Quantifying and optimising user experience: Adapting AI methodologies for Customer Experience Management.

Transcription

1 Quantifying and optimising user experience: Adapting AI methodologies for Customer Experience Management. Jan Kaczmarek MOST Foundation program.org Dominik Ryżko Warsaw University of Technology ABSTRACT: Over the recent decade CRM systems have made a considerable breakthrough in the IT landscape of major companies in many sectors like telecommunication, financial services, etc. Despite this success, these systems have failed on delivering many of the promises to concentrate on the components of customer experience as too much effort was directed to the highly quantitative, technical aspects instead. CEM systems bring the hope of coming back to the original ideas, while at the same time pose formidable challenges of modelling these high level ideas in computer systems. This paper analyses the meaning behind customer experience and discusses possibilities of using AI methodologies to model concepts such as brand, environment and the experience itself. INTRODUCTION The paper has been structured around 7 sections. Firstly we provide a glance on the history of Customer Experience Management with purpose to set a right context, then we link CEM with the concept of massive personalisation of services, thirdly we take a closer look on the term experience, its different meanings in psychology, cognitive sciences, management and every day speech, then on the psychocognitive aspects of experience with particular focus on the role of emotions in experience gaining, fifthly we try to point on the relationship of CEM with qualia, then we map opportunities in application of AI technologies in CEM, and finally make an attempt of formal definition of experience in the pursuit of an ambitious goal to come up with a conceptual framework allowing for measuring, quantification, modelling and optimization of customer experience. 19

2 CEM Informational Approach to Driving User Centricity CRM vs. CEM BACK TO THE ROOTS Customer Relationship Management systems promoted what we refer to as structural homogeneity between business processes across organisations and sectors with regard to customer management policies. CRMs being complex computer systems, albeit composed of standardised components, has revolutionised customer management largely through technology push. Operational alignment advocated by CRM technology has gradually dominated innovative strategic marketing thinking. CRM systems have represented a benchmark for the adequate customer base management policy for many years. Consequently, as following best practices does not advance the state of the art, there has been significant deficit of innovative customer oriented strategies leading to a gap that welcomed the emergence of Customer Experience Management concept. CEM comes back to the initial ideas that once provided ground for CRM and takes these to an extreme. CEM approach in its puritanical form is radical about customer centricity and thus individual treatment of a customer. CEM AND MASS CUSTOMISATION Evidently, CEM calls for mass customisation. Mass customisation a concept coined by Davis [4] involves individual treatment of the customers like in the pre industrial era still upholding operating processes apt for mass market economy. In more practical terms mass customisation is a system or strategy that makes use of information technology, agility and flexibility of business processes to deliver tailored made products and services adapted to individual needs of each customer without compromising on the economies of scale achieved by mass production [12]. The underpinning thought behind mass customisation is that there does not have to necessarily be a trade off between personalisation and productivity. Mass customisation can take different forms and reach different degrees which could be marked on a continuous line between pure personalisation and pure standardisation as too opposite extremes [16]. Management science literature is reach in diverse proposals of mass customisation typologies [27] none of which prevails. Nonetheless it is evident that if mass customisation is to be applied in CEM, in a way that it is fully in line with CEM vision, it should be taken it its most pure form that implies focus on 20

