Introduction: The Customer Data Modeling Dilemma
You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? Yeah, that one. Well, here’s the kicker: we’ve been doing it wrong. Or at least, not entirely right.
For years, we’ve been obsessed with creating these grand, top-down customer data models. You know the type – those massive diagrams that supposedly capture every single attribute and behavior of our customers. They’re like the architectural blueprints of the marketing world, with every demographic, psychographic, and behavioral trait meticulously mapped out. In theory, they’re supposed to give us a complete picture of our customer landscape. In practice? Well, that’s where things get a bit messy.
The problem, you see, is that customers don’t stand still. They’re not static entities that we can capture in a single snapshot. No, customers are more like hyperactive toddlers on a sugar rush – constantly moving, changing, and occasionally breaking our beautiful segmentation models. We’ve got new channels popping up, customer journeys zigzagging all over the place, and don’t even get me started on the ever-changing privacy regulations. By the time you’ve finished your beautiful, all-encompassing customer model, it’s already out of date.
Real-world example: Imagine you’re modeling customer data for an omnichannel retail brand. You’ve got your customer entity with basic attributes, purchase history, and website behavior – all neat and tidy. Then suddenly, the company decides to launch a mobile app with a loyalty program, start selling on social media, and open pop-up stores. Boom! Your neat little model just exploded. Now you need to account for app usage data, social media interactions, in-store beacons, and a whole host of other data points you never saw coming.
But here’s the thing: this isn’t a failure of customer data modeling as a concept. It’s a failure of our approach. We’ve been trying to force the dynamic, ever-changing nature of customer behavior into static, rigid models. It’s like trying to catch a river in a bucket – you might get some water, but you’re missing the essence of what makes it a river.
So, what’s the solution? Well, that’s where things get interesting. The world of customer data modeling is evolving, and we’re here to guide you through this brave new world. We’re talking about approaches that embrace change rather than resist it. Methods that allow our customer data models to be as dynamic and flexible as the customers they represent.
In this guide, we will explore concepts like transitional modeling for customer profiles, the power of event logs for customer behavior, persistent staging for raw customer data, real-time customer data capture, and much more. We’ll look at how Customer Data Platforms (CDPs) and cloud technologies are changing the game. And we’ll see how we can combine bottom-up and top-down approaches to get the best of both worlds.
By the end of this guide, you’ll have a new perspective on customer data modeling. One that doesn’t fear change but embraces it. One that sees customer data modeling not as a one-time exercise but as an ongoing process that evolves with your customers and your business.
So, are you ready to challenge everything you thought you knew about customer data modeling? Great! Let’s dive in and see just how deep this rabbit hole goes.
Transitional Modeling: The Lego Approach to Customer Data
Now that we’ve established that traditional customer data modeling is about as effective as using a paper map to navigate the changing labyrinth of customer behavior, let’s talk solutions. Enter transitional modeling, the cool, laid-back cousin of traditional data modeling that’s about to shake up your Customer Data Platform (CDP).
Transitional modeling is like the Lego of the customer data world. Instead of trying to build a perfect, complete customer model from the get-go, it starts with small, standardized pieces of information – let’s call them data atoms (or atomic data). These atoms are the building blocks of your customer data model, and they can be combined and recombined in countless ways to represent complex customer realities.
Building Blocks of Customer Understanding: Data Atoms Explained
So, what exactly is a customer data atom? Think of it as the smallest, indivisible unit of customer information. It could be a customer’s email address, a product view on your website, or a support ticket submission. Each atom typically consists of a subject (which customer we’re talking about), a predicate (what aspect of the customer we’re describing), and an object (the actual value). For example:
Subject: Customer#1234
Predicate: hasEmailAddress
Object: "john.doe@example.com"
This approach gives us incredible flexibility. Got a new social media channel? No problem, just add some new data atoms for social interactions. Launched a new loyalty program? Cool, create some atoms for points earned and rewards redeemed. It’s like having a Lego model of your customer base that you can constantly tweak and expand.
The Flexibility Factor: Adapting to Change at Lightning Speed
The real power of transitional modeling comes from its ability to handle change over time. In traditional modeling, if you need to add a new customer attribute, you’re often looking at a schema change in your CDP – a process that can be time-consuming and risky. With transitional modeling, you simply add new atoms to represent the new information. The old atoms don’t go away – they’re still there, representing the historical state of your customer data.
Let’s look at an example. Say your customer, John Doe, changes his email address. In a traditional model, we’d update the email field in the customer record. In transitional modeling, we’d add new atoms:
Subject: Customer#1234
Predicate: hasEmailAddress
Object: "john.new@example.com"
Timestamp: 2023-07-24T10:00:00Z
The old email address atoms are still there, giving us a complete history of how to contact John. This approach naturally captures the temporal aspect of our customer data, allowing us to easily reconstruct the state of our customer profiles at any point in time.
Atomic Power: How Small Data Points Pack a Big Punch
But here’s where it gets really interesting for us marketing folks.
With transitional modeling, you’re not just creating a static customer profile—you’re creating a living, breathing representation of your customer’s journey with your brand. Every interaction, purchase, and customer service call becomes part of a continuous stream of data atoms. It’s like having a real-time news feed of your customer’s relationship with your brand.
This stream of customer data atoms can be used to derive any number of views or models of your customers. Need a traditional CRM view for your sales team? No problem, just aggregate the relevant atoms. Want a graph model to analyze customer influence networks? You can build that too, using the same underlying atom stream. Looking to build a machine-learning model for churn prediction? The atomic data provides a perfect input, capturing the full richness of customer behavior over time.
Transitional modeling also aligns beautifully with modern marketing concepts like customer journey orchestration and real-time personalization. Your stream of customer data atoms becomes your source of truth, from which you can derive any number of customer views optimized for specific marketing use cases.
From Theory to Practice: Implementing Transitional Modeling
Now, I know what you’re thinking. “This sounds great in theory, but how does it work in practice with customer data or something like a ‘composable CDP’?” Well, implementing transitional modeling does require a shift in how we think about and work with customer data. It often involves specialized databases designed to handle this kind of atomic, temporal data. Some modern CDPs are starting to incorporate these concepts, allowing for more flexible and evolving customer data models.
It also requires a shift in how we query our customer data. Instead of simple SQL queries, we often need to use more complex temporal query languages or rely on derived views for simpler querying.
But the payoff is enormous: unparalleled flexibility in customer data modeling, built-in customer history tracking, and the ability to evolve our customer data model as quickly as our marketing strategies change.
Challenges and Considerations: Navigating the Atomic Data Universe
Transitional modeling isn’t a silver bullet—no approach is. It can be more complex to implement than traditional customer data modeling, and it may require more storage space to store all those historical atoms. But for marketing teams operating in rapidly changing environments (and let’s face it, who isn’t these days?), the benefits often far outweigh the costs.
So next time you’re faced with modeling a complex, ever-shifting customer base, remember: you don’t have to build the perfect customer model upfront. Start with your customer data atoms, and let your model grow and evolve organically with your customers and your marketing efforts. It’s time to embrace the Lego approach to customer data modeling!
Activity Schema Modeling: Capturing the Customer Journey in Action
Now that we’ve got our Lego blocks of customer data, let’s talk about another game-changing approach that’s shaking up the world of customer data modeling: Activity Schema Modeling.
