DEMYSTIFYING B2B PURCHASE INTENT DATA:

UNDERSTANDING THE BASICS

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DEMYSTIFYING B2B PURCHASE INTENT DATA

1 What is intent data?

2 Does intent data work?

3 What are the benefits of intent data?

4 What are the different ways to capture intent data?

5 How does the intent data from the three methods differ?

6 What are common pitfalls with intent data?

7 What are the recommendations to move forward with intent data?

THIS REPORT COVERS THE FOLLOWING

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DEMYSTIFYING B2B PURCHASE INTENT DATA

1. WHAT IS INTENT DATA?

B2B purchase intent data (intent data) captures online buyer

behavior, tracking a specific company’s research level with

regard to a specific product/solutions category. This data is used

to generate an intent score. As research activity with regard

to a given category increases, the intent score also increases,

reflecting an ever higher likelihood that the researching company

will purchase products or services in that category.

2. DOES INTENT DATA WORK?

Aberdeen has worked with clients to conduct over a dozen

assessments of intent data accuracy. To begin, these clients

provide us two things — a list of companies, and a particular

product offering. The list of companies should include a mix of

known opportunities, as well as companies that do not have an

active opportunity. This mix helps identify false positives.

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DEMYSTIFYING B2B PURCHASE INTENT DATA

We then “go back in time” and look at historical

intent data – from multiple intent data providers –

in order to track the research activity of the

companies in question.

An example of a visualization of the results can be

seen above. The black line represents research

activity over time. After we performed our analysis,

the client added the teal dot (opportunity open date)

and red dot (opportunity close date).

Research Activity

Opportunity Open Date

Opportunity Close Date

Inaccuracies, including false positives (identifying

intent where there was none) and false negatives

(missing intent signals for known opportunities),

varied significantly by intent data vendor.

In other words, intent data providers are not all the

same. Companies need to vet intent data providers

in order to select the one that can provide the most

accurate data for their market.

The visualization shows what you would expect:

Active research activity increases above a baseline

at the beginning of the process and continues

to increase, with some plateauing, until the end.

Accuracy rates (the percentage of total accounts

with known opportunities where the data revealed

measurable increases in research activity) on the tests

we’ve conducted range from 53 - 91%, depending

on the category and the intent data provider.

INTENT SCORE VS TIME

Account 5

INTE

NT S

CORE

TIME

Account 6

Account 7

DEMYSTIFYING B2B PURCHASE INTENT DATA

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3. WHAT ARE THE BENEFITSOF INTENT DATA?

Most B2B companies are flying blind when it comes to identifying companies currently in-market to buy what

they sell. Indeed, the majority of B2B marketing budgets are spent trying to make up for this lack of visibility

with highly inefficient and non-scalable tactics. As our tests have shown, however, it is possible to determine,

well ahead of a buying decision, which companies are in-market to buy, with up to a 91% accuracy.

EFFICIENCYWith this information, marketing and sales efforts can be focused on precisely these accounts, dramatically reducing the amount of marketing dollars and sales time wasted on companies that will not purchase.

PIPELINE QUALITYSince the opportunities being created are intent-qualified at the account level – we’re not talking about individuals that simply stopped by a booth or viewed a piece of content – pipeline quality also increases. As a matter of fact, intent-qualified opportunities tend to be larger, move through the pipeline faster, and convert at a higher rate.

WIN-RATESAn increased focus on in-market companies most often leads to higher win-rates. Insight into the actual buyers’ journey, including what companies were researching when, allows companies to better understand what influences sales outcomes. Sales and marketing can then develop battlecards and targeted talk-tracks to increase win-rates.

Combined, these benefits lead to

significant return on investment ( ROI ) by

companies effectively applying intent data

to their marketing and sales efforts.

Several early adopters of intent data shared internal

reports with us showing

3-5x ROI

on their intent data programs.

DEMYSTIFYING B2B PURCHASE INTENT DATA

4. WHAT ARE THE DIFFERENT WAYS TOCAPTURE INTENT DATA?

ACAPTURING ONLINE BUYER BEHAVIOR

First, they must capture the online behavior of B2B buyers.

