Customer Journey Analytics: a Comprehensive Approach

The vast majority of companies are currently working on a digital transformation initiative. Although the digital environment has dominated the way of doing business in recent years, there are organizations that have learned – the hard way – that copy-paste and one-size-fits-all solutions are not good enough, since they lead to a great waste of time and money. Customer experience design (UXD) has been shown to be a very valuable tool to clearly define a particular and personalized solution for a given business, aligning its strategy and vision in the short and long term. However, UXD also presents great challenges, starting with the collection of data across the entire customer journey for future analysis and management.

The most common way to design a customer experience is to start with collective design sessions in which experts brainstorm ideas to identify what customers enjoy or suffer throughout the value chain. However, in practice, many of these sessions come up with intuitive and subjective concepts because there are not enough data to reach more objective and quantitative conclusions.

In an initial stage, it is sufficient to ask a small group of people what their experience has been and what suggestions they have to improve it. Similarly, at the beginning, the opinion of a group of employees who, based on their knowledge, can propose improvements in the internal processes of the organization, can be used. However, these types of exercises are, at best, a version 1.0 of the proper solution.

It is important to understand that managing the customer journey is a living, dynamic and heterogeneous process. For this reason, it is necessary to enable technologies that allow data to be collected in real time from all customers, through all points of contact. This is not a minor enterprise; it demands the design of a comprehensive and automated solution.

Given the need for data and analysis tools, the interaction between the customer journey and disruptive technologies is of great importance. Business intelligence models serve to integrate all elements in a strategic effort. We can define a business intelligence system as a comprehensive solution, including both methodologies and tools, which aims to develop the following four components within the organization:

1. Data Mapping: In this component, all relevant data sources, both internal and external, of the organization are identified. The data to be collected, the systems that contain them, and the devices that generate them are clearly defined. These sources are then constantly updated in a process of continuous improvement.
2. Extraction, Transformation and Loading (ETL) Process: For each data source, a solution is designed that enables data collection, as well as its validation and any required transformation. There are currently many disruptive technologies that enhance this component, such as the Internet of Things, artificial intelligence, and specialized robots.
3. Data and Storage Governance (DWH): This component is fundamental since, after validation and structuring, it stores and governs all the organization’s data. This is where potential insight into the entire customer experience lies, across all contact points and all value elements.
4. Data Analytics and Decision Making: This is the last layer of the system, which focuses on identifying and extracting all the valuable knowledge that has been collected and structured. In this layer, there are different types of analysis and algorithms that make it possible to find a solution to complex problems. This component is the one that provides all decision makers with the same version of what is happening inside the organization and in its environment. There are three types of analysis that can be carried out: descriptive (what happened?), predictive (what will happen?), and prescriptive (what should I do?).

The most common mistake organizations make when executing data analytics projects is to focus on one of the components and lose sight of the system as a whole. Many companies focus on collection and, as a consequence, manage to accumulate a large amount of data, but do not know what to do with them. Others focus on analytics and visualization tools, but don't have sufficient data to reach interesting conclusions and findings.

The challenge is to unite each and every one of the components of the system and align them to a user experience strategy. Companies that are winning the competition for customers today have managed to balance the comprehensive development of their business intelligence capabilities, technology adoption, and advanced customer-centric analytics.

The authors are professors at EGADE Business School.

Article orginally published in Forbes.