3.1.1. Adapt Big-Data Analytics Process for Service Innovation
A semi-structured interview from all key informants can summarize the process of adopting big-data analytics for service development, according to Table 3
Process for opportunity identification from adapt data analysis of customer interaction for new service development, leading to new service development (NSD) process. The informants mainly focus on using various technologies applying to data analysis and customer insight. To identify opportunities in the process of service innovation development, there are different opinions on the procedures. Considering an informant who is in direct service segment gave an opinion on opportunity identification that first process should determine value of identifying customers’ and market needs and indicating source of data, which covers overall customer experience with organization through all channels for discovering for all needs, get feedback from customer directly, which is outside-in approach, and able to identify customer’s pain–point. Consequently, collecting customers’ conversation dialogue which was requesting service, inquiring for information, ordering the service, notifying service problems, including data from customer relationship management, is very beneficial information for the organization. It is, therefore, necessary to specify as the first process on how to gather data and ways to gather information from the voice of customers, which is different from defining requirements or identifying opportunities based on management perspective or business perspective solely, which probably causes weakness in meeting the needs of the market and customers of service development. Especially today, when customers have changed their behavior quite fast, resulting organizations regularly and closely monitored informants no. 01, 02, 04, 07, 08.
On the contrary, another group of informants from those organizations supporting leading service pointed out that, firstly, it should begin with business objective or business requirement starting from demands to identify business requirement, having a clear objective at the beginning to help define the information needed to collect data that meets business needs, resulting in the efficiency in cost and resources—furthermore, having opportunity identification for the clear objective of verification and what to explore according to informant no. 03, 05, 06, 09, 10, 11. There are different opinions on the starting point of data analysis in service innovation development besides that more than half of the informants support the approach of starting by proceeding with the business objective and requirement. The researcher considered this as the issue not to be overlooked, as there is no clear evidence from documentary research and the semi-structured interview. Additionally, from the interview, the researcher is able to identify the gap of documentary research in the process of the problem and customer insight identification, which informants stated this process is very important because it is a process of finding value and opportunity of service innovation development, to be comprehensive and consistent into seven steps, which are (1) Business Objective and Requirement, (2) Data Identification and Data Acquisition, (3) Data Understanding and Preparation, (4) Identifying the problem and customer insight, (5) Analysis and Modeling, (6) Evaluation, (7) Applying findings to the service development of the innovation process, according to Figure 3
• Step 1: Business Objective and Requirement
It is to set objectives or goals for getting customer feedback or executing this project in order to have a clear understanding of the desired goal of the project or interested matters, and identify the objective, which is to determine opportunity identification for understanding the overall picture of the operation, which is not too broad or too narrow. This process is to verify and explore customer requirements and opportunity identification; therefore, if it is too narrow, it will cause a lack of identifying an opportunity, but to analyze data for those operation issues that already have clear answers.
• Step 2: Data Acquisition
In order to meet the business objective and requirement determined in step 1, it is essential to define the appropriate data input that will be used for analysis. The focus of this research is to design and develop new services for the customer, so data related to customer services, such as customer feedback (voice of customer) must be acquired. Since customer services are provided via a digital channel, communication data can be captured and stored in digital form. These unstructured communication data are mainly used as input for analysis and interpretation of customer requirements and customer’s experiences from existing services.
• Step 3: Data Understanding and Preparation
The customer services related to data obtained from the various sources have to go through the data cleansing process. The data cleansing process detects and correct (or remove) corrupt, inaccurate data. This can be done manually or by using data cleansing tools that apply a pre-defined set of rules for data verification and correction. Data from various sources with different forms and formats have to be standardized and integrated. It is essential to have a clear understanding of data at this step. The correct data definition is crucial in data analysis. Furthermore, the quality of data must be examined. Experts in data analysis, who are familiar with the data, need to perform initial analysis to verify quality of the data and ensure that data is valid for further analysis.
• Step 4: Identifying the Problem and Customer Insight
At this step, valid and qualified customer service data obtained from the previous step undergo preliminary data analysis by the domain expert. The purpose of preliminary data analysis is to identify problems or issues that customers have encountered, as well as customer requirements on the products or services. The findings are then compared with existing products or services to identify the business gap, hidden or unknown demand of the customers. To achieve this, in-depth service comparison and differentiation with systematic problem root cause analysis methods are used by having experts brainstorming to generate ideas for solutions or services to solve existing problems. Then, idea validation is conducted with front line staff to determine the validity of the solution idea and determine any pitfalls that may occur.
• Step 5: Analysis and Modeling
The solutions or services ideals obtained from the previous step are a preliminary level and required in-depth detailed analysis. The further analysis aims to discover data patterns, customer behavior patterns, or hidden knowledge. The data analysis models or data algorithms are used to analyze large amounts of customer service data. The models used for analysis can be Data Association, Data Classification, Data Clustering, and Factor/Variable Effect Modeling. Many data analysis and statistical analysis tools are widely available for performing these complex data analyses.
• Step 6: Evaluation
The results of the data analysis using various methods must be evaluated to verify efficiency of the model as well as accuracy of the results. Mainly, the results of the analysis should address the solutions to the problems and meet the business objectives. It should also be evaluated for its reliability by comparing the results with analysis by domain experts. The discrepancy is then used to adjust the variables or factors of the model to improve the accuracy of the analysis model. This step requires an expert level of data or statistical analysis.
