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…
1. 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
1. To store customer data and do analytics
2. To enable use of that data (real-time engagement system)
‣
How it compares to other products
• The CDP is an evolution of the CRM category. The Five CDP Capabilities:
‣
Data Collection
‣
Data Management
‣
Unified Profile
‣
Segmentation and Activation
‣
Insights / 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
‣
System of insights (learning about the customer OVER interacting with them)
‣
System of engagement (using data to influence the customer journey)
‣
Enterprise Holistic CDP - combining insights and engagement

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

• 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
1. Know your customer
2. Personalise every interaction
3. Engage across channels and surfaces they touch
4. 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

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

• 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?

• 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

• 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
1. To store customer data and do analytics
2. 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
‣
Data Management
‣
Unified Profile
‣
Segmentation and Activation
‣
Insights / AI
• 2/3 types of CDPs
‣
System of insights (learning about the customer OVER interacting with them)
‣
System of engagement (using data to influence the customer journey)
‣
Enterprise Holistic CDP - combining insights and engagement
• 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…

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

• 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

• 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

• 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
1. Exploration: using statistics and visualisation techniques to find patterns in data
2. Experimentation: applying design of experiments methods to develop and test hypotheses under controlled conditions
3. 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
‣
Regression = Predicting a numerical value
‣
Classification = Classifying customers and prospects
‣
Clustering: Finding structure in numerical data generally involves creating groups or clusters that have some internal similarity while being relatively distinct from other groups
‣
Dimensionality Reduction
‣
Neural Nets
• 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%

• 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

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)

• 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)