Guide to Predictive Lead Scoring

Lead scoring has an important role to play in modern B2B
marketing and sales. It is a useful system for gauging a
prospect’s likelihood of buying, allowing salespeople to
prioritize their targets. In this guide, we will cover what
lead scoring is in more detail, the ways it can be improved
through predictive analytics, and how and why you should
use predictive lead scoring system to stay ahead of your
competition.

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What is Lead Scoring ?
Lead scoring is a methodology to rank prospects against a scale that represents the perceived value each
lead represents to the organization. The resulting score is used to determine which leads a receiving function
(e.g sales, partners, teleprospecting) will engage, in order of priority. I
Through assigning scores to prospective customers or clients to gauge buying intent and readiness, the
following benefits can be achieved:
Increased sales efficiency due to the prioritization of leads.
Marketing efforts made more effective.
Tighter marketing and sales alignment.
In a typical lead scoring system, a prospective customer will have a variety of criteria that makes up their
score. With attributes like company, budget, and location, the prospect is assigned a score of how likely
they are to buy. If we add in actions such as signing up for a newsletter, clicking on a sales link or filling
out a form, we add behavioral criteria that adds to the score.
“
All Names
Prospect
Engaged
MQL
Sales Lead
Opportunity
Customer
Early stage content:
Attend webinar:
Visit any webpage/blog:
Visit careers pages:
Pricing pages:
Watch demos:
Mid-stage content:
Late-stage content:
+3
+5
+1
-10
+10
+5
+8
+12
Behaviors
Job Title:
Generic email
Industry:
Technology:
+20
-5
+10
+5
Demographics

Lead scoring tools require users to select the attributes and actions on which to score the leads and to
determine the actual score. However, it’s largely down to human intuition to set the values and weights
in the first place.
Most marketing automation providers recommend sales and marketing teams have a large brainstorming
session to decide what is an ideal lead and its score. This appears good on the surface and it asks for
cross-departmental cooperation. However, the resulting lead scoring is based a lot on assumptions the
team makes. They may be educated assumptions, but assumptions nonetheless.
This lead scoring system - one that relies
on assumptions, can quickly result in sales
disenchantment, to the point where good leads
are routinely ignored and demand generation ROI
suffers accordingly.
Companies often launch scoring schemes that just
don’t work; by the time the scheme is modified,
sales teams need to be re-sold and re-trained on
what they now regard as inaccurate and irrelevant data. Even if sales teams haven’t been through this
process, they may opt to ignore the scores attributed to leads and use their intuition to pick the winners.
It can even happen that the marketing team effectively guesses the lead scoring scheme and the sales
team ignores the scores entirely. This is bad for the marketing department, as lead scoring optimization
is a critical part of ensuring that companies reap the greatest benefit from the investment in marketing
technology. It is doubly bad for sales, as they spend a lot of their time chasing leads that don’t convert
and most likely pass over leads that are great fits for the company.
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Problems with Traditional Lead Scoring

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Big Data and Predictive Analytics
A SiriusDecisions report stated that ‘94% of all Marketing Qualified Leads will never convertII. This
means with all of Marketing’s efforts only 6% are leading to customers. Evidently there’s some way to go
in improving the lead scoring processes. This is where predictive lead scoring comes in.
The first step in building a lead scoring process that works is to accurately define what success looks like
for the organization. In the cases of most B2B organizations, success looks like closed won opportunity
information inside of the Customer Relationship Management (“CRM’’) system. Once success is
established, the next place to look is at the digital footprint of that company.
Your customers all have a digital footprint online – through their social networks
like Twitter, blogs, press releases, job openings and the technology on their
corporate website. This abundance of data on the public web and in your CRM
system can be leveraged to find the ideal customer for your business. But rarely
is this data well structured; any organization with a CRM has been collecting a
goldmine of data, but most are not leveraging it to draw actionable insights and
find meaningful correlations.
Predictive analytics can help. Through machine learning a predictive model can
crunch through large data sets and structure data properly to deduce patterns
that went previously unnoticed. Predictive lead scoring companies like Fliptop
are able to take this data structure it properly. We’re also able to find previously unseen relationships
between data sets and predict likely outcomes through predictive modeling.
Predictive modellng is a commonly used statistical technique to predict future behavior. Predictive modeling
solutions are a form of data-mining technology that work by analyzing historical and current data and
generating a model to help predict future outcomes. In predictive modelling, data is collected, a statistical
model is formulated, predictions are made, and the model is validated (or revised) as additional data
becomes available. III
“

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It starts with your
existing CRM
opportunity data
It adds thousands of
person and company
attributes
We automatically create
predictive scoring
models tailored for you
It applies scores to your lists or
directly within your sales and
marketing applications
How Predictive Lead Scoring Works
A predictive model always starts by understanding what success looks like and for most B2B enterprises,
this is “closed won” opportunities.
The system can analyze thousands of public and internal data points in order to construct a “fingerprint”
of what a good lead or account looks like.
This information could include fields like:
Size of company, revenue, legal status
Social network memberships and activity
Marketing actions - e.g. prospect clicked A, B or C
Technology the company uses on their website
In the near future, predictive engines tied into marketing automation systems will be able to automate
the entire process of assigning scores to activities, so marketing teams no longer have to guess the value
of downloading a white paper or attending a webinar.

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Once the predictive algorithm has learned which data signals typically result in a sale, new leads can
be scored instantly. No longer are marketing teams throwing all the leads over the fence to sales and
waiting weeks or months to see the results.
Because most marketers are not doing any form of predictive analytics today, the potential for upside is
huge. Imagine if your business could definitively answer the following questions:
Which leads should marketing send to sales, nurture or discard?
What accounts or contacts should sales be prospecting next?
Which programs should marketing scale back or double-down on?
How likely is sales to make quota this quarter?
Which accounts are ripe for cross-sell/upsell?
Which accounts are most likely to churn?
In the absence of predictive analytics, these questions inevitably are answered by HiPPO - the highest-
paid person’s opinion. Those who operate by gut instinct are at a distinct disadvantage in the marketplace
against those who leverage data.

5 Reasons You Should Adopt a Predictive Lead Scoring
Technology
Predictive analytics clearly gives a multitude of key advantages over traditional lead scoring models.
In summary, five of the most important factors for choosing a predictive lead scoring technology are
below:
How You Can Get Started with Predictive Lead Scoring ?
If you would like your own predictive scoring model, please contact us at info@fliptop.com
or call 888 373 7533.
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1. Base decisions on data, not intuition – many established lead scoring systems are set up based
on intuition – a predictive technology removes this risk.
2. See the bigger picture through more data points – since predictive lead scoring technology
can use thousands of different signals and data points, you’re likely to get a much deeper
understanding of your prospects.
3. Understand previously unseen patterns through algorithms – these signals are sent through
a machine learning system, which can spot previously unnoticeable patterns in prospect
behavior.
4. Machine learning will optimize your lead scoring patterns – over time, your lead scoring
system will have a deep understanding on the behaviours and combinations that drive a sale
specific to your market, removing possible errors of human judgement.
5. Get a closer alignment between marketing and sales – an improved lead scoring system will
almost certainly lead to a better alignment between these two departments, given marketing
qualified leads will have a better chance of converting.

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References
Famico, Jay, “What is Lead Scoring, Anyway?”,
www.siriusdecisions.com, January 14, 2013
http://www.siriusdecisions.com/blog/what-is-lead-scoring-anyway/
Gartner IR Glossary
http://www.gartner.com/it-glossary/predictive-modeling
I
II
III
Sirius Decisions
http://www.siriusdecisions.com/