The Importance of Data for Marketers
Marketers need easy access to large data streams so they can personalize customer engagement and experiences. There are options to satisfy this need, but it is important to understand the benefits and setbacks. One major problem, of course, is that poor data quality inhibits the ability of the enterprise to deliver the personalized experiences customers expect in today’s real-time world. Consider a recent Dynata survey commissioned by Redpoint, where 70 percent of customers said they will only shop with brands that demonstrate a personal understanding of them. This means the brand knows they are the same customer across all channels and is able to deliver a highly relevant experience at the moment of interaction.
What is a Data Lake?
A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.
Meeting this expectation requires access to an enterprise-class dynamic data repository that continuously refreshes and links the totality of an organization’s customer data in a central hub with a single point of operational control: a robust, dynamic CDP.
Knowing the key differences between a data lake and a dynamic CDP will help marketers understand why the latter is often the No. 1 revenue-generating solution of the enterprise, and why high-engagement brands in industries such as retail, financial services, telco, travel and hospitality, and healthcare are choosing the Redpoint rgOne customer experience platform as their no-code, data management platform to manage the brand experience.
One of the biggest limitations of a data lake and other highly code-dependent systems is the inability to scale – providing personalized experiences for tens or hundreds of millions of customers. By itself, this makes a data lake entirely inadequate for enterprise-wide adoption and CX use cases. It also limits the power of AI and machine learning to provide differentiated experiences; offline, coded models go stale over time. Data scientists build models that, once in production, become outdated as soon as there is new data or as soon as the business decides to optimize a different metric.
Because transformations are pushed downstream, marketers must build their own aggregate values, derived attributes and other critical information. And with these data values driving segmentation decisions, audience selections, campaign triggers and real-time personalization decisions, missing it, doing it wrong or inconsistently strips data of much of its value and sub optimizes the overall return on investment.
What is a CDP?
A Customer Data Platform – CDP – is a software that creates a unified customer database that is accessible to other systems. The CDP centralizes customer data from different platforms and sources and then shares the data with other systems for marketing campaigns, customer service, and all customer experience initiatives. Through solutions such as the Redpoint Digital Acquisition Platform powered by LiveRamp, marketers can activate and match activity from downstream ad platforms and networks back to a CDP, giving them control over the customization of a customer journey across digital touchpoints.
Satisfying customer expectations for a holistic customer experience precludes data, process and channel siloes because the expectation isn’t just for a personalized experience on every channel, it’s for a seamless experience that spans every interaction with a company. The delivery of a core brand experience transcends marketing.
This is why platforms that integrate customer data might claim the CDP mantle, but solving for customer data complexity demands far more. As the customer-centric approach outlined by Gartner suggests, meeting the modern customer expectations head-on requires engaging with customers with a relevant, personalized experience in the context of a customer’s individual buying journey across all touchpoints. This in turn requires the integration of customer data from any source and of every type, immediately structuring data at ingest and applying advanced identity resolution capabilities to create a true single customer view that is updated in real time.
A digital customer experience platform that also embeds automated machine learning, a real-time decisioning engine and intelligent orchestration capabilities tackles the inherent complexity, giving marketers a single tool with which to meet customers’ lofty expectations for a seamless, personalized customer experience. This is the enterprise-grade technology that is needed to drive new revenue with innovative customer experiences.
In the battle between CDP vs Data Lakes is one with a clear winner. The cleaner the data the more meaningful and accurate customer interactions become. For real growth and customer loyalty, these interactions are critical. With the right tools and technology in place, facing complexity does not have to be an uphill battle. With the right approach, a simpler higher-fidelity interaction can be achieved by defining a customer-centric strategy around the messages they must receive in a specific order and then delivering those via a real-time engine anywhere the customer appears.