CSEPARATING INTENT SIGNAL

Third, they have to separate the signal of true intent to purchase from the noise of

ongoing activity, especially from companies with large numbers of employees.

BCATEGORIZING CONTENT

Second, they need to categorize the content consumed into keywords or

topics that are in turn tied to potential purchases.

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DEMYSTIFYING B2B PURCHASE INTENT DATA

BIDSTREAMBidstream data refers to the meta data made available through programmatic advertising platforms. When a user visits a website with programmatic ad inventory, the data provided for automatic bidding includes information that associates the visitor to a company and the page content consumed to keywords relevant to purchase categories.

The leading provider in this model is The Big Willow.

WEBSITE TAGSWebsite owners agree to place a javascript tag or pixel on their website and share their visitor data with 3rd parties.

The leading provider here is Bombora.

REGISTERED USERSUsers must register to download information or to access a site’s full functionality. Companies may target ads to collections of users (e.g., LinkedIn) or purchase their contact info directly, if the users have knowingly or unknowingly given consent to the website owner to share their contact information with all of the website owner’s customers (e.g., Tech Target, G2 Crowd)

A. CAPTURING ONLINE BUYER BEHAVIORIntent data is captured in three primary ways: bidstream data; website tags; and registered user websites.

The characteristics of the intent data derived from these three approaches are quite distinct and should be

understood before companies attempt to apply any of them to their businesses.

DEMYSTIFYING B2B PURCHASE INTENT DATA

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BIDSTREAMCategorization of web pages is done

via Natural Language Processing (NLP) at the keyword level. Each

category is defined from the bottom up based on keywords in use by

vendors and their competitors. As a result, the vendors in the space define

the category and it evolves as the space evolves.

WEBSITE TAGS There is no categorization at the

keyword level but, instead, top- down definition at topic level. In this case, it’s up to the intent data vendor to choose which topics are relevant,

without being able to explicitly connect topics to relevant keywords.

REGISTERED USERThe title of the document downloaded

determines the category in which the registrant is placed. No broader

visibility into buyer behavior is categorized or provided.

B. CATEGORIZING CONTENT CONSUMEDINTO INTENT CATEGORIES

Once the data is captured, the next challenge is to categorize the data by keyword or topic to associate it with

potential purchases. Each of the three approaches uses a fundamentally different approach to categorize the

content consumed. Bidstream takes a bottom-up approach based on keywords in use by vendors. Website

tagging uses a top-down approach at a topic level, with each topic reflecting a range of potential keywords.

Registered user sites simply use the title of the downloaded document. These different approaches must be

understood before companies attempt to apply any of them to their businesses.

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DEMYSTIFYING B2B PURCHASE INTENT DATA

BIDSTREAMA company-specific model of activity by keyword on a trailing 6-month basis is created. Current activity is compared to this baseline to determine true spikes in research activity by keyword.

WEBSITE TAGSA weekly normal distribution model is created that compares activity across companies at a topic level. Companies with the most activity receive the highest score. The risk in this approach is that large companies will consistently receive a high score and thus generate false positives.

REGISTERED USERSNo model of intent signal here. Every download is assumed to signal intent.

C. SEPARATING INTENT SIGNAL FROM NOISEOnce buyer behavior is captured and categorized, it must be analyzed to distinguish a purchase

intent signal from the ongoing noise of online research activity. Each of the three methods takes

a different approach to this last challenge as well.