3 Quantifying and optimising user experience: Adapting AI methodologies for the needs of each end every customer. But are the customer needs really the right focus? Is it not the narrow understanding of top experience as needs satisfaction, the very reason why CRM has failed? This discussion will be continued in the following section, but before we move on to it let us first formulate an important claim. Evidently, focus on the individual characteristics, whether needs or experience, of customers is not a new idea. Customer Experience Management however verbalises perfectly what it is all about: influencing the experience of a customer, the very foundations of his cognition, so that it was optimal for him and compliant with the objectives of the service provider. We propose an approach to CEM that is strongly focused on transactions, and to be more precise: focus on each and every transaction with a given, unique customer. Under this set up we perceive service delivery as a discrete process composed of transactions of smallest granularity possible each of which produces some output of experience quanta. Three major challenges that emerge from this set up are: (i) how to quantify and measure experience, (ii) how to conduct transaction to optimise experience outputs and (iii) how to do it in a mass personalised way. UNDERSTANDING AND QUANTIFYING EXPERIENCE Experience is a term and concept that is widely used across many disciplines of science: philosophy, psychology, theology, management and also in everyday language. Interestingly, a query experience in Encyclopaedia Britannica Online [7] results in 5,936 hits but there is no entry devoted entirely to the term, similarly in the MIT encyclopaedia of cognitive sciences (1999) the term is used excessively still it is not indexed. Historically experience was primarily dealt with in philosophy, epistemology in particular, and psychology. Currently the interdisciplinary cognitive sciences: cognitive psychology, philosophy of mind and neuroscience in particular delve into experience as a cognitive phenomena. One cannot talk about Customer Experience Management in scientific terms until experience is properly defined, so that the term carry a common meaning. This appears to be the major challenge, still some important endeavours have been made and will be hereby presented. In psychology, experience is understood closely to its everyday meaning as subjective (conscious) appreciation of stimulus event or the knowl- 21

4 CEM Informational Approach to Driving User Centricity edge resulting from this [8]. Though the above mentioned definition is variously rephrased, all of the definitions found in the literature point at the following particularities of the phenomena: Experience is subjective experience is born in the mind of an individual. We can talk about group experience [19] but these are derivatives of individual experience; Experience emerges as a result of changes in the environment experience is triggered by events involving the subject as an observer or active participant. Experience is a result of complex cognitive process experience is primarily shaped by perceptions, but also emotions, previous experiences, and interpretation assigned to it by the human mind [14]. Experience is memorized experience is registered in memory [25] and as such it is translated into new knowledge, thus it plays a key role in learning [23] Consequently experience is accumulated and constantly transformed by human mind in a very dynamic process influenced by memory volatility. Experience shapes human behaviour human activity is driven by goals set internally in response to the given state of reality [22], conscious experience builds an internal replica of the external world allowing the subject to orient oneself and adapt its behaviour to the given reality [18] in the pursuit of one s goals. Nagel [24] in the essay What it is like to be a bat? noticed that conscious experience defines act of being, underlying thus the very subjective character of experience. Mind is that which thinks and experiences, thus thinking and experiencing defines a minded creature, including a human being [11]. As experience is a product of activity of human mind it depends on both conscious and unconscious mind processes. Karl Lashley provocatively states that no activity of the mind is ever conscious, as all that is conscious in human mind is build on the inaccessible unconscious information processing in the brain. It is important to distinguish three meanings of the word experience used in different contexts. Firstly experience can be associated with procedural knowledge or skills (know how), secondly experience can mean the body of knowledge about the environment that is gained by participat- 22

5 Quantifying and optimising user experience: Adapting AI methodologies for ing or assisting different events over time, finally it can be understood, in line with the definition by Eysenck, as appreciation of stimulus event or the knowledge resulting from this. In this paper we are considering experience in the second and third meaning, as appreciations of stimulus events accumulated (remembered) over time that build knowledge. EXPERIENCE AND EMOTIONS Modern psychology defines emotion as negative or positive reaction to a perceived or recalled object, event or circumstance accompanied by a subjective feeling [5]. The evolution of emotion theories from classical Cannon Bard theory [2], Lazarus [18] cognitive approach, to recent theory of LeDoux (2000), which says that emotions are result of both physiological reaction of brain and body, and/or mental interpretations related to a given situation claiming that there are different brain sub systems for different types of emotions, clearly shows that emotions should be seen holistically as neurocongnitive phenomena. Given the above, as well as the fact that both emotions and experiences are subjective and are characterised by valence allows us to risk to state that there is no good reason for our considerations to distinguish between emotions and experiences. Emotions could be simply seen as experiences of particular type or integral component of experiences that determine their valence (positive neutral negative) and intensity (high low). In this perspective, the problem of sequentiality of emotion and cognition, in other words what comes first the emotion or cognition and how these two interact, looses importance as these two become irrelevant concepts that are embraced by a more universal concept of experience, and experiences that are remembered states of mind. As stated earlier current experience depends on previous experiences. Furthermore own experience provides the basic material for the imagination, whose range is therefore limited. It can be claimed then that quality of subjective experience depends on the quality of previous experiences. This is important observation in view of customer experience management as customers may have different requirements depending on past service, not necessarily form the same provider. 23

