ABM quick guide

everything you need to know about

B2B intent data

ABMquick guide

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Intent data lets you see through your customers’ eyes like never before. It helps you understand key phases along the B2B journey to purchase. By following the trail of clues left by this digital footprint, intent data lets you craft better messaging. Messaging, after all, is about taking a customer from A to B, where A is an initial fl icker of interest and B is a purchase decision. Until now, you were left to make assumptions about the former. No longer.

That is the promise of intent data and helps explain a steady and material increase in interest in the subject, as illustrated by the Google Trends chart below.

To follow the logic of intent data, this increase in search activity offers a offers a strong signal of interest. As it is with those looking to understand more about intent data – its application, its suppliers and so on – so it is with your products and services.

In this quick guide, we will discuss how such logic can be applied to B2B sales and marketing activity. We will examine the basics of intent data, its benefi ts,

uses and limitations and how it can be deployed in the context of account based marketing (ABM).

We see intent data as an important means of understanding your customers. We see it as enabling ABM-at-scale. It has the potential to provide a steady fl ow of strategic ABM and ABM Lite prospects by identifying those ‘long tail’ accounts ready to engage.

1 Nov 2007 1 Aug 2010 1 May 2013 1 Feb 2016

100

75

50

25

Interest over time

(source: Google Trends)

Enriched by other sources of insight and intelligence, and coupled with human know-how, we will explain how intent uncovers active accounts hidden in plain sight.

First, some defi nitions.

introduction

what is intent data?

First-party data (internal intent data)

• This includes information drawn from your in-house customer relationship management (CRM) or DMP systems, inbound traffi c to your websites, social channels, newsletter/marketing automation activity and other hosted channels

• The data is held and owned by your organisation

• Given your information is directly aligned to the services and products you offer, fi rst-party data can be seen as a strong predictor of interest and intent

• Note, however, this data only shows investigation into a topic through the context of your brand. As such it doesn’t reveal the whole story of customer interest and intent

• Meanwhile, volumes are likely to be low compared to third-party sources

Third-party data (External intent data)

• Determined through behavioural signals from across publisher sites and social networks

• Activities monitored include browsing, downloads, video streaming and trackable search queries

• Collected at IP level and enriched through user registration or via cookies, third-party data is shared with multiple organisations

• A would-be purchaser is much more likely to seek out third-party content (and therefore generate third-party data) to learn about a category of products and services before they browse vendor websites for the specifi cs

• While this might dilute the predictive strength of the signal, it does allow an organisation to understand an account’s interest much earlier in the cycle and much nearer the top of the sales funnel

How intent data aids ABM

Sales and marketing teams can use intent data to:

1 Monitor buyer intentions and mindset across a broad range of accounts

2 Understand intent across a single account, even a single branch-offi ce

3 Identify interest, or likely triggers, earlier in the sales cycle

4 Understand account needs earlier in sales cycle

5 Create or customise content for the customer’s needs or intent

6 Develop messaging for the customer’s needs or intent

7 Identify common themes across accounts

8 Target contestable accounts

9 Identify outliers and redeploy resources accordingly

There are two flavours of intent data: internal data, often referred to as first-party data, and external or third-party data

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0 20Internal Intent External Intent

40 60 80 100

0

Hybrid cloudPrivate cloud

Cloud securityPublic cloud

Managed cloud2 4 6 8 10

External Intent v Internal Intent Another useful form of visualisation is to compare levels of interest as expressed on third party websites (3rd party intent) as against interest expressed on a vendor website (1st party intent). In this example, the topic is cloud computing and the relative 1st and 3rd party score per key sub-topic are set side by side (see chart). In this scenario, ‘managed cloud’ generated the greatest interest on external sites while ‘private cloud’ proved the most salient subject for those on the vendors own site. Typically, accounts are more likely to spend time on vendor sites the nearer they are to a purchasing decision.

comparing 1st and 3rd party intent

These limitations should not put you off using third-party data but in order to get true value from it, make sure to:

• Validate the data. Do your target accounts show in suffi cient volumes within the third-party intent data?

• Defi ne what success looks like. Start at the end and map what type of intelligence is required to draw actionable insights. Then see if the available data matches need.

• Enrich intent data with other meaningful insights.

Before we discuss further the implementation of intent data as part of ABM campaigns, it’s worth acknowledging the potential limitations of this third-party intelligence. Intent data is imperfect. It doesn’t reveal who read the article, downloaded the whitepaper or watched the video since access to this kind of information runs counter to data protection regulations around the world. In addition, some data sets may end up being small to negligible once organisation and topic classification are cross referenced.

the limits of intent data and how to spot them

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Third-party intent data provides one more powerful tool to the insights’ mix, one that can help scale ABM activity. Instead of looking at 20 accounts, an intent model enables an organisation to monitor hundreds of accounts ready to redeploy resources when it detects a spike in interest, sometimes in unexpected places. Monitored at scale, these accounts can then be managed through strategic ABM and ABM Lite campaigns (see graphic).

