“All CDPs look alike and talk the same thing. Then, how would you decide what works best for you?”
As mentioned in the earlier segment that describes different types of CDPs, you would notice that each CDP as it moves through the evolutionary scale gathers more functions to handle larger customer data and identification problems. A CDP’s functions build a framework for you to further evaluate its capabilities.
Some common Functions performed by CDPs all across:
- Data Ingestion from different marketing sources of information and interaction.
- Customer Data classification and Analytics
- Customer segmentation and Cohort Management
- Recommendation Engine and Predictive Analytics
But among these capabilities, there are some nuances that make a CDP work perfectly for your Enterprise. Here is a list of capabilities that you should look out for.
Data ingestion is an inherent function of a Customer Data Platform. But, what matters the most to you is whether the data is in a usable format. For instance, FirstHive is designed to eliminate duplicate entries while collating data from across multiple data systems. Any poor data formats such as irrelevant characters, are highlighted and can be rectified accordingly.
Uniquification & Data Standardization
Data cleansing helps to further standardize data. Standardized data is used for the creation of unified customer data and unique profiles. This process involves removing inconsistencies caused by data entry errors, lack of entry standards, and inconsistent standards in different systems. When data is available in a certain format but is not consumable, then a CDP uses the capability to reconfigure for other information systems.
Standardized data is collated together and is associated with individual customer profiles. Such data leads to a unified identity of each customer across multiple platforms, enabling marketers to provide each one a unique experience.
While the previous two capabilities are concerned about the data at large, CDPs are also equipped to dive deep into cleansing individual customer profiles and data. Data enhancement is a process that uses behavior, demographics and other parameters to tweak the existing data. It also accepts triggers and rules defined by marketers to enhance data. Even better, smart CDPs that are AI-enabled use algorithms to constantly check the data quality and replenish them back for different needs.
For marketing campaigns, customer profiles, and system integrations, data is classified and segmented. The classification by format, marketing channel, cohorts, source of interaction, and others, helps in automating customer profile segmentation.
Just like ‘data enhancement’, with the use of triggers, rules, and parameters, a CDP auto-segments cohorts. AI-enabled CDPs use machine learning to further enhance auto-segmentation, minimizing human effort on redundant activities.
CDPs that use AI and machine learning enrich new and existing cohorts with continuous recommendations. CDPs monitor each customer profile holistically, across all marketing channels and feed the new data for enriched and unique customer IDs. For instance, the platform not only uses deterministic customer identification methods such as tracking a person’s shopping behavior but also uses probabilistic methods by considering the family’s shopping behavior for future recommendations.
Also, the ongoing reverse customer mapping with enriched data enhances the processes administered by a CDP. It further automates any process initiated within the system.
AI-enabled CDPs are equipped to use machine learning that empowers a marketer with ongoing recommendations. Such CDPs have an advantage of tailoring exclusive recommendations for each campaign, marketing channel, cohort, and customer profile; increasing profitability and marketing ROI.
This list helps you gather that a Customer Data Platform can be customized to your needs not only based on scale but also your industry and Enterprise’s unique business needs.