring the data and proactively recommending ways to

increase revenue.

Below is a simple way of thinking about the impact of predictive lead

scoring. When we first build a model and present the results, it is very

common to see that the top 30% of leads account for the vast majority

of the pipeline (green bars) and the bottom 70% of leads have very little

pipeline impact.

Infer also provides advanced analytics to measure sales effort. This is

tracked by looking at the phone calls being logged and emails being

sent out by sales reps. While most companies are using marketing au-

tomation platforms, they’re still spending lots of cycles on leads that are

dead weight. If you don’t have confidence in your scoring, the safe bet

is to leave no stone unturned. But this wastes time, damages your sales

team’s trust in marketing and makes it difficult for your sales reps to hit

their goals.

See The Infer Difference

When we turn on Infer, something magical happens.

There is an instant change in behavior across all your reps. All of a sud-

den your effort is almost perfectly aligned with impact.

“What Infer is doing represents the future of data-driven business.”

-Brennan O’Donnell, VP of Sales

SurveyMonkey

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Before Infer:

The reduced effort on low scoring leads can be shifted into more profit-

able endeavors. Maybe that’s doubling down on their top scoring leads

and following up more aggressively. Or maybe marketing is able to use

Infer to find new sources of good leads.

For sales, you want to increase average deal size and conversion rate.

For marketers, you want to increase your ability to target the best op-

portunities, improve your pace of innovation and drive down your cost

per good lead.

After Infer:

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Hear From Our Customers

Continued Learning

There are essentially two main problems that sales and marketing

teams leverage predictive to solve - too many leads or too few. Find

out how companies are tackling the second challenge in our Predictive

Playbook, “Expanding Your Footprint in an SMB Market.”

“Infer helped us drive a 3x increase in qualified leads sent over to sales.”

-Gina O’Reilly, COO

Nitro

“Now, our marketing investments produce bigger payoffs.”

-Elissa Fink, Chief Marketing Officer

Tableau

“Infer is able to automatically research leads and identify MQLs. That saves our sales reps time and energy, but just as important, it gives us an objective way to measure lead quality.”

-Ash Alhashim, Director of Sales Development

Optimizely

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Path To Predictive Intelligence30-Second Summary

• There are five levels of sales and mar-

keting maturity. At each stage, customer

insight grows and automation increases.

• The five levels are: Level 1: Group Con-

tact Management, Level 2: Sales Auto-

mation, Level 3: Marketing Automation,

Level 4: Predictive Intelligence and Lev-

el 5: Data Scientists.

• At the lower levels, your insight is very

limited and you don’t have a true under-

standing of who your best customers

are.

• Companies at the higher levels have a

huge advantage. They have a fuller pic-

ture of their customers and statistical-

ly significant lead scores that reps fully

trust.

• Every company should be able to achieve

Level 4.

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As your organization grows, it naturally pro-

gresses toward greater data sophistication,

which increases the efficiency at which you

can operate. In sales and marketing, we typ-

ically see five levels of CRM and marketing

automation maturity. We’ll walk you through

each level so you can assess where you are

today, and where you want to be in the future.

Map Your Journey

As you can see, there is a natural progression from

group contact management to predictive intelli-

gence. At each stage customer insight grows and

automation increases. Companies that achieve level

four and beyond are able to work with much greater

efficiencies than companies stuck at lower levels.

Level 1: Group Contact Management

Once you have a shared database with all of your customers and pros-

pects, you can pull lists and view activity history. This helps your orga-

nization reduce operational inefficiencies, but doesn’t usually produce

deep insight into who your best customers are.

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Level 2: Sales Automation

By deploying an automation system like Salesforce, you can standard-

ize your processes and capture transactional data. This allows you to re-

port on the status of your leads and opportunities, and begin to identify

trends across your customer base by looking at converted vs. archived

leads and wins vs. losses. However at this stage, your insight is limited

to the data that your reps collect.