6 CEM Informational Approach to Driving User Centricity To sum up, accumulating experience is a complex psychocognitive process of particularities that make experience difficult to measure, quantify, model and optimise. Experience which is critical for human behaviour is: (i) complex, (ii) subjective, (iii) dependant on the unconscious, (iv) dynamic, (v) intangible. From the management point of view this makes experience management, strictly speaking its optimisation, an ill defined problem. It is evident however that experiences can be assessed in a qualitative way. Experience can be positive result in feeling of pleasure, or negative result in lack of pleasure or pain. Furthermore experiences could be ordered according individual preference: one can say that experience x is better, worse or equals to another experience y. Naturally, as experience itself is intangible this will be ascribed to a given stimulative event (whereas physical, sensual, emotional or mental) causing experience x and y. Evidently, as experience is subjective the same stimulative event can result in experiences of different quality depending on the subject, or circumstances (varying in time and space). This makes experience assessment and comparisons extremely difficult and complex, though psychological literature provides some examples of both theoretical and empirical scientific endeavours challenging this problem. Maslow [20] developed a notion of peak experience, which he defined as the moment of highest happiness and related it to self actualisation. Similarly, the optimal experience was described by a Hungarian psychologist Csikszentmihalyi, who introduced a concept of flow [3], the moment of top experience when one is confronted with a demanding challenge, still attainable with one s own capacities, and deeply enjoys the moment of stretching intellectual capabilities, and thus learning and increasing self esteem. We can therefore talk about maximisation of subjective experience. Schmitt, considering experience in the Customer Experience Management context [26] proposes that customer experience comprises: (i) customer satisfaction, linked to functional aspects of product or services, (ii) customer emotions, linked to psychological comfort or pleasure, and (iii) social comfort achieved by social fulfilment. This however seems too a simplistic approach implying, unnecessarily, dichotomy between the components of experience. This is also not clear why one type of emotions: social comfort is treated separately form others. 24

7 Quantifying and optimising user experience: Adapting AI methodologies for Instead we suggest to look at experience as sequence of remembered states of mind shaped by cognitive process (process of information processing influenced by internal and external stimuli) the purpose of which is to allow the subject to pursue one s goals. CEM AND QUALIA Having looked on experience in this perspective and bearing in mind the necessity to measure experience it is inevitable to reflect on what we may call experience quantum. If experiences are states of mind could these be somehow quantified? In this context and interesting concept of quale (plural: qualia) comes up. Qualia are defined as experimental properties of sensations, feelings, perceptions, thoughts, desires, etc., in other words qualia include what is like to have experiential mental states. Though the existence of qualia raises controversies [6] it seems to be a highly relevant concept from the point of view of experience measurement. However, being a relatively new so far insufficiently explored concept its practical application in CEM seems still distant. ARTIFICIAL INTELLIGENCE (AI) AT THE SERVICE OF CEM In order to understand customer actions and extract usable patterns of behaviour data mining, machine learning and other AI inspired techniques have been extensively used to support customer centric systems like CRM. At the beginning clients were regarded as a mass, not as individuals. The goal was to model how an average customer behaves. Deducing rules, decision trees etc. provided general ways of dealing with the client population and creating general policies. This allowed to reduce the probability of offering wrong products to a customer, however the possibilities for improvement as the new data arrived were very limited. To overcome these limitations clients were divided into segment using clustering techniques and the process of finding patters was repeated in each of the segments. This resulted in the more accurate fit of profiles to the customers. It was then crucial to classify each new customer into the right segment so that his behaviour could be predicted more accurately. It is possible to go down even further with decreasing this granularity, but this makes sense only to a certain extent. By creating too fine grained segments we risk that the concepts learned by the system suffer from over fitting to the training 25

