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The Use of Artificial Intelligence (AI) in Analysing Consumer Behaviour Patterns in the Budget Hotel Segment: A Qualitative Analysis (Colour-Coding)

Written by Jess C.

 

Abstract

This qualitative analysis explores the use of artificial intelligence (AI) in analysing consumer behaviour patterns in the budget hotel segment. This emerging technological trend is already changing the ways marketers explore consumer data and establish marketing communication with prospective customers in many industries. Its potential value for budget hospitality organisations suggested the need to analyse its potential more in-detail. The unique nature of the studied subject suggested that the qualitative data should be processed using a thematic analysis. The findings indicated that budget hospitality organisations may need extensive preparation to gain the full advantage of this technology. However, it also has the potential to provide them with the capabilities that are presently possessed by their major counterparts and change the competitive situation in the market.

 

Primary Benefits of AI Implementation at Hospitality Industry Organisations

The interviewed expert provided a number of interesting insights into the benefits offered by AI to the hospitality industry. The following table summarises the key advantages mentioned by the interviewee. The full transcript with colour-coding is provided in the Appendix.

Table 1: Key Benefits of AI Implementation at Hospitality Industry Organisations

 

The implementation of AI tools could primarily benefit the quality of consumer communication and servicing since the capability to intelligently process large amounts of data could provide a number of insights critical for analysing consumer behaviours in the hospitality industry (Zsarnoczky, 2017). First, the repetitive patterns identified in past purchases information can allow hotel owners to build high-quality predictive models. For example, they can identify the seasons of highest and lowest demand to dynamically adjust their pricing and plan various refurbishing and repair works accordingly. This way, the knowledge of consumer purchase decision patterns can allow them to avoid the loss of profits and ensure that they maximise the efficiency of assets utilisation. Second, these opportunities may be highly valuable for targeting prospective customers more effectively (Murphy et al., 2019). While this may require access to social media data and advanced image processing, AI instruments may allow marketers to primarily target the clients who chose similar companies and offerings in the past rather as well as those who may be more interested of such offerings in the future.

 

This concept is closely associated with the theme of services personalisation. As noted by the interviewed AI expert, positive outcomes in this sphere could be achieved on the basis of two types of analysis. On the one hand, the appraisal of past decisions could be used to predict the future preferences of existing loyal clients and make their consumer experiences more favourable (Kaartemo and Helkkula, 2018). This could also be utilised for upselling purposes to provide customer-specific auxiliary offerings expanding the core product or service. On the other hand, the predictive analytics technologies supported by AI systems could reliably anticipate the preferences of future clients having similar behavioural and psychographic characteristics. However, this opportunity requires high-quality data collected from multiple consumers, which can make this option unsuitable for micro- and small-organisations with limited customer flows (Brougham and Haar, 2018).

 

Another positive contribution noted by the expert was greater organisational self-awareness of hospitality organisations implementing AI systems. The collection and processing of big data allow decision-makers in the hospitality industry to have clearer reference points for comparing their performance against their past results (Cain et al., 2019). From the predictive analytics standpoint, this also allows marketers and owners to measure the effectiveness of AI predictions and quickly recognise any flaws in company decision-making processes. At the same time, image recognition and passive monitoring instruments might also be invaluable for social listening. For example, hotel brands could instantly learn about the photos of their hotel uploaded by internet users and visitors or their positive and negative reactions (Ko, 2018). Quick responses to such queries are presently considered a sign of good customer service quality, which can allow smaller hospitality organisations to imitate the capabilities of their larger counterparts. However, the costs of such systems could still be sufficient and could not substantiate their acquisition in the case of small hotels focused on single-time customers.

 

An Analysis and Prediction of Consumer Behaviour Patterns

The earlier mentioned big data analysis was reported as the most universal method for analysing and predicting consumer decisions. Specifically, the expert mentioned the use of AI technology to record information about the name of the person and their family composition. The brief reference to these facts could make follow-up messages more personalised and human-like since the marketers were referring to a particular individual rather than a generic buyer from a certain consumer segment (Ko, 2018). However, this example could be viewed as a controversial practice according to the new GDPR requirements regarding the collection and storage of personal data. The overall effectiveness of consumer analysis was linked with the availability of company information dating back 1-3 years (Appendix). Another example in this sphere referred to the use of services for business customers such as professionally equipped office spaces or photocopying services. While the general ‘rule of thumb’ outlined by Cain et al. (2019) implies the provision of such amenities on the basis of psychosocial segmentation, AI technologies offer an alternative route.

 

According to Kaartemo and Helkkula (2018), individual companies could develop their unique offering by studying the preferences of their past visitors and listening to their suggestions. This could refer to both the optimisation of existing products and services and the introduction of new ones to make the marketing mix highly unique. Artificial intelligence solutions could aid marketers with this task by exploring social media data, company records, consumer feedback, and other information sources to build the ideal customer profile for every targeted segment. This analysis could allow the owners and managers to predict the behavioural patterns of future consumers and address their needs before they formulate them (Zsarnoczky, 2017). Similarly, this could identify the actual demand for personalisation. While most marketers blindly follow some general trends supporting either in-depth adaptation to individual consumers or the cost benefits provided by extreme standardisation, the answer to this debate may be unique for every hospitality organisation. The expert suggested that the use of AI was quickly becoming mandatory for making informed marketing decisions and reliably predicting the patterns of consumer decision-making in the studied industry.

