Customer Experience Personalization
Industry reports consistently state that consumers are more engaged when they receive relevant, personalized customer experiences in a timely manner. But how do you make sure personalization is respectful and helpful, not unwanted or creepy? It is to cooperate with the data infrastructure.
Personalization is about respectfully collecting relevant user data and using that data across touchpoints within your marketing and sales funnel. In reality, personalization can look like many things, each with varying degrees of complexity to deploy and impact the customer experience. Adding the customer name to the email subject line is a simple example of a personalized customer experience.
On the more complex side, brands can even build fully contextualized customer experiences through a series of multi-threaded user interactions. For example, an e-commerce company can personalize a shopper's catalog recommendations based on the shopper's current browsing or search behavior.
InfiTekPro Customer Data Platform (CDP) is a software application that supports marketing, product analytics, and customer experience use cases by unifying a company's customer data from multiple data sources and making it available to additional systems. CDPs make it easier for teams to access customer data and use it to optimize the timing and targeting of messages, offers, and customer engagement activities, power individual-level personalization, and to support analysis of customer behavior.
Here's what to look for in a capable CDP:
Privacy-forward:
More than user flagging, you need a proper framework dedicated to consent management and compliance and the ability to respond to data subject requests
Identity resolution and data quality tooling:
For effective personalization, you need to know both who users are (on the individual level) and how they behave. This ensures you can recognize and reach the right consumers across any channel.
Depth and breadth of integrations:
True interoperability means a real-time API-driven approach. This speeds up adding tools to the stack, minimizes duplication of effort, and protects data from issues such as inconsistencies and duplication.
AI Personalization:
Ability to enrich your own datasets with AI insights and use those insights to enable better personalization across channels.
Real time:
As with personalization, customers live in real time and so should your personalization.