8 CEM Informational Approach to Driving User Centricity examples. Therefore the proper balance should be found to acknowledge differences yet to take advantage of similarities. Also other more advanced techniques need to be adopted in order to fulfil the promises of CEM and allow for mass customization of products and services. By adopting mass customization approach we give the customers possibility to influence the way products are designed and allow the voice of customers to be translated into product specifications. To facilitate this machine learning can be applied in order to model the relation between customer needs and product specifications. Yu [30] shows how a system can translate user needs into a set of useful rules which become guidelines for product engineers while designing new products. One of the most promising ways to approach the problems of mass customization is the use of Multi Agent Systems (MAS). MAS propose not only a new architecture for building information systems but a set of paradigms in which a central concept is an intelligent, autonomous and proactive agent. These features seem to be fitting perfectly into the needs of mass customization. Distributed computation and autonomy allows each of the agents (or a group of agents) to adapt to the required task, without involving the whole system to do so. Also the proactiveness of the agents will be an advantage. As long as an agent has a clear set of goals, it will pursue it by initiating suitable actions. In most models of MAS agents can interact and exchange information, which allows them to share solutions and reuse valuable knowledge. Smirnof [28] shows how the concepts of agents together with mass customization can be utilized in a corporate knowledge management system. Agents have also been used as facilitators in the data acquisition process for collecting information on customer choices [27]. The idea of MAS is also in line with the paradigm of ubiquitous computing. With more and more products performing computations on its own and interacting with each other, delivering the right customer experience becomes a difficult task. MAS gives the promise to model such distributed, knowledge intensive environments and to cope with this challenge. For this purpose a new term MMAS (Massively Multi Agent Systems) has been proposed [13]. Yet another powerful tool to support mass customization are ontologies. Even with highly efficient knowledge reuse it is never certain whether two customers request the same product or service unless the true mean- 26

9 Quantifying and optimising user experience: Adapting AI methodologies for ing of the concepts they use is established. Ontologies give tools to perform semantic analysis of meaning and relation of different entities. Building system domain ontology allows analysis of large amounts of information often described in different terms [28]. One of the most important aspects of building customer experience is understanding how single actions and events together lead to the formation of a certain experience by the customer. Or how experience of several customers leads to the perception of a company by a group or the entire society. These are examples of emergent phenomena. The study of emergence has been long present in the work of AI researchers. Our understanding of these processes is still limited, however it is worth looking at them carefully. Research on emergence and self organization in the context of economy has already been conducted [29]. Some more AI inspired methodologies will be described in the next chapter related to the concept of experience. MEASURING EXPERIENCE In the previous section we have been talking about machine learning as an important tool to understand customer behaviour. Now lets look at the process of customer company interaction from a different angle. We can observe that it is the customer, who performs learning. Through various events like buying products, viewing advertisements etc., which can be regarded as training examples, his perception of the company is shaped. If we were dealing with a machine we could teach it all the desired concepts. So this striking perspective seems to be quite attractive. But of course we are dealing with a human and the learning process we try to handle is a complex one. We are not able to control the algorithm which processes the training examples. Even worse, we will never fully understand how this algorithm works. Transactions will be appearing irregularly, some of them planned, other unplanned or even undesired. Information related to the company, product or brand will be received by the customer through several channels, by interaction with other users, media, competition etc. Despite these obstacles, viewing client experience as a learning process gives us several advantages. Firstly, we can treat in a homogeneous way all the events related to client and the company. So even actions of the competi- 27

