Author

Martin Kihn, Christopher B. O’Hara

Year

2020

Review

Marketing is more about data and technology than ever. MarTech helps companies get the right customer, the right message at the right time. Customer Data Platforms (CDPs) occupy the centre of the modern marketing tech stack. CDPs promise to solve some of marketings biggest problems.

If you understand CDPs and the systems they interact with then you understand digital marketing. This book is incredibly useful if you’re new to (OR a little rusty on) the world of customer data management.

Loved

I didn't enjoy reading this book. I found it to be a bit of a slog. I felt it repeated itself in places and tailed off towards the end. Yet it delivered on 'demystifying the Product Marketing Manager (PMM) role - which is why I picked it up.

andrewclark.co.uk

Hacking Growth

This book is much more professional and methodical than the name would have you believe - don't judge this book by its cover. Perhaps the best explanation of the importance of Acquisition, Activation and Retention I've come across. It balances exciting anecdotes with more practical advice on how to influence your metrics.

andrewclark.co.uk

Product-Led Growth

Product-Led Growth is a go-to-market strategy that relies on using your product as the main vehicle to acquire, activate and retain customers. It's an alternative to using a sales team. It requires an easy to use product that can quickly deliver value to new users.

andrewclark.co.uk

Key Takeaways

The 20% that gave me 80% of the value.

In attempt to respond to changes in technology companies quickly added new teams and new tools to their marketing operations. That in turn caused…

A ‘Martech Frankenstack’
Data Silos
Fragmented teams: mobile, brand, email
Difficulty coordinating the customer experience

The ideal marketing infrastructure is easy to define but hard to achieve:

A unified user profile
Smart segments - perform segmentation, machine learning
Plan and react capability - campaign management and decision capability
Engagement - directly engage with the customer (email, SMS)
Optimisation - capturing signals, recommending improvements, reporting results

Without effort and strategy → data becomes siloed

‣

Typical data silos…

CRM, Loyalty Service	Salesforce, AWS, SQL Server
Email, Mobile, Social	Salesforce - Marketing Cloud, LinkedIn, Instagram, Youtube, Facebook
Sales	Salesforce - Commerce Cloud, Magento, IRi, SAP HANA, Nielsen
Web Analytics	Adobe Analytics, Google Analytics
Paid Digital Ads	Instagram, twitter, snap, Google Ads, Google Adsense, Amazon
Programmatic Ads	Turn, Moat, The trade desk, tubemogul, appnexus,
Customer Databases	Data lakes, propensity scores, internal models
Agency Reporting	Excel, google sheets, pdfs.

To make the most of your data → you need to break silos → by resolving customer data + creating data portability across applications. Break down the people silos that prevent collaboration by unifying the data completely.

Enter the Customer Data Platforms

To store customer data and do analytics
To enable use of that data (real-time engagement system)

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How it compares to other products

	Data Warehouse	Custom Integration	DMP	CRM	CDP
Unified Customer Data	✅	❌	❌	🔶	✅
Persistent	✅	✅	❌	✅	✅
Packaged System	❌	❌	✅	✅	✅
Real-time Capability	❌	🔶	✅	❌	✅
Open Access	✅	❌	✅	🔶	✅

The CDP is an evolution of the CRM category. The Five CDP Capabilities:

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Data Collection

Collect a variety of customer data
Batch/ API / SDKs / Tags
Both Known and Unknown
Interoperability is key to get to the ‘golden record’

‣

Data Management

Classification and Schema
The manipulation of disparate customer data into a common format

‣

Unified Profile

Move from singular contacts - to rich profiles
Aligns all data → including loyalty, call centre, third party
Unification and fidelity of the profile are really important

‣

Segmentation and Activation

We need segments - because we can’t quite deliver a 121 personal experience yet
The challenge of segmentation previously - has been the multiple places it happens
Unifying where marketers do segmentation - making them available to others
CDPs can then activate those segments - service as a source of truth for decision making

‣

Insights / AI

Use AI to build segments
Predict the right content, the best time of day to trigger a message, or the engagement score
As CDPs are the source of truth - they are the source of data for AI

Anonymous to Known → Start capturing data when the customer is still anonymous or pseudonymous.
A system of engagement → providing real-time engagement, such as channel optimisation, next best-offer management, and dynamic creative optimisation
A System of insight → providing a more persistent single view of the customer or customer 360, for the purposes of in-depth analysis, modelling and measurement
Ultimately all of this is in service of creating a more personalised customer experience.
Marketing = Right Customer + Right Message + Right Time
Typically, lack of data isn’t the issue. It’s data unification that’s the problem.
Known Customer Resolution → taking data from a number of sources, and creating a single, unified profile for every customer (the Golden Record)
Data Portability → Once you’ve resolved customer data - you want to make available to other systems.
A PLATFORM NOT A PRODUCT

To unify and integrate the landscape, using customer data as the glue
To enable existing Martech products → and enable a new generation
The operating system that marketing and advertising plug into

2/3 types of CDPs

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System of insights (learning about the customer OVER interacting with them)

Sorting out data silo problems
Golden record of the customer
Query data easily
Adding propensity scores (receptiveness to marketing)
Getting a better understanding of customer data - unified, connected, queryable
Serving as the single source of customer truth - aggregation point for many different sources of data

Looking for these capabilities

Data Unification	federated IDs	lead scoring	enriching records
Data Cleansing	Golden Record	creation and deletion	managing consent
De-duplication	Single source of truth	Stitching IDs together	building segments
+ computed attributes	Single place to query

‣

System of engagement (using data to influence the customer journey)

Requires real-time customer profile store, ready to be accessed by other applications in milliseconds
Fast-moving, near real-time. Managing real-time profile of customer data - activating them
Requirements

serve web visitors the right page based on profile	centralise predictive segmentation
decide which offer to email customer	predictive real-time offer management
up-sell/cross-sell - matching next best	deliver the right mobile offer
decide the next message and next channel	manage next best action on different endpoints
power dynamic creative optimisation	automatically update propensity score in real time
automatically move profiles across segments

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Enterprise Holistic CDP - combining insights and engagement

Known and unknown data unification - it’s not possible to truly understand customers without accounting for their interactions across PII and pseudonymous data modalities
A business user friendly UI - unlocking the value of data to more people
A platform ecosystem - take advantage of new applications

