Unlike marketing automation solutions, a distinction is made not according to functional scope such as enterprise or best-of-breed, but according to the customer data platform's functional focus. In other words, the right kind of CDP can be selected already in advance, depending on how requirements are defined.
A total of three categories of customer data platform are distinguishable:
- Data CDP
- Engagement CDP
Customer data platforms - overview of features and the three classifications
The data CDP in this context is analogous to a customer database, because it ensures data merging, data aggregation, identity matching and data persistence by means of standard connectors within the CDP. The solution should include some functions for diagnostics, backup and monitoring of data quality so that a high data quality can be ensured within the CDP already during integration of data.
An analytics CDP firstly enriches the CDP's internal customer database with segmentation information and customer profiles, and creates scores. Secondly, an analytics CDP uses data and information, partly with the help of artificial intelligence, to perform selections and determine target groups for the purpose of subsequent utilization in downstream journeys. State-of-the-art representations of analyses and key figures round off the image of a modern analytics CDP.
An engagement CDP unites the disciplines comprising customer database, analysis/selection and campaign. Through standard connectors and identity matching, it creates the customer perspective necessary for campaigns , produces segments and selects target groups. These target groups then receive specific offers in multi-step and multi-channel campaigns. A campaign CDP thus focuses on orchestration of customer journeys.
Not every customer data platform is suitable for every scheme
A closer look at the individual classifications raises the following questions, particularly with regard to the data and engagement CDPs:
Does a data CDP fit into my desired architecture?
A look at the source systems to be connected helps answer this question. Do I have many source systems which I can connect to my data CDP with standard connectors, and can I merge customer data relatively easily with an identical customer number?
If so, then a data CDP might be the first choice here. An interesting constellation can then arise through connection of an engagement CDP to a data CDP. Data transfer between the CDPs should also be taken into consideration. Alternatively, certain options like creation of a golden record, consolidation of customer data from different sources, or data persistence can be realized in a dedicated, upstream data or customer hub. Though standard connectors can then no longer be used, data modelling and consolidation can be achieved via modern ETL tools.
Which functions in digital channels does engagement CDP offer out-of-the-box?
Einige Engagement-CDP-Anbieter bieten sehr ausgereifte Personalisierungsfunktionalitäten für die Touchpoints Web, Mobile App oder auch E-Mail an, die ein wirkungsvolles Engagement der Kunden an den entsprechenden Touchpoints sicherstellen. Daher haben wir eine weitere Unterteilung der Engagement CDP vorgenommen.
A detailed description of each category can be found here.
Selection of the right provider and associated creation of use cases - necessity or nuisance?
Use cases should be created if certain parts of a business objective or business model are to be represented in a system. A use case accordingly describes a system's behaviour or a specific function from the user's perspective.
To define requirements concerning the functions of a particular CDP and assess these functions, one should know which use cases can be covered by which part of a CDP. The individual CDP types thus also differ significantly in terms of the degree of coverage, depending on the individual use cases.
A data CDP mainly covers the following sample use cases:
- Consolidation of customer information from multiple data sources, including data from websites, as well as ERP and CRM data
- Implementation of data cleansing and standardization of different data formats during import
An analytics CDP mainly covers the following sample use cases:
- Analysis of campaign conversion (responses such as orders)
- Creation of precisely matched customer profiles, segments or personas for selective addressing via e-mail or at the website
An engagement CDP mainly covers the following use cases:
- Connection of a data management platform, export of an audience to a DMP, creation of a look-alike match
- Communication of web visitors to a data management platform (DMP) for the purpose of re-targeting
When choosing the right kind of CDP, one should always consider the key areas covered by the individual types of customer data platform. If requirements emphasize an integration of customer data, a data CDP is the first choice; if the focus is on analytics or complex journeys, then an analytics or engagement CDP should be selected. Previously created use cases representing one's own business model as specifically as possible are essential for selecting a customer data platform from the right category.
The third and final post of the CDP blog series deals with how to select, and make decisions about, a customer data platform: How do I evaluate? Which steps must be taken? What must be borne in mind here? And: Which typical mistakes can be avoided?