Leadscoring1

INTRODUCTION
There is a need to move from traditional scoring techniques based
simply on BANT to a new scoring model fit for new age marketers. This
whitepaper sheds light on the factors that should be considered while
designing lead scoring methodologies for present day marketers.
PROBLEM STATEMENT
In the world of KPIs and executive summaries, where
everyone is hard pressed for time, making sense out of data deluge can
be tedious. In this context, significance of “Lead Scoring” in prioritizing
leads & measuring campaign success has been widely agreed upon.
However, same cannot be said about consensus on “Lead Scoring”
practices and norms. While, the old and beloved “BANT” is still the
most used scoring norm, there are many views that passionately point
out the limitations of “BANT”. But they haven’t yet concurred on a
single, comprehensive yet flexible standard.
In addition to Lead Scoring Norms, accuracy or effectiveness of Lead
Score is also a function of data collection techniques. Since realization
of a “Lead Scoring Algorithm” is affected by limitations in data
collection techniques, it is pertinent to quickly examine them for
arriving at the best possible scenario.
Data collection is possible through either information sought explicitly
or tracked implicitly.
“
”

Explicit Data Collection:
The data points are obtained from a prospect or lead by asking them
questions explicitly and then tabulating them. Such data collection is
generally done through web forms or during conversations (on call/at
event etc). Some of the short comings of explicit data collection are:
The number of data points that can be solicited, is likely to be
limited by responder fatigue.
Accuracy of data can’t be taken for granted as
q Respondents may deliberately provide in-accurate data to
avoid contact by sales.
q In case of pending decisions, the data provided may differ
from the actual, once these decisions are made.

Implicit Data Collection:
The behavioral data points (& psychographic attributes that manifest
as behavior) are obtained by trailing the lead or prospect. The
demographic data points in this case are obtained from relevant
databases that have already accumulated information. The biggest
advantage of implicit data collection is minimal (if not “zero”) gap
between intent and actual behavior. Some of the short comings of this
method of collecting data are:
The number of data points that can be collected just by trailing
the lead/prospect.
Possible privacy infringement
Before web-analytics, reverse-ip technology and cookies have gained
prominence and acceptance, it would have been very difficult to trail
B2B prospects (either online or offline) and analyze their behavior.
Hence the dependency on explicit data collection would have been
indispensible. But then, given the possible respondent fatigue, data
collection instruments would have been limited to collecting data
points that were considered most important. BANT, in our opinion
may have suffered from this limitation as it restricts itself to very few
parameters, while every “B2B Marketing Resource” emphasizes on the
complexity involved in engaging a prospect and converting them into a
customer.
But with advancements in web-tracking and web-analytics, the
scope for collecting different data points (& types) has increased
tremendously. In this context, it would be in the best interest of a
company to trail the web behavior of prospect, to collect all the data
points necessary for a scoring algorithm and resort to explicit data
collection only a) to avoid privacy infringement and b) in cases where
collecting specific data points implicitly is not possible.

Such an effort helps in identifying scoring parameters based on the best
practices and guidelines defined by different “B2B Marketing Resources
and Research” and the below scoring algorithm is an effort in that
direction.
In addition to recommendations from different marketing resources
& thought processes, other aspects considered while arriving at these
parameters are:
Avoiding double counting (i.e. giving credit to a single activity
manifested in multiple forms, multiple times)
Defining these parameters against the backdrop of “Marketing
Automation” platforms.
The parameters thus identified were:
Purchase Readiness (or) Decision Making Stage (P)
Degree of Intent (of prospects) revealed (I)
Engagement or Interest (Time Spent, Recency) (E)
Demographic factors and any purchase barriers defined by Sales
(eg: Revenue & other demographic data like Title etc) (D)
Identity of individual contact or prospect established (being able
to attribute behavior to an individual decision maker) (I)
Off-site (& Off-line) Activities (O)

PURCHASE READINESS (OR) DECISION STAGE
Significance & Implications
Research1 suggests that, since the time a need or problem is felt,
prospects (individual consumer or an organization), especially in case
of high-involvement products, go through below decision making stages
before actual transaction
i. information gathering
ii. evaluation (or trial) of alternatives
iii. purchase ready
Identifying the right “Purchase Decision Stage”, will help in:
Identifying activities and campaigns that connect with the
prospect most
Identifying activities and campaigns that propel prospects to the
next stage of decision making
Prioritizing based on immediacy exhibited
How to obtain data points
“Purchase Decision Stage” of a prospect can be obtained from the pages
visited on the website.
“Information Gathering Pages” are the ones which establish your
industry/sector, as the key to solving prospects’ need or problem.
They don’t speak about the superiority of your brand or the specific
solution you offer. They instead provide all the information that a
prospect should be aware of, to take a decision that serves him (or the
organization) the best.
“Evaluation (or trial)” pages are those which position your brand
(or your solution) as the best suited to serve the needs of prospect.
Whether it is explaining your solution in depth or the industry
feedback your company has received, the objective is to establish the