10 CEM Informational Approach to Driving User Centricity tion can be taken into account and by the nature of the process we will be able to embrace them, possibly neutralize or even utilize to reach our own goals. Secondly such approach is suitable to model differences between customers. Each and every one of them is modelled explicitly as a separate entity, which performs its own learning process. Nevertheless, we can utilize the patters of transactions (training examples) which are successful among several customers and conduct them coherently reducing our costs in the spirit of mass customization. Yet another advantage is that machine learning theory gives us powerful tools to measure the progress of the learning process. Computational learning theory gives several interesting results, which can help us to understand how the customer perceives the company/brand etc. For example Bayesian Inference take into account believes prior to the evidence collected during the learning process [9]. Machine learning theory brings also results of learning multiple concepts, which can be very useful when we deal with several brands, products and values simultaneously. Yet another models allow us to treat undesired transitions as misclassified examples and bring tools for dealing with them. Finally, the perspective we have taken allows us to formulate the following definition of experience shaped thorough a learning process, which is at least to some extent measurable. Definition: The experience of a customer with regard to the company/ product/brand etc. at a certain point of time t is defined as a set of concepts about this company/product/brand believed to be true by the customer at t, which were learned through a series of previous discreet transactions representing training examples. For example during the course of interaction with company X a customer can believe at some point that {Company X is technologically innovative, Products of X are generally more expensive then those of competition, Brand X is trendy among middle aged part of population, Kids regard advertisements of X as funny}. Important aspect of experience defined as above is its time dimension. We have to assume, that as the time passes experience will change even without any new transactions. This is due to the fact that, as explained earlier, experience is memorised by human brain and therefore is being steadily forgotten. 28

11 Quantifying and optimising user experience: Adapting AI methodologies for So what is need in order to perform the task of teaching the customer the right concepts? As mentioned above we believe machine learning theory should be the base of this approach. Some of the results tested in other fields have already been pointed out. In order to predict the experience of customer population, simulation systems can be built. Once again Multi Agent Systems come to help here. Multi Agent Simulation is a well developed branch of AI with several applications in different areas [1]. Finally it is crucial to model at least to some extent the way a customer processes transactions into the perception of company or brand they are related to. Some answers to these needs are given by common sense reasoning [21] or BDI (Belief Desire Intention) model for MAS. However, it seems important results from Cognitive Sciences are required in order to perform this task. CONCLUSIONS Experience is discussed here as remembered states of mind resulting from appreciation of stimulus events that determine human behaviour. Gaining experience is a psychocognitive process that is complex, subjective, dependant on both the conscious and the unconscious, dynamic, and intangible. However, in business context customer experience could be defined as a set of concepts about a service provider internalised by a given customer. It has been proposed to look at experience gaining as a learning process, and treat transactions as training examples. Such approach allows not only to make experience measurable and quantifiable, but also opens doors to application of machine learning theory in Customer Experience Management. Under this set up CEM is about teaching the customers the right concepts. All in all, in order to reach the ambitious way described above a wide programme of interdisciplinary research is needed involving expertise from Information Technology, Economy as well as Cognitive Sciences. We believe such a joined effort should lead to achieving really important results in the area of CEM. REFERENCES [1] Blecic,(2007), An Agent Based Model for Supporting Tourism Policies. In Proc. 10th Intl. Conference on Computers in Urban Planning and Urban Management [2] Cannon, W.B (1927), The James Lange Theory of Emotions: A Critical Examination and an Alternative Theory, The American Journal of Psychology, Vol. 39, No. 1/4 (Dec. 1927), pp