Platforms spur innovation

lower the barrier for entry for new entrants
more speciality apps

Consent is key

Customers are increasingly resistant to data collection
Customers are increasingly resistant to advertising
4 parties involved in data privacy: Web Browsers + standards bodies, government regulators, consumers and companies

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The Seven Principles of GDPR

Transparency → the consumer should know what data is being collected by whom
Purpose limitation → data can only be used for the explicit purpose granted by the customer
Data minimisation → only as much (and no more) data can be collected to serve the purpose above
Accuracy
Storage limitation → limits to how long data can be retained
Security → must be stored in a way that it cannot be accessed by unauthorised parties
Accountability → the data ‘controller’ must provide ability to audit and edit data

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6 ways to build trust

Get consent
Restrict processing of data
Honour the right to be forgotten
Allow subject requests for data stored
Keep data secure
Capture data use preferences

A healthy majority of customers are willing to share data in return for relevance - as long as it is collected and used in a way that makes them feel comfortable

Transform your company using data

Goal: get better at understanding and engaging with their customers

Know your customer
Personalise every interaction
Engage across channels and surfaces they touch
Measure everything and use that data to improve

Transformations come together around people, process and technology. While you need all three, the people are probably the most important aspect.
You need a platform operating philosophy. A common language, enables creativity by allowing extensible model that lets people develop on them

A platform approach is what gets value from the CDP
Democratise data in the organisation. Make data easily available to anyone who needs it

Data unification for it’s own sake doesn’t provide value
The idea is to produce a flywheel

Tech gives access to highly enriched and unified customer data set
Analytics team builds better customer segments
Marketing activates segments
Campaigns create more data that further enrich the dataset

Better data leads to better segmentation. Better segmentation leads to better engagement. Better engagement leads to better data. Rinse and repeat
A working maturity model

Channel Coordination (single, multi, cross, omni)
Engagement Maturity (segmented, lifecycle, individualised, connected)

Segmented → by segment
Lifecycle → by segment + funnel stage
Individualised → customer + funnel stage + status
Connected → profile + different systems + personalisation

From touchpoints → journeys → experiences

Journeys requires multi-channel and better identity capability and better lifecycle awareness
Experience → move from channel based interactions to real time in the moment sending the right message and creative

Advertising

You need to go beyond your own customers data to find and acquire new customers and fill the top of the funnel and drive new revenue

Those customers are on social networks, they’re browsing your site anonymously
How do you reach them? Through advertising - How do you target them? Using the DMP and building segments of pseudonymous consumers

Why Pseudonymous data is still important

The number of internet connected devices is only going to increase
Most consumers interact with brands anonymously before they volunteer information
Consumers digital exhaust signals can reveal a deep understanding of intent

Ad networks let you send first party data to them - so you can target the customers you know (by email address) etc. Facebook will return a match about 70% of the time.

CDPs therefore can define a segment and send it to facebook or google and execute an ad campaign against it

Beyond Marketing

Customer data can make a better experience across marketing, support and sales
An experience can either be seamless or disjointed, depending on how good companies are at using customer data

Abandoned baskets are noticed and customers are emailed.
Purchases are noticed and customers are emailed (new purchase flow)
Start with suppressing adverts to customers who’ve already purchased that product to reduce costs
Once a customer has purchased we have high value deterministic data to develop propensity models

Connect this data to unknown users. You could send emails for abandoned baskets
Combining highly granular attributes of unknown visitors with known customer purchase data can yield effective predictive models
The purchase isn’t the end of the journey → but the seed that can grow into a new top-of-funnel campaign

The single source of truth → creates a platform that others can build on
Householding → target households not just individuals
Track targetable attributes → engagement of emails can help with propensity modelling
How can marketing data be an input into the sales, service and commerce functions
Every team gets more in value than they individually contribute (Platform / Centre of excellence mindset)

Other Highlights

Customer data in a warehouse serves no purpose - it must be activated to yield value
Top Uses for AI among marketers

Personalise individual channel experiences 80%
Improve customer segmentation / lookalike audience modelling 77%
Automate interactions over social channels / messaging apps 76%
Drive next best actions in real time 76%
Surface insights from data 78%

Clever targeting: Inventory availability - so you have the stock to follow through

Deep Summary

Longer form notes, typically condensed, reworded and de-duplicated.

Introduction

The marketing space is getting harder

Loyalty to consumer brands is declining (FAAG - being the exception)
Cost of advertising is rising
People are becoming less receptive to adverts (40% don’t/or barely respond)

The Impact of Personalization

The Personalisation Flywheel:

Personalisation → better experience → more usage → more data → better personalisation
Amazon’s recommendations are 2x better than other large retailers - because data!

Consumer expectations

dConsumers want more relevant messaging and promotions
Consumers expect personalisation.
Consumers (69%) expect connected experiences (entering information once)

Personalization works

Example: Pizza shop increases conversion by 16% after using preferences in emails
Better personalisation on e-commerce → +15-20% conversion, +30% engagement.
Targeting ads based on website visits → +200% click through
Personalised emails → +6% open rate

From segments to personalised → +20% Lifetime Value, +30% engagement.

Challenges of Personalization

Privacy Paradox: Customers want personalised experiences → but are wary of providing access to the data that enables them (behavioural, attitudinal, demographic)
Privacy and Regulatory Challenges

Consumer attitudes to data collection, storage and access are changing
A decline in consumer trust and increase in ad-avoidant and marketing-hostile behaviour
GDPR in Europe and CCPA in California
People are OK with the right data, being shared with the right people for the right reasons
Marketers are becoming more mindful with personalisation

Managing and connecting data

If we had a magic data wand → personalisation would be unlocked.
Disconnected data is a key challenge
Companies are unhappy with their data …

Data Quality	37%
Data Timeliness	34%
Data Integration	34%
Consent Management	34%
Identity Reconciliation	33%

1 Customer Data Platforms

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CDP vs Data Warehouse, Custom Integrations, DMPs, CRMs (not from the book)

	Data Warehouse	Custom Integration	DMP	CRM	CDP
Unified Customer Data	✅	❌	❌	🔶	✅
Persistent	✅	✅	❌	✅	✅
Packaged System	❌	❌	✅	✅	✅
Real-time Capability	❌	🔶	✅	❌	✅
Open Access	✅	❌	✅	🔶	✅