Take, for example, would-be accounts from the oil and gas industry where cluster-based insights show broad interest in digital transformation. These initial insights might include a senior executive delivering a keynote address at a conference or a piece of intelligence drawn from a sales meeting. By layering across intent data, sales and marketing teams can identify where and when interest in digital transformation’ is expressed. Associated terms may provide more detail. They may indicate, for example, that many in the sector are looking to introduce document collaboration software. The volume and coverage of those intent signals will help you decide whether the relationship opportunity is tactical or strategic.

The decision to download a whitepaper may be one signal but so is social media engagement and a search query. Crucially, so too are activities that happen offl ine – meetings, internal promotions, company acquisitions, keynote addresses to name but four.

To get a full view of the customer’s mindset, sales and marketing teams need to allow for all those factors. For example, you can collect and analyse fi rmographic data, on-the-ground intelligence as well as content, customer announcement and infl uencer engagement signals. Want to understand how well a target CIO, for example, is plugged into your offer? It’s about segmentation, profi ling, propensity mapping and understanding what each piece of data is telling you.

how can intent data be applied to ABM?

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See graphic on page 7

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deal

size

1-2-few, clustered accounts with ABM budget,

mix of customized and tailored marketing

1-2-many, segmented into strategic groups to focus messaging and approach

Standard b2b marketing, segmented into groups of thousands with persona and industry-based insight, heavily automated

ABM as a system

1-2-1, large accounts with dedicated ABM budget, bespoke at individual account

1-2-few, clustered accounts with ABM budget, mix of customized and tailored marketing

1-2-many, segmented into strategic groups to focus messaging and approach

Standard B2B marketing, segmented into groups of thousands with persona and industry-based insight, heavily automated

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The best ABM methodology is always insights led. High quality third-party intent data will send very strong signals of purpose and behaviour. From there, it is possible to map a likely sales cycle and overlay that with messaging, propositions, content and communications. From account selection to execution and engagement, therefore, intent data provides value across the ABM lifecycle:

Planning & Insight

Intent signals for ABM Lite Clusters and to identify Strategic

ABM outliers

Messaging & Propositions

Content logic to refl ect cluster needs, strength of intent and

stage along sales cycle

Content & CommsAudit; Curation;

Repurposing; Customising

Execution & EngagementEvaluation; Real-time,

programmatic and top-level view

intent meets ABM: a methodology

Account selection

Finding; clustering; prioritising

Planning & Insight

Intent signals for ABM Lite Clusters and to identify Strategic

ABM outliers

Execution & EngagementEvaluation; Real-time,

programmatic and top-level view

Account selection

Finding; clustering; prioritising

creating transformation deals

There are two further benefi ts from this approach. First, cross-functional teams can align, making the sales and marketing process more effective and effi cient. Resources can be pooled where the sales lead is most promising. Second, the strength of interest and knowledge of need informs messaging which will be far more persuasive as a result.

All of which means the greater likelihood of success – a deal closed, a solution that more closely matches need and the start of a sustainable vendor-client relationship. And there’s no reason to stop monitoring your new customer’s intent. Do so to retain and strengthen the relationship.

Imagine being able to identify four clusters of accounts that map against four key products or services. If one of those clusters has a propensity towards transformation and a willingness to consider a broad set of solutions, that presents a promising opportunity for a sales team.

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Ideally, the best way to score intent is through a combination of internal and external data plus other signals about how an account and its stakeholders are ‘trending’ on the web. For example: how frequently is a business featured within news headlines? Has it recently made an important investor announcement? Or is a significant C-suite stakeholder joining?

As we know, external data identifi es intent across third-party websites. Triggers for intent include an article read, a video viewed or a whitepaper downloaded. Data aggregators collect all this data, match it to an organisation, apply algorithms to identify signifi cant intent ‘spikes’ and then categorise it according to specifi c topics of interest. Topics of interest to an IT vendor, for example, might include ‘cloud infrastructure’, ‘cloud security’ and ‘cloud storage’. Not only are target accounts monitored against topic interest but across individual divisions, headquarters and offi ces too.

Once topics and behaviours have been identifi ed, we give each organisation a baseline score and monitor engagement overtime, looking to identify exceptional and sustained spikes in interest.

Ideally, third-party data spikes should be ‘weighted’ with other factors that are meaningful to your business. For example, you may decide that multiple intent spikes across a series of related topics is more signifi cant; or that intent is more meaningful if you have an existing connection with an organisation (e.g. via Twitter).

Third-party intent across 2+ subtopics

“Transformational outreach activity”

With proprity weighting...

M360 people engagement signals

M360 company announcement signals

M360 content signals

how to score intent

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which companies are in-market?