If transitional modeling is like building with Legos, then activity schema modeling is like creating a flip book animation of your customer’s journey.
What is Activity Schema Modeling?
Activity Schema Modeling focuses on modeling the events and activities that make up a customer’s interaction with your brand. Instead of trying to create a static snapshot of who your customer is, it aims to capture what your customer does. It’s like the difference between a posed portrait and a candid action shot – both tell you something about the subject, but the action shot gives you a much better sense of what they’re really like.
Here’s how it works:
Events as First-Class Citizens: In Activity Schema Modeling, events or activities are the stars of the show. Each customer interaction, whether it’s a website visit, a purchase, a support call, or a social media engagement, is modeled as an event.
Rich Context: Each event carries with it a wealth of contextual information. Who performed the action? When did it happen? Where did it occur? What was the outcome? It’s like each event is a mini-story in your customer’s journey.
Temporal Nature: Activity schemas are inherently temporal. They capture not just what happened, but when it happened, allowing you to reconstruct the sequence of a customer’s interactions over time.
Flexibility: Just like with transitional modeling, activity schemas are incredibly flexible. Need to track a new type of customer interaction? Just define a new event type and start capturing it. No need to restructure your entire customer model.
Let’s look at an example. Imagine you’re running marketing for a trendy fitness app. Here’s how some customer interactions might be modeled using an activity schema:
Event: App_Open
Timestamp: 2023-07-24T08:00:00Z
User: User#5678
Device: iPhone12
Location: GymA
Event: Workout_Start
Timestamp: 2023-07-24T08:05:00Z
User: User#5678
WorkoutType: HIIT
Duration: 30min
Event: Achievement_Unlocked
Timestamp: 2023-07-24T08:35:00Z
User: User#5678
Achievement: FirstHIIT
Reward: 50pts
Event: Social_Share
Timestamp: 2023-07-24T08:36:00Z
User: User#5678
Platform: Instagram
Content: Achievement
See how this captures the flow of the user’s morning workout? It’s like we’re writing the play-by-play of their fitness journey.
Now, here’s where it gets really powerful for us marketers:
Customer Journey Mapping: Activity schemas make it super easy to map out customer journeys. Want to know the typical path from sign-up to first purchase? Just follow the sequence of events.
Behavioral Segmentation: Instead of segmenting customers based on static attributes, you can create dynamic segments based on behavior. “Users who work out 3x times a week” or “Users who always share their achievements” become a breeze to identify.
Predictive Modeling: Activity data is a goldmine for predictive models. Want to predict churn? Look at patterns of decreasing activity. Want to identify upsell opportunities? Watch for sequences of events that often lead to upgrades.
Real-Time Personalization: Because you’re capturing events as they happen, you can use this data to drive real-time personalization. “Oh, you just completed your first HIIT workout? Here’s a congratulatory message and a recommended cool-down routine!”
Attribution Modeling: By capturing all touch points as events, you can more easily attribute conversions to specific marketing activities or customer interactions.
But wait, there’s more! Activity Schema Modeling plays really nicely with the other approaches we’ve discussed. You can use it alongside transitional modeling, with events becoming another type of “atom” in your customer data model. And it fits perfectly with the event streaming and log-based architectures we’ll discuss later.
Now, I know what some of you are thinking. “This sounds great for digital-first businesses, but what about my brick-and-mortar store?” Well, fear not! Activity Schema Modeling can work for physical interactions too. A store visit becomes an event. A purchase becomes an event. A conversation with a sales associate? You guessed it – an event!
With the rise of IoT and mobile technology, we’re increasingly able to capture physical world interactions as digital events.
Of course, like any approach, Activity Schema Modeling has its challenges. It can generate a lot of data, which means you need robust storage and processing capabilities. Making sense of all those events can be complex – you might need to invest in stream processing technologies or complex event processing systems to really leverage the power of this approach.
But for many businesses, especially those operating in digital-first environments or striving for omnichannel experiences, the benefits of Activity Schema Modeling are hard to ignore. It gives you a dynamic, behavior-focused view of your customers that’s hard to beat.
So, next time you’re thinking about how to model your customer data, don’t just think about who your customers are – think about what they do. Embrace the action, capture the events, and get ready to see your customers in a whole new light. Welcome to the world of Activity Schema Modeling – where your customer data is always on the move!
The Power of the Log: Your Customer's Digital Diary
Now that we’ve discussed breaking down our customer data into atoms and capturing every interaction as an activity, let’s zoom out a bit and discuss how we can capture and store all these customer interactions over time. Enter the unsung hero of customer data modeling—the event log.
No, not the kind you’d find in a quaint bed and breakfast. I’m talking about the digital log of customer events, the record of every click, swipe, purchase, and customer service interaction. It’s time this silent workhorse of the CDP got its moment in the spotlight.
What is an Event Log?
The event log is like the diary of your customer’s journey with your brand. It records every single thing that happens in the order it happens. Every page view, every add-to-cart, every email open – it’s in there. Every customer data atom we create in our transitional model? Yep, that too. Every activity we capture in our schema? You betcha. It’s the most granular representation of your customer’s behavior you can get. And guess what? It’s incredibly powerful for us marketers.
Let’s break down why customer event logs are so awesome:
Completeness: Logs capture everything. And I mean everything. Every interaction, no matter how small, gets recorded. This gives us a complete history of our customer’s behavior, allowing us to reconstruct their journey at any point in time.
Order: Logs are sequential. Each entry has a specific order, typically represented by a timestamp. This lets us know exactly what happened, when, and in what order. Did the customer view the product before or after receiving that promotional email? The log knows.
Immutability: Once something is written to a log, it’s there forever. We don’t go back and edit log entries. This immutability is crucial for maintaining our customer data’s integrity and compliance with data privacy regulations.
Efficiency: Appending to a log is one of the fastest operations a computer can do. This makes logs incredibly efficient for capturing high-volume customer interactions in real-time.
You might be thinking, “That’s great, but how does this help me create better marketing campaigns?” Well, my friend, this is where it gets really interesting. From this simple log of customer events, you can derive any customer view or segment you want. It’s like having the source code of your customer’s behavior – with enough time and processing power, you can recreate any view of your customer base.
Let’s look at a few examples:
Customer 360 View: Want a complete view of a customer? Just replay your log, applying each event in order. The end result? An up-to-the-minute snapshot of your customer’s current state.
Customer Journey Analysis: Need to analyze the path customers take before making a purchase? Replay that log again, but this time, focus on building a sequence of touchpoints leading up to conversion events.
Activity-Based Segmentation: Remember our Activity Schema Modeling? Logs are the perfect companion. Want to segment customers who’ve completed a specific sequence of activities? Just query your log for those event patterns.
Cohort Analysis: Building a retention analysis? Replay the log once more, but now group customers by when they first engaged with your brand and track their behavior over time.
Time Travel: Want to know what your customer base looked like right before you launched that big campaign last year? Just replay the log up to that point in time. It’s like having a time machine for your customer data.
This approach, where the event log is your source of truth and all other customer views are derived from it, is the foundation of some powerful CDP architectures. It’s used by companies like Netflix and Spotify to handle massive amounts of user interactions while maintaining a complete picture of each customer’s preferences and behavior.