superiority of your brand (vis-à-vis competitors or bench marks) and
inspire trust in your brand and solution.
“Purchase Readiness” pages are the contact-us, RFx and similar pages
that a prospect visits to make the contact when he is more or less ready
for the final transaction.
Cheat Sheet
Examples of Information Gathering pages: Whitepapers, Blogs,
Webinars etc
Examples of Evaluation/Trial pages: Case Studies, Testimonials,
Demo, Free Trial, Pricing etc
Examples of Purchase Ready pages: Contact Us, Request for
Quote etc
If a prospect visits pages pertaining to multiple “Decision
Making Stages”, the most advanced of these “Decision Making
Stages” should be attributed to him
It is possible that some of the white papers or blogs are intended
to establish the superiority of your specific brand or solution. If
the content is clearly captured in the anchor text or title, such
white papers or articles should be considered as Evaluation/Trial
pages

DEGREE OF “INTENT” REVEALED
Significance & Implications:
Understanding the intent of a prospect (his “use-cases”, “purchase
barriers”, “opinions” etc), will help in identifying (or developing) the
right communication that captures their attention, establishes the most
appropriate associations, helps in overcoming any purchase barriers and
closing the deals faster.
The more we know about the “intent(s)” of prospect, the more it can be
used for our advantage in making the connection with the prospect. Hence
capturing as much intent as possible is desired. So prospects or leads have to
be scored based on how much of their intent is “known”.
Through web analytics, the information about intent of a prospect can be
identified from:
The pages (related to specific solutions/products/utilities/use-
cases/dispositions etc) visited by the prospect
The “search queries” used by the prospect in your website
internal search.
The “search queries” used in different search engines to arrive at
your website
The referral sources specific to the prospect (if they indicate any
specific context)
Any related comments by the prospect on different social
networks, blogs etc
Cheat Sheet
It is possible that some pages provide data points for both “intent” of the
prospect and also “purchase readiness” of the prospect. For example, consider
this webinar “Learn How To Turn More of Your Web Traffic Into Qualified
Sales Leads” by a marketing automation vendor. A visit from a prospect to
this page suggests that the prospect is in “Information Gathering” stage of
decision making. It also indicates the use-case/utility that is relevant to him.
In such cases, a visit to this page should be counted in both the sections of
scoring. That is because; this single act has provided multiple insights.

ENGAGEMENT (OR) INTEREST
Engagement impacts brand recall, strength of brand associations and
thus response of prospects to brand communication or sales pitch. The
higher the engagement, the more likely they are to warm-up to your
communication. Engagement can be captured in:
Total Time Spent
Recency of Last Visit or Last Few Visits
Information pertaining to a) time spent and b) date and time of last
visit is captured by Marketing Automation or Web Analytics vendors.
This information will help in determining the score for this particular
section.
Cheat Sheet
Page views & total repeat visits can also act as metrics for engagement.
But they are not considered since each additional page view or repeat
visit contributes towards time spent and thus results in “double
counting” when “Time Spent” is taken into account already. Also,
“Total Time Spent” is a better metric than the other two as it is sum of
products of page views and stickiness of each page or sum of products
of repeat visits and stickiness of each visit. Hence only “total times
spent was considered” to ensure single counting.

DEMOGRAPHIC & OTHERS PARAMETERS (MOSTLY
PURCHASE BARRIERS) DEFINED BY SALES
Inspite of all the interest evinced by prospects, there could be some
purchase barriers that can’t be overcome. They could be either
external, like revenue of the prospect (not sufficient to allocate budget
for your solution), laws of land in geography of prospects etc or internal
like unavailability of solution in the geography of prospect.
So, such demographic (or other similar factors) factors that result in
strong purchase barriers should be considered for scoring. Since it
is “Sales Team” that is more likely to be aware of such factors and
barriers, from their personal interaction with prospects during closure,
it is recommended that the parameters for this section of scoring are
obtained from them.
Obtaining these metrics or parameters from Sales Team will also
ensure higher acceptance for marketing qualified leads among Sales
Team.
Some of the demographic details would be captured in your web
analytics platform (for eg: geography). Rest of the details can be
obtained from various databases integrated with your marketing
automation platform. If the information is still not available, it should
be researched or sought explicitly.
Cheat Sheet
The most important (and compulsory in most cases) parameter in this
section is “Annual Revenue” of the prospect. This is because, annual
revenue of recent times determines current and future budgets in most
cases. It can also determine the potential for up-sell and cross-sell
opportunities.

IDENTITY OF INDIVIDUAL CONTACT OR LEAD
ESTABLISHED
All the leads or contacts (specific individuals) who have identified
themselves on your website or through email campaigns:
Allow you to track their personal web-behavior (only on your
website) and thus let you customize website experience and
nurturing content for them (at individual decision maker level).
Reduce your overheads in researching about the prospect and
identifying contact details of decision makers who are more than
happy to be contacted.
Hence such leads should be scored for these advantages they offer.
The necessary data points will be captured automatically by your
marketing automation (or web-analytics) vendor.

