How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service

Figures & data

Table 1. Key BDA technologies

BDA technologies	Description
API	Provides access to data sources like sensor data, clickstream data, and social media data [3]
Data lake	Stores data in its native format until it is needed; used in combination with, for example, a Hadoop framework, this technology allows firms to analyze large and/or unstructured data much faster than relational data warehouse systems do [37]
Stream analytics	Analyzes streaming data in real time in order to identify patterns and trends and/or to detect current and/or future deviations from normality [53]
Web analytics	Analyzes clickstream data logs to provide insights on customers’ online activities and reveal their browsing and purchasing patterns [3]
Mobile analytics	Analyzes clickstream data logs and sensor data (e.g., location data) generated by mobile devices to provide insights on customers’ mobile activities and movement patterns [3]
Social media analytics	Analyzes social media data (e.g., user posts) to provide insights on customers’ activities, sentiment, opinions, and preferences [3]
Predictive analytics	Uses statistical techniques to analyze current and historical facts to make predictions about future events and/or behaviors [3]
Rule-based system	Applies predefined sets of rules to initiate actions based on the interaction between input and the rules [53]
Visualization application	Transforms the results of data analytics into visually comprehensible and customizable dashboards [3]

Table 2. Overview of case organizations

	Company A	Company B	Company C	Company D
Country	Switzerland	Switzerland	Austria	Germany
Industry sector	Insurance	Banking	Telecommunications	E-commerce
Number of employees	4,000	20,000	8,600	1,700
Turnover in 2015	US$11.0 billion	US$28.0 billion	US$2.8 billion	US$1.0 billion
Big data vision	Increase customer centricity and the firm’s position as a digital leader	Provide efficient but high-quality personal advice	Enhance core business by offering excellent individual service at all contact points	Tailor customer interactions and extend the service value chain
BDA technologies	Full-blown BDA infrastructure with data lake and in-memory technology	Full-blown BDA infrastructure with data lake and in-memory technology	High-performance data warehouse; predictive analytics	High-performance data warehouse; web and mobile analytics

Table 3. Interview partners

Firm	Interview number	Participant’s position	Interview number	Participant’s position
A	1	Head of Digital Business	5	Head of Data Analytics
	2	Head of Sales Applications	6	Big Data Architect
	3	Head of Community Management	7	Project Manager
	4	Head of Digital Innovation	8	Transformation Manager
B	1	Head of Strategic Marketing	5	Solution Architect
	2	Head of Digital Innovation	6	Senior Manager Big Data
	3	Head of Customer Management	7	Head of IT Architecture
	4	Head of Online Communication	8	Head of Big Data
C	1	Chairman of the Board	4	Head of B2B Service
	2	CEO	5	CTO
	3	Head of Sales and Marketing	6	Head of Analytics
D	1	Senior Manager Business Strategy	5	Senior Manager of CRM
	2	Manager Business Strategy	6	Head of Business Intelligence
	3	Head of Business Operations	7	IT Project Manager
	4	Head of CRM	8	CTO

Table 4. BDA-enabled service innovation at Company A

Organizational goals	Material features of BDA	BDA-enabled service innovation
Provide tailored service to customers at the right moment	Sensor data Data lake Stream analytics Rule-based systems	Automatically taking action on the customer’s behalf in response to triggers (material agency)
	Clickstream and social media data Data lake Web and social media analytics, predictive analytics Visualization applications	Approaching the customer in response to triggers (i.e., lifetime events) through a human service actor who adapts the contact strategy to the customer’s individual needs (human and material agencies)

Table 5. BDA-enabled service innovation at Company B

Organizational goals	Material features of BDA	BDA-enabled service innovation
Provide individualized and consistent service at all touchpoints	Clickstream data, historical customer data Data lake Web analytics, predictive analytics Rule-based systems	Automatically customizing user interfaces (e.g., providing tailored content) in response to triggers (material agency)
	Digital trace data from various sources compiled in the customer profile Data lake Predictive analytics Visualization applications	Interacting with the customer in accordance with the customer’s preferences, as derived from previous interactions and behavior (human and material agencies)

Table 6. BDA-enabled service innovation at Company C

Organizational goals	Material features of BDA	BDA-enabled service innovation
Provide the product offering or problem solution that is most relevant to each customer	Digital trace data from various sources that is compiled to a customer profile Data warehouse Predictive analytics Visualization applications	Interacting with the customer in accordance with the customer’s preferences derived from previous interactions and behavior (human and material agencies)

Table 7. BDA-enabled service innovation at Company D

Organizational goals	Material features of BDA	BDA-enabled service innovation
Provide individualized service to customers and extend the service value chain	Clickstream data Data warehouse Web and mobile analytics, predictive analytics Rule-based systems	Automatically customizing digital user interfaces in accordance with customer preferences (material agency)
	Sensor data Data warehouse Mobile analytics (location-aware analysis) Rule-based systems	Automatically customizing user interfaces (e.g., providing tailored content) in response to triggers (i.e., a customer’s current location) (material agency)

Figure 1. Theoretical model of BDA-enabled service innovation

Table 8. Service automation and human-material service practices

	Service automation	Human-material service practices
Goal	Service individualization through automated activities that are carried out without human intervention	Service individualization through interaction of the customer with a human service actor who interacts with a digital service actor
Role of agency	Focus on material agency in delivering the service	Interaction of human and material agencies in delivering the service
Nature of outcome	Deterministic provision of service	Nondeterministic provision of service: technology provides space for action

Table 9. Key material features of BDA technologies

BDA material features	Description	Examples of underlying BDA technologies
Sourcing features	Features for collecting and integrating digital trace data from various sources	APIs for accessing sensor data, clickstream data, social media data
Storage features	Features for storing digital trace data	Data lake
Event recognition and prediction features	Features for detecting and predicting events (i.e., deviations from a normal state)	Stream analytics, predictive analytics
Behavior recognition and prediction feature	Features for analyzing customers’ behavioral patterns and predicting their future behavior	Web analytics, mobile analytics, social media analytics, predictive analytics
Rule-based features	Features for initiating automated actions	Rule-based systems
Visualization features	Features for making outcomes available to employees	Visualization applications

Table 10. Automation of customer-sensitive service processes

Organizational goals	Service innovation	Material features of BDA	Example
Provide individualized service to customers	Automated trigger-based service action describes activities that are independently carried out by a system to create value for a customer.	Features of sourcing, storage, event recognition and prediction, behavior recognition and prediction, rule- based actions	Starting an alarm or calling the police in response to a forced entry into a customer’s home, as detected by a sensor
	Automated preference-sensitive service action describes activities that are independently carried out by a system to adjust user interfaces in accordance with customer preferences.	Features of sourcing, storage, behavior recognition and prediction, rule- based actions	Providing tailored content in response to certain user behavior on an online channel

Table 11. Human-material service practices

Organizational goals	Service innovation	Material features of BDA	Example
Provide individualized service to customers	Trigger-based customer service interaction describes the interplay of human and material agencies in providing individualized interactions with customers.	Features of sourcing, storage, behavior recognition and prediction, visualization	The system provides trigger information to human service actors who then proactively approach and interact with customers.
	Preference-sensitive customer service interaction describes the interplay of human and material agencies in providing individualized interactions with customers based on their preferences.	Features of sourcing, storage, behavior recognition and prediction, visualization	The system provides recommendations for actions based on customer profiles, which allow human service actors to adjust their customer interactions.

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