Self-Service Technology Adoption’s Effect on Customer Experience
Part 1: Introduction
Background Information
ADTRAN is a leading manufacturer and provider of telecommunications equipment for distribution of data services and communications globally. However, the empowerment of consumers and businesses in the age of digital technology has commoditized manufacturing strength, distribution power, and information distribution and largely dissolved competitive boundaries, leading to consumer choice as the main differentiator and necessitating an enterprise shift to customer experience management (du Plessis & de Vries, 2016). Companies seeking to maximize customer lifetime value are dependent on customer retention and look to self-service technologies (SST) to reduce operational cost, structure the customer experience, and quantify every interaction of the customer lifecycle, with SST as the critical factor in controlling costs and improving customer experience (Considine & Cormican, 2016). ADTRAN’s lack of SST adoption is common in the telecommunications industry, but as they shift to an “as-a-service” business model, the number of customer interactions will grow, making customer experience a critical factor for revenue growth and customer retention.
Management Dilemma
The management of ADTRAN are responsible for the customer success, retention, controlling operational costs, and meeting revenue growth targets. Rapidly changing customer expectations, greater information parity, and the ability to quickly change providers introduce new risks to the business; thus, improving customer experience is critical to business sustainability (du Plessis & de Vries, 2016). While reducing the volume of customer issues are important to managing costs and profitability, “as-a-service” or subscription-based business models rely on customer retention for the majority of revenue, making customer retention and lifetime value critical. An objective approach to evaluating SST would allow ADTRAN’s management to minimize bias and organizational belief that may be reluctant to change (Landrum, 2014). Therefore, it is imperative ADTRAN understand the impact of SST in revitalizing its customer experience to ensure customer retention.
Ethical Concerns
This study analyzes secondary data which must be handled in accordance with ethical practices. Ethical decisions may be swayed by human nature but following basic fundamental principles about how participants and their data are treated may reduce the likelihood of unethical practices (Landrum, 2014). According to Tripathy (2013), “the fundamental ethical issues related to secondary use of research data remain”, but “they have become more pressing with the advent of new technologies” as “data sharing, compiling and storage have become much faster and easier” (p. 1478). The primary concerns with using secondary data include potential exposure or harm to the original participants and issues surrounding consent (Tripathy, 2013).
Tripathy (2013) recommends selecting secondary data that lacks identifying information and is anonymized.
Open access data, available online through various mediums, can be used freely and analyzed, with acknowledgement given to the original data owner (Tripathy, 2013). Appropriate data collection should be sufficient and necessary without exceeding the parameters of the stated research and should be reviewed to ensure it meets the standard for relevance, including “the methodology of data collection, accuracy, period of data collection, purpose for which it was collected, and the content of the data” (Tripathy, 2013, p. 1479). Additionally, all reasonable measures to protect the data must be taken while in the care of ADTRAN, and upon completion of its use, properly disposed (Tripathy, 2013).
Research Question and Hypothesis
The research question is: Does access to SST improve ADTRAN’s customer experience? The hypothesis is: SST will improve ADTRAN’s customer experience, thereby improving customer retention and lifetime value, which will reduce operational costs and improving overall profitability.
Part 2: Literature Review
Background Research
Consumer access to SST is a relatively new phenomenon, and Considine and Cormican (2016) define it as “technological interfaces that enable customers to produce a service independent of direct service employee involvement” (p. 104). However, for successful implementation and adoption, quality must be measured multi-dimensionally, including functionality, enjoyment, security, assurance, design, customization, and convenience (Considine & Cormican, 2016). Considine and Cormican (2016) collected data on functionality, security, design, and customization from 182 knowledge workers in a financial services organization and found positive feedback on the use of STT to get work done more efficiently and improve their work experience, but a lack of personalization and user centered design received the lowest scores, indicating improvements in these areas can positively impact user experience and perception of SST.
Timing SST adoption within the customer lifecycle is important, because establishing the initial customer relationship is important. Scherer, Wunderlich, and von Wangenheim (2015) analyzed longitudinal customer data to compare the ratio of SST to personal service use over time and its impact on value creation. The findings in this research suggest a combination of SST and personal service within the initial three months significantly decrease the likelihood of customer defection and access to SST is most important at the beginning of the customer-firm relationship, as the customer becomes acquainted with the firm (Scherer, Wunderlich, & von Wangenheim, 2015). This research suggests that while customers may need more personal interaction at the beginning of the relationship, SST availability is an important factor in deciding to remain with a provider and possibly in the initial selection of the provider.
