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May 6, 2024

Data or Die: How High-Quality Data Fuels AI-Driven Agile Marketing

In the past decade, there has been a remarkable shift from product-centric to customer-centric marketing as brands in every industry recognize the importance of providing a personalized customer experience (CX). Coincident with that shift, the concept of agile marketing has arisen and evolved so that agility is now about improving CX rather than just selling more product. That is, marketing must consistently produce meaningful and relevant CX outcomes – which depends on rapid course changes to keep up with dynamic customer changes.

Agile marketing is a strong driver of the customer data platform (CDP) space, particularly with the ascendance of artificial intelligence (AI), Large Language Models (LLMs), and generative AI (GenAI) as key marketing trends. This blog will break down the elements that factor into an AI-driven agile marketing landscape best suited for producing optimal business outcomes.

Data, Data, Data

The core of agile marketing is a focus on understanding and responding to customer needs based on customer data. First-party data is of particular importance with Google eventually eliminating third-party cookies on Chrome, following Safari and Firefox. But a focus on first-party data does not mean simply accumulating as much data as possible. To support agile marketing, i.e., rapid response to constant customer change, first-party data must be cleansed, validated, and ready for business use: Data quality rather than quantity. An enterprise CDP that supports an agile marketing framework should perform all data hygiene tasks as data is ingested into the system.

Any data – even data that might not spring to mind as being important today – may become critical to quickly support and respond to emergent use cases. This is one area where AI is both a source of – and reason for – an agile approach. As an example, AI can help identify data sources that marketers might overlook as being pertinent to a specific use case. Conversely, high-quality and relevant data might become relevant to launch a new GenAI capability, such as an automated customer service assistant. Clean, pristine and fit-for-purpose first-party data is essential to fuel a virtuous cycle of AI-driven personalized experiences. When clean data is a constant, marketers are better positioned to think about tomorrow’s use cases today. So when the next disruptive trend burst on the scene, they’re not wasting time with data prep before pouncing.

Give Data Quality its Due

Because clean and fit-for-purpose data is so critical, it is worth noting that not all CDPs treat data quality with the same level of importance. Some bring data together and rely on a third party to perform data quality tasks somewhere downstream. Or they consider identity resolution or simple validation a stand-in for data quality, claiming that bringing records together is a form of data hygiene. But if we think about agile CX use cases that depend on clean, high-quality data, data hygiene at ingestion is critical. It’s the difference between understanding a customer as a unique individual (or in the context of a household) vs. having a muddled view and, ultimately, an inferior CX. The former – a deep understanding – ultimately depends on having data that is complete, accurate and matched appropriately for a specific use case. One consequence of putting data quality off until later is the potential for false matches, such as failing to match addresses that were entered incorrectly, creating duplicate customer records. When data quality is embedded into the identity stitching process as data comes into the system, marketers are confident that a unique customer profile is just that – unique. A customer ID assigned to a customer is guaranteed to be the intended customer for any business use case.

Eliminate Data Bottlenecks

Perfecting data at data ingest is also key for marketers to stay in the cadence of a customer journey. Raw data eventually requires processing. If processing occurs only at the point data is needed for business use, latency is introduced. The amount of latency will depend on several factors, among them how many processes, systems and channels are impacted. Different processes for different systems and/or channels will also introduce inconsistencies. The result of latency and inconsistencies is a strong potential that a customer journey outpaces the ability of a marketer to keep up.

In a similar vein, putting off data hygiene makes it almost impossible to create an omnichannel CX. With multiple channels, different cadences, inconsistent data and new data always entering a CDP, the issue when data is cleansed on the way out but not on the way in is that every channel has to manage its own process and queues. The data flow stops at the channel, with large amounts of data sitting at a channel waiting to be cleansed and aggregated, creating a bottleneck and impacting downstream CX. There is no cross-channel consistency. For a real-world example, consider having to delay outbound emails because you’re waiting on real-time updates from your digital channels.

An AI-Driven Virtuous Data Cycle

With customer data as the common denominator in producing a personalized CX, AI can be seen from two sides of the equation. That is, as AI and machine learning models become more accessible, less expensive and out-of-the-box (without the need for data analyst teams) more data (models, aggregates, predictions, and responses) can be made available to the CDP. On the flip side, for an AI framework (driven by ML and LLMs) to coach a company with better customer insights and suggested responses, the AI needs accurate and up-to-date data. LLMs work best when they know more about what a user is trying to achieve, and that knowledge stems from cleansed, fit-for-purpose data.

The constant for both sides of the equation is customer data. With pristine data as a foundation, as both a destination and source, an AI-driven virtuous cycle is created where data drives better outcomes, which in turn creates better models and better insights. In this context, the importance of data quality becomes clear; models are only as good as the data that’s fed into them, and feeding actual results – human responses to those models – back into the cycle ensures that the next models are better. Without data quality, models will degrade and measurements become skewed. In a virtuous cycle, models collectively become better, but without good data, they become progressively worse.

A Composable, Agile CDP

The convergence of the growth of agile marketing with the demand for high-quality, first-party data explains the popularity of agile CDPs as the foundational technology for capitalizing on emerging trends such as AI to deliver differentiating CX. In this context, an “agile CDP” or “composable CDP” is a short-cut reference to a broader composable architecture – a modular approach to integrating best-of-breed technologies that does not force a company to duplicate the functionality of components they may have already invested in, or to replace things they want to keep using (e.g.., an analytics system or a data cloud).

When agile marketing is the goal, it’s important to ask a few questions about your CDP:

  1. Does it support the connectivity (data sources, engagement channels, AI frameworks) you need, or will you have to engage in a long and complex services journey?
  2. Can it run in your chosen data environment (on premises or cloud), or will you have to move or replicate your vital customer data?
  3. Does it allow you to license and use the specific business capabilities you need, or force you to manage a landscape of overlapping, superfluous, and technically-oriented tools?
  4. Does it offer the flexibility and configurability you will need as martech, other enterprise tools and frameworks, and your data environment itself change in the future?

A CDP that supports a modular approach becomes an orchestration engine, geared toward improving CX and business outcomes. And that CDP should be able to grow with you as you your use cases, CX, and outcomes evolve.

The Redpoint CDP supports agile marketing in a few different ways. First, Redpoint meets companies where they are. A unified customer profile can be built on top of an on-premises database a private cloud, a data cloud or on top of something Redpoint helps a company set up. Second, Redpoint offers unmatched data observability and measurement features. From data ingestion through segmentation and activation, Redpoint lets users see any potential data problems before they reach the customer. Full transparency over the health of data providers users with confidence that data flowing through the system is clean, accurate and ready for use. Third, Redpoint provides the foundation of a consistent, accurate and real-time customer profile by completing all data hygiene at ingest, fueling AI models and providing deeper insights and outcomes (i.e., a superior, personalized CX) that are then fed back into the CDP.

Clean, fit-for-purpose data, embedded AI and a composable architecture elevate Redpoint as the only enterprise CDP ready to meet you where you are today to help you accomplish your business and CX goals now and in the future, whatever they might be.

Steve Zisk 2022 Scaled

Ian Clayton

Chief Product Officer

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