OFF-SITE (& OFF-LINE) ACTIVITIES
Marketing Automation Platforms or web-analytics tools will only be able
to track online prospect behavior or engagement on your website (limited
tracking on other portals like social networks maybe available). Some of
the key parameters that web-analytics tools will not be able to capture
“automatically”, but still are relevant are:
Offline engagement (at events, exhibitions etc) and insights
(behavioral or psychographic) developed during these interactions.
Milestones (like a “Meeting” set-up) which are influenced by
different offline, offsite, online interactions and confirmed offline
(eg: on phone)
Psychographic factors that don’t translate into website behavior
The data points have to be exported into “Scoring Algorithm” from
offline data collection instruments to ensure that output from the scoring
algorithm is comprehensive.
SUMMARY
Established based on website behavior captured
Higher engagement is larger mindshare
Based on demographics & sales team preferences
Higher the score, the more likely they are to be accepted by sales
Identified from form fills, email campaigns, webinars etc
Establishes the exact contact for the Sales Team to pitch
{ }

How does “BANT” translate in the new scoring
Algorithm (“PIEDIO”)?
In the new scoring algorithm, “Budget” from “BANT” translates as
“Revenue” of the prospect (a Demographic parameter defined by sales),
which indicates the scope for budget allocation either immediately
or in near future. This also addresses one of the major criticisms laid
against “BANT” that, it is responsibility of marketers and sales team
to get a prospect allocate budget by effectively communicating the
need & benefits even if there was no prior budget allocation and hence
considering “Budget” as a parameter is short sighted.
n Extracted from Databases
n Explicitly asked in web forms
n Tracked over time by web analytics
& MA Tools
n Exported from offline interactions
n Explicitly asked & tracked from
form field data
n Translated into behavioral attributes to
track automatically using web analytics
n Exported from offline interactions
n Explicitly asked & tracked from form field
data

In the times of marketing automation and contact database
integration, nurturing activities are targeted at engaging only the
decision makers with right authority. Thus the parameter “Authority”
is implicitly addressed in most of the present day marketing efforts.
In case it is not addressed or if there is a need for additional emphasis
on decision makers’ titles and seniority, necessary inclusion in
“Demographic parameters defined by sales” will serve the purpose.

“Need” can be established based on the level of “Engagement” &
degree of “Intent” revealed by the prospect.
“Timeline” can be established based on “Purchase Readiness” (or
Decision Stage).

So the new scoring algorithm (“PIEDIO”) addresses all the aspects of
“BANT” and also a host of other very relevant aspects.
Scoring based on “Social Media” activity in “PIEDIO”
If your web-analytics or marketing automation solution is able
to track all necessary social interactions of your prospects, they
can be scored appropriately under “Degree of Intent Revealed” or
“Engagement”.
If all the interactions of your leads (or prospects) on social platform
are not tracked automatically, they may have to be captured using
other instruments and uploaded into your scoring system under
relevant sections or under “Off-Site” activities as appropriate.
Thus this scoring algorithm is capable of bridging the gaps in scoring
your leads or prospects based on their “social” activities.
Score Depreciation in “PIEDIO”
This scoring algorithm also allows for score depreciation based
on inactivity. All data points pertaining to “Recency” (within
“Engagement”) that are updated in real-time (or periodically) affect
score depreciation along with other score changes automatically.
Scoring – Other Points of Note
n Scoring is not just for prioritizing or quarantining leads.
It can also be used2 to identify objectives and processes for
future lead nurturing efforts (like AB testing landing pages
for a prospect scoring low on “Time Spent” or trying new
channels to reach a prospect scoring low on “Recency” or
trying new link baits for a prospect scoring low on “Degree of
Intent Revealed” etc).
n Increase in score can be considered as the common denominator
(objective) for all lead-nurturing campaigns. Hence, it can
be regarded as common KPI and can be used for normalizing
performance measurement across campaigns.

}
n In case, implicit data collection falls short in identifying
necessary data points, your marketing automation
solution can provide you custom dynamic form fields (based
on past behaviour & form fills) to capture necessary
information (explicitly). Data thus obtained from these form
fields can augment data points (collected implicitly) across all
the sections of scoring.
n Company level score is not a simple addition of individual
“scores” of all the visitors/decision makers from the company,
but is an outcome of scoring aggregated activity of all
individuals from the company.
n Having maximum score limits across various
scoring parameters will help in normalizing and ensures ease of
comparison between leads.
n Weightage distribution across different scoring parameters
may be influenced by various internal factors (like resource
availability etc), external factors (like economic conditions
etc) and hence may have to be changed with time as
appropriate. Weightage distribution can also be used to drive
specific desired behaviour across organization.
Note
n In recent times, some of the marketing folks3 have even tried
to expand the traditional stages of decision making1.
For this particular scoring algorithm, only traditional stages of
decision making are considered for ease of application and wider
acceptance.
n The name “PIEDIO” (Purchase Readiness, Intent Revealed,
Engagement, Demographics & Purchase Barriers, Identity of
Lead (or Contact) Established, Off-site (& Off-line) Activities)
was used only for ease of recall. It doesn’t have any other
significance and hence is dispensible.
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Leadscoring1

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