Level 3: Marketing Automation

Bringing in a marketing automation platform like Marketo or Eloqua is a

good next step because it allows you to capture behavioral data from

your website, email campaigns and social channels. This provides a full-

er picture of the customer, but the lead scoring capabilities in these sys-

tems have their limits.

Most require you to manually define point values for different types of

behaviors. For example, if a lead clicks on an email, that’s worth 5 points,

and if they download a white paper, that’s worth 10 points. Without data

science to prove which signals are statistically significant and determine

the proper weights, you often end up with scores that your reps don’t

fully trust.

“68% of companies use marketing automation to do lead scoring, yet only 40% of salespeople agree or strongly agree that lead scoring is effective.”

-Jay Famico, Practice Director SiriusDecisions

*State of Marketing Automation, April 2014

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The other missing link with the scoring in marketing automation is that it

doesn’t tap into external data. If you’re relying solely on the information

passed through form fills, it is difficult to get an accurate assessment of

fit.

Level 4: Predictive Intelligence

Companies that reach level four have a huge leg up on their compe-

tition. They’re able to accurately predict which prospects are likely to

become great customers. In the past, only the most sophisticated com-

panies achieved this kind of data maturity because it required data sci-

entists, a software stack, lots of external data, a testing framework and

integration with CRM and marketing automation.

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Today, world-class predictive scoring is available to companies of all siz-

es, because companies like Infer have solved the problem end-to-end

with solutions that can be implemented in a matter of weeks.

Level 5: Data Scientists

While most companies will opt for predictive-as-a-service, some compa-

nies may also have their own team of data scientists in-house.

For example, AdRoll and Optimizely have brilliant data scientists, but

most of them are focused on the product. Building a proprietary predic-

tive scoring model for your own use is a bit like writing your own CRM

software. It can be done, but there are economies of scale when you tap

into a service.

Infer provides an end-to-end solution, but that doesn’t mean it can’t be

customized. We can incorporate other sources of data your organiza-

tion might have, such as application usage data. We can also make our

library of external signals available to you if you want to model off it.

Continued Learning

Eager to know more? Check out our Predictive Playbook, “How To Ana-

lyze Predictive Models” to discover the best ways to successfully eval-

uate the accuracy, efficacy and performance of your company’s model.

“Increasingly automated testing will allow incremental optimization despite constant changes in customer interests, product availability, creative executions and offers.”

-David Raab, Raab Associates

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Creating Your Predictive Model30-Second Summary

• Building accurate predictive mod-

els requires seven key steps - Step 1:

Start With Your Existing Data, Step 2:

Add Thousands Of External Signals,

Step 3: Determine Which Signals Are

Predictive, Step 4: Create The Optimal

Formal, Step 5: Test The Accuracy Of

The Model, Step 6: Push It Live Into

Production, and Step 7: Measure The

Results.

• Once your model is complete, Infer

helps you understand the results by

translating the data into simple visuals.

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Once you’ve recognized the value of predictive lead scoring,

it’s important to understand the process. Building accurate

predictive models requires seven key steps.

Step 1: Start With Your Existing Data

To create a predictive model, you first want to pull in your existing data

and look at historical outcomes. This might include converted leads vs.

archived leads, wins and losses, purchase history, and additional data

for each record like company name, email address, and opportunity

amount.

That’s why at Infer, we’ve built easy connectors for Salesforce, Eloqua,

Marketo and Pardot. The more data you have, the better. And our solu-

tion is designed to work with data that is sparse or dirty.

Step 2: Add Thousands Of External Signals

The next step is to expand the data by adding external signals about

your leads and customers. For example, with nothing more than a com-

pany name or email address, Infer can add thousands of factors like

their relevant job postings, employee count, patent filings, social pres-

ence, website traffic and even the technology vendors they use.

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Step 3: Determine Which Signals Are Predictive

Once you have all the available data in place, the next step is to use ma-

chine learning to determine which signals are predictive. In some cas-

es they’ll be positively correlated with conversion, and in some cases

they’re negative signals. You can even set up your model to weight sig-

nals toward large deals, so you can prioritize your efforts where they’ll

have the biggest revenue impact.

