CDP vs Data Stack

Every business aims to get closer to its customers and establish a ‘single source of truth’ for their data, but this can be incredibly challenging. Customer data is generated and stored across multiple platforms, departments, and systems, leading to fragmentation and inconsistencies. In fact, organisations with fragmented data struggle to leverage up to 73% of their collected information (Gartner). Integrating data from multiple sources while managing data quality, duplication, and outdated information presents significant obstacles to creating an accurate and comprehensive customer profile. So what can businesses do?

The Rise of Customer Data Platforms (CDPs)

A key technology aimed at solving this problem in recent years is the Customer Data Platform (CDP). These platforms, such as Segment and mParticle, are designed to collect, unify, and manage customer data from multiple sources in one place. CDPs have been particularly popular with marketing teams as a way to consolidate customer data and drive personalised engagement. But what are their pros and cons? And could a data stack achieve the same outcome without the downsides?

Off-The-Shelf CDPs

Customer Data Platforms (CDPs) are purpose-built tools that aggregate customer data from different channels into a centralised system, offering businesses a structured way to analyse customer interactions and drive insights.

Pros:

Data Consolidation: CDPs unify data from multiple sources, such as CRM, marketing automation, and e-commerce platforms. This enables businesses to gain a 360-degree view of customer interactions, leading to more informed decision-making.

Event Tracking: Operating on an event-based model, CDPs capture detailed customer interactions in real time, helping businesses better understand user behaviour, preferences, and engagement patterns.

Segmentation: CDPs allow businesses to segment audiences based on various attributes and behaviours, facilitating personalised marketing campaigns that can improve engagement rates.

Cons:

Scalability Costs: Many CDPs charge based on events tracked, which can lead to significant cost increases as user interactions grow. Businesses report spending over 20% of their marketing budget on CDP-related costs alone (MarTech Alliance, 2023).

Limited Data Science Integration: While CDPs are solid for marketing automation, they often struggle to integrate advanced metrics such as customer lifetime value (CLV) or churn predictions, requiring additional engineering time.

Redundancy with Data Warehouses: Many businesses already store key customer data in data warehouses. CDPs often replicate this logic, leading to data silos between marketing teams and the rest of the organisation.

The Alternative: A Data Stack Approach

Instead of relying on an off-the-shelf CDP, businesses can replicate many of its features within their own data stack, avoiding vendor lock-in and unnecessary costs. But what exactly is a data stack? It is a combination of tools used to collect, process, transform, visualise, and analyse data within a company’s existing infrastructure. Below is an example of tools we commonly recommend:

Pros:

Not Dependent on One Tool: A modern data stack leverages scalable cloud solutions, such as AWS, Google BigQuery, or Snowflake, avoiding reliance on a single CDP provider.

Custom Metrics and Modelling: Unlike CDPs, data stacks offer complete flexibility to define and build business-specific metrics, making them ideal for analysing longer-term trends such as customer lifetime value.

True Single Source of Truth: Since all data is stored within a central data warehouse, there is no risk of creating a separate silo for marketing teams, ensuring consistency across the organisation.

Activation: Tools such as DinMo, Census and Hightouch can be used to send your modelled data back into the systems and tools your team uses. For example, crunching all of your transactional data to discover which customers are at risk of churning, and then create a flag in your CRM

Cons:

Higher Upfront Cost: Implementing a data stack requires initial investment in infrastructure and engineering resources. However, once the system is in place, costs are lower in the long run compared to continuously scaling a CDP. Data infrastructure costs are typically tied into the volume of data you are working with and how often that data is processed. In many cases 173tech have built scaleable infrastructures that have represented a significant cost saving against CDPs.

Speed Of Implementation: Not only is there the process of setting up a data stack, but there is also the process of integrating data sources, modelling the business concepts and then activating that data. Modelling can take weeks at a time which means that if you want to centralise, numerous data sources, you extend your time to value where CDPs can offer some sort of functionality out of the box.

Conclusion: Which Approach is Best?

If you have not yet implemented a CDP, and have tens of thousands of customers, then our advice is to leverage a data stack instead. This approach is more cost-effective in the long run and offers all the functionalities of an off-the-shelf tool without its limitations.

If you are already using a CDP like Segment or mParticle, we are not suggesting you replace it immediately. However, consider limiting its use to event tracking, rather than using it for complex data science models. In the long term, transitioning to a fully integrated data stack could offer a higher ROI and greater flexibility.

Want to explore how a data stack could work for your business? Need to personalise at scale but not sure where to start? Book a consultation with the friendly 173tech team today.