Customer Data Platform

Data Governance

Data Infrastructure

Data Platform

Data Products

Data Strategy

The marketing lab

Industries:

Finance & Insurance - Retail & FMCG - Transportation - Energy & Utility - Life Science - Industrial

Solutions:

Data Governance - Data Infrastructure - Data Platform - Data Products - Data Strategy

Technologies:

Aws - Azure - Google Cloud Platform - Confluent - Snowflake - Vertica - Power BI - TIBCO - Tableau

Scopri

Overview

Although most organizations have adopted a best-of-suite approach for their martech stack, ad-hoc solutions for individual point solutions gravitating around the core marketing automation platform have steadily grown over time, often driven by strongly channel-oriented logics.

This articulation in the application landscape has led to a high degree of fragmentation in data and business logic, resulting in increased management costs and development time for new marketing solutions.

In this context, many organizations are investing in rationalizing their martech stack by trying to bring together some key functional elements that are today replicated in several places. One of the most important of these is certainly the management of customer and prospect data via a Customer Data Platform that centralizes the collection, consolidation, analysis and sharing.

Challenges

The main problems related to the lack of a CDP are:

Silos between business functions and costs of integration in omnichannel logic
Lack of a single source of truth on which customer marketing models can be based
Increased costs of managing a constantly growing and redundant volume of data for different needs
Lack of or poor exploitation of first-party data, also in view of the end of third-party cookie support by Google (end of 2024)
Difficulties in monitoring and verifying the use of customers’ personal data in a compliant manner

52% of marketers believe that integration

is the biggest obstacle to success in martech

Ascend2 Marketing Technology Trend Survey

Architecture

Data sources directly owned by the company or second/third parties are integrated, normalized, cleaned and modeled within the data management layer.

In the data analysis layer, more advanced analytics models can be implemented, which also involve statistical analysis or artificial intelligence algorithms such as customer base segmentation or recommendation systems.

The data is then made available for consumption in the data activation layer, where targeting and reverse ETL logic can be implemented, for example.

The entire data and metadata cycle, with a special focus on sensitive data, is managed by the transversal data governance and compliance layer.

The complete route

1. Identification of a use case

Assessment phase with the business to identify the best use case to be used for the construction and validation of the Customer Data Platform

2. Customer Data Management

Analyzing the various sources of data, bringing them together in one place, cleaning them so that they can be reconciled with each other and organizing them into a scalable data model for other use cases

3. Customer Data Analysis

Exploratory data analysis and realization of the use case models directly on data from the customer/prospect base

4. Customer Data Activation

Exposure of data and results of processing and analysis to reports, applications used by the marketing area. Restart scaling the cycle

Make or Buy?

There are three possible options for implementing a CDP: adopt the CDP solution offered by your own marketing automation suite, select an external product offered by a pure play vendor, develop your own CDP as an extension from the data platform. Each option has advantages and disadvantages.

The general trend is to follow a hybrid approach in which the basis of the CDP (data management and data analysis) is developed as an extension of the corporate data platform in order to have an open infrastructure that is highly customisable according to one’s needs.

The more activation-oriented parts can instead be developed by selecting the best mix of custom and off-the-shelf solutions focused on specific use cases (e.g. audience definition, journey orchestration, etc.) to increase ROI and time to market.

The main advantages of building the CDP as an extension of your data platform are:

Capitalisation of investments made on the data platform

Much of the customer’s data is often already present and integrated within the data platform, so building the CDP on top of it is a natural evolution that allows reusing technologies and skills already acquired.

Managing customer data as a corporate asset

Since several departments need access to a unified view of customer data (Marketing, Finance, Operations, Customer Care, Risk Management, etc.) the aim is to rationalize the integration effort and avoid having multiple independent repositories that are costly to maintain over time.

Increasing agility and reducing lock-in (marketing cloud fatigue)

The marketing cloud platforms adopted in most large organizations for centralized customer data management present problems related to ease of integration with external systems, poorly scalable pricing and inflexible data and analytics models.

Reducing risks in a market that is still too fragmented and volatile

Pure-play vendors have evolutionary dynamics that are still too uncertain and subject to the constant uncertainty regarding possible acquisitions.

Benefits

Reduction of infrastructure costs related to customer data management

Increased time to market in the development of omnichannel solutions

Holistic view of the customer/prospect journey

Ability to provide personalized and more engaging user experiences

More transparent and privacy complaint management of customer/prospect personal data

Events

Hybrid Event

Netcomm Forum 2022

Date and Time: 03/05/2022

Quantyca participated in Netcomm Forum 2022, the Italian event dedicated to E-commerce and Digital Transformation. Andrea Gioia, CTO, and Francesco Gianferrari Pini, co-founder of Quantyca guide us, in their speech,...