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Customer Experience Management architecture for enhancing corporate customer centric capabilities Dominik Ry˙zko1 and Jan Kaczmarek1 Warsaw University of Technology, Intitute of Computer Science, Ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

Abstract. The paper introduces a novel architecture for Customer Experience Management (CEM) for modelling customer experience with respect to company, brand or product and its relation to consumer decistions. The process for customer experience approximation and decision taking is proposed. User experience gaining has been modeled as learning so application of various machine learning algorithms is possible. Each customer is modelled as an intelligent agent, which reflects the distributed nature of the problem and allows for autonomy of its elements. It is shown how the architecture can be utilised with existing resources e.g. Customer Relationship Management systems as a source of data for CEM.


Background and research problem

Schmitt introduced the concept of Customer Experience Management (CEM) by which he popularised the approach to customer relations and loyalty management, but also strategic marketing at large, to which customer experiences are of central significance [10]. The important contribution of Schmitt was the observation and emphasis on that customer experience should not only be measured by customer satisfaction linked to functional aspects of product or services but also to customer emotions, and social fulfilment derived from the consumption. Still he provides little insight in qualitative and quantitative character of the experience, which is critical if his approach is to be of any use for commercial IT systems. In [4] we have defined the experience as remembered states of mind resulting from appreciation of stimulus events that determine generically any human behaviour, and particularly consumer decisions. In case of a consumer decision, customer experience can be modelled as a set of learned concepts about an object of consumption (brand, product, service, provider, etc.) internalised by a given customer. It has been proposed to look at experience gaining as a learning process, and treat transactions and consumption related events as training examples. Here we will further develop this idea and translate it into a more formal model.

We see potential in applying Schmitt approach in AI enabled CEM support systems, in particular the use of machine learning theory for modelling customer experience to support planning and executing marketing strategies that would aim at optimising customer experience either via adapting the offer to it, or teaching the customers the relevant concepts.


Customer knowledge, experience and intentionality

The study of human experience, either in the context of consumer behaviour or any other, is intrinsically related to the study of human consciousness and in particular intentionality of human mind. Though a comprehensive discussion on the implications of philosophy of mind on customer experience management does not fit in the scope of this paper it is important to outline the philosophical stance that constitutes epistemic underpinnings for formalisms introduced in the paper later on. This philosophical stance has been developed by John R. Searle [11] [12] [13]. As mentioned earlier in the widest sense we define experience as remembered states of mind. This entails certain qualities of experience which relate to the qualities of human consciousness, of which the most important are dependability on memory and its volatility (temporal dimension of experience), emotional value (emotional dimension of experience), integrity and intentionality. The last two qualities may require further explanations. Integrity of human consciousness embraces two important characteristics of human experience. Firstly the fact that at a given moment our cautious appreciation of the surrounding world is unitary in the sense that different stimuli we receive at the same time are converted into single experiential states. Secondly, these unitary states follow and relate to one another creating thus a coherent flow of experience in the sense that one experiential state comes for the previous one and turns into the following one. Integrity of human consciousness allows to model experience as a learning process where events (stimulus events) serve as training examples, because it justifies the casual relationship between past events and current experience. Intentionality of human mind in turn, as defined by Searle [11], embraces the phenomenon that the mind always relates to external world, always is directed at or about something. This aboutness allows to represent experience by means of formal logics. Intentional states typically have a structure such that they are composed of two main elements: psychological mode (S) and propositional content (p): S(p), for instance: Hate (I wake up in the morning). The main consequence of this is that human experience, therefore customer experience as well, can be comprehensively represented by means of first order logic.