But the power of logs doesn’t stop there. They’re also crucial for real-time personalization and marketing automation. Technologies like Apache Kafka, often used in modern CDPs, use log-based approaches to stream customer events between systems in real-time. This allows for loosely coupled architectures where different marketing tools can consume the same log of customer events and build their own view of the customer.
Logs also play a crucial role in ensuring data consistency and recoverability in your CDP. If there’s ever an issue with your derived customer views, you can always go back to the log and rebuild them.
And here’s where it gets really exciting: logs are the perfect foundation for implementing both Transitional Modeling and Activity Schema Modeling. Each log entry can represent a data atom in your transitional model or an activity in your schema. You get the best of both worlds – the flexibility to model customer data as atomic facts and the ability to capture rich, contextual customer activities.
Now, I know what some of you are thinking. “This sounds great, but isn’t storing every single customer interaction going to take up a ton of space?” It’s a valid concern. Customer event logs can indeed grow large over time. But with modern cloud storage solutions and clever techniques like log compaction (where obsolete entries are removed), this is becoming less and less of an issue.
The benefits of log-based approaches often far outweigh the storage costs. The ability to have a complete, ordered history of all your customer interactions is incredibly powerful. It enables advanced analytics, makes debugging your marketing automations easier, provides natural audit trails for compliance, and allows for flexible, evolving customer data models.
So next time you’re designing your customer data architecture in your CDP, don’t just think about the current state of your customers. Think about how you’re going to capture and leverage the complete history of their interactions with your brand. Remember, in the world of customer data, the log is king. Embrace its power, and you’ll unlock new levels of customer understanding and personalization in your marketing efforts.
After all, if your customers could keep a diary of their interactions with your brand, wouldn’t you want to read it? Well, with event logs, you can! Just remember that your customers are trusting you with this data and don’t be evil.
Persistent Staging: Your Customer Data's Scrapbook
Alright, marketing data aficionados, we’ve talked about breaking down our customer data into atoms, capturing every interaction as an activity, and logging every event. Now it’s time to tackle another crucial concept in modern customer data architecture: persistent staging. If the event log is your customer’s diary, think of persistent staging as their scrapbook – a place where raw customer data is collected, organized, and kept for future reference.
In traditional ETL (Extract, Transform, Load) processes in CDPs, staging areas were often temporary holding pens for data. Customer data would be extracted from source systems, land briefly in a staging area for some initial processing, and then be loaded into the target customer profile or segment. Once the data was loaded, the staging area would typically be cleared out, ready for the next batch of customer updates. It’s like if you took a Polaroid of your customer, quickly sketched their details onto your master customer canvas, and then tossed the original photo. Not ideal, right?
Persistent staging turns this idea on its head. Instead of being a temporary waypoint, the persistent stage becomes a permanent part of your customer data architecture. It’s a place where raw, unaltered customer data is stored indefinitely. It’s like keeping a detailed scrapbook of every customer interaction, complete with ticket stubs, receipts, and candid snapshots.
Let’s break down why this is so powerful for us marketers:
Data Preservation: By keeping a copy of your raw customer data, you preserve the original context and granularity. This can be invaluable for auditing your marketing efforts, debugging personalization algorithms, or reprocessing customer data later when you have new ideas for segmentation or analysis. It’s like being able to go back and re-examine every piece of evidence in your customer relationship journey.
Flexibility: With your raw customer data safely stored, you have the flexibility to transform it in different ways for different marketing purposes. Need to change how you calculate customer lifetime value? No problem, just reprocess the data from the persistent stage. It’s like having a time machine for your customer data – you can always go back and look at things from a new angle.
Historical Processing: Want to apply a new customer segmentation model to historical data? With persistent staging, you can easily reprocess months or years of historical customer data using new logic. Imagine being able to retroactively identify your most valuable customers from three years ago using today’s advanced analytics – that’s the power of persistent staging.
Data Quality Management: Persistent staging provides a clear demarcation between raw and processed customer data. This makes it easier to implement and manage data quality processes, ensuring your marketing efforts are based on clean, reliable data. It’s like having a pristine archive of customer information that you can always trust.
Support for Multiple Modeling Approaches: Persistent staging can support both transitional modeling and activity schema modeling. You can store your customer data atoms and activity events in their raw form, giving you the flexibility to apply either modeling approach (or both!) as needed.
In modern CDP architectures, the persistent stage plays a crucial role. It’s the landing zone for all incoming customer data before it gets modeled into the actual customer profiles and segments. This separation allows for a clear distinction between data acquisition and data modeling.
Let’s look at a few examples of how persistent staging might be used in marketing scenarios:
Multi-Channel Retail: Imagine you’re running marketing for a hip clothing brand that’s all over the place – website, mobile app, physical stores, maybe even a VR shopping experience (because why not?). You could use persistent staging to store raw customer interaction data from all these channels. This data can then be processed in various ways:
For transitional modeling: Break down each interaction into atomic facts about the customer.
For activity schema modeling: Capture each interaction as a rich, contextual event.
For real-time personalization: “Hey, looks like you’ve been eyeing that jacket online, want to try it on in our VR store?“
For customer segmentation: “Let’s create a ‘Metaverse Fashionista’ segment“
For churn prediction models
And if next year you come up with a brilliant new way to analyze cross-channel behavior, guess what? The raw data is right there, ready to be reprocessed. It’s like having a time capsule of your customers’ shopping habits.
Subscription Service: Let’s say you’re marketing for a streaming service that’s trying to be the next Netflix. You might use persistent staging to store raw usage data from your platform. These could then be processed into various customer health scores and engagement metrics. Are people binge-watching your original series? Are they sharing accounts (naughty naughty)? All this raw data goes into your persistent stage. Then, if you later refine your definition of what constitutes an “engaged” customer, having the raw data in persistent staging allows for easy reprocessing of historical data with the new logic. You could potentially identify previously overlooked super-fans or spot early warning signs of churn that you missed before.
FinTech Revolution: Picture this: you’re the data guru at a cutting-edge fintech startup that’s shaking up the banking world. You might use persistent staging to store raw transaction data, customer service interactions, and even data from that quirky financial health chatbot you launched. This provides an immutable record of all customer touchpoints, which is crucial for:
Regulatory compliance (because fintech and regulations go together like peanut butter and jelly)
Building a comprehensive customer journey map using activity schema modeling
Identifying key moments in a customer’s financial life for targeted marketing
Applying transitional modeling to track changes in customer financial health over time
Now, you might be wondering, “Isn’t this just another form of data lake?” And you’re not wrong – there are certainly similarities. Both persistent staging and data lakes involve storing large amounts of raw data. But persistent staging is typically more structured and integrated into your overall customer data pipeline. It’s not just a dumping ground for data, but a crucial step in your customer data processing workflow.
Implementing persistent staging does come with some challenges. It requires more storage space, as you’re keeping two copies of your data—the raw version in staging and the processed version in your final customer profiles and segments. It also requires careful management to ensure data in the persistent stage remains accessible and doesn’t become a “data swamp” where information dies.