CRMs have been improving: response rates, satisfaction, throughput, sales, market share, closing rates → since the 90s. They upped the game for many consumer companies.
The CDP is an evolution of the CRM category

Data Ingestion
Data Processing
Identity Management
Segmentation
Machine Learning and AI
Cross-Channel activation, reporting and optimisation

A Customer Data Platform must provide:

Anonymous to Known

Start capturing data when the customer is still anonymous or pseudonymous,

Includes capabilities associated with DMP (Data Management Platforms)

Insights and engagement

A system of engagement → providing real-time engagement, such as channel optimisation, next best-offer management, and dynamic creative optimisation
System of insight → providing a more persistent single view of the customer or customer 360, for the purposes of in-depth analysis, modelling and measurement

For personalised, trusted, one-to-one messaging and marketing, yielding a better customer experiences.
Reliable, future-proof and customer driven

2 The Customer Data Conundrum

Marketing = Right Customer + Right Message + Right Time
Typically, lack of data isn’t the issue. It’s data unification that’s the problem.

Without effort and strategy → data becomes siloed

Where is data stored? Who can use it? For what can it be used?

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Data Silo Table

CRM, Loyalty Service	Salesforce, AWS, SQL Server
Email, Mobile, Social	Salesforce - Marketing Cloud, LinkedIn, Instagram, Youtube, Facebook
Sales	Salesforce - Commerce Cloud, Magento, IRi, SAP HANA, Nielsen
Web Analytics	Adobe Analytics, Google Analytics
Paid Digital Ads	Instagram, twitter, snap, Google Ads, Google Adsense, Amazon
Programmatic Ads	Turn, Moat, The trade desk, tubemogul, appnexus,
Customer Databases	Data lakes, propensity scores, internal models
Agency Reporting	Excel, google sheets, pdfs.

Known data = PII = Personally identifiable information

Valuable - typically stored in a CRM
Often stored in a CRM, often fragmented (sales support, marketing, commerce) and rarely combined stored in a way that can be made us of in a customer journey.

Customer Relationship Management (CRM)

is the OS for customer data that the sale organisation plugs into
should be the source of truth

To make the most of your data → you need to break silos → by resolving customer data + creating data portability across applications
Known Customer Resolution → taking data from a number of sources, and creating a single, unified profile for every customer (the Golden Record)

FirstName and First_Name need to be reconciled
You need to create a data model to standardise those fields - and map them accurately

Data Portability. Once you’ve resolved customer data - you want to make available to other systems.

Deciding what data to port - to what granularity is an art.

The CDP aims to solve unification of customer data - and provisioning a golden record that marketers can rely on an leverage.
Unknown data

Prior to logging in - or giving PII - it’s hard to know who’s doing what

Customers have many devices
Customer journey’s are often multi-device

Data Management Platforms (DMPs) were created to solve identity. Resolving unknown customer data is much harder
GDPR and other privacy regulations also make this is a difficult area

Fact Check:

4.3 connected devices per US adult
41% to 67% of online purchases are the culmination of a multi-device journey
66% retailers can’t tell a customer has switched devices during a path to purchase
Trends → more unknown data, more multi-device usage

Marketers might no longer be buying DMPs - but they’re still going to want and need to capture signals from the beginning of the buying journey
Cross-Device Identity Management → recognising customers across devices, gather contextual and behavioural data about the customer → add you to a segment

You can get a rich view of the customer doing this
You need a mechanism for tracking consent and the right to be forgotten
You need to capture different consent flags - else you create a binary system, 100% in or out

Types of Consent: Data collection, analytics, targeting, cross-device, data sharing, re-identification
Connecting the known and unknown - the identity problem. Connecting them gives you a full view of the customer funnel.
Build awareness and interest (top of funnel) → desire and action (at bottom of funnel)
The CDP can connect known and unknown customer data

Direct Engagement and Known (CRM) : Direct Mail, Email, Sales, Call Centre, Commerce, SMS
Passive Engagement and Anonymous DMP: Display, Video, Mobile Web, Mobile App, SSP/DOC, Social

Data Onboarding → the process of matching unknown (Cookie / Device ID) to known (Person/ Email)

LiveRamp specialised in just that

Company provides email addresses (100m) and gets cookies in return (40m)

Match rates are typically 30-90%

Without the connection - brands suffer from observational bias - only seeing what they can see

People Silos - organisation structures can often dictate the data silos.

Analytics, CRM, Online advertising, Commerce team, Service team

Break down the people silos that prevent collaboration by unifying the data completely

then build incentives for everyone to enrich the golden record
having the data in one place will encourage customer-driven thinking and collaboration

Customer-Driven Thinker: Kevin Mannion

What companies are most interested in:

Predictive marketing and advertising
Customer profile management and expansion
Customer segmentation
Development or enhancement of marketing automation systems
Content delivery systems that reach the right person at the right stage of the lifecycle

Key Themes:

Data Silos | Known Data | Unknown Data | Connecting known and unknown data | People data silos

The History of Customer Data

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History of Marketing (1870 to present)

1870 → Direct mail circulars
1894 → Sears catalog.
1958 → Lester Wunderman coined ‘Direct Marketing’
1961 → ‘Society of the Divine Saviour Data Processing Center’

Crude predictive models and LTV calculations were made using IBM processors and tape

1970 → E.F.Codd invents the relational data model
1980s → Database Marketing

and into the 90s → Relational databases and SQL enabled personalised marketing.

