Marketing is no longer oriented on a brand or a channel; it is rather centered on the client. In order to make recommendations at each touchpoint with the customer, this strategy of marketing, which is centered on delivering individualized experiences to each customer throughout their unique purchase journey, requires solid, trustworthy data. A well-defined customer data management approach will ensure that valuable customer data is not locked up in legacy systems but instead flows freely across the organization, providing a single source of truth to marketing and other customer-facing functions like sales and service, as well as enabling real-time, data-driven responses to customer activities.
Fundamental elements of a customer data management strategy include:
1. Data Acquisition and Integration
2. Data Management
3. Data Analysis
4. Data Activation
Data Acquisition and Integration
This is the first step toward developing a comprehensive customer data management strategy. “On average, between 60 % and 73 % of the data within a business remains underutilized for analytics,” according to a Forrester blog article. Most businesses are dealing with a data deluge these days, with millions of data points pouring in from thousands of client touchpoints across several channels, platforms, and devices. To properly design a customer data management framework, marketers must first determine what data must be consumed in the system.
Recognizing the external forms of critical data is accompanied by bringing it into a central system and running it through an ETL process – ingesting the critical data, ‘transforming it’ in terms of basic redundancy and formatting, and loading – which refers to attempting to bring all of the critical data into the preferred central marketing data platform such as a database system, a customer data platform (CDP), or a data management platform (DMP). The final outcome is that you have access to all of the information you require in a single location and in a single format.
Data management refers to the phase of joining the dots between data points in order to create comprehensive, unified profiles of specific users or segments. To guarantee compliance, methods such as probabilistic or deterministic identification resolution, establishing identity graphs, 360-profiles of clients, and incorporating consent into customer data will be used with first-party data for mar-tech applications. In order to create ‘look-alike’ segments, data from ad-tech apps that must be used through a DMP be anonymized.
The data is now ready to use at this stage. Marketers may create focused groups of similar consumer characteristics, do predictive research to forecast campaign outcomes, and so on. Intelligent systems may also begin advising the optimal next steps and personalized interactions and experiences for each consumer based on the data.
Having all of the data in accordance, even if it is categorized into unified customer profiles, is required but not sufficient to ensure that the data can be used to perform well-planned marketing campaigns across many channels. A thorough customer data management plan also considers how data will be integrated into marketing technology platforms and utilized to perform data-driven marketing initiatives in the last mile. To do this, marketing systems must be interconnected not just with data and with one another, but also with performance tracking and analytics systems in order to optimize campaigns in real-time.