Step 4: Create The Optimal Model

Advanced machine learning is used to test millions of scenarios and

produce the optimal model for your business. It will determine the pre-

cise cutoff points (e.g. optimal sized customer has between 124 - 207

employees) and the proper weight (e.g. employee count accounts for

1.7% of the overall score).

“Infer really allowed us to take the guessing out of what the leading indicators are for conversion.”

-Jim Cyb, VP of Sales

Zendesk

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Step 5: Test The Accuracy Of The Model

When the model is complete, you should use historical backtesting to

determine how predictive it is. Visualizations like Infer’s can help you

understand the results. For example, a concentration of green bars on

the left side means that the model is identifying winners and pushing

them to the top of your ranked list.

Step 6: Push It Live Into Production

Once you have confidence in your model, it’s time to push the scores

into production within your sales and/or marketing automation systems.

From your rep’s perspective, they should be able to view their leads au-

tomatically prioritized by the predictive lead scores in Salesforce.

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Infer offers pre-built connectors for Marketo, Eloqua, and Pardot that al-

low you to use the score to trigger workflows, route leads into different

paths and personalize campaigns.

Step 7: Measure The Results

Infer helps you measure the impact of predictive scoring through useful

visuals. You should be able to see higher conversion rates, lower cost

per good lead, and reduced effort on bad leads.

Continued Learning

As companies start experiencing the benefits of predictive scores, they

typically seek out more places where predictive can add value. Discov-

er the winning strategies in our Predictive Playbook, “Surfacing Gold

from Nurture Leads with Behavior Scoring.”

“We have one set of leads that converts at 4x the baseline, and one set that’ll never convert. Infer helps us tell the difference between the two.”

-Suresh Khanna, Chief Revenue Officer

AdRoll

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Getting Buy-In On Predictive30-Second Summary

• Win over any naysayers by proving val-

ue of predictive lead scoring.

• Predictive lead scoring allows you to

keep pace with or surpass the compe-

tition.

• A predictive model raises the batting av-

erage of your reps by ensuring they’re

only swinging at strikes.

• Unlike the lead scoring found in market-

ing automation applications, this lead

scoring actually works.

• Predictive scoring is low risk and low

overhead, with fast adoption and fast

time-to-value. Without it, you don’t have

a true understanding of who your best

customers are.

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Not every company is ready to jump into a new technology,

so it’s important to think about how you’ll win over naysayers

that don’t recognize this opportunity. Here are four key bene-

fits of implementing predictive lead scoring:

Keep Pace With Or Surpass The Competition

If your competition is leveraging predictive scoring and achieving 100%

lift in their win rates or conversions, it’d be irrational not to follow suit.

This is one of those trends where you need to be ahead of the curve vs.

lagging behind.

Raise The Batting Average For All Your Reps

To use a baseball analogy, this raises the batting average of your reps

by ensuring they’re only swinging at strikes. If a good rep can work 100

leads a month, you want to make sure every lead they call is a fit for the

product you’re selling.

No More Manual Lead Scoring

Many people are familiar with the lead scoring found in marketing au-

tomation platforms. It’s manually configured and based on guesswork

- which creates lead scoring that isn’t accurate.

Infer is bringing the predictive power of Google to sales and marketing.

We’re crawling the web and acquiring thousands of signals. And we

use machine learning to build state-of-the-art predictive models based

upon millions of simulated combinations. Finally, lead scoring that actu-

ally works!

Don’t Let Marketing Automation Slow You Down

Many companies feel like they’ve got to get their marketing automation

right before tackling predictive. But the two can actually work great in

parallel tracks. Predictive is less risky, delivers faster time-to-value, and

its impact is more easily quantified. And because of all the external data

used in our predictive models, it works regardless of the current state

of your data.

Marketing automation is important plumbing that every company needs

eventually, but you’ll also find that predictive solves similar challenges

in a more elegant way. For example filtering, prioritization, nurture and

campaigns are all places where Infer makes a big impact quickly.