Customer experience and consumer decisions

The reason why we concentrate on experience is because of its significance to the human decision making, in particular we are interested in what is customer

experience, how to model and measure it and how it determines customer decisions. We recognize that from the point of view of a company any CRM or CEM system is of value only if it contributes to improved business results, higher sales in particular. This can be achieved by providing insights on how purchase decisions are made with relation to customer experience. Importantly, the overview of CEM and CRM solutions currently available on the market reveals that ’customer experience’ has become a common buzzword meaning of which is incoherent and often vague. It is often confused with GUI usability especially when online interactions with customers are involved. We want to stress that we understand customer experience much wider, as this part of customer’s knowledge that relates, directly or indirectly to consumption of a given product or service, in particular in parts it can be represented in computer systems and used for supporting customer relationship management and marketing activities. Let us consider the nature of consumer decisions in the context of customer experience management system. Consumer decision is a special case of a decision, which in general terms is defined on the grounds of decision theory (e.g. [3]) as choice between available alternative ways of conduct and acting upon it. This therefore requires an intentional state of the agent that is followed by the action, so internally accepting that an agent wants to do something and eventually does it. A consumer decision therefore would always be about whether an agent wants to consider to buy anything in first place, then which of the available offers it will decide to accept and eventually purchase. The moment when the decision can be considered made is when an agent makes a legally binding commitment to purchase the offer. Consequently this decision is a two step process. Firstly an offer must match an intentional state of an agent that is a desire or commitment that fits the offer, we would say in such a case that an agent has a need corresponding to the offer. Secondly, an agent will consider alternative options available to him that fit the set of desire or commitment type of intentional states constituting the need as to assess which of these options would fit it best. At each of these steps an agent engages in what is called rational deliberation. Searle [13] points out that rational deliberation consists in appraising the motivators (desires or commitments) for their validity and for conflicts between motivators, and appraising the effectors and constitutors (the available optional offers) in such a way as to bring about the maximum satisfaction of the motivators with the least expenditure of other motivators in satisfying the effectors and constitutors. From the perspective of a CEM system or any other system supporting the customer relationship management the following questions are of primary importance: 1. How a customer would respond to an offer 2. What is the best moment to present an offer to a customer 3. How to shape the offer to make it fit with desires or commitments (the need) of a customer 4. How to influence customer’s experience to provoke desires or commitments corresponding to the presented offer

5. How to influence a customer’s experience to make him choose the presented offer from all others satisfying the need. To be able to answer these questions a CEM system would need for each agent an artificial reasoner simulating the deliberation of a customer. This reasoner could be implemented by applying selected method of defeasible reasoning, e.g. default reasoning. The overall idea behind the customer experience modelling framework presented in this paper is to be able to embrace possibly largest range of methods proposed in AI literature to simulate human decision making, in particular reasoning about actions, that are based on sentential logics. These methods however always need to be selected, and likely adapted, in a way that they could embrace the particularities of human experience and decision making that we think are critical. One of this critical characteristics is dependability on emotions.


Consumer decisions and emotions

There is enough evidence from neuroscience to claim that emotions play a central role in human and animal decision making [2] [5]. Based on this evidence let us postulate that any intentional state, which is as explained earlier reflections of external world in human mind, has certain emotional value, which is characterised by valence (positive or negative) and intensity (arousal level). The emotional value of intentional states that build up the total reason for action does influence the choice made by the agent. Emotions are central to trigger decision and actions [2] as well as in deciding which option to choose. Saltzman and Newsome [9] research on neural mechanisms for forming perceptual decision shows that a deciding brain presents increased neuronal activity in separate locations as if it ’accumulated’ simultaneously arguments for different available options each represented by a different group of neurons. The eventual choice is made the moment one options ’prevails’ which is manifested by visibly strongest neuronal activity in one of the parts taking part in this ’neuronal dispute’. This suggest that decisions are made based on conscious evaluation of available options by internal collection of arguments for and against available alternative, which is biased by emotions. For this reason we propose that for the purpose of customer experience representation we take intentional states, formally represented as predicates, and introduce an additional variable emotional value, which describes valence and intensity of emotional content of intentional state. Emotional value will have influence on vividness of experience, i.e. time during which the experiential state will stay in agent’s memory, as well as will bias the choice between options available to an agent. Consequently to represent the decision making process of a consumer agent a formalism must be selected and adapted as to be able to relate the modelled experience to consumer behaviour. It is intended that the experience modelling framework presented below be compatible with many logical approaches used

to represent deliberation of an intelligent agent. We believe that different approaches to consumer decision modelling may fit better different implementations. However we believe that the most promising system in this case is BDI [1], for example KARO framework proposed by Van Linder [6] or its variations describing also cognitive behaviour of agents such as attitude regarding emotions [7], [14]. Taking BDI system as example, customer experience would form a part of agent believes (B). The way how customer experience would relate to customer decisions under our customer experience modelling framework and the BDI system is presented by the below diagram