90s - 00s → Digital Marketing Wave 1: the rise of the internet and email
1990s → Marketing Automation
00s - 10s → Digital Marketing Wave 2: the rise of mobile and social
2000s → Multichannel Campaign Management
2010s → CDP

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The timeless elements of the marketing stack

Data input - ingest data from multiple sources
Storage - persistent available storage
Data processing - transform data into useful forms
Activation - get signals from the data
Engagement - send them to different channels
Measurement - track and optimise results

Customer Relationship Management (CRMs) emerged to automate the process

No need to write SQL
Easy campaign management
Automated engagement (sending of emails)
Expanded to data beyond email creating a feedback loop (A/B testing)

Customer Engagements Channels → Email / Direct Mail / SMS

Email is still popular → it’s multichannel and easily measurable

To keep up companies quickly added tools and teams. That lead to…

A ‘Martech Frankenstack’
Data Silos
Fragmented teams: mobile, brand, email
Difficulty coordinating the customer experience

The need for Customer Data Platforms was clear

To store customer data and do analytics
To enable use of that data (real-time engagement system)

Ideal marketing infrastructure would include:

A unified user profile
Smart segments - perform segmentation, machine learning
Plan and react capability - campaign management and decision capability
Engagement - directly engage with the customer (email, SMS)
Optimisation - capturing signals, recommending improvements, reporting results

Ideal Marketing Architecture

Engage Across The Entire Customer Journey	Ads · Social · Email · Mobile · Website · APIs
Personalise the Engagement	+ Plan Campaign Management + React Real-Time Interaction Management
Know the person	Smart Segments → Segment Discovery, Cont. Recommendations
Analyse the results	Unified User Profile → Identity Service · Metadata Management

3 What is a CDP, Anyway?

2013 the term CDP was first coined by David Raab
2016 the first CDPs became available on the market
2019 CDPS started to become popular
Suffered from confused definitions, blurred boundaries and vendor hype
Key Components of a CDP

Acquire - natively collect and extract data from many source s
Process - cleanse and manage attributes and IDs
Expose - make data available in a persistent store
Analytics and decisions - execute decisions and perform predictive analysis
Delivery - send signals to engagement systems

This is nearly all of marketing, so it’s ambitious.
CDPs are there to organise customer data and make it available to the systems that need it.
Promises made by the CRM vendors of the past - are similar to those made by CDP vendors today
CDPs aren’t new → they’re the latest evolution of CRM and expanding beyond just marketing.
What marketers wanted a CDP to do:

Cross-channel campaign management (frequency and sequence of messages)
Segmentation (better segments, LTV segmentation, High Value Customer identification)
Loyalty (offer management and loyalty)
Measurement (ROI of marketing tactics)

Features they expected:

Profile building - known or unknown
Data ingestion - from common systems
Identity management - tie records to existing accounts, fuzzy matching, cleansing, deduplication, household mapping
Normalisation - manual and automated for attributes in tables
Marketer friendly UI - SQL not needed, drag and drop segmentation / rule building

A PLATFORM NOT A PRODUCT

To unify and integrate the landscape, using customer data as the glue
To enable existing Martech products → and enable a new generation
The operating system that marketing and advertising plug into

Five CDP Capabilities

‣

Data Collection

Collect a variety of customer data
Batch/ API / SDKs / Tags
Both Known and Unknown
Interoperability is key to get to the ‘golden record’

‣

Data Management

Classification and Schema
The manipulation of disparate customer data into a common format

‣

Unified Profile

Move from singular contacts - to rich profiles
Aligns all data → including loyalty, call centre, third party
Unification and fidelity of the profile are really important

‣

Segmentation and Activation

We need segments - because we can’t quite deliver a 121 personal experience yet
The challenge of segmentation previously - has been the multiple places it happens
Unifying where marketers do segmentation - making them available to others
CDPs can then activate those segments - service as a source of truth for decision making

‣

Insights / AI

Use AI to build segments
Predict the right content, the best time of day to trigger a message, or the engagement score
As CDPs are the source of truth - they are the source of data for AI

2/3 types of CDPs

‣

System of insights (learning about the customer OVER interacting with them)

Sorting out data silo problems
Golden record of the customer
Query data easily
Adding propensity scores (receptiveness to marketing)
Getting a better understanding of customer data - unified, connected, queryable
Serving as the single source of customer truth - aggregation point for many different sources of data

Looking for these capabilities

Data Unification	federated IDs	lead scoring	enriching records
Data Cleansing	Golden Record	creation and deletion	managing consent
De-duplication	Single source of truth	Stitching IDs together	building segments
+ computed attributes	Single place to query

‣

System of engagement (using data to influence the customer journey)

Requires real-time customer profile store, ready to be accessed by other applications in milliseconds
Fast-moving, near real-time. Managing real-time profile of customer data - activating them
Requirements

serve web visitors the right page based on profile	centralise predictive segmentation
decide which offer to email customer	predictive real-time offer management
up-sell/cross-sell - matching next best	deliver the right mobile offer
decide the next message and next channel	manage next best action on different endpoints
power dynamic creative optimisation	automatically update propensity score in real time
automatically move profiles across segments

‣

Enterprise Holistic CDP - combining insights and engagement

Known and unknown data unification - it’s not possible to truly understand customers without accounting for their interactions across PII and pseudonymous data modalities
A business user friendly UI - unlocking the value of data to more people
A platform ecosystem - take advantage of new applications

Platforms spur innovation

lower the barrier for entry for new entrants
more speciality apps

This new platform is going to create a second golden age or martech. Anyone is going to be able to spin up speciality apps that leverage CDPs

4: Organising Customer Data

High level key steps for data management…

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1) Data ingestion

Identify the different sources of customer information across your systems
Collect relevant data and point it towards a centralised architecture

Batch processing: Asynchronous ingestion at bounded intervals. Good for high volume of data for specific time periods (e.g. daily point of sale purchase data)
Stream processing: Synchronous ingesting of data stream (e.g. web analytics)

Just because data is collected - doesn’t mean that it’s available. Often harmonisation is required before it can be useful.
3 ways data can be ingested for use: Extracted, collected in real time, collected in batches.

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2) Data Harmonisation

Ingested data requires transformation before it can be used for analytics, decisions or processes.
Prepared, cleaned, modified, enriched so it can be fit for a business use case
You need an agile, flexible harmonisation strategy
Harmonisation is the process of mapping fields from different data sources that represent the same type of data
You can either integrate by using middle ware or making changes at the source
CDPs allow you to map data fields from different sources in a drag and drop way
Best practice is to start with a standard data model - and customise it for your requirements. Doing so saves time and reduces errors.

