Normal market volatility, combined with sudden economic disruption, put immense pressure on integrated supply chain systems to meet evolving consumer demands for immediacy in the delivery of customized goods and services. Integrated supply chains built on static, list-based systems are wholly incapable of meeting the challenges posed by this disruptive environment, especially in an increasingly dynamic global economy.
The same is true for meeting the demands for a personalized customer experience by the always-on, continuously connected consumers as they move through an omnichannel customer journey, particularly as they change behaviors and move to a more digital experience. Customer engagement strategies that rely on lists are likewise incapable of keeping pace with a dynamic, omnichannel customer journey.
Customer engagement strategies and systems that are rules-based, dynamic, multi-channel driven and flexible are needed to consistently deliver relevance with a personalized customer experience at any touchpoint. Evidence shows that this type of data-driven approach provides a direct line between data and revenue, which has elevated marketing as the tip of the spear for the enterprise as a revenue-generating engine.
Dynamic systems of engagement are required for marketing to embrace this responsibility. Complexities such as an influx of communication channels, online and offline engagement touchpoints and personas, and continuous updates to preferences and permissions cannot be met using lists of data, nor can they be bound by legacy technology designed for the age of outbound, batch or drip communications through audience lists.
With this context in mind, here are seven reasons why a customer data platform (CDP) that derives audiences, channels, actions and preferences dynamically – at the time an action is taken – is a requirement for keeping pace with a dynamic, omnichannel customer journey.
Rules are dynamic
In a rules-driven environment, marketers are not locked in by pre-defined, static lists of data, customers, or prospects that paint a picture of a moment in time at odds with the entirety of a customer journey. The dynamism and flexibility of a rules-based system underscores every reason why a rules-based approach is superior to a reliance on lists.
Rules accommodate changes in a data-model
Being dynamic, rules can be re-oriented into whatever a changing business requirement or KPI dictates. Lists, being inflexible, are static in a table. Models based on lists will therefore become outdated the moment a business objective or KPI changes.
Rules enable multi-channel
A marketing campaign based on a static list – such as sending a credit card application to a list of prospects – is likely not just impersonal, but it runs a risk of being irrelevant if a prospect signs up for the credit card after the list is created. Because a rules-based system is dynamic, eligibility for one channel versus another channel – or multiple channels – is decided at the time the communication is initiated based on the totality of a consumer’s actions. They allow the user to define an audience and then keep that definition consistent across channels and journeys.
Rules enable journeys
A list-based system is locked in time and cannot keep pace with a customer in a non-linear, non-sequential journey. A rules-based system dictates a next-best action relevant to a customer’s journey at the precise moment and channel of engagement. It does not rely on a list that recommends an action for a customer based on an engagement that may have lost its relevance.
Rules are reusable
Lists are a one-and-done proposition. As soon as a customer takes an action – signs up for the credit card, buys the product, etc. – the list is obsolete. Rules are dynamic, which means they can be updated in real time based on a customer’s behaviors and re-used as a “living” document. A rule about what constitutes a gold customer means that the “list” of gold customers is fluid and is reusable within an automated machine learning model.
Rules are flexible
Once created and activated, a list cannot adapt to an unforeseen change in conditions. When an item on a grocery list is not in stock and the shopper makes a substitution on the fly, the list is relegated to the trash bin. Likewise, a static list of customers loses relevance when any change occurs that is not directly accounted for in the list.
Rules are secure
The General Data Protection Regulation, the California Consumer Privacy Act and other regulations governing data privacy make it important to apply customer permissions dynamically in the customer lifecycle – especially because regulations frequently change. A list of customer preferences and permissions becomes outdated with each new provision, introducing compliance risk and creating a self-defeating cycle where a new list becomes irrelevant almost the moment it’s published.
Applying rules dynamically at the time of each customer interaction seamlessly integrates all segment and preference data into the process at each stage of the journey, making it dynamic and eliminating the potential of frustrating a customer by ignoring or mishandling their preferences, or by ignoring where the customer is in their journey. A CDP that creates rules for how, where, and when customer data is ingested and how it is managed, rules that dictate identity resolution capabilities, and rules for how code-free automated machine learning models recommend a next-best action within the context and cadence of a customer journey enable segment-of-one marketing at scale. A system that relies on lists is incapable of matching this power.