But with the right tools and processes, these challenges are manageable. Modern cloud storage solutions make it cost-effective to store large amounts of customer data. Extract, Load, and Transform (ELT) using tools like dbt has largely replaced ETL. And data catalog tools can help keep your persistent stage organized and discoverable, ensuring that treasure trove of raw customer data doesn’t turn into a jumbled mess.
The key is to think of persistent staging not as a cost but as an investment. You’re investing in flexibility, the ability to easily reprocess customer data, and better audibility and traceability of your customer information. In a world where customer expectations and marketing strategies are constantly changing, this investment can pay off many times over.
So, next time you’re designing your customer data pipeline in your CDP, consider making room for a persistent staging area. Give your customer data a scrapbook where it can collect memories in their raw, unaltered form. You never know when you might want to flip back through those pages and rediscover something valuable about your customers!
Change Data Capture (CDC): The Play-by-Play Announcer of Customer Behavior
If your customer data was a thrilling sports game, Change Data Capture, (CDC for short) would be that enthusiastic announcer, breathlessly narrating every move, every score, every change as it happens.
So what exactly is CDC in the context of customer data? At its core, CDC is a set of software design patterns used to determine and track the changes made to customer data in your various systems. It’s like having a vigilant observer watching your customer database, CRM, website analytics, and every other customer touchpoint 24/7, noting down every new sign-up, every profile update, every purchase, and every support ticket.
But why is this so important for us marketers? Well, in our real-time world, knowing what’s changed in our customer data, and when, is crucial. CDC enables us to:
Synchronize Customer Data: Keep multiple systems (like your CDP, CRM, and marketing automation platform) in sync by propagating customer data changes from one to the others. No more “which system has the most up-to-date email address” headaches!
Build Real-Time Customer Analytics: Feed changes immediately into analytics systems for up-to-the-minute insights. Want to know how many people signed up right after your Super Bowl ad aired? CDC’s got you covered.
Audit Customer Interactions: Maintain a complete audit trail of all customer data modifications. This is not just for compliance (though your legal team will love you for it), but also for understanding the customer journey in minute detail.
Enable Event-Driven Marketing: Use customer data changes as events to trigger personalized marketing actions. Did a customer just abandon their cart? CDC can help you fire off that perfectly timed email reminder.
There are several ways to implement CDC, each with its own pros and cons:
Trigger-Based CDC: This method uses database triggers to capture changes. It’s precise but can impact database performance. It’s like having a dedicated spotter for each player in a sports game – accurate, but potentially overwhelming.
Polling-Based CDC: This approach periodically checks for changes, often using timestamp columns. It’s less real-time but also less intrusive. Think of it as taking regular snapshots of the game rather than continuous footage.
Log-Based CDC: This method reads the database’s transaction log to capture changes. It’s efficient and low-impact but can be complex to set up. It’s like reading the official game log rather than watching the game itself.
API-Based CDC: Some systems provide APIs that emit change events. This is great when available but isn’t a universal solution. It’s like getting play-by-play updates directly from the team’s official app.
Real-world example: Imagine you’re running marketing for a hip direct-to-consumer brand that’s taking the world by storm. You’ve got customer data flowing in from your e-commerce platform, mobile app, in-store point-of-sale systems, customer service platform, and even that quirky IoT-enabled product you launched last year.
Without CDC, you might resort to periodic full data dumps and reloads, which are slow, resource-intensive, and definitely not real-time. It’s like trying to understand a football game by looking at a summary of player stats once a day. You’d miss all the exciting plays!
With CDC, any change to a customer profile (new sign-up, address update), interaction (product view, add-to-cart), or purchase is immediately captured and propagated to all the relevant systems. Your CDP always has the latest customer data, your marketing automation platform can trigger timely messages, and your analytics dashboard shows real-time customer behavior. It’s like having a super-powered sports announcer giving you play-by-play updates on every single fan in the stadium.
CDC isn’t without its challenges, though. Handling high volumes of changes (think Black Friday levels of customer activity), dealing with schema evolution (because your customer data model will definitely change over time), and managing the ordering of changes across multiple systems are all complexities you’ll need to tackle. Tools like Debezium, Striim, or Attunity can help by providing robust CDC solutions that integrate with various data sources and streaming platforms.
When combined with the concept of persistent staging we discussed earlier, CDC becomes even more powerful. The changes captured by CDC can be fed into your persistent staging area, providing a complete, historical record of all changes to your customer data. This allows you to reconstruct the state of your customer profiles at any point in time, or reprocess historical data with new logic. Imagine being able to rewind and replay your entire customer database like it’s a DVR – that’s the power of combining CDC with persistent staging.
In the world of modern, real-time marketing, CDC is often a crucial component of your customer data infrastructure. It’s the nervous system that keeps all parts of your marketing technology stack informed and in sync. So next time you’re designing your customer data pipeline, remember to invite the sports commentator. Your customer data game will never be the same.
Beyond Traditional CDPs: The Rise of the Composable CDP
It’s time to talk about the new kid on the block that’s shaking up the Customer Data Platform (CDP) world: the Composable CDP. Now, I know what some of you are thinking: “This sounds great in theory, but do I really need to go full-blown traditional CDP to get the benefits?” Fear not, my data-savvy friends, the Composable CDP is here to give you all the CDP goodness with a hefty dose of flexibility on top.
The Composable CDP: Your Data Dream Team
First things first – let’s break down this “composable CDP” concept. Imagine if instead of buying a pre-built car, you could assemble your dream ride using the best engine, the slickest wheels, and the most comfortable seats from different manufacturers. That’s essentially what a composable CDP lets you do with your customer data platform.
In the composable CDP world, you’re not locked into a single vendor’s vision of what a CDP should be. Instead, you get to pick and choose the best-of-breed components that fit your specific needs. It’s like building your own data Avengers team, with each component bringing its own superpowers to the table.
Here’s how a composable CDP might incorporate the modeling approaches we’ve discussed:
Data Storage and Processing: This is your foundation. You might choose a cloud data warehouse like the Snowflake AI Data Cloud or BigQuery. This is where you’ll implement your persistent staging, storing all that raw customer data in its pristine, unaltered form.
Transitional Modeling Layer: On top of your raw data, you might implement a transitional modeling layer using a tool like dbt. This is where you’ll break down your customer data into those atomic, flexible data points we talked about.
Activity Schema Processing: To capture and process customer activities, you might use a stream processing technology like Apache Kafka or Apache Flink. This allows you to implement activity schema modeling, capturing rich, contextual customer events in real time.
Event Logging: Remember our discussion about the power of the log? You might implement this using a tool like Apache Kafka or Amazon Kinesis, creating that immutable record of all customer interactions.
Data Activation: To put all this customer data to work, you might use a tool like Hightouch or Census. These reverse ETL tools can sync your customer segments and personalized content to your various marketing channels.
Identity Resolution: To tie all these customer interactions together, you might use an identity resolution tool like Merkle Merkury or Twilio Segment.
Machine Learning Layer: For predictive analytics and advanced segmentation, you might add a machine learning tool like DataRobot or H2O.ai.
The beauty of this approach is that you can start small and grow as your needs evolve. Maybe you start with just data storage and basic transitional modeling, then add activity schema processing and advanced activation as you grow.