Fig. 1. Relation of customer experience and decisions in BDI framework

Customer experience results from events registered by the CEM system. These events are in turn, to important extent, consequences of customer decisions. The customer takes decisions based on experience that form part of one’s believes. This gives us a relational loop between experience, believes, decisions and events as illustrated on the above diagram.


Experience modelling

We define customer experience as a set of remembered intentional states resulting from appreciation of stimulus events related to a consumption object that can be attributed with emotional valence and intesity. Importantly these intentional states are of �belief� type and have temporal dimension. Using predicate calculus we can define experiential intentional state more formally as p(a1 , a2 , ..., an ) where p is a predicate symbol and a1 to an its attributes. In the case of a customer experience the psychological mode is a belief and propositional content is a predicate of argument x, where x is a consumption object. By consumption object

we define a product, a service, a brand or any entity beyond the experiencing agent at which the intentional state is directed at, for which the experience is modelled. Consequently we can model the emotional value of an experiential intentional state as a variable that is a number or a logical value. Now let us formally define customer experience in the following way: Exp(t) = {< p, v >: p is a predicate believed to be true at time t, and v is its emotional value} The experience is gained over time which can be modelled as learning process. Any learning process involves training data, which goes through the learning algorithm and results in a set of learned concepts (intentional states). In the case of CEM, we will consider training data to be a set of events related to a particular consumption object and customer, ordered according to the time of their occurrence. More formally we will define it in the following way T (t) = {(e1 ..en ) : ∀k ∈ (1, n) time(ek ) > time(ek−1 ) and time(n) <= t} where time(e) is the time of occurrence of e Each event will be a tuple e =< d, t, c, v > where d - description t - time c - class (e.g. offer, advert, sales etc.) v - vividness The learning algorithm simulates a process taking place in the customer’s mind, which takes the training data described above as input and generates experiential intentional states as outputs. The learning function L will process the experience in the following way Exp(t) = L(T(t), Exp(t-1)) The architecture presented in the paper is general in the sense that it does not assume any particular formalism for representing user knowledge, in particular experience and the experience learning algorithm. However, we argue that some form of defeasible reasoning seems a suitable approach to reflect the commonsense way of human reasoning. We will show how this can be done in the case of default logic as defined by Reiter [8]. In such a setting, the learning function L will be modelled as a default reasoning process. This process has two features important in this case. Firstly, it is non-monotonic, so that adding new facts does not always result in adding new conclustions but can lead to invalidation of some of them. This reflects nicely the way humans reason. With limited information at hand, we first take assumptions to make preliminary conclusions and then revise them in case of new evidence. Second important feature of default reasoning is that the order of applying the rules is important and can lead to different conclusions. This means a single theory can have multiple extensions. Such phenomenon is also common in human thinking when taking decisions. People construct different concurrent alternatives and weight arguments for each of them, before finally committing to

one of them. This is confirmed by neurological studies of human brain referred to earlier. In this model it is natural to use valence as a driver for rule priorities. Example: Let us assume the following default theory modelling the user experience learning function: D = { expensive(X):durable(X) , plastic(X):−durable(X) } durable(X) −durable(X) If both expensive(X) and plastic(X) are known two extenstions exist, one containing durable(X) and another with −durable(X). To choose one of them, we wieght the emotional value related to the input events and give priority to the one with higher value. The temporal dimension is one of the most important aspects of the customer experience. As the customer is confronted with new events, the experience gained from the old ones will be steadily forgotten. As forgetting is intrinsic to learning the model of experience must take this into account. A variable ”vividness” can be defined that will allow to model the memory volatility linked to experiential outputs of events. The vividness of experience can be assessed by applying known forgetting algorithms worked out under machine learning. Again the CEM framework we propose shall integrate with many approaches and algorithms dealing with memory loss. To give an example Wo´zniaks algorithms based on spaced repetition could be used [16].


