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Predictive Lead Generation
Predictive Lead Scoring
A buying guide from experts to:
Understand the emerging Predictive market
Evaluate vendors In North America and Europe
Master common use cases & scenarios
Predictive Analytics for
marketing and sales
The market for B2B Predictive Analytics SaaS technologies has enjoyed
uncontested success in recent years. Many early adopters have testified
to the value of these innovative solutions, lauding their ability to forecast
future business successes based on existing data patterns.
Though they are often associated with pure
lead generation, Predictive Analytics solutions
are not limited to identifying new prospects,
but can in fact be used at different stages of
the sales cycle. When applied to new leads,
these technologies can help gauge buyer
intent.
Outbound prospecting, campaigns, account-based marketing
Inbound leads, demand generation programs
Opportunities, pipeline forecasting
Proposals, pricing
Upsell, cross-sell,
churn
Existing leads can likewise be evaluated
according to their propensity to convert, and
the outcomes of existing opportunities can
also be assessed. Finally, additional scoring
scenarios are possible according to the
configuration of the technology (see below).
Predictive lead generation and
predictive lead scoring
Predictive lead scoring
Predictive opportunity scoring
Predictive opportunity scoring
Predictive lead scoring
Considered a subcategory of Predictive Analytics, Predictive Lead Generation, which is dedicated
to scoring new leads, along with Lead Scoring, which focuses on scoring inbound and existing
leads, are the most commonly used types of solutions. Essentially, Predictive Lead Generation is
used as a capability enhancement technology for marketers looking to drive conversion and build
a steady and predictable sales funnel.
Market dynamics in North
America and Europe
Adoption Rates and Existing Customer Base
Predictive solutions are
enjoying a rapid adoption
rate around the globe from
a variety of customers.
Unsurprisingly, the IT
industry is spearheading
the use of Predictive
Analytics, with an adoption
rate of 78%1. The overall
existing customer base
includes large,
multinational corporations
(Amex, Oracle, La Poste,
Dell, GE, Microsoft, BT,
Lloyds Bank, IBM, Adobe,
among others), as well
as small,up-and-coming
entities such as Docusign,
Marketo, Optimizely,
Tableau, AdRoll, Box, and
Zendesk. This would seem
to indicate that
company size is not a
barrier to adoption. In fact,
SMBs have embraced
predictive lead scoring
much faster than large
companies, with 17%2 of
B2B SMBs ($250m to $1B
in revenues) already using
predictive lead scoring.
“When considering different solutions, Predictive Lead Generation for
prospecting makes sense for SMBs who may not have largescale lead
generation programs in place. On the other hand,bigger organizations with
a steadier lead stream can use predictive analytics to refine existing data,
helping prioritize sales and marketing activities all along the funnel.”
David Raab, consultant in marketing technology evaluation and analytics.
The good news for Predictive Analytics suppliers is that the market is far from saturated:
according to influential research firm TOPO, 37%3 of high growth companies plan to invest in
predictive analytics over the next 12 months.
At publication of this ebook, we estimate that roughly 800 companies are currently using
Predictive Analytics for marketing and sales purposes in North America and Europe. Indeed,
some companies actually implement multiple solutions in order to cover different geographic
areas. This overlap is sometimes necessary since Predictive Analytics providers are limited by
the regions covered by the data providers they work with.
1 Sirius Decisions -Predictive Lead Scoring study - (2014)
2 Sirius Decisions -Predictive Lead Scoring study - (2014)
3 TOPO -B2B Predictive Analytics Technology Report - (2015)
Differences between North America and Europe
The market for Predictive Analytics applications for B2B sales and marketing professionals
differs slightly between North America and Europe. The majority of American vendors, such as
Infer, Lattice Engines, 6sense, and Fliptop, have chosen to build their offer around scoring
inbound or existing leads and opportunities, complimenting a strong culture of inbound
marketing in the States.