12 CEM Informational Approach to Driving User Centricity [3] Csikszentmihalyi, M. (1975), Beyond boredom and anxiety, San Francisco, CA, Jossey Bass. [4] Davis, S., From future perfect: Mass customizing, Planning Review 17 (2) (1989), pp [5] Damasio, A.R. (1999), The Feeling of What Happens: Body and Emotion in the Making of Consciousness, Harcourt Brace & Company, New York. [6] Dennett, D. (1988), Quining qualia, [in] Marcel, A., Bisiach, E. [eds], Consciousness in Contemporary Society, Oxford University Press, New York. [7] Experience, (2009), In Encyclopadia Britannica, Query entered February 04, 2009, from Encyclopadia Britannica Online: [8] Eysenck H. J., Arnold, W., Meili, R., [eds.] (1972), Encyclopedia of Psychology, London: Search Press. [9] Gaudard, M. (2004), Convergence of posterior probabilities in the Bayesian inference strategy. In Foundations of Physics, Volume 15, Number 1, Springer Netherlands, January, 1985 [10] Gilmore, J. Pine, The four faces of mass customization, Harvard Business Review, 75 (1) (1997) pp [11] Graham, G., Philosophy of Mind: An Introduction, Blackwell Publishers, Oxford [12] Hart, C., Mass customization: Conceptual underpinnings, opportunities and limits, International Journal of Service Industry Management 6 (2) (1995), pp [13] Ishida, T, (2005), Massively multi agent systems I: first international workshop, MMAS 2004, Kyoto, Japan, December [14] Johnson, M.K., Verfaellie, M., Dunlosky, J., Introduction to the special section on integrative approaches to source memory. Journal Of Experimental Psychology: Learning, Memory, And Cognition, 2008 Jul, Vol. 34 (4), pp [15] Kotler, P., From mass marketing to mass customization, Planning Review 17 (5) (1989), pp [16] Lampel, J., Mintzberg, H., Customizing customization, Sloan Management Review 38 (1996), pp [17] Lazarus, R.S. (1991), Emotion and adaptation, Oxford University Press, New York. [18] Lehar, S., (2006), The Dimensions of Conscious Experience: A Quantitative Phenomenology, [in] Batthyany, A., Elitzur, A. [eds], Mind and Its Place in the World: Non reductionist Approaches to the Ontology of Consciousness, Ontos Verlag, Frankfurt. [19] Littlepage, G., Robison, W., Reddington, K., Effects of Task Experience and Group Experience on Group Performance, Member Ability, and Recognition of Expertise, Organizational Behavior and Human Decision Processes Volume: 69, Issue: 2, February, 1997, pp [20] Maslow, A.H. (1971), The Farther Reaches of Human Nature, Viking Press. [21] Minsky, M.(2006), The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. New York. [22] Mises, L. von, Human Action: A Treatise on Economics, Yale University Press, New Haven [23] Morgan, D.L., Essentials of Learning and Cognition, McGraw Hill

13 Quantifying and optimising user experience: Adapting AI methodologies for [24] Nagel, T. (1974), What is it like to be a bat?, Philosophical Review, October [25] Reyna, V.F., (1995), Interference Effects in Memory and Reasoning: A Fuzzy Trace Theory Analysis, [in] Dempster, F.N., Brainerd, C.J. [eds] Interference and Inhibition in Cognition, Academic Press Inc. [26] Schmitt, B.H. (2003), The Customer Experience Management: A Revolutionary Approach to Connecting with Your Customers, John Wiley and Sons. [27] Silveira Da, J., Borenstein,D., Fogliatto, F.S, Mass customization: Literature review and research direction, Internatinal Journal of Production Economics, 72 (2001), pp [28] Smirnov, A.V., Pashkin, M., Chilov, N., Levashova, T. (2003), Engineering Applications of Artificial Intelligence 16 (2003) , Elsevier. [29] Vincent M., Darley, Kauffman, S. (1997), Natural rationality, [in] Arthur, W.B, Lane, D, Durlauf, S.N. [eds] (1997), The Economy as an Evolving, Complex System II, Addison Wesley. [30] Yu, L., Wang, L., Yu, J. (2008), Identification of product definition patterns in mass customization using a learning based hybrid approach. In The International Journal of Advanced Manufacturing Technology, Volume 38, Numbers 11 12, Springer London, October,