Salesforce uses CIM (Cloud information model), a subset of which is the customer information model

‣

3) Identity Management

Identity management seeks to reconcile customer IDs → using identifiers such as names and addresses, mobile phone numbers, emails, anonymous browser cookies etc
Helps power more effective multi-channel engagement, improve targeting and creative tactics and makes measurement more accurate.
Identity management can also help provide a more consistent experience for customers
Marketers want to link devices and browsers to individuals because so much of the customer journey can happen on another device or before logging in.
Acxiom, Experian, Epsilon and KBM maintain authenticated identities for almost every customer and business in developed countries
Benefits of identity management

Improved media effectiveness → Efficiency, targeting and audience extension
More relevant content → Personalisation of messages and offers, and sequencing across channels and devices
Better analytical insights → higher profile accuracy for segmentation, predictive models, recommendations and measurement.
More informed governance → the ability to meet consumer and regulatory requirements for opt-outs, preference management etc.

The Spectrum of Identity

Identity isn’t about device matching and it isn’t as easy as classifying at known or unknown.
Identity is more of a spectrum:

Location

Device

Multi-device

Anonymous persoon

Identified person

Authenticated person

Pseudonymous identifiers: point to locations and devices not a person (IP address, Device ID)
Known identifiers: resolve to a person (email, phone, names, addresses)

Identity management is built around keys like (cookies, hashed emails, IP addresses, and customer IDs)

Identity management is about matching one to the other - they can be probabilistic or deterministic

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4) Segmentation

The practice of grouping customers or accounts together based on similar characteristics
Helps with data-driven approach to marketing. Segmentation can be used in many ways:

Product development, pricing, targeting, messaging and measurements.

Smart segmentation is a key step toward meeting customer’s demands for more relevant experiences.
Different types of segmentation: Psychographic, Behavioural, Benefits, Geography, Occasions
Can be identified manually or through clustering.
Most common segments:

Customer value - tiering by potential value to the company
Product Preference - types of products bought
Loyalty - share of wallet and stickiness, correlated to value but not the same as
Price sensitivity - often tied to product margin (business vs personal buyer)
Use - Depth of engagement or frequency of use

Perception that 5-8 segments is best - but you can have as many as are useful
Attributes are really important to segmentation. Data harmonisation and identity management are requisites
Attributes come in three basic types (are, do, think)

Type	Uses	Data Type	Sources
Are	- Media targeting - High-level filters - Offline media buying - Predictors for attitudes	- Demographic - Filmographic - Income + credit - Family size	- CRM systems - Marketing Service Providers - Public sources - Third-party sources
Do	- Product Preference - Channel preference - Locations visited - Price sensitivity - Content interests - Likelihood to respond	- Online behaviour - Purchases - Location data - Devices used - Social media use - Content consumption	- Campaign management systems - Point of sale - Digital analytics - Email services - Media platforms
Think	- Messages - Advertising Campaigns - Competitive positioning - Call scripts	- Psychographic - Affiliations - Sentiment and valence - Responses to content	- Inference models - Surveys - Social analytics - Voice of customer

Businesses love to be able to explore customer profiles with queries → to help with hypothesis and experimentation.

‣

5) Activation

Taking all the collected, identified, harmonised, and segmented customer data nad putting it to work → to drive operational systems
Activation intersects with engagement channel technology

Campaign management tools, email, mobile and social platforms
The other systems execute decisions developed, managed, and tracked based on the data and profiles sitting within the customer data platform

Key Activation Activities (how the platform communicates with engagement systems)

Engagement Activation	- send segments to engagement systems - personalisation of websites, dynamic creative - sequenced multistep journeys - A/B testing support
Realtime-Event Based Marketing	- triggered messages and other treatments - predictive sending and scoring
Predicative Analysis	- product recommendations - content recommendations
Reporting and Insights	- dashboard / reporting - AI consumer insights

The platform needs to be able to send segments to many different channels
Realtime event-based marketing requires fast response times (e.g. product recommendations)
Should support File transfer, APIs and Queries.

5: Build a first party data asset with consent

Consent and compliance are part of a transparent, trusted data-value exchange with customers
Customers are increasingly resistant to data collection
Customers are increasingly resistant to advertising
4 parties involved in data privacy: Web Browsers + standards bodies, government regulators, consumers and companies
Browser Market Share: Chrome 65%, Safari 18%, Edge 4%
Safari blocks third-party tracking cookies by default
Google ending third-party cookies by end of 2023
The future is first-party data, consumer who opt in and the use of aggregate rather than user-level information

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The Seven Principles of GDPR

Transparency → the consumer should know what data is being collected by whom
Purpose limitation → data can only be used for the explicit purpose granted by the customer
Data minimisation → only as much (and no more) data can be collected to serve the purpose above
Accuracy
Storage limitation → limits to how long data can be retained
Security → must be stored in a way that it cannot be accessed by unauthorised parties
Accountability → the data ‘controller’ must provide ability to audit and edit data

Marketers must collect consent alongside data, and provision the infrastructure to be compliant with GDPR
Practical level you need to classify data, determine user entitlements and ensure compliance

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6 ways to build trust

Get consent
Restrict processing of data
Honour the right to be forgotten
Allow subject requests for data stored
Keep data secure
Capture data use preferences

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4 ways customers feel

Lack of control 81%
No benefit 81%
Confusion 59%
Paranoia 72%

Customers are happy to share if you’re transparent, there’s a clear value exchange and you give the consumer control
Consumers will happily share data with companies that they trust
4 Ways to gain trust:

Reliability: security and uptime
Legality: compliance and flexibility to react to legislation
Transparency: collection and use of customer data
Value: sufficient benefits received for exchange

A healthy majority of customers are willing to share data in return for relevance - as long as it is collected and used in a way that makes them feel comfortable
The Privacy Paradox:

We all say that we value our personal data, but we regularly surrender intimate information to platforms such as Google, Facebook, Amazon etc.