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A Home Run Initiative

Predictive scoring is low risk and low overhead, with fast adoption and

fast time-to-value. It can be deployed rapidly so you see value in less

than 30 days. All you have to do is provide access to your sales and

marketing systems.

Then we use machine learning to build a personalized model, and pres-

ent it to you. Once you approve it, you can flip the switch and start ben-

efiting from predictive scoring immediately.

Predictive Scoring

Marketing Automation

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Amazing Customer Stories

Infer’s community includes some of the fastest-growing companies on

the planet — AdRoll, Cloudera, Box, New Relic, Nitro, Tableau, Zendesk,

and many others. This offers a terrific opportunity to learn from for-

ward-thinking sales and marketing leaders.

Continued Learning

Integrating predictive scoring into your sales and marketing workflows

drives real business value and internal alignment. Explore a real life ex-

ample in our Predictive Playbook, “Improving Sales Efficiency Through

Predictive-Driven Lead Routing.”

“What I love about Infer is that it supercharges our revenue.”

-Bill Macaitis, CMO

Zendesk

“We’re organizing for rapid scale, and Infer’s predictive scoring is

critical to our success.”

-Matt Cooley, Chief Revenue Officer

Mixpanel

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Predictive Playbooks30-Second Summary

• Once you’ve built your predictive model

and tested its accuracy, the next step is

to determine how to apply the scores to

unlock the most value.

• Sending low-score leads straight to nur-

ture frees up your reps and leads to in-

creased sales rep productivity.

• Prioritizing leads based on predictive

score allows your reps to align their ef-

fort where it will have the biggest reve-

nue impact.

• With predictive scoring, you can go back

and automatically research and re-score

all of your leads. Depending on the size

of your database, you might surface

thousands of high-scoring leads.

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Once you’ve built your predictive model and tested its accu-

racy, the next step is to determine how to apply the scores in

order to unlock the most value. Here are the most common

use cases and some frameworks for measuring ROI.

Use Case #1: Filtering

A predictive model will identify some subset of leads that have almost

zero chance of converting. By routing these leads around sales and

straight to nurture, your sales reps can focus on the best opportunities.

To measure the amount of effort you’ve freed up, you can look at the

percent of your reps’ tasks that were logged against low-scoring “D”

leads. When you turn on predictive scoring, you should see that per-

centage go down. If you multiply the delta by the total cost to operate

your inside sales team, you can identify real cost savings.

Before After Impact

Revenue from D leads $0 $0 $0

% of your reps’ activities

logged against D leads9% 0% +9%

Cost of working D leads $4,725 $0 +$4,725

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It is worth noting that many companies don’t stop working their “C” and

“D” leads completely. They may have a one-touch policy, or have reps

focus on those leads only after they’ve exhausted their “A” and “B” leads.

Any small decrease in revenue as a result of this approach is usually tiny

compared to your cost savings from decreased effort.

Use Case #2: Prioritization

Predictive lead scoring lets reps stack rank their leads, so they can align

their effort where it is going to have the biggest revenue impact. This

means that they’re calling good leads faster and they’re less likely to

give up one call too soon.

“95% of our closed/won opportunities are identified because of Infer’s lead scoring model.”

-Stephan Blendstrup, Global Sales Operations

Zendesk

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With that change in behavior, you should see an increased win rate

amongst your “A” and “B” leads. And you may find that your average

deal size goes up, especially if you’re also prioritizing leads within the

top scoring bands.

A Leads Before After Impact

Monthly revenue $1M $1.2M +$200k

Win rate 46% 49% +3%

Average deal size $19,500 $24,800 +$5,300

Another way to measure ROI is to look at filtering and prioritization to-

gether. Were you able to put more energy into high-scoring “A” and “B”

leads? And what was the impact on bookings?