Why Composable CDPs are Changing the Game
Now, you might be wondering, “This sounds cool, but why should I care?” Well, let me tell you why composable CDPs are becoming the talk of the town:
Flexibility: With a composable CDP, you’re not locked into one vendor’s roadmap. Need to swap out a component or add a new one? No problem! It’s like having a LEGO set for your customer data – you can always rebuild it to fit your changing needs.
Best-of-Breed Solutions: Instead of settling for a jack-of-all-trades traditional CDP, you can choose the best tool for each specific function. Want the best-in-class machine learning capabilities? Just plug it in!
Cost-Effectiveness: Why pay for features you don’t need? With a composable CDP, you only invest in the components that deliver value for your business.
Scalability: As your business grows and your data needs evolve, a composable CDP can scale with you. Just add new components or upgrade existing ones as needed.
Future-Proofing: Technology moves fast, especially in the world of customer data. With a composable CDP, you can easily adopt new technologies as they emerge without having to replace your entire platform.
Support for Multiple Modeling Approaches: Remember our discussions about transitional modeling and activity schema modeling? A composable CDP lets you implement both approaches side by side, giving you the best of both worlds.
Real-World Example: The Composable CDP in Action
Let’s bring this down to earth with an example. Imagine you’re running marketing for “SuperFitness,” that hip new fitness app we talked about earlier. Here’s how you might build a composable CDP:
Data Foundation: You start with Snowflake as your data warehouse, implementing persistent staging to store all your raw customer data.
Transitional Modeling: You use dbt to implement transitional modeling, breaking down customer data into atomic facts. This allows you to create flexible customer profiles that can evolve as your business needs change.
Activity Schema Processing: You implement Apache Kafka to capture and process customer activities in real-time. Every workout, achievement, and social share is captured as a rich, contextual event.
Event Logging: Kafka also serves as your event log, creating an immutable record of all customer interactions.
Data Activation: You use Hightouch to sync your customer segments to various marketing tools. High-churn-risk customers get enrolled in a retention campaign in your email tool, while your “fitness enthusiasts” segment gets pushed to Facebook for lookalike audience targeting.
Identity Resolution: You implement Amperity to tie together customer interactions across your app, website, and physical gym locations (because SuperFitness is omnichannel, of course).
Machine Learning: You add DataRobot to build predictive models, like forecasting which customers are likely to upgrade to a premium subscription.
With this setup, you’ve got a CDP that gives you the flexibility of transitional modeling, the rich context of activity schema modeling, the power of event logging, and the activation capabilities to put all this customer data to work. And the best part? If a new, game-changing customer data technology comes along next year, you can easily incorporate it into your composable stack.
Challenges and Considerations
Of course, every approach has its challenges. Building a composable CDP requires some serious data engineering chops. You’ll need to ensure all these different components play nicely together, which can be complex. Data governance and security also become more complex when you’re dealing with multiple tools instead of a single, integrated platform.
However, for many businesses, especially those operating in rapidly changing environments or with complex, unique customer data needs, the benefits of a composable CDP often far outweigh the challenges.
So, the next time you’re evaluating your customer data platform options, don’t just think about traditional, all-in-one CDPs. Consider taking a composable approach. It might just be the key to building a customer data platform as dynamic and adaptable as your customers. Remember, in the world of customer data, flexibility is king. Long live the Composable CDP!
The "CDC to Iceberg" Composable CDP Approach
Now, let’s add another exciting layer to our composable CDP discussion. Enter Apache Iceberg, the cool new kid on the block in the world of open table formats. It’s like giving your customer data a time machine with an open-source twist, leveraging modern table formats for maximum flexibility. Great Scott!
Here’s how a “CDC to Iceberg” approach might work in your composable CDP:
Change Data Capture (CDC): Use CDC to capture changes from various customer data sources. It’s like having a fleet of tiny data drones constantly monitoring every twitch in your customer database.
Stream to Iceberg: Stream these changes into Apache Iceberg tables. Think of Iceberg as a super-smart, infinitely expandable filing cabinet for your customer data.
Leverage Iceberg’s Superpowers: Use Iceberg’s snapshot isolation and time travel capabilities to access historical versions of customer data. Yes, you read that right – time travel for your data!
Now, you might be wondering, “What’s so special about this Apache Iceberg thing?” Well, my curious friend, Iceberg is an open table format that provides some seriously nifty features:
Schema Evolution: You can add new customer attributes without breaking existing queries. Decided to start tracking your customers’ favorite emoji? No problem! Just add it to your schema, and all your existing reports and dashboards will keep humming along happily.
Partition Evolution: Change how you organize your customer data over time as query patterns evolve. Maybe you started out partitioning by country, but now you need to partition by both country and customer segment. Iceberg lets you make that change without a massive, painful data migration.
Time Travel: Query customer data as it existed at any point in time, or roll back to a previous version. It’s like having a DVR for your entire customer database. Want to know what your high-value customer segment looked like right before that big holiday campaign last year? Just rewind and take a look!
ACID Transactions: Ensure customer data consistency, even with concurrent readers and writers. No more worrying about halfway-updated customer profiles or inconsistent data across systems.
Here’s where it gets really interesting. Snowflake, the AI cloud data platform that’s been taking the analytics world by storm, has added support for external Iceberg tables. This means you can have your open-source cake and eat it in the cloud too!
With Snowflake’s support for Iceberg:
You can query Iceberg tables stored in your cloud storage (S3, Azure Blob, etc.) directly from Snowflake. It’s like having a universal translator for your customer data.
You get the benefits of Snowflake’s powerful compute engine while working with open-format data. Speed and flexibility? Yes, please!
You can seamlessly integrate Iceberg tables with your existing Snowflake workflows. No need to rip and replace – just enhance what you’ve already got.
Let’s look at an example of how this might work in a marketing context.
Real-world example: Imagine you’re running marketing for “SuperFitness”, our hip fitness app that’s taking the world by storm. You’ve got customer data coming in from all angles – app usage data, wearable device integrations, in-app purchases, customer support interactions, you name it.
With the CDC to Iceberg approach in your composable CDP:
Your CDC system captures every change in customer data across all these sources in real-time. New user sign-up? Captured. Workout completed? Logged. Customer support ticket resolved? Noted.
These changes are streamed into Iceberg tables in your data lake. Each change is timestamped and added to the appropriate table, building up a rich, historical view of your customer data.
Your data scientists can now run complex analyses on this data using tools like Apache Spark or Snowflake’s Snowpark and Cortex to look at how customer behavior evolves over time. They might discover, for example, that users who log three workouts in their first week are 50% more likely to become paying subscribers.
Meanwhile, your BI team can use Snowflake to query this same data, building dashboards that show real-time customer engagement metrics and cohort analyses.
When you launch a new feature – let’s say, social sharing of workout achievements – you can easily add this new data to your schema without disrupting existing processes.
Six months down the line, when your CEO asks “How has our customer engagement changed since we launched social sharing?“, you can use Iceberg’s time travel feature to compare customer behavior before and after the launch, down to the minute.
And if you ever need to comply with data privacy regulations like GDPR or CCPA, Iceberg’s ACID transactions ensure that when a customer requests their data be deleted, it’s deleted consistently across all your systems.