European players like IKO System are instead more likely to position themselves as an outbound
prospecting or Account-Based Marketing solution, focusing on scoring net new leads.
Vendors in Europe typically target the top of the sales funnel, addressing the need to generate
new prospects that yield higher conversion rates than traditional outbound leads.
SiriusDecisions explains the disparity between European and American vendors:
“Most vendors in the North American market have spread to a broader part
of the sales funnel. This can be attributed to the revival of outbound
marketing in the U.S. that until now had been largely ignored due to a strong
inbound culture.”
Kerry Cunningham, research director at SiriusDecisions
Predictive Lead Generation at a glance
Predictive Lead Generation solutions identify and score leads by buying intent
probability. The technology relies on past sales successes, business environment and
extensive external data to qualify the buying propensity of new leads on a given market.
This allows the prioritization of marketing and sales actions relating to
higher-conversion and higher-velocity leads.
The WHY: specific
predictive scenarios
We have seen that predictive technology can be applied at different stages of the funnel, and
can address different business-related goals. Although American and European vendors have
made an effort to differentiate their respective offers, recent surveys show that marketers’
chief business concerns are practically equivalent on both sides of the Atlantic:
Top Marketing priorities: North America vs EMEA
Source: Hubspot State of Inbound 2014
30
20
10
0
Increase the
number of leads
Convert leads to
customers
Predictive lead
generation
Predictive oppty
scoring
Reach relevant
audience
Upsell and
cross-sell
Predictive lead
scoring
Below, we will detail common use cases for Predictive Analytics that address
these concerns, including:
• Building an outbound machine with Predictive Lead Generation
• Uncover the buying probability of inbound or existing CRM leads
• Build an Account-Based Marketing process
• Score opportunities’ potential, forecast revenues
• Other applications
Scenario 1: Build an outbound engine with Predictive
Lead Generation
Defining an optimal demand
generation strategy often
boils down to determining
the most effective mix of
inbound and outbound
marketing techniques to
implement. While most
companies have favored
inbound tactics over the
past few years, its inherent
limits - issues like content
resources, growth
inertia, and scalabilty - have
prompted businesses to
start exploring new
outbound prospecting
strategies. I dicative of this
new orientation is the fact
that leadingnames on the
inbound marketing scene,
such as Hubspot, Adobe,
Marketo, Oracle/Eloqua and
others, have all turned to
Predictive Lead
Generation as a main
resource. These
technologies are put in
place primarily to fortify
outboundteams charged
with ensuring the acquisition of new customers and
prospecting large, complex
accounts.
Facilitating this recent
interest in outbound are
the myriad of new tactics
in sales automation, social
selling, and inbound-outbound aggregated
intelligence. These tools
increase outbound
engagement metrics and
lead conversions, making it
easier to track progress and
ROI.
The acquisition of Fliptop
by Linkedin in August 2015
confirms that there is
indeed a strong market
need for leads based on
predictive analytics, as
opposed to more traditional
“database leads” filtered
merely by job title or other
similar criteria.
“Predictive Analytics brings the most value at the top of the funnel, before
Sales Reps have a relationship with prospects. Finding the best leads is
important, but engaging these mature leads before your competitors do is
critical.”
Matt Heinz, president of Heinz Marketing Inc
How does it work?
Predictive Lead Generation is changing the face of outbound marketing by scoring the buying
probability of all potential leads on the market for their customers’ offer.
By identifying dynamic patterns - also known as buying signals - from existing customers (i.e.
past successes), the Predictive Lead Generation vendors create the DNA signature of the ideal
customer profile (ICP) and scan all potential leads on the market to score their buying
propensity.
Challenges addressed
Predictive Lead Generation helps marketers address strategic and tactical goals:
• How do I identify new leads with a high probability of converting?
• How can I create a streamlined system to identify, engage and qualify a given number of new
leads per day in a routine and scalable way?
• How do I build a reliable and predictable sales funnel?
