Digital Customer Experience: Part 4
Slow moving data traffic represents the greatest obstacle when it comes to implementation of the Digital Customer Experience (DCX), according to the results of the DCX Study (IDG 2017). The decision-makers surveyed seem to be at their wits end with both data processing (insufficient data quality) and data organization (island solutions): both are considered the most important data-related obstacles standing in the way of better (= continuous) DCX. Yet, there are ways out of the data silo misery: in the 4th part of our DCX blog series, we questioned data expert Monika Freiburghaus about the success factors pertaining to master data management.
Monika, what is master data management and what role does it play when it comes to the digital customer experience (DCX)?
Monika Freiburghaus: In brief, master data management describes where the data is “at home” and both how, and how often, it is to be synchronized. Central data is crucial for a continuous, digital customer experience. In the course of digitalization, calls for the breaking up of data silos and improvement of data quality are growing ever louder. Hardly to be ignored are demands from the marketing, service and sales departments for a central location, a single point of truth, or this “home” for the data. These calls, however, frequently bump up against entrenched IT and organizational structures. Added to this are the cultural and political aspects (“that is my data”) which play a role in the recognition that only the breaking down of silos and improving data quality will lead to success.
How has the role of data changed within the scope of digitalization?
Monika Freiburghaus: Data has become the measurement indicator of good decisions. With the right data we attain relevance and high closing probabilities. Necessary for the effective use of data is the integration of peripheral systems – one of BSI’s core competencies.
Which peripheral systems play a role in an optimized DCX?
Monika Freiburghaus: Next best actions, for example, are only attractive to customers if they are highly relevant – the right offer at the right time, through the preferred channel. Such high relevance requires data – and the right data! Data is available in the systems: in the webshop (customer’s shopping behavior and times, unchecked out shopping carts, sizes, preferences, etc.); in the order system (leads, offers, contracts, orders, claims); in the ERP (stock); in the app or in the self-service portal (geodata, inquiries, uploaded documents); in the partner system (partner data, address, birth date); on Facebook (contents that my customer likes and has shared); on my website (clicks, configuration, pages visited) and, of course, in the CRM (communications, business cases, hobbies and interests, relationships).
"A change in thinking is needed throughout the company – away from small island solutions, which may have been quickly implemented, but are difficult to integrate in the big data world, as well as data architecture coordinated throughout the company."
Monika Freiburghaus, software developer and data expert at BSI
How can the many departments, systems and touchpoints attain high data quality and consistency?
Monika Freiburghaus: It is important to avoid data silos. Ideally, the relevant data for marketing campaigns and reports is already consolidated in a data warehouse (DWH) or is integrated through interfaces. Cross-system, coordinated master data management is essential for attaining high quality and consistency. This clarifies where the data is “at home” and, depending on the technical usage case, how and how often they are synchronized.
As a master data management expert, how do you technically accomplish this herculean task?
Monika Freiburghaus: Luckily, no demigods or superhuman powers are needed for this (laughs). You reach the goal faster with intelligent solutions and experience than is often anticipated. The basic prerequisites are regular duplicate checks and merging by the master system. The defining, refining and checking of duplicate rules is a long-term task that can only be partially automated. Testing and correcting manual data is time consuming. Various technologies are available for the technical interface connection: CSV imports (manual or automatic), database replicas, SOAP or REST Web services offer various advantages and integration depths, depending on the system, the data volume and the technical requirements.
What, besides the technology, is to be considered?
A change in thinking is needed throughout the company – away from small island solutions, which may have been quickly implemented, but are difficult to integrate in the big data world, as well as data architecture coordinated throughout the company. To find good, sustainable solutions, what is needed are both good communications between those in charge of the application and enterprise architects, as well as the willingness to compromise. The change process is not to be neglected – decisions must be understood all the way down to the users and also be borne by them.
What results can be expected from central master data management?
Monika Freiburghaus: Do I have the relevant data on hand; do I know which product the customer reaches into his or her purse for: such as for one of the last pair of boots that a customer liked so much, in the right size and the right color. Through a personalized newsletter I draw her attention to the boots, which she then can have delivered to her directly at home or work, or pick them up at the local sales outlet. And all this is orchestrated through marketing automation, fully automated and optimally linked through the system to all other activities (purchasing, paying, delivering, next best actions, etc.). This is how the DCX cycle of a good customer experience closes – and, at the same time, opens numerous options for competitive advantages and positive business developments.