Analytics that Matter: How to Turn Raw Data Into Actionable Insight Through Hypothesis-Driven Thinking

1
Copyright © 2022 Demandbase

2
© 2021 Demandbase |
Agenda
1. Mindset Shift
2. KPIs that Matter
3. Common Blockers
4. Ingredients for Hypothesis
Driven Analysis
5. Real World Use Cases

3
For marketing to make the most impact. . .
What you say How you say it
(Positioning)
Data behind it

4
Don’t just read the news, make the news
VS
47 MQLs! Action!

5
All marketers have the same goal
What do I do
more?
What do I do
less?
What do I do
different?

6
What are the
KPIs that matter?

7
C-suite Marketers
vs
Depends on the audience

8
What’s next!
KPIs differ depending on your audience
C-suite
● Pipeline and Revenue (Attribution)
● # of Opportunities
● # of leads
● # of Meetings
● Read out of what happened

9
KPI Sandwich
Executive KPI = Pipeline and revenue
What are you going to do next? (Hypothesis)
Marketing engagement metrics

10
Measurements that matter
New Leads
● Did we get the right people?
○ Who are they
○ Are they engaging
○ Are they part of your ICP
Existing Leads
● Are they engaging
● Are they engaging more
● Are they showing indications
of buying
● Can we get them take the next
step

11
Think different about the data to optimize
marketing
Looking at the Data
● Who they are
(demographics)?
● What did they do?
Marketing Analysis –
Manipulating the data
● Cohort
● Create a testable
hypothesis
● Prove or disprove
Repeat

12
Common fallacies to hypothesis-driven
analysis
● Conflating correlation with causation
● Non-testable hypothesis
● Averaging out the Insight

13
Conﬂating causation with correlation
How many MQLs did nurture create?
This Gartner report was the best content that drove the highest
amount of pipeline

14
How do we create more MQLs?
● Figure out what they did and do
more of that
● Whole population has many
paths
● Not hypothesis driven

15
Measurable
Helps you narrow
down the next
testable
hypothesis
Helps you answer
what are you
going to do next
(Actionable Insight)
Prove or disprove
What is a useful testable hypothesis?

16
Turning observations into questions that
lead to hypothesis-driven testing
Use Case #1
Observation Question
MQLs aren’t
producing
enough
Meetings
What can I do
to better
identify the
people who are
most likely to
take a meeting?

17
● Persona
● Series of engagements
○ Contact Us
○ Demo
○ Trial
○ Gartner Report
Hypothesis-driven analysis
Of the people that take meetings, what common
behaviors or characteristics do they exhibit?
vs

18
Use Case #2
Nurture emails
had low
engagement
Should I put in
any effort in
nurture by
doing the same
or change what
I’m doing?

19
Existing leads
● Engaged prospects >1 year
New leads
● Purchased lists
● Content syndication
● New Paid Social
Averaging out the insight
Cohorting reveals insight
vs

20
Existing leads
● Engaged prospects >1 year
New leads
● Purchased lists
● Content syndication
● New Paid Social
In there a difference in engagement rate
between existing leads and net new?

21
Measurement: What’s the next thing that you
want them to do?
● Did you acquire right people
● Engage on website
● Download more content
● Attend a Demo/Meeting
● Account missing the right buyer
● Showing Intent on 3rd party site
● Net new from paid media
● Engaged not MQLed

22
Move from emotion to
logic with thought
leadership and
education. Focus on validation
and consensus
across the entire
buying committee.
Align Your GTM to the Account Journey
Early in the journey, focus on building
your brand on a foundation of trust.
Drive adoption and
retention with best
practices.
Engage
when
accounts are
in-market
and actually
interested in
hearing from
you.
Aware
Engaged
MQA
Opportunity
Customer
MQA (Cust)
Qualified
Opp (Cust)
Win (Cust)

23
Use Case #3
Content
syndication
produces low
number
meetings
Which content
syndication
vendor is
working so I
can put more
$$ on those?

24
Content Syndication Vendors
● Vendor A
● Vendor B
● Vendor C
In there a difference in nurture engagement rates,
website visits or unsubscribes between CS vendors

25
Action:
● Go back to vendor and fine
tune audience
● Right ICP but no
engagement – create the
next program to try to
engage them
Vendor B has some engagement. Now
what?
Questions:
● Are they bringing in the
right people? (Job level,
industry, accounts)
● What percentage of the
accounts brought in are in
the right ICP?

26
Use Case #4
Events have the
highest
attribution $$
Where should I put
my marketing
dollars? How do I
understand how
Events are
performing? Should
I do more or less of
them?

27
Typical Post Event Analysis
● How many leads
● How many net new
● How many MQLS
● How many meetings – why are they so low
● Why hasn’t anyone bought anything yet

28
Dimensions
● Existing leads vs Net New
● Customers vs Prospects
● Accounts with higher aggregate
amount of engagement across
people
● Account with more people vs less
Do we see increase in the next logical engagement
if we slice the data by different cohorts?

29
Get real insights based on data, not hunches
Retention
Cross-sell
Customer
Opportunity
MQA
Engaged
Awareness
Qualified
Measure site engagement
See which roles are engaged
Track core journey metrics
Identify impactful programs
Follow an account’s engagement
Measure: Connect the dots across the buyer journey

30
Action:
● Target follow up to prospects
net new people to accelerate
path to engage on the website
(retargeting, top of funnel)
● Target follow up to customers
on check up, or assessment on
other problems that we could
solve. (Xsell/Upsell)
We have identiﬁed differences in meeting conversion rates.
Now what?
Questions:
● Are prospect accounts and net
new people less likely to
proactively ask for a follow up
meeting?
● Are customers who don’t engage
unaware of our full value
proposition?
I care more about the people who aren’t engaging because I can do
something about that

31
Use Case #5
Registration for
a Webinar on
topic X broke
every record and
the platform
Should I
change my
webinar
strategy and do
more of those?

32
Follow the lily pads
● 5X the number of registrations and attendance
● Very engaged with content
● Significantly lower number of meetings
● Significantly longer deal cycle

33
● Cohort on interest
● Cohort on no shows vs attended
● White glove follow up for no shows to
drive more meetings
I care more about the people who aren’t engaging because I can do
something about that

34
Use Case #6
Content isn’t
working
What content
should I
produce more
of?

35
● Cohort by:
○ New lead vs existing leads
○ Engagement
○ Customers vs prospect
The ebook that drove the highest number of leads, are
those engaging the people we care about or driving net
new leads that are the right people.

36
Heatmap Analysis: Content Engagement
Analysis by:
○ Target Accounts
○ Persona
○ Stage
○ Segment

37
The story is in the outliers

38
Thank you!
YOU’RE
DEE
BEST!

Analytics that Matter: How to Turn Raw Data Into Actionable Insight Through Hypothesis-Driven Thinking

Recomendados

Recomendados

Más contenido relacionado

Similar a Analytics that Matter: How to Turn Raw Data Into Actionable Insight Through Hypothesis-Driven Thinking

Similar a Analytics that Matter: How to Turn Raw Data Into Actionable Insight Through Hypothesis-Driven Thinking (20)

Más de Demandbase

Más de Demandbase (20)

Último

Último (20)

Analytics that Matter: How to Turn Raw Data Into Actionable Insight Through Hypothesis-Driven Thinking