Adopting capabilities to improve customer experience are highly complex and require executive sponsorship to be successful, because they span a “complex mix of strategy, integration of technology, orchestrating business models, and brand management” which must converge on a common goal (du Plessis & de Vries, 2016, p. 24). Du Plessis and de Vries (2016) surveyed multiple telecommunications providers to understand the intricacies aligning people, process, and technology to improve customer experience, and they found that while many providers espouse customer experience management as a strategic priority, “efforts are still distributed across the enterprise” and lack a “single, holistic approach” (p. 24). Impeding a holistic approach were operational goals not aligned to customer experience, a lack of customer-centric culture, minimal comprehension of customer experience, and lack of expertise to execute a customer-centric operational model aligned to business drivers (du Plessis & de Vries, 2016). Improving customer experience via SST must encompass executive led, organizational change to existing operating models, championing a customer-centric culture, and alignment with clear business goals.
Successful SST programs will inevitably experience some type of malfunction as part of the lifecycle, so anticipating customer response is vital, since many customers may have already experienced subpar performance in the past. Early SSTs were prone to error, such as 25% of online shoppers encountering issues, voice technology accuracy reaching only 18%, and thousands of self-service U.S. Postal Service kiosks not in-use (Zhu, Nakata, Sivakumar, & Grewal, 2013). Understanding customer-recovery expectancy (CRE) can aid the design of SST to improve the likelihood of re-engagement when SST failures occur (Zhu, Nakata, Sivakumar, & Grewal, 2013). CRE was improved by “greater internal attribution, perceived control, and SST interactivity;” therefore, identifying methods for customers to engage in self-diagnostic and self-help reduce the risk of customers disengaging from SST when encountering difficulty (Zhu, Nakata, Sivakumar, & Grewal, 2013, p. 25). Co-creating customer communities and public knowledge bases alongside SST could introduce collaborative methods to resolving issues without any direct provider intervention.
Part 3: Data
Types of Secondary Data
Internal and external sources of data will be reviewed. Internal sources include “customer data, information about competitors, and industry-wide data,” such as customer surveys and financial statements (Landrum, 2014, 11.1, para. 5). This data will be valuable in quantifying the financial performance predicted by customer loyalty, and feedback from existing customers will indicate gaps in the current customer experience that can be addressed through greater access to SST.
External sources will include existing research, analyst reports, and professional publications. As this is a new strategy for ADTRAN, external perspectives are important for understanding the pitfalls, limitations, strategies, and successes of others. The combined analysis of internal and external data will allow for acceptance or rejection of the null hypothesis, or the assertion the results of this research will not significantly alter the existing state (Landrum, 2014).
Scales and Benchmarks
The impact of SST on customer experience can be measured along an ordinal scale. With ordinal scales, the numbers directly relate to significance, allowing the data to be ranked (Landrum, 2014). Common internal and industry scales used to measure overall customer experience include customer satisfaction (CSAT) and Net Promoter Score (NPS) which measure customer satisfaction and loyalty along an ordinal scale. For example, a CSAT score of 8 indicates higher customer satisfaction than a CSAT score of 6, and a NPS score of 9 indicated higher customer loyalty than a NPS score of 5. Additionally, while ordinal scale measure along a continuum, the intervals between each number are not necessarily equal, so the difference between CSAT scores of 10 and 9 cannot be assumed equal to the difference between scores of 6 and 5 (Landrum, 2014).
ADTRAN’s current CSAT and NPS surveys will be used as benchmarks, which can be compared to new CSAT and NPS surveys to identify any impact to overall customer experience. NPS benchmarks can also be obtained from third party firms, which collate measures across industries. Using internal and external benchmarks allow ADTRAN to measure improvement against its own past performance, as well as its competitors.