• Step 7: Applying the results to service innovation
Taking knowledge extracted from big-data analytics to utilize in service innovation, which is considered as the front end of the new service development plan. That helps reduce confusion and ambiguity of the front end of service development, or called “fuzzy front-end of service innovation.”
3.1.2. The Benefits of Adopting Big-Data Analytics in Service Innovation
From the interview found all informants commented that “adopt Big-data analytics is very beneficial for service innovation development” from 11 informants who have experience being experts in service innovation development for more than ten years, gave two main reasons as follows:
Firstly: All of the 11 informants stated correspondingly that analyzing data on customer interaction, which are massive and unstructured data, can help to have a better understanding for customer needs and customer insight and service request patterns which are unique, identifying individual customers’ preferences. Furthermore, they can capture the shifting trends of customers’ behaviors timely because of using big-data analysis through various channels with connecting the relationship to customer journey leading to have a more precise vision for overall customer experiences, unlike market surveys, which only some part of can be visualized.
Especially the organization, whose goal is to be number 1 in the field of services, they must use information related to customers with the data of customer interaction to analyze for both broad and in-depth understanding in order to improve the service or offer the new services to meet customer needs at the individual level. Apart from having an understanding of customer needs at the right time, an organization can specify whether or not they can serve customer needs, how much difference there is of current service offerings to what the customer requires, and to what extent of their needs, which is advantageous for using correct information received directly from customers.
The essential of key informants:
Informant no.01 mentioned that “apart from being able to understand what customers need, also we can comprehend the gap that company did not meet customers’ needs, and how important those gaps were to the customers in order to prioritize issues to be fixed and things to be improved, to identify matters that customers value, and itss level of importance to facilitate the prioritization for things to be modified for improved.” Informant no.02 added that “transaction information done by customers through various systems can be analyzed to understand customer behavior to have more understanding on what customers want, and to realize trends from customers’ perspective of what has changed; moreover, it is accurate customers’ data to identify right timing and period to serve customer’s needs.”
Similarly, Informant no.04 stated that “Currently, the company has many customer interaction transactions helping them to identify what products to be developed, which one is suitable, finding new prospects, and it is useful for better understanding the customer. Nowadays, they are using this information to improve their current interactions, and initiate new things for better customer experience, develop current offers for the most positive impression. Therefore, it is essential for those companies that wants to be number 1.” Besides, informant no.05 commented that “Analyzing data from customer interaction is useful, which is to be able to understand the nature of customer request that is beneficial, because if we really understand the needs of the customers, we can improve the service to serve their needs as much as possible. The improvement means to come up with services which meet customers’ needs”.
Secondly: All eight informants stated that adopting data analytics, including analyzing a large amount and unstructured data of customer interaction, can develop service innovation, which is to develop existing services and develop new services to meet the needs of customers both current and prospective. Analyzing by groups, classifications, and at the individual level can specify different needs to offer a personalized offering and identify current market gaps which are yet to be fulfilled, create opportunities to present new services are undisclosed demand in the market, creating current competitive advantage. Today, service is much different from before because customer’s needs and their behaviors are shifting rapidly and differently. It is necessary for the service sector at present to develop and improve services regularly in order to maintain its base market and grow continuously.
The essential of key informants: Informant no.03 pointed that “analyzing of customer interaction data help to visualize specific personalized needs, which can be used to develop and design customize service according to the preferences of each customer for the reason that nowadays the way of service has been obviously changed so that apparently we are not able to provide general mass services anymore, as the unique needs of each customer are more different than before” Informant no.07 indicated that “utilizing the benefits of data analysis for collecting customer data from their inquiries or service usage via a digital channel in service innovation development. Alternatively, compatible technology can indicate the needs that best match the target customers”.
Moreover, informant no.06 added that “with this process, they could assess the situations based on actual data rather than predictions or the sense in order to perform service innovation” Meanwhile, informant no.09 pointed out that “service innovation is time-consuming and costly. The issue has a new service that failed to meet customers’ needs; although, it has gone through a market survey or market testing, resulting in harmful consumption of cost, time, and resources in the development process that does not generate positive results. That is because the process of conducting market research creates high discrepancies from assigning group samples, the insufficient number of samples, formulating questions, conducting fieldwork, until the final process, which is to analyze and explain the results. As a result, the new service development process (NSD) is not as successful as it should be”
3.1.3. Characteristics and Types of Data for Service Innovation
Most of the informants gave the same information classifying data into three types; 1st type, structured data from customer relationship management system (CRM), which have been recorded in the system after the service, including systems that have transactions via operation system in each industry or creating several transactions, visiting various pages on the website where there is visiting history record, data from this channel consists of the type of customer profile, product profile, transaction history, customer requested history, service requested history, including other customer behavior and voice of customer. 2nd type, unstructured data which are customer interaction dialogue with the employee, Social Media message from Facebook, Chat, or Website. Both of these data are useful for analysis in order to proceed into the process of new service development. 3rd type, data from market research, which is the additional information to type 1 & 2. Usually, in the past, we only use market research data to develop service innovation. Currently, it is not enough to identify the needs of a diverse market; therefore, it uses the supplement information only.