The “CDC to Iceberg” approach isn’t just a technical solution – it’s a game-changer for how we think about and work with customer data. It allows us to move beyond static, point-in-time views of our customers to a dynamic, ever-evolving understanding of their behavior and preferences.
Of course, it’s not without its challenges. Implementing this approach requires some serious data engineering chops. You’ll need to set up and manage CDC processes, design your Iceberg table structure, and potentially retool some of your data pipelines. But for marketing teams that are serious about leveraging their customer data to its fullest potential, the benefits often far outweigh the costs.
So, as you’re charting your course in the world of composable CDPs, don’t forget to consider the power of open table formats like Apache Iceberg. With support from major players like Snowflake, it’s a technology that’s poised to reshape how we store, manage, and analyze our customer data. Who knew customer data could be so…cool?
Data Activation: Hightouch and the Composable CDP Revolution on Snowflake
We’ve talked about collecting customer data, organizing it, and understanding it. Now it’s time for the grand finale – putting all that juicy customer data to work! Let’s dive into the world of data activation, with a special spotlight on Hightouch and the composable CDP concept on Snowflake. Buckle up, because we’re about to turn your data warehouse into a marketing powerhouse!
The Composable CDP: Your Data Dream Team
First things first—let’s discuss this “composable CDP” concept by returning to the car example. Imagine if instead of buying a pre-built car, you could assemble your dream ride using the best engine, the slickest wheels, and the most comfortable seats from different manufacturers. That’s essentially what a composable CDP lets you do with your customer data platform.
In the composable CDP world, Snowflake acts as your high-performance chassis. It’s the rock-solid foundation that stores and processes all your customer data. But a car isn’t much use without an engine and wheels, right? That’s where tools like Hightouch come in – they’re the specialized components that turn your data warehouse into a fully functioning, high-octane marketing machine.
Enter Hightouch: Your Data Activation Turbocharger
New tools like Hightouch are like the turbochargers for your composable CDP. It takes the rich, unified customer data sitting in your Snowflake warehouse and activates it across your entire marketing tech stack. No need to move your data into yet another platform – Hightouch brings the activation directly to where your data lives.
Here’s how Hightouch turns your Snowflake data into marketing gold:
Segmentation & Audience Building: Hightouch gives marketers a choice – you have a modern, clean interface for building our Audiences and Segment, or you can use a cool new BI tool like Sigma Computing. Love SQL? Hightouch lets you use good ol’ SQL, or better yet, dbt to define customer segments right in Snowflake. No need to learn a new query language or interface!
Real-Time Syncing: As your customer data updates in Snowflake, Hightouch can automatically sync those changes to your marketing tools. It’s like having a real-time data pipeline from your warehouse straight to your marketing channels.
Reverse ETL Magic: Hightouch specializes in “Reverse ETL.” Instead of just loading data into your warehouse, it pushes data out to where it’s needed. It’s like turning your data warehouse into a data distribution center. You can put that valuable information back into your CRM, CS systems, and others, putting the right information in front of the right people just at the right time.
Multi-Channel Activation: Whether you need to update customer profiles in Salesforce, trigger emails in Mailchimp, or adjust ad audiences in Google Ads, Hightouch has got you covered with a wide range of integrations.
Data Activation in Action: Hightouch and Snowflake Edition
Let’s look at some real-world examples of how this Hightouch + Snowflake dream team might work:
1. The “High-Value Customer” VIP Treatment: Your data team has created a sophisticated “high-value customer” model in Snowflake using purchase history, website behavior, and support interactions. With Hightouch:
- Automatically sync this segment to your email marketing tool to enroll these customers in a special loyalty campaign.
- Update their status in your CRM so your sales team can give them white-glove treatment.
- Adjust your ad bidding strategy in Google Ads to show these valuable customers your premium products.
2. The “Churn Risk” Rescue Mission: Your data scientists have built a churn prediction model in Snowflake. As soon as a customer hits a high-risk score:
- Hightouch can trigger a personalized "We Miss You" email campaign.
- Update their profile on your customer service platform, so support reps know how to handle them with extra care.
- Add them to a retargeting audience in Facebook Ads with special win-back offers.
3. The “Product Recommendation” Personalization Engine: You’ve got a fancy collaborative filtering model running in Snowflake that generates personalized product recommendations. Hightouch can:
- Sync these recommendations to your e-commerce platform to personalize the shopping experience in real-time.
- Push the data to your email marketing tool to power personalized product recommendation emails.
- Update dynamic product ads in Google and Facebook with each customer's top recommendations.
Why This Combo is the Future of Data-Driven Marketing
The Hightouch + Snowflake composable CDP approach is gaining traction for good reasons:
Flexibility: You’re not locked into a monolithic CDP. Need to swap out a component or add a new one? No problem!
Cost-Effective: Why pay for a separate CDP when you can leverage the Snowflake infrastructure you already have?
Data Freshness: With Hightouch’s real-time syncing, you’re always working with the latest customer data.
Scalability: Snowflake can handle massive amounts of data, and Hightouch is built to keep up.
Data Governance: Keep your customer data in Snowflake, where you already have strong security and governance controls.
Developer-Friendly: Data teams love working with SQL in Snowflake, and marketers love the easy-to-use Hightouch interface. Win-win!
Challenges and Considerations
Of course, no approach is without its challenges:
Skill Set Requirements: You’ll need a team that’s comfortable with SQL and data modeling in Snowflake.
Integration Complexity: While Hightouch simplifies things, you’re still managing multiple tools rather than a single CDP interface.
Change Management: Moving to a composable CDP approach might require some adjustment for teams used to traditional marketing tools.
The Future is Composable
As we peer into our crystal ball, the future of CDPs looks increasingly composable. Here’s what we might see:
AI-Powered Activation: Imagine ML models running in Snowflake, with Hightouch automatically activating the insights across channels.
Even More Real-Time: As Snowflake and Hightouch continue to evolve, expect even faster, more real-time activation capabilities.
Expanded Ecosystem: Look for more specialized tools that plug into this composable CDP framework, each bringing unique capabilities to the table.
So there you have it, folks! The dynamic duo of Hightouch and Snowflake is revolutionizing the CDP landscape, turning the concept of data activation on its head. It’s no longer about moving your data to where it can be activated – it’s about bringing activation to where your data lives.
Remember, in the world of modern marketing, it’s not just about having a CDP – it’s about having a CDP that can flex and grow with your needs. The composable approach with Snowflake and Hightouch gives you that flexibility, letting you build the exact customer data platform you need, piece by piece.
Ontologies and Data Catalogs: The Dynamic Duo of Customer Understanding
Now that we’ve explored various ways to model and store our customer data, let’s zoom out and discuss how we can make sense of all this information. Enter ontologies and data catalogs—the dynamic duo of customer understanding that’s about to take your data game to the next level.
Ontologies: The Wise Elder of Your Customer Tribe
An ontology is like the wise elder of your customer data tribe. It provides the big picture, the conceptual understanding of your customer domain at a high level. But what exactly is an ontology in the context of customer data management?
At its core, an ontology is a formal representation of a set of concepts within a domain and the relationships between those concepts. It’s a way of organizing and representing knowledge about your customers that both humans and machines can understand and reason about. In the marketing world, an ontology might define entities like “Customer,” “Product,” “Campaign,” and the relationships between them, like “Customer purchases Product” or “Campaign targets Customer Segment”.