• How can I improve my lead-to-opportunity conversion rate?
• What strategy can I put in place to rapidly attack green fields (i.e. new markets)?
• How can I improve department efficiency so that the sales team can fully focus on
worthwhile leads?
Predictive benefits
Predictive Lead Generation provides a flow of net new
leads ranked by buying probability, along with
significant signals or patterns relevant to that lead.
Some vendors provide additional capabilities useful
for lower down the sales funnel that allow
prospectors to automatically engage with leads via
email workflows or ad retargeting. They might also
allow prospectors to measure the effectiveness of new
leads provided by predictive analytics by evaluating
conversion rates or velocity in the sales cycle.
Predictive Lead Generation
solutions can also usually
provide new leads from a list
of named accounts, in which
the territory is delimited,
or from a dynamic territory,
using criteria such as
demographics and
firmographics.
Scenario 2: Uncover the buying probability of inbound
or existing CRM leads
By matching leads with a given ICP (Ideal Customer Profile), Predictive Lead Scoring
solutions can also evaluate the buying probability of incoming or existing leads in a
client’s CRM. In this scenario, vendors rely not only on demographics, firmographics
and external lead or company data, but also intensively analyze the past CRM activity
of each lead, including lead source, campaigns and activities. The more criteria there
is to analyze, the better the modeling and scoring becomes. Behavioral data from
marketing automation sources can also be incorporated into the predictive model if
such technology is in place.
Challenges addressed
Predictive Lead scoring addresses the following questions often posed by marketers:
• Which dormant leads in my CRM are worth warming up right now?
• How do I prioritize incoming leads? How do I establish incoming routing?
• How do I encourage my sales reps to accept and contact leads?
• How do I segment my contact database to generate the best leads?
Predictive benefits
First, custom fields are
created in the CRM or
marketing automation
platform to display lead
scores. Lead routing rules
are then established to
helps sales and marketing
departments better
prioritize lead engagement,
attribute prospects to
each sales rep, build
custom reports, or run
specific campaigns for
mature or high-value
dormant leads. Predictive
Lead Scoring can also be
leveraged to measure the
success of marketing
actions (sources/campaigns/programs) by
evaluating the quality of the
leads they produce.
Some predictive vendors
also provide ‘scoring
reasons’, or insights that
explain why a lead is
given the score it has been
attributed. They may also
append CRM fields with
relevant lead or account
data missing in the client’s
CRM from their own
sources.
Scenario 3: Build an account-based marketing process
Jon Miller, co-founder of Marketo and now CEO of Engagio,
defines ABM as, “Intentional go-to-market activities that
coordinate personalized marketing and sales efforts to open
doors and deepen engagement at a specific account.”
Essentially, ABM aims to allow marketing and sales
departments to align and focus their efforts to target a
limited number of key accounts.
Many new technology layers and organizational processes
have recently emerged to provide customers with a
consistent Account-Based Marketing strategy. TOPO deftly
summarizes how Predictive Analytics can be applied to an
ABM Strategy using the following five steps4:
”Intentional go-tomarket activities
that coordinate
personalized
marketing and sales
efforts to open doors
and deepen
engagement at a
specific account.”
4 http://blog.topohq.com/the-account-based-marketing-technology-stack-emerges/
• Build a list of target accounts using Predictive Analytics
• List the best contacts into these accounts with Predictive Lead Generation
• Engage leads with a personalized approach: either through email workflows or targeted
advertising (some predictive vendors provide solutions and third party integrations)
• Personalize the website experience for targeted accounts
• Manage the CRM with an account-focused approach (for example by linking leads to
accounts in SFDC or analyzing the CRM metrics by accounts)
Challenges addressed
Predictive Lead Generation addresses the following challenges inherent in ABM:
• How to uncover a list of high-potential accounts to target for a specific offering?
• How to find the best contacts to address in specific accounts?
