Customer Data Platforms: The Next Evolutionary Step for Marketing Automation
For the past couple of years, customer data platforms (CDPs) have been all the rage. Custom or off the shelf, these newish shiny software packages aim to break silos, consolidate data, target, personalize, and sell like never before. CDPs are supposed to grant full control over multi-touch interaction data pulled from various sources and channels, feed information to external systems, and integrate tools for analysis, prediction, and personalization.
Combining CRM, DMP, offline tagging, and more, CDPs approach the idea of a persistent, dynamic, holistic, marketer-managed customer database. Due to the variety of data sources, they provide automation possibilities far beyond basic mailing lists or cookie-based DMPs. They are also somewhat less threatened by the General Data Protection Regulation (GDPR) and other emerging data laws and are fully expected to become only more prominent in the years to come.
So What Does This Mean for Marketing Automation?
Despite the growing concerns over personal data, what consumers want—and are ready to be tracked for—is relevant, in-context recommendations.
But marketers often have trouble moving past generalized statistics to determine the context and motivations behind each stage of the customer journey. Relying on static and scattered data leads to limited customer profiles based on hard identifiers like email addresses and phone numbers, as well as to short-lived massive data pools.
This results in automated scenarios where a lot of content gets pushed and targeted via emails, ads, and other channels at what is assumed to be a susceptible audience or its segments. In the best-case scenario, the guesswork proves right and prospects convert into customers. In the worst case, there are no conversions and potentially negative customer experiences.
That's where CDPs are poised to enhance the process.
It’s All in the Journey
The textbook notion of a buyer's journey is a rather straightforward, linear path followed in many marketing automation platforms. It’s presented as a series of triggers corresponding to a number of consecutive steps, one leading to another, ideally fully logical and non-emotional.
Yet customers can often skip things intended for them, move sporadically, or get carried away by their emotions or sudden impulses. They abandon their paths halfway for some reasons outside of your control, and they pick up later from a different device or location. In this case, it’s too bad if you were running A/B tests, or your brick-and-mortar proximity tag game wasn’t on point.
To be ready to not just personalize but contextualize, marketers need a holistic view of real-time data from multiple channels. Marketing automation systems, with their limited data and reach, are but a cog in the complex CDP machine that can collect, process, and leverage information through various tools and applications.
Ideally, CDPs are supposed to better identify and profile customers, help analyze their historical behavior, figure out real-time wants, and predict possible future actions. Such systems paint a much more complete picture filled with consolidated data from multiple platforms, channels, and touchpoints. This helps marketers truly understand customers’ contextualized journey and decision-making process over a period of time. This means better personalization, more authentic automated communication, and swift reaction to change.
AI Game Stepped Up
Artificial intelligence (AI) has been on the lists of top marketing automation trends for years, expected to bring about innovation, better segmentation, personalization, and content targeting. However, marketing automation tools and platforms are still largely geared toward manual creation of scenarios and journeys. Based on assumptions and limited information, they might leave a lot to be desired.
Even with a more widespread implementation (in recommender engines, for example), there’s still a question of the quality of data that AI and machine learning (ML) algorithms operate on. This is another field where CDPs leave their alternatives in the dust: The sheer amount, scope, and variety of the data they operate with means better pattern identification and less possibility of bias or false equivalencies when AI is integrated. This in turn means more accurate behavioral analysis, targeting, and predictions.
A Test Is a Test Is a Test
CDPs started as expanded data warehouses, but in a few years have steadily evolved into a “package deal”-type of software. New features and integrated tools redefine them as multifunctional sales and marketing platforms, covering data collection, storage, emailing, channel integration, reporting, campaign testing, and much more.
To carry out wide-scale automated campaigns requires segmentation, nonstop split testing, quality assurance, and analysis. One obvious strength of CDPs is that they can work with micro-segmented audiences; but the downside is that such campaigns require just as much—if not more—testing and fine-tuning.
The solution is automated, ML-powered testing that involves launching series of campaigns against others of equal proportions. This way, it’s possible to test not only campaigns but entire strategies, with unlimited numbers of micro-segments.
The majority of emerging analytical tools aim to provide ways to understand audiences, personalize communications, and increase conversions. With the possibility of marketer-managed, micro-segmented, automated incremental testing of strategies en masse, it’s not hard to see how CDPs have a very impressive chance to deliver on their promise.
Marketing automation moves businesses toward a unifying, interconnected, full-life-cycle approach and overarching strategies, and CDPs look like the embodiment of this idea at the moment.
Such platforms keep to the core principles of segmentation and a continuous approach but expand on them by adding context, behavior analysis, and a much more complete view on customers and their actions. While still far from being standardized, CDPs have the potential to integrate, consolidate and shape the disparate bits of digital marketing. This interconnected marketing future is likely to make all the processes a lot more relevant and fruitful for both the B and the C ends of business-to-consumer relationships.
Elena Yakimova is the head of the web testing department at software testing company a1qa. She started her career in QA in 2008. Now Yakimova’s in-house QA team consists of 115 skilled engineers who have successfully completed more than 250 projects in telecom, retail, e-commerce, and other verticals.