Key benefits of customer data ontologies include:
Common Language: Ontologies provide a shared vocabulary for describing your customer domain, reducing misunderstandings between marketing, sales, and customer service teams. No more debates about what constitutes a “loyal customer” or an “abandoned cart”!
Semantic Relationships: They capture not just customer attributes but the meaningful relationships between different aspects of the customer journey. This allows for more sophisticated analysis and inference.
Flexibility: Ontologies can evolve more easily than traditional data models, accommodating new concepts and relationships as your understanding of your customers changes. Launched a new loyalty program? Just add it to your ontology!
Interoperability: By providing a standardized way of describing your customer domain, ontologies can facilitate data integration across different systems and even different brands within a larger corporation.
But here’s where it gets really exciting: ontologies and transitional modeling are like peanut butter and jelly – they just work beautifully together!
Remember how transitional modeling breaks down customer data into atomic facts? Well, these atoms are the perfect building blocks for creating knowledge graphs based on your ontology. Each atom, with its subject-predicate-object structure, naturally maps to the nodes and edges of a knowledge graph. It’s like we’re building a vast, interconnected web of customer information, where each strand represents a relationship or an attribute.
Let’s break it down with an example. Say we have these customer data atoms:
(Customer#123, hasName, "Jane Doe")
(Customer#123, madePurchase, Order#456)
(Order#456, containsProduct, Product#789)
(Product#789, hasCategory, "Fitness Equipment")
Our ontology might define concepts like “Customer,” “Order,” “Product,” and “Category,, along with relationships like “makes purchase,” “contains product,” and “belongs to category.” When we combine these atoms with our ontology, voilà! We’ve got ourselves a mini knowledge graph showing that Jane Doe bought some fitness equipment.
But here’s where it gets really powerful. As we accumulate more of these atomic facts, our knowledge graph grows and evolves. We start to see patterns and relationships we never noticed before. Maybe we discover that customers who buy fitness equipment are also likely to purchase nutritional supplements. We might also notice that customers who make purchases across multiple product categories tend to have a higher lifetime value.
These knowledge graphs, built from our transitional model and guided by our ontology, give us a rich, multi-dimensional view of our customers and their behaviors. It’s like we’ve gone from looking at a flat map of our customer base to exploring a 3D model where we can see all the connections and relationships.
The best part? Because we’re using transitional modeling, our knowledge graph can evolve over time just as easily as our customer data model. Add a new type of customer interaction? No problem; just define it in your ontology and start adding those atoms to your graph. It’s like your customer understanding is growing and adapting in real-time, just like your customers themselves.
Real-world example: A travel company might use an ontology to model complex concepts related to customer trips and preferences. This could include types of travelers (business, leisure, adventure), destinations, amenities, and how they all interrelate. By combining this ontology with transitional modeling, they could create a knowledge graph that reveals intricate patterns in customer behavior. For instance, they might discover that business travelers who extend their trips for leisure are more likely to book high-end accommodations, opening up new opportunities for targeted marketing and personalized recommendations.
Data Catalogs: The Librarian with a Photographic Memory of Your Customers
If ontologies are the wise elder, data catalogs are like librarians with a photographic memory of your customers’ data. They know where every piece of customer data is, what it means, and who’s been using it. A data catalog is a detailed inventory of all the customer data assets within your organization, including datasets, databases, APIs, and even reports and dashboards.
Key features of customer data catalogs include:
Metadata Management: Catalogs store and manage metadata about each customer data asset, including its structure, format, owner, update frequency, and more. It’s like having a detailed card catalog for your customer data library.
Data Dictionary: They often include a data dictionary that defines the meaning of various customer data elements. What exactly does
"customer_status = 2"mean? Your data catalog knows!Lineage Tracking: Many catalogs can track data lineage, showing how customer data flows through various systems and transformations. This is crucial for understanding where your customer insights are coming from and how they’re being used.
Search and Discovery: Catalogs typically provide powerful search capabilities, making it easy for marketers to find the customer data they need. Looking for all datasets that include customer email preferences? A good data catalog will find them in seconds.
Collaboration Features: Modern data catalogs often include features for data stewards and users to collaborate, such as the ability to rate datasets, leave comments, or request access. It’s like a social network for your customer data!
Real-world example: A large e-commerce company might use a data catalog to manage its vast array of customer data assets across different product lines and geographical locations. The catalog could help marketers quickly find relevant customer data for their campaigns, understand its origin and quality, and see how it’s been used in other marketing initiatives across the company.
The Power Couple: Ontologies and Data Catalogs Together
Now, here’s where the magic really happens. When you combine ontologies and data catalogs, you get a customer data management power couple that’s greater than the sum of its parts.
Imagine a world where your data catalog doesn’t just tell you where a customer dataset is and who owns it, but also how it fits into your overall understanding of the customer journey. Or where your customer ontology isn’t just a theoretical model, but is directly linked to actual customer data assets in your organization.
By integrating ontologies with data catalogs:
Contextual Understanding: Marketers can understand not just where customer data is and what it contains, but how it fits into the larger customer experience context.
Improved Data Discovery: The semantic relationships defined in the ontology can enhance search and discovery in the catalog, helping marketers find related customer data they might not have known to look for. Looking for purchase data? The system might also suggest related browsing history or customer service interactions.
Enhanced Data Governance: The combination can support more sophisticated customer data governance, using the ontology to define and enforce business rules and data quality standards across your entire marketing tech stack.
Powerful Analytics: By understanding the semantic relationships between different customer data assets, you can enable more sophisticated analytics and AI applications. Want to predict customer lifetime value? The system can automatically suggest all relevant data points based on your customer ontology.
Together, ontologies and data catalogs are the dynamic duo of customer understanding. They help us move from just collecting data to truly comprehending it, from drowning in information to surfing on waves of customer insights. With knowledge graphs built from our transitional model and organized by our ontology, and data catalogs helping us navigate this vast sea of information, we’re not just seeing individual data points – we’re understanding the entire customer ecosystem. It’s like we’ve upgraded from reading customer tea leaves to having a customer crystal ball!
Real-world example: A telecommunications company could use this combined approach to great effect. The ontology might define concepts like “Customer”, “Device”, “Plan”, “Network Usage”, and “Customer Service Interaction”, along with their relationships. The data catalog would inventory all the actual datasets related to these concepts across various systems – CRM, network logs, billing systems, etc.
When a marketer is looking for data to build a churn prediction model, they could use the catalog to find all relevant customer datasets. The ontological information would help them understand how these datasets relate to other concepts like network quality or customer service satisfaction, potentially uncovering valuable features for their model they might have otherwise missed.
In the fast-paced world of modern marketing, having customer data is not enough—you need to understand it, find it quickly, and use it effectively. Ontologies provide the conceptual framework for understanding your customers, while data catalogs provide the concrete inventory and usability layer for your customer data assets. Together, they form a powerful combination that can take your customer data management and utilization to new heights.
So, as you’re building out your customer data architecture, don’t forget to invite both the wise elder and the librarian to the party. Your future self (and your marketing team) will thank you!