Filtering + Prioritization Before After Impact

% of activities logged

against C & D leads27% 9% -18%

% of activities logged

against A & B leads73% 91% +18%

Bookings from C & D leads $250k $180k -$70k

Bookings from A & B leads $1.7M $2.1M +$400k

Net increase in monthly

bookings+$330k

Use Case #3: Nurture

Do you ever wonder how much hidden potential is buried in your lead

nurture? Many businesses have leads that slip through the cracks. May-

be on the surface it didn’t appear to be a good lead, maybe the timing

was off because they weren’t quite ready to buy, or maybe your inside

sales team was spread too thin at the time the lead came in. Over the

years, who knows how many good opportunities you’ve lost out on.

“My Infer ‘A’ leads win 15 times more often than my ‘D’ leads with 10x the average deal size.”

-Kevin Gaither, VP of Inside Sales

ZipRecruiter

26 infer.com

With predictive scoring, you can go back and automatically research

and re-score all of your leads - whether you have 10 leads or 10 million.

And since predictive scoring leverages so much external data, you can

rescore leads even if they haven’t been active on your website.

Depending on the size of your database, you might surface thousands

of high-scoring leads to put on specific paths with special offers such as

invitations to events or 1:1 consultations. Or maybe you can carve off one

or two reps to focus on re-engaging those high-scoring nurture leads.

To estimate the value of your nurture database, you can look at how

many archived leads fell into A, B, and C buckets and place a value on

them. What would you pay per name to buy a list of A leads? Clearly

your own lead quality is better than what you can get elsewhere, be-

cause these people expressed interest in your product at some point.

A Leads B Leads C Leads

# of leads 3,000 8,000 15,000

Est. value per lead $50 $15 $5

Est. value in nurture $150k $120 $75k

27 infer.com

This isn’t an exact science, but it can help you determine whether or not

your nurture database warrants a dedicated sales team. And as they call

down these leads, you can begin tracking what percent convert, how

many tasks you’re putting in, the average deal size, and ultimately the

revenue per task.

A Leads B Leads C Leads

Conversion rate on archived

leads1.9% 0.1% 0.04%

Avg. deal size for archived

leads$24,800 $19,500 $8,750

Avg. # of tasks 3 2 1

Cost per task $30 $30 $30

Pipeline per task $157 $98 $35

Once you have predictive lead scoring in place, you will probably see

diminishing returns for your nurture database over time. The hope is

that by doing a better job of filtering and prioritization, your reps will no

longer give up on the good leads without a fight. That said, because

timing is often a factor in whether or not a deal closes, you’ll still want to

implement a process for bubbling up good nurture leads and checking

back in.

28 infer.com

Additional Use Cases

Campaigns

With a simple formula field on the Sales-

force campaign object, marketing can be-

gin to measure cost per good lead. This

provides an apples-to-apples comparison

across campaigns.

AdWords

In AdWords, you can adjust your bidding

strategy to optimize for good leads rather

than a form complete. With instant feed-

back, you can move money to the right

keywords and campaigns.

List Imports

If you purchase lists, you can often get a

48-hour out clause written into the con-

tract. You can be upfront with the vendor

and tell them you’re going to run the list

through Infer to see how well it matches

with your target prospect. If it does well,

there’s a great relationship to be had. Oth-

erwise, it’s probably best to know right

away and move on.

Territories

With Infer, you can objectively look at the

quality of leads and compare that to rep

performance. You might also use the Infer

score to help with dividing up territories

evenly.

Sales SLAs

Many companies create Sales SLAs to de-

termine how many phone calls and emails

a prospect should get before giving up.

Using the Infer score helps you dictate the

right level of follow up.

Events

If you’ve ever seen Infer’s booth at a trade

show, you’ll be familiar with our score-o-

matic. A prospect can walk up to the booth,

enter their email address, and see how

good of a fit they are for Infer. We highlight

the top 10 signals so they can see why,

which creates an educated conversation

and increases our conversion rate.

Continued Learning

These days marketers are generating more leads than ever before. Find

out how to unlock enormous value for your company by filtering out the

noise and focusing reps where they’ve got the best shot at winning in

our Predictive Playbook, “Sales Prioritization: Aligning Effort With Im-

pact.”

29 infer.com

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