Predictive benefits
As long as an agreed-upon
ICP exists, Predictive Lead
Generation vendors can
evaluate the potential of all
addressable accounts on a
market and rank them by
buying probability. This
strategy yields much better
results than simply
guessing which accounts
will be the most fruitful
based on static lists. In
most cases, one predictive
model is used per product
or per offer, although
occasionally individual
models can be created per
sales territory. Once a
territory has been
established and if the model
has been configured to
surface individual leads,
the predictive solution will
score the buying
propensity of each contact
- in all likelihood a decision
maker - within each
targeted account.
Scenario 4: Score opportunities’ potential, forecast revenues
Trying to forecast how many open opportunities will close within a given
timeframe has always been a challenge for VP Sales professionals. Similarly,
sales reps are also constantly asked to prioritize their prospecting efforts based
on which leads seem more promising, a task which is not always easy to
accomplish due to lack of relevant data. Predictive Opportunity Scoring vendors
can step in to help solve these problems, and can also often predict close rate,
deal size, and close date.
As opposed to scenarios 1 through 3, predictive models based principally on the
ICP are not sufficient here to evaluate the propensity of winning an opportunity.
Predictive Opportunity Scoring instead leverages data other than demographics,
firmographics or corporate buying patterns, such as the sales actions, on-going
deal momentum and the structure of an opportunity (price/terms/product...).
Challenges addressed
Predictive Opportunity Scoring solutions address these questions:
• What is the quality of the pipeline?
• How much revenue will we close this quarter?
• Which of the current opportunities require additional attention from sales reps?
• Are there open opportunities which could pay more than we anticipate?
• Which sales-forecasted opportunities are at risk?
Predictive benefits
Predictive Opportunity Scoring solutions rank each
opportunity by its propensity to close, at an expected amount
and at a certain time. VPs of Sales build reports from these
predictions, (though some reports are automatically created
by thesolution), to identify the lists of opportunities at risk
(low score), to evaluate the total bookings expected for a
period, and to identify opportunities where ‘money is left on
the table’.
Other scenarios
Some predictive vendors focus on other parts of the funnel.
For example, they might predict the success of marketing
programs, allow one to optimize pricing, predict renewal/
churn potential of recurring customers, or predict upsell/
crosssell opportunities in certain accounts.
Essentially, the technology that supports predictive analytics
can be applied to any consistent dataset of high quality. One
vendor (InsideSales.com) even uses predictive technology to
evaluate the potential productivity or performance of
tobe-recruited salespeople by comparing the candidate’s
‘skill DNA’ against a model of existing top sales performers.
5 Sirius Decisions -Predictive Lead Scoring study- (2014)
Benefits seen
by current users5
90%
of users say that predictive
lead scoring provides more
value than traditional lead
scoring.
98%
of current users of Predictive
Lead Scoring say they would
purchase again.
250%
average ROI from
implementing predictive
lead scoring.
Predictive Analytics in
action: How does it work?
Acquiring internal data
Typically, predictive vendors will first build the ICP model that will be used to evaluate the
potential success of leads or opportunities. In order to build this profile, potential clients must
provide some sample data: a certain number of sample won deals or customers, examples of
failed deals, or the definition of a target market (demographics, firmographics, buying
patterns, etc.,). This data is usually extracted or synchronized with the CRM or the Marketing
Automation platform in use by the potential client.
Building the ICP model
Derived from a core
scientific principle of data
analysis, the customer’s
historical data is used to
predict future activity.
Models for lead scoring,
opportunity scoring
or pricing optimization rely
on different datasets.
Since many vendors in the
market use similar data
science algorithms,
differentiation is often
based on data inputs and
how they are incorporated
into predictive models, and
not on the algorithm itself.
create the ideal customer
profile (ICP).
When a solution is used for
predictive lead generation,
the lead scoring model is
built by first analyzing the
customer’s past client
successes and their
business environment.