Likert-type scales are a type of ordinal measures, with statements ranked by respondents “on a scale such as 1 = strongly disagree, 2 = disagree, 3 = neutral,” and so forth (Landrum, 2014, 4.4, “Scales of Measurement and Statistics,” para. 13). With these types of ordinal measures, the numbers exist on a continuum and imply varying perspectives, without adjacent numbers having exact intervals (Landrum, 2014). CSAT scores are used to understand current customer satisfaction and the scales vary between organizations, as they seek to understand their particular customers. NPS was introduced as a standardized metric in 2003 as a predictor of growth, based on customer loyalty, and a predictor of whether a respondent is a “promoter” or “detractor” based on their responses with the score calculated as the proportion of promoters minus detractors (Keiningham, Cooil, Andreassen, & Aksoy, 2007). These data are available from ADTRAN’s internal sources and customer surveys and can be analyzed to understand trends separating highly satisfied, loyal customers from those who are not.
Considine and Cormican (2016) employed Likert scale measurements to “examine participant’s perception of SST in terms of the technologies’ functionality, security, design and customization features” (p. 105). Maduku (2017) measured “performance expectancy,” “effort expectancy,” and “structural assurance” on “a 5-point Likert-type scale with anchors ranging from 1 (strongly disagree) to 5 (strongly agree)” to measure self-efficacy (p. 896). This trend is common with the existing literature, and Likert scale measurements allow for granular analysis while adhering to common disagree-agree response scale patterns, which will reduce the complexity of coding data for this research.
Plans for using observations, focus groups, interviews or surveys
This research is based on secondary data; therefore, primary data collection methods such as observations, focus groups, interviews, or surveys will not be used. Secondary data are found in existing research, so these primary methods are not necessary for sampling and analysis in this study. Internal sources will be collected from ADTRAN’s existing CSAT and NPS surveys, financial statements, and sales performance data, since this data most accurately reflects current and past performance. Internal data are preferred because they are proprietary and exclusive to the business (Landrum, 2014). External sources will be gathered from public databases, such as EbscoHost and ProQuest, in addition to analyst reports, third-party industry benchmarks, and government data. These sources will include relevant studies of SST adoption and customer satisfaction across industries and a framework for building a customer experience strategy.
Plans for analyzing data
Content analysis is one statistical method that may be used to quantify verbal or qualitative data, such as the feedback collected during customer satisfaction surveys, and this method analyzes the content of feedback to identify patters or trends based on the frequency of specific words or concepts present in secondary data sources (Landrum, 2014). ADTRAN’s existing customer surveys include written feedback which lends itself to this method, and external sources include similar, industry-wide language patterns. Quantitative CSAT scores and financial data require another technique. Multiple regression is employed to make relevant predictions from the data by describing the relationship between variables, forming a prediction about the relative effect between predictor and criterion variables, and framing a theory of the relationship between variables (Landrum, 2014). The t-test is useful when comparing two groups, such as those with access to SST and those without, against a dependent variable, such as CSAT or NPS, and will help determine whether or not the null hypothesis can be rejected (Landrum, 2014). Because the secondary data are qualitative and quantitative, these statistical methods will help determine the relationship between SST and customer experience to assist management’s forecast of potential revenue growth.
Part 4: Results
Plans for presenting results
Results of the analysis will be presented in graphical and table formats. Selecting an efficient, relevant method to display data is vital to communicating its importance and implications without obfuscation or misleading the audience (Landrum, 2014). Graphical representations, such as scatter plots, can be used to represent the relationship between measurements and allow for recognizable systematic or causal relationships between the data in a regression analysis (Presenting numerical data, 2012). Underlying the graphical representation, tables provide a structured format for quantified data which allow finer analysis and organization, though the interpretation may be obscured (Presenting numerical data, 2012). Data residing within a table also lends itself to additional analysis, since it can be sorted and manipulated within numerous tools.
Reject or fail to reject the Null Hypothesis
The null hypothesis presumes there is no statistically significant effect observed during the research and is “used for the assumption that nothing will change from the status quo as a result of the research effort” (Landrum, 2014, 5.1, para. 2). The goal of this research is to ascertain whether or not there is enough statistically significant evidence to reject the null hypothesis in favor of the alternative hypothesis, also known as the research hypothesis (Landrum, 2014). To determine whether or not the results are statistically significant, a confidence level, or p value, is chosen to test the null hypothesis, and the “standard accepted p value is 0.05,” representing 95% confidence the results are not due to chance (Landrum, 2014, 5.2, para. 6). This study will use a p value, or alpha, of 0.05. Therefore, if the resulting p values from the statistical tests are above 0.05, the null hypothesis will fail to be rejected, meaning there is no statistically significant relationship between SST and customer experience. However, if the resulting p values are below 0.05, the null hypothesis will be rejected.