Conclusion: Embracing the Dynamics of Customer Data - A New Era in Marketing
Well, folks, what a wild ride through the customer data landscape! We’ve zoomed from the atomic level of data modeling, through the action-packed world of activity schemas, all the way up to the stratosphere of customer ontologies and data catalogs. If your brain feels like it just ran a marathon, don’t sweat it – that’s just the sweet burn of your customer data horizons expanding.
So, what’s the big picture here? What’s the takeaway from this grand tour of modern customer data concepts? Well, we’re witnessing nothing less than a revolution in how we think about, manage, and leverage customer data. Let’s break it down, shall we?
From Static Snapshots to Customer Journey Livestreams: Remember when we thought we could capture our customers in neat little profile boxes? How adorable we were! We’ve now embraced the chaos of the customer journey, moving from static data models to dynamic, ever-evolving representations of our customers. Whether it’s transitional modeling with customer data atoms, activity schemas capturing every interaction, or flexible formats like Apache Iceberg, we’re all about capturing the customer’s journey in all its messy, glorious detail.
From “Who They Are” to “What They Do”: We’ve shifted from just describing our customers to chronicling their actions. Activity Schema Modeling has us thinking in terms of events and behaviors, giving us a play-by-play of each customer’s unique story. It’s like we’ve gone from writing customer bios to directing customer action movies!
From Data Silos to Rivers of Information: We’re no longer content with customer data sitting in lonely silos, waiting for the next batch job to come along. Nope, we’re now thinking in streams, baby! Change Data Capture (CDC) and event logs are turning our customer data into rivers of real-time information, flowing through our marketing tech stack like caffeinated electrons. It’s like we’ve gone from using carrier pigeons to telepathy for our customer communications!
From “We’ll Figure It Out Later” to “We’re Ready for Anything”: With persistent staging and data lakes, we’re storing raw customer data like squirrels hoarding acorns for winter. But unlike our furry friends, we’re not just saving for one season – we’re preparing for any future use case our caffeinated marketing brains might dream up. Schema-on-read approaches mean we can adapt to new ideas without needing a time machine to go back and change our data collection.
From Data Lockboxes to Flexible Treasure Chests: Remember when changing your customer data schema was about as fun as a root canal? Those days are gone! With modern approaches like Apache Iceberg (now with extra Snowflake goodness!), we can evolve our customer data models faster than customers can change their minds – and that’s saying something!
From “What Does This Data Mean?” to “Let Me Show You the Customer Story”: This is where ontologies and data catalogs come in, turning our customer data from a bunch of cryptic tables into rich, interconnected customer narratives. Ontologies provide the big picture, the conceptual framework that helps us understand how different pieces of customer data relate to each other. They’re like the wise elders of our customer data tribe, providing context and meaning to every data point we collect. Meanwhile, data catalogs are like having a librarian with a photographic memory for your customer data. They keep track of what data you have, where it came from, what it means, and how it’s being used. Need to find all datasets related to customer purchase history? Your data catalog has got you covered. It’s like we’ve gone from fumbling through filing cabinets to having a customer data GPS! Together, ontologies and data catalogs are the dynamic duo of customer understanding. They help us move from just collecting data to truly comprehending it, from drowning in information to surfing on waves of customer insights. It’s like we’ve upgraded from reading customer tea leaves to having a customer crystal ball!
From “We’ll Get To It Eventually” to “Here’s What’s Happening Right Now”: Batch processing? That’s so last season. We’re increasingly living in the now with real-time customer data architectures. It’s like we’ve upgraded from customer postcards to customer telepathy!
From One-Size-Fits-All to Custom-Tailored Data Solutions: We’re breaking free from monolithic customer data platforms and embracing a mix-and-match approach. With composable CDPs built on powerhouses like Snowflake, activated by tools like Hightouch, and organized with the help of ontologies and data catalogs, we’re crafting bespoke customer data wardrobes. Fancy!
The name of the game here is flexibility and adaptability. In a world where customer behaviors change faster than social media trends, our customer data architectures need to keep up. The approaches and technologies we’ve explored—from transitional modeling and activity schemas to composable CDPs and Apache Iceberg—are our secret weapons in staying as dynamic and unpredictable as our customers.
But here’s the kicker: this isn’t just about cool tech and flashy buzzwords. It’s about a fundamental shift in how we approach customer understanding. We’re moving from a “capture and analyze” mindset to a “listen, understand, and adapt” philosophy. We’re building systems that can learn and evolve over time, just like our customers do.
So, what does this mean for you? Here are your marching orders:
Embrace the Chaos: Don’t fear changes in your customer data models or marketing requirements. Build data architectures that are as flexible and adaptable as your customers.
Think in Customer Moments: Start seeing your customer data as a never-ending stream of customer moments and interactions. Every click, every purchase, every support ticket is a story waiting to be told.
Action is Everything: Remember, it’s not just about who your customers are, but what they do. Embrace Activity Schema Modeling to capture the vibrant tapestry of customer behavior.
Hoard That Raw Data (But Make It Organized): Use persistent staging or data lake approaches to keep all your customer data options open. You never know when that seemingly useless data point might become the key to your next big campaign.
Go Real-Time or Go Home: Look for opportunities to shift from batch to real-time processing. In a world of instant gratification, real-time customer insights are your secret weapon.
Make Your Data Tell Customer Stories: Invest in ontologies and data catalogs to bring meaning to your customer data. Understanding the “why” behind the data is just as important as the “what”.
Keep Your Options Open: Use technologies like Apache Iceberg that allow you to evolve your customer data schemas over time. Your understanding of your customers will change, and your data model should be able to keep up.
Be a Data Mixologist: Don’t be afraid to combine different approaches to meet your specific customer data needs. The best solution might be a cocktail of various technologies and methods. Shake it up!
Remember, there’s no one-size-fits-all solution in the world of customer data. The key is to understand these different approaches and tools, and then choose the right combination for your specific needs. Maybe you need the full power of a composable CDP for your core customer data, but a simpler data lake approach for experimental projects. Perhaps you need real-time CDC for your e-commerce site, but batch updates are fine for your email marketing system.
The beauty of modern customer data architecture is that it gives us options. We’re no longer constrained by rigid database schemas or clunky ETL processes. We have the tools to build customer data systems as dynamic and multifaceted as our customers.
As we look to the future, we can expect this trend towards more dynamic, flexible customer data architectures to continue. Emerging technologies like AI and machine learning will put even more emphasis on our ability to quickly adapt our customer models and extract insights from diverse, rapidly changing data sources. And you can bet that ontologies and data catalogs will play a crucial role in making sense of it all, helping us navigate the ever-expanding universe of customer data.
So, the next time you’re staring at a massive customer entity-relationship diagram, wondering how you’re going to keep up with your ever-changing customers, remember: there’s a better way. Embrace the atom, capture the activity, respect the log, stage with persistence, capture those changes, store with flexibility, guide with ontologies, and catalog with care.
The world of customer data isn’t static, nor should our approach be. Welcome to the brave new world of dynamic customer data architectures. It’s a wild ride, but trust me, the view of your customers from up here is spectacular. Now, if you’ll excuse me, I have some customer data atoms to wrangle, activities to model, a real-time customer insight stream to dive into, and a whole lot of data to catalog!
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