Future Proof: How to Create a Lead Scoring Model That Scales

Future Proof: How to Create a
Lead Scoring Model That Scales
Kylie Alexander | Marketing Automation Manager

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Speaker
Kylie Alexander
Marketing Operations Manager
Adobe

Growing your marketing
team can mean
growing pains

When growth is fast and furious...
New marketers come onboard fast.
• They specialize in different regions,
priorities, products, etc.
• They build one-on-one relationships
with sales.
• They each may have different goals—
and different opinions of how lead-
scoring should work.
When lead scoring isn’t fully coordinated
across your entire organization, problems
can occur.

Inconsistent data
There is no consistent definition of a qualified
lead. This means:
 You cannot analyze qualified and accepted
leads across your entire organization,
because you’ll be comparing apples to
oranges, pomegranates, etc.
 You can’t find out if your scoring models are
working
 Marketing attribution is a lot more difficult

Unpredictable results
BDRs and sales reps never know what
they’re going to get.
 The quality of leads offered each BDR
may be wildly different.
 Leads may be allocated based on one-
on-one relationships.
 Lead scores may be manually adjusted
on the fly so marketing and/or sales can
meet short-term targets.

Two years ago, Marketo
faced the very same
challenges

Marketo Marketing Operations—2018
 We had a small marketing ops team—
one internal employee and a few
contractors.
 The company had onboarded all kinds of
marketers—customer marketers,
demand gen marketers, event
marketers, etc.
 We had a standardized lead-scoring
model set up in our instance of Marketo
Engage, but no formal governance or
process for changing it.

Our challenges
Things had to change
In some situations, marketers
would manually change system-
generated lead scores to:
 Meet short term goals
 Make lead scores more
accurate for their region or
strategic focus
Occasionally, marketers would tinker
with the companywide lead-scoring
model, which led to:
 Inconsistent lead-scoring data
 Difficulty getting meaningful
reports on marketing
performance

We got buy-in from leadership
and stakeholders.
In every region, we talked
to:
 Sales
 Sales operations
 Business development
 Customer success
 Marketing

It took many iterations, but we got there
We updated regional lead-scoring criteria to be more flexible.
 Lead scoring now relies on three types of scoring, which may be weighted differently
for each region:
1. Behavioral
2. Demographic
3. Ideal customer profile
 We implemented a closed loop process for BDRs to provide feedback on
their leads
 We assembled a governance committee and defined a process for periodically
evaluating and updating our scoring model and responding to questions.

Then we spread the word
Communicating our new lead-scoring
processes was critical to their acceptance.
So:
 We onboarded everyone—marketers,
BDRs, sales—with formalized training
sessions.
 We made sure everyone knew what to do
when they had questions or concerns
about lead scoring.

We became...
More effectiveMore agile More knowledgeable

More agile
 When sales and marketing teams have
questions about lead quality or quantity, our
governance committee addresses them.
 We use data-driven analysis to investigate
potential problems.
 If we find them, we adjust our approach.
– For example, we discovered that leads from
content syndication were being qualified too
early.
– We updated the lead-scoring model, which led
to higher-quality leads.

More knowledgeable
 With consistent scoring data, we can get
a better understanding of how well our
marketing programs are performing.
 We’re now confident our reports are
accurate, so we use them to make
forward-looking planning decisions.

More effective
 By continually evolving our lead-scoring
model and using lead scores to inform our
marketing strategies, we set the stage for
rapid growth.
 Even during our acquisition by Adobe, we
were able to keep growing our pipeline.
 The same agile approach is working for us
today.

Future Proof: How to Create a Lead Scoring Model That Scales

Future Proof: How to Create a Lead Scoring Model That Scales