Part 5: Conclusion
Take Home Message
Consumer expectations have changed, with access to limitless services on-demand via smartphones and web-enabled apps, and consumer-grade self-service experiences are influencing the expectations of B2B transactions and engagements. As ADTRAN repositions itself from a hardware manufacturer to a service provider, customer engagement and experience will play a more significant role in the vendor-customer relationship. As millennials and digital natives mature into upper and executive management roles, their buying experiences will have been shaped by SST in their personal lives, so their expectations of vendors will be heavily influenced. SST will become table-stakes for attracting new customers, and while service-oriented products are more profitable, they also carry more risk as customers are empowered to more easily change vendors at the end of the contract term. Service-oriented business models rely on recurring revenue, so the company’s continued success and growth will be highly dependent on customer satisfaction and retention.
Understanding the complex relationship between SST, customer experience, and revenue is vital for management to guide ADTRAN’s continued growth and competitiveness. Analyzing existing SST frameworks, optimal adoption patterns, and customer feedback will provide a predictive analytical model for initial investments in SST and establishing criteria for success. While SST can improve the customer experience from initial purchase through the operational lifecycle, it also acts as a real-time feedback mechanism for the company to understand how the customer consumes the purchased services. Ongoing, this framework can be adapted to include direct, real-time data points from the SST tool, customer feedback, and lifetime value, so management has the relevant metrics to make more informed decisions.
Strengths and Weaknesses and Suggestions for Future Research
This research will provide ADTRAN with a deeper understanding of how SST can be utilized to maximize customer satisfaction and experience, thus improving customer loyalty, retention, and lifetime value. Because of rapidly growing SST adoption in B2B markets, there is a plethora of data available for analysis across industries. Additionally, ADTRAN’s own CSAT data provides a wealth of information to parse and glean a better understanding of its own customers. Combined, these external and internal data sources will provide new insights into the pulse of customer needs and guidance on successfully implementing SST to maximize customer adoption. The conclusions from this research will significantly improve management’s understanding of the broader SST ecosystem and shifting expectations of B2B clients.
Due to time and budget constraints, there is no direct collection of data in this research, there may be unaccounted for variables and the predictions are not a certainty they will be successful if implemented. ADTRAN’s lack of SST precludes the collection of primary data and lacking standardized industry terminology increases the risk of misinterpreting qualitative feedback from customers, since there is no direct surveying or participation in focus groups. Because of the proprietary nature of SST, direct analysis of comparable services is unlikely; therefore, recommendations will be theoretical rather than empirical.
Future research should include direct customer involvement and feedback specific to the nature of SST, expectations of B2B SST, and perceived customer experience. Furthermore, pilot programs or A/B testing with select customer segments would provide primary data points to help establish a minimal viable product to bring to market. Due to the complex nature of customer experience, focus groups would provide greater comprehensive feedback and nuance in the data collection, thus improving management’s ability to adapt to shifting customer demands.
References
Considine, E., & Cormican, K. (2016). Self-service technology adoption: an analysis of customer to technology interactions. Procedia Computer Science, 100, 103-109.
Du Plessis, L., & de Vries, M. (2016). Towards a holistic customer experience management framework for enterprises. South African Journal of Industrial Engineering, 27(3), 23-36.
Keiningham, T.L., Cooil, B., Andreassen, T.W., & Aksoy, L. (2007). A longitudinal examination of Net Promoter and firm revenue growth. Journal of Marketing, 71, 39-51.
Landrum, R. E. (2014). Research methods for business: Tools and applications [Electronic version].
Maduku, D.K. (2017). Customer acceptance of mobile banking services: use experience as moderator. Social Behavior and Personality, 45(6), 893-900.
Presenting numerical data. (2012). University of Leicester.
Scherer, A., Wunderlich, N.V., & von Wangenheim, F. (2015). The value of self-service: long-term effects of technology-based self-service usage on customer retention. MIS Quarterly, 39(1), 177-200.
Tripathy, J.P. (2013). Secondary data analysis: ethical issues and challenges. Iranian Journal of Public Health, 42(12), 1478-1479.
Zhu, Z., Nakata, C., Sivakumar, K., & Grewal, D. (2013). Fix it or leave it? Customer recovery from self-service technology failures. Journal of Retailing, 89(1), 15-29.