Account 9

Account 11

Account 5

Account 3

Account 1

Account 4

Account 12

Account 7

Account 13

Account 15

Account 14

Account 16

Account 10

Account 6

Account 2

Account 8

The future of intent and ABM is likely to mean:

• Enriching account data with external data such as growth rates, funding information, credit risk data and technographic data indicating what technologies they are using.

• Applying modelling techniques to create predictive prototypes for scoring customers and prospects based on how likely they are to buy and when.

• Making predictive scores and account-level insights/data available in real-time to ad platforms, marketing automation systems and CRM systems.

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how to visualise intent data

Charts and graphs help bring intent data to life and make insights and trends easier to spot. By visualising third party data, for example and combining it with account signals, you can generate detailed reports about the buying behaviour of key account around particular topics (see graphic). Under each main topic – in this case Cloud computing – the graph shows relative interest by sub-topics as measured by clicks, views, download and so on. This in-market account analysis allows for intelligent targeting for maximum relevance. +

tactical opportunities strategic opportunities

1 2 3Strategic Value

Stre

ngth

of I

nten

t

54 60

50

60

70

80

90

100

Prudential Financial

Citigroup

PepsoCo

Fannie Mae

Home Depot

Cardinal Health

General Electric

Microsoft

IBMUnitedHeath Group

AmerisourceBergen

Express Scripts Holding

Boeing

Phillips 66

Berkshire Hathaway

Walmart

CVS Health

Amazon.com

Lowe’s

Target

Costco

Wells Fargo

Comcast

AT&T

Bank of America Corp.

UPS

Ford Motor

McKesson

AIG

Sector

TelecommunicationsTechnologyRetailProfessional ServicesOtherMediaHealthcareFMCGFinancial ServicesEnergyAutomobileAerospace

Generating detailed reports about the Intent and potential purchasing behaviours of accounts

In Market Account Analysis:Related to a particular topic such as Cloud or IT Sourcing, to determine the topics under this that the account has been looking at – highlighting in market accounts.

Intelligent targetig for maximum relevance:Real time account prioritisation for marketing activity

Third party intent

Account signals

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Storage

Application as a service

Public cloud

IT infrastructure

Infrastructure as a service

Virtualisation

Hybrid cloud

Private cloud

Cloud Security

Software as a service

Data centre

Colocation

Managed cloud

50 55 60

65

70 75 80

85

90

95

100

Cloud Tech – February 2017

Score (%)

visualising granular interest

Drilling further into Cloud computing data reveals a more granular level of interest through a range of sub-topics. Meanwhile, the metro score refl ects interest against the topic within a specifi c geographical (metro) area and benchmarks geographical differences against global and national scores. The data can even be mapped to specifi c offi ces, providing the potential to target teams and individuals with highly relevant content. There is also the option to include postcode data.

An example

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> 11 billion content consumption events per quarter

> Nearly 4 billion content consumption events every month

> +1 million companies monitored > 18 billion interactions tracked globally

to date

how intent data informs ABM actions

1. Real-time programmatic response (tactical)

When intent changes, your digital marketing programme should react accordingly channel by channel:

Marketing automation: adapt nurture programme messaging based on changing intent signals.

Social: identify accounts showing highest intent and engage with those individual infl uencers you have some contact with on a one-to-one basis. For example, invite them to connect on LinkedIn, share an infographic on Twitter and so on.

Paid: refi ne advertising creative to adapt to the changing intent status of the account. At MomentumABM we link intent and insights to ad platforms to allow us to update creative in real time.

2. Top Level response (strategic)

Beyond the immediate and programmatic, changes in intent allow you to take a longer term, strategic view. A quarterly review, for example, will identify industry trends that will feed into future ABM campaign management. It will also identify more nuanced changes in the behaviour of a specifi c account, allowing you to adapt. A longer-term view also allows you to track activity against anticipated moves across the sales funnel. Equally, you should be constantly monitoring changes in the topics used to categorise intent as these themselves help you stay ahead of buying signals. For example, the phrase ‘intelligent applications’ is emerging as a common phrase now artifi cial intelligence and machine learning technologies are adopted.

Intent data at scale

Momentum uses third-party intent data from a pool of data suppliers and co-operatives covering over 2,500 publisher sources. This gives clients access to topic based signals from:

> 11 billion content consumption events content consumption events per quarter

> Nearly 4 billion content consumption content consumption events every month

> +1 million companies monitoredcompanies monitored> 18 billion interactions tracked globally interactions tracked globally

to date

Intent data at scale

Momentum uses third-party intent data from a pool of data suppliers and co-operatives covering over 2,500 publisher sources. This gives clients access to topic based signals from:

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Intent data and account based marketing (ABM) are the perfect match. ABM is designed so you can get under the skin of your key accounts – to understand what makes them tick. Intent data provides signals of interest around the products and services you provide far earlier in the buying cycle than would otherwise be possible. MomentumABM is the only specialist, full spectrum ABM agency. We work with nine of the world’s 10 largest IT companies and we deliver significant financial impact.

Get in touch now to fi nd out more

next steps

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