 

Previously, it was expensive and difficult to perform the necessary analyses and reliably predict the shares of consumers demonstrating positive behaviours and response patterns regarding standardisation or adaptation. AI solutions may be the most cost-efficient solution to this problem allowing budget hotels to identify the optimal competitive strategy in a scientific manner and fully exclude the guesswork (Brougham and Haar, 2018). This could also allow them to see changes in this sphere and predict future reactions to pricing decisions, marketing campaigns or various communication strategies. At the same time, the availability of advanced automated instruments may allow small companies to study consumer reactions first-hand by sending various messages and monitoring responses. This way, budget hotels could identify the optimal follow-up strategies and modify their targeting policies to address their selected audiences more effectively (Ko, 2018).

 

The Perspectives of AI Implementation in the Budget Hotel Segment

The third objective referred to the potential of AI solutions for improving the competitiveness of budget hotels. It was addressed by the expert in questions 3 and 4 exploring the main barriers to AI implementation in these organisations and the competitive opportunities associated with this option (Appendix). According to the interviewee, the costs of implementing these technologies have been steadily decreasing and have already become affordable to small and medium organisations. However, the primary barrier was associated with the need to explore the quality data collected by the hotel itself in order to build reliable predictions on the basis of such analyses. Large chains usually have access to such information. Hence, they may use the existing records to identify common consumer behaviours such as the usual demands of business travellers, family travellers or consumer responsiveness to auxiliary offerings (Ko, 2018). Budget organisations lack this opportunity and have to either rely on purchased third-party data or use the limited amount of data available to them to build predictions with low generalisability and reliability.

 

Table 2: Main Barriers to AI Implementation in the Budget Hotel Segment

 

However, these complications could be balanced by the opportunities offered in some dimensions. For example, the mentioned use of chatbots could improve customer service quality and allow small hotels to offer basic 24/7 support to their guests (Cain et al., 2019). Similarly, the utilisation of behavioural analysis and prediction tools could provide for the more effective identification of loyal clients. This way, budget hotels could focus on building loyalty and move away from the short-term strategy based on the maximisation of profits obtained primarily from single-time reservations (Ko, 2018). Another interesting advantage not found in the secondary literature reviewed earlier was the capability to replace some of the live staff with AI-based chatbots. While this solution could not fully substitute a well-trained serviceperson, software tools were a single-time investment. They do not require salaries, corporate insurance plans, income taxes, social security payments or paid holidays, days off, and maternal leaves. All benefits combined, they may be the most cost-efficient solution in the customer service domain that is presently available to small and medium hospitality organisations.

 

While this decision may detract from the anthropomorphic ‘small family hotel’ experience mentioned by Murphy et al. (2019), it could also reduce the workloads on existing staff members and allow them to focus on the more critical tasks at hand. Additionally, the statements of the expert confirmed the concerns voiced by Cain et al. (2019) and Ko (2018) regarding the fact that AI technology could be a disruptive innovation. In the scenario outlined by the interviewee, the companies unable to learn the new competencies and prepare themselves for the wide adoption of artificial intelligence may experience a critical decrease in their competitiveness in the hospitality industry. This problem was not directly related to the size of these organisations but to their capability to utilise the advantages offered by this technology that were expected by the consumers. This is generally in line with the findings of Zsarnoczky (2017) who noted that the increasing personalisation of both marketing communication and customer service required the use of big data analysis instruments rather than the competence of individual managers and hotel owners. However, it is not clear if new hotels without prior experience can fully utilise the advantages of AI technologies without possessing quality data from their industry segment.

 

References

Brougham, D. and Haar, J. (2018) “Smart technology, artificial intelligence, robotics, and algorithms (STARA): employees’ perceptions of our future workplace”, Journal of Management & Organization, 24 (2), pp. 239-257.

Cain, L., Thomas, J. and Alonso, M. (2019) “From sci-fi to sci-fact: the state of robotics and AI in the hospitality industry”, Journal of Hospitality and Tourism Technology, 1 (1), pp. 1-13.

Kaartemo, V. and Helkkula, A. (2018) “A systematic review of artificial intelligence and robots in value co-creation: current status and future research avenues”, Journal of Creating Value, 4 (2), pp. 211-228.

Ko, C. (2018) “Exploring Big Data Applied in the Hotel Guest Experience”, Open Access Library Journal, 5 (10), pp. 1-17.

Murphy, J., Gretzel, U. and Pesonen, J. (2019) “Marketing robot services in hospitality and tourism: the role of anthropomorphism”, Journal of Travel & Tourism Marketing, 1 (1), pp. 1-12.

Zsarnoczky, M. (2017) “How does Artificial Intelligence affect the Tourism Industry?”, VADYBA Journal of Management, 31 (2), pp. 85-90.

 

Appendix