DEMYSTIFYING B2B PURCHASE INTENT DATA

5. HOW DO THE INTENT DATA FROM THETHREE METHODS DIFFER?

The three methods for capturing intent data result in significantly different intent data outcomes. A comparison

of the different characteristics of these three primary methods are listed in the table below:

BIDSTREAM

Billions of web pages

Global coverage today

White Box — page visits connected to company, geographic location, page URL, and device

Time-series data visualization of actual buyers’ journey across webpages

Company-specific baselines by keyword to determine true signal

Available across 12 billion web pages

Web visitor resolution to zip code + 4

REACH: THE WEB

REACH: GEOGRAPHIC

GRANULARITY OF INSIGHT

BUYERS’ JOURNEY

SIGNAL FROM NOISE

AD TARGETING AT MOMENT OF

INTENT RESEARCH

GEOTARGETING

REGISTERED USER

1 website or closed community of sites run by one company

Primarily English-only sites based in the US

Registration info from download of a piece of content

No visibility beyond single download —1 data point

No visibility into buyers’ journey

No intent targeting. Standard display ads on a few websites to all visitors

Information provided in form fill or registration

WEBSITE TAGS

Tens or hundreds of relevant sites for a given product, based on opt-in

Primarily English-only sites in US and Europe

Black Box — No visibility into underlying data

Black Box — No visibility

No company-specific models — companies with the most activity each week receive highest score

Not available

Company location only, web visitor may be anywhere 10

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DEMYSTIFYING B2B PURCHASE INTENT DATA

6. WHAT ARE COMMON PITFALLS WITH INTENT DATA?The value obtained from intent data is limited by the following traps we see companies falling into:

CHECKBOX

Intent data is treated as just another score, one that gets

added to a marketing automation or ABM tool and ignored along with all of the other scores and

attributes. If it is selected as a field (“all companies above 70 on a 100 point scale”) in an email campaign,

for example, no rigorous A/B testing is done to determine

the performance improvement provided.

Accurate intent data is not just another attribute. It is

THE SIGNAL of purchase intent.

A checkbox approach never confirms the accuracy of the

data and, if it is accurate, never ties the data to a specific,

appropriate action.

ROUND PEG IN SQUARE HOLE

Larger companies often have their own internal predictive

analytics teams that have built predictive scoring models

based on discrete attributes and regression modeling. Intent data

is a near-continuous variable that must be modeled against

behavior across the internet, not just against one company’s

data set.

This makes it challenging for intent data to be incorporated into existing predictive models,

and many companies have given up trying to make it work.

BLACKBOX

Many companies grow frustrated with the black box nature of some intent data providers and stop their

initiatives. An inability to see the underlying data points that roll up into intent scores is a major challenge for analytic teams that need to test and

prove the validity of intent signals.

The Big WIllow does provide a white box approach for teams that require such granular-level

intent data visibility.

SEEN ONE, SEEN THEM ALL

Intent data providers vary widely in their capture

mechanisms and ability to deliver accurate, actionable

data. Companies closely evaluate the alternatives in all solution selections and

should do the same with intent data.

FALSE POSITIVES

Depending on the chosen intent data approach, false positives

can be a significant factor. False positives can lead to a significant

loss of time. When working accounts who are not in-market to buy, support from the sales

organization can be lost.

Companies should evaluate intent data providers for their

ability to minimize false positives.

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DEMYSTIFYING B2B PURCHASE INTENT DATA

LEARN MORE

7. WHAT ARE THE RECOMMENDATIONS TOMOVE FORWARD WITH INTENT DATA?

Getting started with intent data can be straightforward. Our recommendations are as follows:

Leading companies gain significant competitive

advantage from intent data. Several we engaged with

for this research have set up CEO-sponsored,

corporate-wide initiatives to leverage intent data

across their business units.

If your company has not begun investigating intent

data, now is the time to do so.

SKIP THE EXPENSIVE SOFTWAREMany ABM software providers are trying to bundle intent data into a large software and integration commitment. Unfortunately, this is expensive, involves a great deal of effort focused on redoing a martech stack, and takes the focus away from getting value from intent data by driving sales. Companies have enough software. They just need to make their existing software smarter with intent data.

CONDUCT AN INTENT AUDITIf you are already using an intent data provider, ask another intent provider to provide an audit to compare the intent signals. This will help identify potential false positives or missed opportunities.

START WITH A DYNAMIC ACCOUNT TARGETING PROGRAMSelect a provider that can deliver an end-to-end solution that doesn’t require any software investment or changing your martech stack. Then focus on identifying in-market companies, creating pipelines, and driving wins.

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