Prices for adverts on Safari are 50-60% lower because of the banning of 3rd party cookies

6: Building a Customer-Driven Marketing Machine

Know, Personalise, Engage and Measure

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Know your customer as much as you can through data collection

Capture data across the known and unknown modalities
Capture all of information customers decide to reveal (email, preferences)
Capture what you can infer about their intent (clicks, video views)
Build the source of truth
The amount of data that’s consented to is important
Garbage in garbage out
You need to build the right dataset - requires consent, truth (resolve identity) and scale (to power AI)

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Personalise every interaction (the right message)

Email is like an ATM machine - but people are getting too many emails as a result
If personalisation can help improve each step of the funnel - then the effects on the total funnel are much larger (10% each step might add up to 50% for the campaign)
Need to personalise through the entire engagement layer

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Engage across the channels and surfaces they touch

Marketing to e-commerce, to call centre, to IRL experiences
Get close to your loyal customers
Make sure each touchpoint is aligned and operates in close to real time
Connecting the touch points can increase lifetime value
Start with connecting advertising to e-commerce (stop showing them things they’ve bought and start showing them things they might buy)
Journey Management

Manage them in a prescriptive way
Provision them in real-time - as the engagement is happening. Real-time interaction management (RTIM) requires that different tools all have real-time capability

can help you connect and relate fast-moving experiences, driving relevance and delivering lift

Orchestrate the way your customer engages across a number of channels
Journey management helps wring maximum value out of known customers through personalisation

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Measure everything and use that data to improve

Analytics, measurement and attribution
Knowing, personalising and engaging leads to insights about your customer
Liberate measurements from silos so they can inform each other
Optimise the experience and value from paid marketing

Organisational transformation

Goal: get better at understanding and engaging with their customers
The software itself cannot solve the problem alone
Transformations come together around people, process and technology
While you need all three, the people are probably the most important aspect
Conways Law: Any organisation that designs a system will produce a design whose structure is a copy of the organisations communication structure.
Conway’s law describes the process by which companies operate based on the structure of their org chart
In every successful implementation we have witnessed, at least one senior executive from these departments has worked to form a centre of excellence approach that made data management a top company priority
You need a platform operating philosophy. A common language, enables creativity by allowing extensible model that lets people develop on them

Platform approach is what gets value from the CDP
Democratise data in the organisation. Make data easily available to anyone who needs it

Data unification for it’s own sake doesn’t provide value
Broad and aspirational CDP use cases are needed to start the project

Get the company aligned about near term success
Showing the exec the juice is worth the squeeze
Every use case needs refinement so that it drives a specific outcome.

E.g: Reduce the cost of customer acquisition → refine customer segmentation to increase conversion rates on targeted campaigns.
E.g: Know more about my customer → Combine POS with digital display data to better understand video media in key segments to increase conversion rates by product

You can’t track the effectiveness of your data transformation without a clear performance framework - and having KPIs that align with is paramount

Many companies start with simple KPIs that show platform adoption

Methodology

Supporting a culture that allows for inquiry and allows for fast results through testing and rewards data-driven insight.

Operating Model

Fund ongoing executive support by aligning data transformation goals with the CEO and CFO
CDP should be Capex - the CDP is a core piece of tech that no company can live without - the system of record for customer data
What is the entry point that gets exec support?

Cost savings, security or operations. A combination of all three will get you funding

The Center of Excellence Model

What about the people stack required to achieve success?
People, process and technology.
Marketing, technology and Analytics stakeholders are key

Marketing must represent the need for customer data as the unifying thread that connects advertising, messaging and website and app experiences across the organisation. The CMO must own the CDP.
Technology - CDP must be the single truth for more that just marketing data. Bless the CDP for capturing, storing and unifying customer data. Migrating things over, owning the process.

Go from being data gatekeepers to data retailers
In exchange for giving everyone access → everyone must enrich the CDP data by returning relevant data → stream attribute data back to to the datastore

Analytics → own the segmentation strategy, and set the metrics for success by building models that can measure the value that customer data brings to the organisation

create rich and dynamic segmentation
should also help create the ROI model - establish KPIs

The idea is to produce a flywheel

Tech gives access to highly enriched and unified customer data set
Analytics team builds better customer segments
Marketing activates segments
Campaigns create more data that further enrich the dataset

Better data leads to better segmentation. Better segmentation leads to better engagement. Better engagement leads to better data. Rinse and repeat

A working maturity model

Channel Coordination (single, multi, cross, omni)
Engagement Maturity (segmented, lifecycle, individualised, connected)

Segmented → by segment
Lifecycle → by segment + funnel stage
Individualised → customer + funnel stage + status
Connected → profile + different systems + personalisation

From touchpoints → journeys → experiences

Journeys requires multi-channel and better identity capability and better lifecycle awareness
Experience → move from channel based interactions to real time in the moment sending the right message and creative

7: Adtech and Data Management Platform

Adtech data is often double siloed - consigned to agency partners without much data sharing
DMPs helped capture, unify and activate through cookies - caused a 10x price increase in ad price for the right customers

Viewing Stock Prices at 5AM in NY on WSJ.com then you’re a high net worth investor
Publishers first used the DMPs - deep segmentation yielded more value from advertisers

A typical user had 20 cookies, multiple mobile ad IDs, and interacts with a number of connected devices
This is pseudonymous identity data but it can still make a rich profile
5 Sources of value in DMP

Audience planning
Audience data activation
Personalisation
Campaign optimisation
Delivering insights

DMPs were the single source of truth of people when they appeared in a pseudonymous state
Any brand suffers form observational bias - if you only understand your customers and not the market
You need to go beyond your own customers data to find and acquire new customers and fill the top of the funnel and drive new revenue

Those customers are on social networks, they’re browsing your site anonymously
How do you reach them? Through advertising - How do you target them? Using the DMP and building segments of pseudonymous consumers

Why Pseudonymous data is still important

The number of internet connected devices is only going to increase
Most consumers interact with brands anonymously before they volunteer information
Consumers digital exhaust signals can reveal a deep understanding of intent

Capture, unify and activate pseudonymous data to connect the customer journey, enrich the knowledge of your customers, and reach net-new customers at the top of the funnel
Ad networks let you send first party data to them - so you can target the customers you know (by email address) etc. Facebook will return a match about 70% of the time.