Then, thousands of sources
of external behavioral data
not found in the user’s CRM
(from the web, social media,
and other third-party data
sources) are leveraged to
The timeframe for the initial
implementation varies from
vendor to vendor. Set-up
can be completed in as little
as one day, or can take up
to two weeks. The amount
of data to be analyzed and
the number of models to
be used are the two factors
that most affect the
implementation times for a
given solution.
Data sources used by vendors
Datasets used for modeling can include:
Demographics: This data can include job title, department, hierarchical position, location,
past companies, time spent in the current/past positions, age, gender, social metrics... Some
vendors provide the ability to reach the lead (valid email, direct number, past responses...) into
the scoring model.
Firmographics: Static company data can include the size, industry, location, financial metrics
(revenue, margin, growth, etc), website metrics, activity description, existing customers,
technologies they use, service providers they have.
Activity data in the CRM: Predictive vendors can leverage the activity data (at a lead or
account level) existing in the CRM or marketing automation platform. This goes from past
calls, status change, downloading an ebook, attending a webinar or visiting the pricing page.
External buying signals: External data is acquired at the corporate (mainly) or the lead levels.
These are thousands of data sources that providers can leverage to build the DNA of a
high-conversion lead. Digging into the types of sources each provider can work out is key.
External dynamic data can be for example:
• The lead works in a department hiring +30% employees in the last 6 months
• The company announces an internal expansion to a country you’re in
• The company issues a job listing on the function X
• The lead is a reference on one of your competitors’ corporate websites
• A press article discloses that the company just raise VC funds
• The lead is just appointed to a new position
Building the ICP model
Once a lead scoring model has been built for a customer,
each new success or failure has to influence and impact the
model. Market conditions change, prices changes, and the
way sales reps work changes also. Vendors must give
visibility on model evolution, either through their customer
success teams or using a continuous evolution process with
machine learning algorithms.
Delivering value: demo, process, routine
Most predictive analytics vendors will stress the importance of both establishing a clear goal
for the predictive solution and arranging a demonstration of the technology as part of the
evaluation process. The goal can be something along the lines of, “show me new leads for this
offer/target, or, “score this sample of my leads/opportunities.” Even if the vendor’s demo runs
on a limited set of sample data, a prospect is nevertheless able to see first-hand the value of
the product, an essential prerequisite to purchase.
Vendors will also insist on the truism that no technology creates success all by itself. Instead,
like any business solution, it is part of an equation which includes the people and the process
implemented alongside the technology in place.
success = people + process + technology
In order to test if a certain vendor’s solution is compatible with a given business model, it’s
essential to review case studies in which the technology has been implemented in a similar
setting. Important to understand is:
• who in the team will use the technology and for what purposes? (such as BDRs for engaging
new leads, sales managers for forecasting bookings, field sales reps for prioritizing
opportunity efforts, marketing program managers for monitoring campaigns, customer
success teams for preventing churn, etc…)
• what is the process for each user, what is the daily routine?
(such as “engage 30 new leads a day” or “weekly review of
an opportunity report in the CRM.”)
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At the core of the marketing technology stack
While there are 2,000 marketing technology vendors on the market fulfilling many marketing
needs, from managing social channels to organizing content creation or running webinars,
there are a few tools that are essential to most ‘technology stacks’:
• CRM: CRMs centralize all customer data (from incoming leads to opportunities, along with
sales activities)
• Web Content Platforms: Web content platforms such as WordPress host websites and most
marketing activities
• Marketing Automation: Marketing Automation platforms automate important marketing and
sales activities like emailing campaigns
• Predictive Analytic Solutions: Predictive solutions prioritize all marketing and sales
activities, from acquiring new leads to converting new customers or retaining existing ones.
They also allow for the accurate forecasting of future revenue.
Read our complimentary free report:
“Scoring predictive scoring vendors”
• Vendors’ differentiators
• List of existing customers
• Starting prices and pricing models
• Customer concerns
predictive scoring
vendors