Model calibration

In a setting as described above, the system has to model the way customer experienced is shaped. This in turn will allow to predict with satisfactory accuracy how the user would react to particular event or design a sequence of events leading to a desired outcome. To achieve this, the feedback process is needed. In the case of experience learning the proactive way of feedback gathering is needed. In the CRM community a concept of the moment of truth has been practiced [15], which is though to be the right moment for assessment of customer loyalty. When we reach the point in which the estimation of user algorithm gives significantly different result then the feedback, we have to reduce the gap. There are two ways of doing this. One is to modify the algorithm, the other is to assume there has been an unknown event, which has to be included in the process. To help with the decision on the approach an additional feedback should be gathered if possible. The process has been illustrated in the diagram below. Example: Let us assume the following default theory reflecting the knowledge of a customer, which we assume he uses to process events and gain experience: , newest(X):prestigious(X) } D = { expensive(X):durable(X) durable(X) prestigious(X) The customer is presented with the offer of a new product, which states that it is the most recent model and the price is high e1 = {expensive(p1 ), newest(p1 ), pink(p1 )}

Fig. 2. Customer experience model calibration

the following fact is deducted and added to experience Expestimated = {durable(p1 ), prestigious(p1 )} Let us assume that after measuring real user experience we get the following result: Expreal = {durable(p1 )} There is a gap to be reduced. After checking that there were no other events influencing the learning process we have to modify our model of the customer learning function. One of the possible options is to take into account other facts, which might be relevant in the current situation. In our case new rules could look like this D = { expensive(X):durable(X) , newest(X):prestigious(X),â&#x2C6;&#x2019;pink(X) } durable(X) prestigious(X) The new theory explains customer conclusions. Obviously in reality the process of modifying customer knowledge can be much more complex and selecting the facts which influence particular experience elements is not easy. However, by means of generalization or various machine learning techniques we will be able to derive meaningful conclusions over significant samples of data.

The purpose of calibrating of the user algorithm is to approximate in a most accurate way how events he experiences result in his personal experience. This in turn will allow us to (1) predict impact of various events we consider and (2) generate an event or series of events that will result with high probability in desired customer experience.


System architecture

In this section a general architecture for a CEM system implementing the ideas described in the paper. The key requirement is to model in parallel all the customers. Therefore, a Multi-Agent System (MAS) is proposed as a central part of the system. By their nature intelligent and autonomous agents will enable differentiation of experience of each customer. Initially each agent can start with a predefined knowledge base. The MAS system will be fed with the stream of events related to particular customers. The source of such events will typically be CRM system or other systems which store knowledge about relevant customer events. Each agent will receive events related to him and will perform the learning process as described in previous sections. This will result in experience gaining. Whenever a real experience will be measured for a particular customer, a model calibration process for corresponding agent will be performed. An analytical module will allow gathering information about current user experience and running reports and statistics over it. The module will also be able to send hypothetical events to the MAS module in order to simulate their impact on user experience.

Fig. 3. Architecture for customer experience management



A new model for Customer Experience Management has been introduced. A formal definition for quantifying experience has been proposed. Tha main novelty is modelling of customer experience gaining as a learning process. Algorithms

for experience calculation and model adjustment have been shown. The paper describes also an architecture for implementing the theoretical results in which CEM can be a valuable extension of an existing CRM system. The approach is generic and allows various defeasible reasoning formalisms to be applied for modeling of customer rationality.

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Customer Experience Management Architecture for Enhancing Corporate Customer Centric Capabilities  
Customer Experience Management Architecture for Enhancing Corporate Customer Centric Capabilities  

Ryżko, D., Kaczmarek, J.M., Customer Experience Management Architecture for Enhancing Corporate Customer Centric Capabilities, [in] Ryżko, D...