CDPs therefore can define a segment and send it to facebook or google and execute an ad campaign against it

CDPs and DMPs are on a crash course to become CDMPs

The best of DMPs (pseudonymous data capture, consent management, real-time profiles, cross-device identity management) functionality will be required in CDPs
Creating a single source of truth for known and unknown, and consent, and segmentation

8: Beyond Marketing

Customer data can make a better experience across marketing, support and sales
Customers data must be at the centre of all our systems - the person should be the currency of identity
An experience can either be seamless or disjointed, depending on how good companies are at using customer data
Abandoned baskets are notices and customers are emailed.
Purchases are noticed and customers are emailed (new purchase flow)
A more seamless experience can increase the LTV of customers and reduce churn
What if the call centre knew your persona / priorities (prioritise location, price, outcomes etc) and was able to tailor their reaction to you

Persona-based contextual buyer consideration journey - connecting service and marketing

Start with suppressing adverts to customers who’ve already purchased that product to reduce costs
Once a customer has purchased we have high value deterministic data to develop propensity models

Connect this data to unknown users. You could send emails for abandoned baskets
Combining highly granular attributes of unknown visitors with known customer purchase data can yield effective predictive models
The purchase isn’t the end of the journey → but the seed that can grow into a new top-of-funnel campaign

The single source of truth → creates a platform that others can build on
Householding → target households not just individuals
Track targetable attributes → engagement of emails can help with propensity modelling
How can marketing data be an input into the sales, service and commerce functions
Every team gets more in value than they individually contribute (Platform / Centre of excellence mindset)

9: Machine Learning and AI

AI started in the 1950s - attempt to make computers do things humans do
Weak AI - specific problems (like interpreting language)
Strong AI - build more general systems
Model that exports a label (Hotdog or Notdog)
Customer data in a warehouse serves no purpose - it must be activated to yield value
Two types of activation

Operational Activation: data is put to use in a process like message personalisation
Analytical Activation: ML and AI

Marketing Data Scientist Tasks:

Measurement, optimisation, experiments, segmentation, predictive modelling, story telling

Quantitative skills + knowledge of the industry + curiosity
Data Science domains

Exploration: using statistics and visualisation techniques to find patterns in data
Experimentation: applying design of experiments methods to develop and test hypotheses under controlled conditions
ML and AI: applying algorithms to build models and make predictions

Spend most of their time locating and cleaning data, or feature engineering → working out what attributes are more meaningful
Defining problems, selecting and exploring data, trying different solutions and weighing trade-offs among them - not usually, writing algorithms
Common marketing data experiments

different version of a website or content
different email subject lines and mobile push messages
item recommendations or incentive offers for different groups
test impact of create treatments or media placements for advertising

ML and AI

About making predictions or finding underlying structures in data
What is a model?

Training - fitting a model to data
Testing - validating the effectiveness of the fitted model

Labelled data is data for which the desired outcome is known, and it is required to build predictive models
Clustering can be used to identify different groups or determine some other underlying structure in the data
No model is perfect - some are more useful than others. The analyst must balance between the two extremes of

Memorising the training (or overfitting)
Building a weak model (or underfitting)

Supervised vs Unsupervised

supervised - using labeled data to build models that make predictions
unsupervised - applying methods to unlabelled data to identify structures

Most marketing prediction models are supervised and use labeled historical data to build a model that can make predictions

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Regression = Predicting a numerical value

Use numerical data to make a prediction of a desired value

Estimating LTV of a new customer based on their first 90 days of purchases
Predicting incremental orders following an advertising campaign
Determining the best frequency to send emails based on historic open rates

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Classification = Classifying customers and prospects

Rather than predict a numerical value, categorical models attempt to classify or label something

Predict whether someone will or will not click on an ad
Determine whether someone is likely or unlikely to purchase a given product
Identify B2B sales leads who are more likely to respond to a pitch

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Clustering: Finding structure in numerical data generally involves creating groups or clusters that have some internal similarity while being relatively distinct from other groups

Discover different groups of visitors to a marketing website based on browsing behaviour
Determine different types of prospects based on demographics and buying patterns
Separate high-value customers into subgroups based on preffered product types
ML can help find structure that is not obvious to an unaided analyst

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Dimensionality Reduction

A variety of complex methods are used to detect underlying structure within categorical datasets (or combined numerical datasets)

Determine which items are to recommend to a customer based on his similarity to other customers
Find evidence of brand affinity on social networks based on identifying logos in photos
Divide customer comments into different topics based on linguistic analysis

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Neural Nets

Deep learning models. Training a network of nodes in various ways, to mimic functions of the human brain. More suited to address highly nuanced and data-intensive tasks such as image recognition (where is the cat in the picture?)

Top Uses for AI amoung marketers

Personalise individual channel experiences 80%
Improve customer segmentation / lookalike audience modelling 77%
Automate interactions over social channels / messaging apps 76%
Drive next best actions in real time 76%
Surface insights from data 78%

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Machine Learning Segmentation

Much of segmentation is still trial and error (despite realtime reporting and A/B testing)
Machine-Learned segmentation can utilise rich data from historical campaigns and determine better segments for future campaigns by analysing which attributes really make people buy

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Machine-Learned Attribution

There are ways to measure and attribute success to a multichannel journey
Accuracy depends on the values you assign to different interactions

Last touch attribution - values the last interaction {most commonly used}
Linear attribution
Time discounting
Sequence discounting

ML can look across thousands of journeys and assign values to various touchpoints

Can look at the most effective sequence

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Image recognition and Natural language processing

Image recognition use cases:

Social: recognising brand logos in social posts
E-Commerce: automate previously manual tasks (labelling, classification of inventory)
Recommendations: recommendation algorithms
Personalisation: automating testing and learning process to include
Search and visual search

NLP use cases in marketing:

Social and email: text editing, AI recommendations to improve emails,
Customer experience: Summaries and sentiment analysis
Content creation: NLP autogenerate text (subject lines etc)
Chat-bots: service applications, to cross-sell, up-sell etc
Voice applications: create voice communications

Can marketers make use of the talent sitting inside and outside company walls?

Don’t assume all data scientists are equal
Provide opportunities for data scientists to learn on the job.
Understand the basic domains of data science

10: Orchestrating a Personalised Customer Journey

Marketing Saying: Right person, Right message, Right time.
Likeliness to buy = mental availability * physical availability
Brands can demand a premium if they align to people’s identity and trigger positive thoughts
Brands need consistent messaging, and to be readily available where the customers are buying

In 1978 Gary Thuerk sent the first marketing email on Arpanet to 400 people promoting DEC computers → it created $13M in sales

By 2010, segmenting email lists and using contextual and behavioural attributes to microtarget customers was more common.

