CRM is not enough

Peter Reinhardt, CEO & Co-founder at Segment

4 billion global users connected
to the internet and growing
MORE PEOPLE CONNECTED
Currently in the U.S. there are
approximately 8 networked
devices per person
MORE CONNECTED DEVICES
90% of all data has been created
in the last two years.
MORE DATA GENERATED
The digital world is changing
Every day more (and different types) of data is coming at us than before

Customer relationships in the past Customer relationships today

How data should
flow throughout the org
How it actually flows

Who your customers are What actions they’re taking Where those actions
are taking place
Who What Where
Requirements for using data to deliver
customer first experiences

Not built to handle the volume
and type of data now available to
record digital customer
interactions, such as activity on
your website and mobile apps
VOLUME OF DATA
Difficult to sync interactions that
happen across 3rd party tools
such as email, push notifications,
support tickets, and payment
systems
DATA CONSISTENCY
Designed to be a “walled garden”
— limited connections to other
tools outside their ecosystem
making it difficult to access data,
Even with new integration
capabilities, will always prioritize
their own solutions.
INTEGRATIONS
Where CRMs fall short...

CRM suites will never be
able to catch up with the
explosion and fragmentation
of digital channels.

The Birth of an Independent Ecosystem

What does the
ideal marketing tech
stack look like?

The CRM Suite is no longer
the center of the tech stack.

The most important
requirement when building
your stack is flexibility.

1. Identify and define what problem you want to solve
2. Choose best-in-class software that is independent and
interoperable
3. Connect this flexible stack together and evolve as needed
4. Repeat
Principles for building the right stack

Brandon Purcell, Principal Analyst at Forrester

Why CRM Is Not Enough In The Age
Of The Customer
Brandon Purcell
February 5, 2020

29© 2020 FORRESTER. REPRODUCTION PROHIBITED.
Success in the age of the customer
requires deep customer understanding
• Behavioral data
• Social data
• Mobile data
• Environmental data
• Sensor data
• Transaction data
• Customer data
• Third-party data
• Financial data
• Sales data
• Product data

Customer insights are the gold buried
within your data

Key phases of the insights lifecycle
Insights
Action
Data

Customer analytics turns data into insights
Customer analytics uses customer data
and analytic insight to design customer-
focused programs that win, serve and
retain customers.

Now let’s explore the menu
Chez CustomerAnalytiques

Companies have a wide range of options to choose
from

Methods that inform contextual marketing
Contextual marketing
analytics methods:
• Text analytics
• Customer location
analysis
• Customer device
usage analysis
Text
analytics
Customer
location
analysis
Customer
device
usage
analysis

© 2015 Forrester Research, Inc. Reproduction Prohibited 39
Case study: customer location analysis
› Business objective: Optimize offers, products, or operations based
on customer location
› Data: Customer location data (either geospatial or in-store)
› Analysis: Develop frequency tables to show where customers have
been and where they’re likely to go
› Action: Deliver targeted marketing based on location, or…
Case in Point: Auto finance

Methods that drive acquisition…
Acquisition analytics
methods:
• Behavioral customer
segmentation
• Customer lifetime
value analysis
• Customer lookalike
targeting
Behavioral
customer
segmentation
Customer
look-alike
targeting
Customer
lifetime
value
analysis

Case study: Customer Lifetime Value at
Royal Bank of Canada
Source: https://www.cio.com/article/2448413/it-organization/customer-segmentation-done-right.html
› Problem: Royal Bank of Canada wanted
to identify subsegments of highly
profitable customers to target
› Solution: Developed CLV model which
identified credit-strapped young medical
professionals and developed program to
meet their needs
› Result: Market share in this subsegment
increased from 2% to 18% and revenue
per customer is 3.7 times average
customer

Methods that increase retention and loyalty…
Retention and loyalty
analytics methods:
• Customer propensity
analysis
• Churn and attrition
analysis
• Social network
analysis

Methods that drive personalization…
Personalization
analytics methods:
• Next best action
• Recommendation
analysis
• Cross-sell and
upsell analysis

Recommendation analysis can inform the digital
and physical shopping experience
› Business objective: Sell more by uncovering correlations between
products
› Data: Transaction data, SKU-level data
› Analysis: Quantify co-occurrence of product purchases
› Action: Recommendation engines or product placement
Case in Point:
Hurricanes

A match made in heaven…
+

Methods that improve the customer experience…
Customer experience
analytics methods:
• Customer satisfaction
analysis
• Customer engagement
analysis
• Customer journey
analysis

But they should not exist in isolation
Source: February 2014 “TechRadar™: Customer Analytics Methods, Q1 2014”

It’s time for a
new “Next Best”
paradigm…
The Next Best
Experience
(Friends call it NBX
for short)

The Next Best Experience focuses on customer
lifetime value optimization

Delivering the Next
Best Experience
requires robust,
real-time customer
data…
Source: Vendor Landscape, Personalization Solution Providers, Q3 2017

All of this requires
robust customer
data…
Can your CRM
provide all this
data?
Source: Vendor Landscape, Personalization Solution Providers, Q3 2017

52© 2020 Forrester. Reproduction Prohibited.
Firms struggle most with data, process, and people
Base: 246 North American analytics and measurement professionals
Source: Forrester/Burtch Works Q3 2019 Global State of Customer Analytics Survey
19%
26%
27%
29%
31%
41%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Standardizing metrics across the organization
Hiring and retaining talent to manage
measurement and analytics
Getting buy-in from business stakeholders on
the value of measurement and analytics
Transforming insights into relevant business
actions
Accessing data from a variety of sources
Ensuring data quality from a variety of sources
Please rank the top three challenges for your
organization when making use of measurement and
analytics.

Start with a minimum viable data approach to prove
value then incorporate more data
Source: the “How To Calculate Customer Lifetime Value For Your Business [157640]” Forrester report.
Example: a minimum
viable data approach to
customer lifetime value
analysis

Analytics team composition – common roles
Strategist and
business lead
Manages the team to optimize a specific business outcome
Project manager Plan, scope, staff, and oversee delivery of insights projects
Technology leader Responsible for insights testing, implementation, and architecture
Domain experts Recognizes insights worth the investment – sits in BUs but participate in pilot teams
Developers Test and implement insights in software and business rules
Data scientists Build models and exercises them to reveal potential insights
Data engineers Gathers, assesses, integrates, prepares, and manages data
Primary researchers Conducts qualitative and quantitative research to provide deeper insight into customers’ needs,
wants and motivations
Facilitator Facilitates customer and internal work sessions (co-creation, journey mapping, ecosystem
mapping, etc.) to turn insights into action

Build a customer
insights center of
excellence

- commitment
- adoption
- applications
- analytics skills
- org structure
- partnerships
- sources
- management
- preparation
- workflow
- prioritization
- execution
- sharing
- production
- consumption
- activation
Assess your capability across six key dimensions
and create your roadmap to analytical maturity
Strategy Structure Data Process TechnologyAnalytics
- methodology
- metrics
- business KPIs
- ROI

The enterprise
martech stack is
evolving to
succeed in the Age
of the Customer

FORRESTER.COM
Thank you
Brandon Purcell
+1 510.926.2694
bpurcell@forrester.com

@Peter Reinhardt @Brandon Purcell
Fireside Chat

CRM is not enough

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