2010 started automating campaigns ‘Customer Journey Management’ is the ability to provision a series of interactions based on a customer's stage in a process

Customers behave similarly → its efficient and effective to automate common journeys

Most have mastered shipping updates → harder to unify customer service etc

Predictive Journeys

Delivering a better journey based on prediction is ‘true journey based marketing’
Email campaign Example:

Customer on web utilises promotion, puts an item in the cart but walks away. It could be because

got distracted or didn’t have time to complete
had reservations about the cost (or shipping costs) and abandoned their cart
left to find a better deal elsewhere

You could create a different journeys to address these issues

Intelligent journeys use rules-based methodology to send customers down a path that advances through time / or steps
Persona Split → build journeys based on contact-level data
Engagement split → segment journeys based on channel we predict consumers are likely to engage on

Call centre, ad, email etc
AI can use predictions to create journeys across multiple channels that increase conversion rates, reduce churn, and join touchpoints like service and commerce

The more data you have - the smarter your decision tree can be:

Marketing, Commerce, Service, LTV, Propensity to buy

Real-Time Interaction Management

Delivering iterative contextually relevant experiences, value, and utility at the appropriate moment in the customer lifecycle via preferred customer touch points

Most journey management tools are email based, which doesn't cover unknown customers
RTIM tools can make immediate personalisation decisions

It’s challenging as journeys are often multi-day, multi-channel and multi-device
CDPs make this easier

Speed, intelligence and channel coordination
RTIMs deliver the ‘Next Best Action’ or ‘Next Best Offer’ at the right moment across channels

even deployed simply - you can lift conversion rates by bringing users back to the last page they visited

Having the capability to build predictive journeys - and to manage interactions in real time across channels

11: Connected Data for Marketing Analytics

Value Chain of Business Intelligence

Business Understanding → ← Data Understanding

Data Understanding → Data Preparation → Data Modelling → Evaluation → Deployment

The Marketing Department and CMO are responsible for

Understanding markets and customers
Create strategy and marketing assets
Manage all the functions required to execute marketing and programs and operations

Some of the most critical categories of support include:

segmentation
customer lifetime value
campaign and channel measurement
media mix and attribution
predictive and propensity modelling
social analytics
dash-boarding and visualisation

For analytics - additional data sources might include

3rd party data and overlaps
voice-of-customer (inc. call centre output)
product and pricing data
loyalty systems
trade promotion and event analytics solutions

Advanced Marketing Analysis

Multi-channel
Nontraditional data → location, social, text and speech analysis, voice of customer, IoT
Greater scale → more that just SQL
Realtime analysis → event processing, analysis of data in motion
Predictive models → next best offer and content recommendations, brand and churn propensity, profitability and lifetime value
More data types → what people are (demographics), what they do (behaviours) and what they think and feel (psychographic and attitudinal)

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Email Analytics

Layer One: Open rate, click through rate and opt-outs or unsubscribes

list growth, deliverability and unsubscribe rates

Layer Two: Demographics, look at performance through age, gender and location impact campaign performance
Layer Two: A/B testing: how components impact engagement: headline, offer type, product etc

or different times of day for different groups

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Data Management Platform Analytics

Slice analytics by vendor and segment

How did this campaign perform for a particular group (male versus female) or a vendor (Yahoo versus Google)?

Which audiences converted faster, based on their exposure to advertising, and manage the delivery of cross-channel advertising
The “sweet spot” in monthly ad delivery. People tended to click, buy, or “convert” when seeing between 5–20 ad impressions per month.

Less is ineffective (people need multiple impressions to enter into brand consideration)
More than that is wasting money - banner blindness

DMP people based view changed the game. A CDP that unifies people data from both pseudonymous and PII-based sources widens the aperture of analytics by introducing attributes that common media analytics never really considered, such as historical purchase data, call center interactions, and loyalty data.

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Multitouch Attribution (MTA)

Consider the impact of one or more customer interactions that lead to a KPI (a sale, a download, a click)
Tend to be digital-only in scope due to data
Traditional attribution models bluntly attribute to a single touch point, for example…

first interaction (search result)
last interaction (used a promo code)

Those models are ruined by the fact that many journeys start and end with a Google Search
MTA attribution modes assign value to the interactions across the journey (emails, website visits, video views, in-store visits). For example…

equal value to every touchpoint
equal value with time decay (closer to the sale is more valuable)

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Media Mix Modelling (MMM)

MTA's but aggregated
Built at campaign level, best bet for attributing offline (TV, print, terrestrial radio) media with digital
Can look beyond the immediate time period, and consider several years' worth of media spend

10,000-foot view of how promotions or seasonality affected sales
CPGs and retailer typically have built robust MMM practices over time, and found them reliable sources of directional attribution for hard-to-measure offline sales, and barometers of soft metrics like brand equity

In future we’re going to see less user-level data released → working with aggregate data is going to be more common → moving measurement away from log files and user-level analytics such as MTA

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Marketing Analytics Platforms

Manual / Batch Dashboards

Crafted marketing performance dashboards and exposed key reports to inform periodic optimisations.
track a campaigns KPIs over it’s lifetime - set alerts for high/low performance
Used batch data and careful manual work.
As a result updates were infrequent, as were optimisations based on the insights shown

AI has helped with:

data capture (smartly fitting disparate data sets together in a persistent model),
analysis (deep learning to surface anomalies in the data)
and activation (automated optimization recommendations)

AI / ML has transformed dashboards into intelligence tools that increase time to value by automating the process of data unification, building data models, and making optimisation available hourly (rather than weekly or monthly)
The CDP unifies people data - The right marketing analytics dashboard unifies cross-channel interaction data

A powerful combination

Clever targeting:

Weather
Inventory availability - so you have the stock to follow through

12: Summary and Looking Ahead

Start sales presentations with a “forward-looking statement”

But customers should make investment decisions based on what we have available to offer today (not our future roadmap)

The move toward aggregate level data is not going away

Shift from user-level data analytics to aggregate-level data

Walled-garden data environments like Google, Facebook, Amazon, Apple
+ continuing privacy legislation by governments
+ properly collected and consented-into first-party data is going to be more valuable than ever
CDPs should have powerful data provisioning services (for replacing third-party marketplace data with second-party partner data)

for clean-room data sharing to suck insights out of walled gardens

MTA and other measurement disciplines are too dependent on digital marketing touchpoints (clicks, email opens, video views, etc.), and biased to data points that are easily captured in systems

We’re going to have to make the most of what we have (sales, service and commerce)