Claims:1. A method for measurement and deployment of customer experiences, the method comprising:
defining, by one or more hardware processors, a plurality of experience indicators corresponding to a plurality of customers and service providers;
mapping, by the one or more hardware processors, a set of a pre-defined quality values corresponding to the plurality of customer experience indicators for obtaining a first set of quantitative values indicating a plurality of services experienced by the plurality of customers;
computing, by the one or more hardware processors, a first set of scores that are unique and corresponding to each of the plurality of experience indicators for obtaining a second set of quantitative values relevant to the plurality of customer experience indicators, wherein the second set of quantitative values indicate a set of gaps between a plurality of customer experiences and service provider experiences;
based on the first set of scores and the second set of quantitative values performing:
(i) computing, by the one or more hardware processors, a plurality of total score values corresponding and unique to a plurality of pre-defined threshold scores, wherein each of the total score value is a summation of the first set of scores corresponding to each of the plurality of experience indicators, and wherein each of the pre-defined threshold score is a summation of a set of pre-defined scores corresponding to each of the plurality of customer experience indicators; and
(ii) determining, by the one or more hardware processors, a third set of quantitative values based upon the plurality of total score values closest to the plurality of pre-defined threshold scores for optimizing a set of quality values for reducing the set of gaps between the plurality of customer experiences and service provider experiences, wherein the set of quality values indicate a plurality of quality level of customer experiences; and
determining, by the one or more hardware processors, a second set of scores based upon an analysis of the set of gaps between the plurality of customer experiences and service provider experiences for optimizing a first set of comparison values for reducing the set of gaps between the plurality of customer experiences and service provider experiences, wherein each of the comparison values is determined based on a comparison between a set of service ratings provided by the plurality of customers and received by the plurality of service providers respectively.
2. The method of claim 1, wherein the step of optimizing the set of quality values is preceded by computing a second set of comparison values corresponding to the plurality of customer experiences based upon an analysis of the plurality of total score values, the plurality of pre-defined threshold scores and the second set of quantitative values, wherein the second set of comparison values are computed for identifying a potential score to be optimized from amongst the set of scores corresponding to each of the plurality of experience indicators for determining a quality level of the customer for the service based upon the plurality of customer and service provider experiences.

3. The method of claim 1, wherein the steps of determining a third set of quantitative values closest to the plurality of pre-defined threshold scores comprises performing a mapping of the plurality of total score values with the set of a pre-defined quality values for optimizing the first set of comparison values, wherein the mapping is performed by taking an aggregate of the first set of scores and performing a comparison of the plurality of total score values with the set of a pre-defined quality values.

4. The method of claim 1, wherein the step of optimizing the second set of comparison values comprises assigning a first set of weightages to each of the plurality of customer experience indicators by performing an analysis of the plurality of total score values, the plurality of pre-defined threshold scores and the first and the second set of quantitative values for computing a second set of weightages to identify one or more weightage values to be optimized, wherein the first and the second set of weightages comprises one or more weightages assigned to each of the plurality of customer experience indicators.

5. A system comprising:
a memory storing instructions;
one or more communication interfaces; and
one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:

define, by one or more hardware processors, a plurality of experience indicators corresponding to a plurality of customers and service providers;
map, by the one or more hardware processors, a set of a pre-defined quality values corresponding to the plurality of customer experience indicators for obtaining a first set of quantitative values indicating a plurality of services experienced by the plurality of customers;
compute, by the one or more hardware processors, a first set of scores that are unique and corresponding to each of the plurality of experience indicators for obtaining a second set of quantitative values relevant to the plurality of customer experience indicators, wherein the second set of quantitative values indicate a set of gaps between a plurality of customer experiences and service provider experiences;
based on the first set of scores and the second set of quantitative values performing:
(i) compute, by the one or more hardware processors, a plurality of total score values corresponding and unique to a plurality of pre-defined threshold scores, wherein each of the total score value is a summation of the first set of scores corresponding to each of the plurality of experience indicators, and wherein each of the pre-defined threshold score is a summation of a set of pre-defined scores corresponding to each of the plurality of customer experience indicators; and
(ii) determine, by the one or more hardware processors, a third set of quantitative values based upon the plurality of total score values closest to the plurality of pre-defined threshold scores for optimizing a set of quality values for reducing the set of gaps between the plurality of customer experiences and service provider experiences, wherein the set of quality values indicate a plurality of quality level of customer experiences; and

determine, by the one or more hardware processors, a second set of scores based upon an analysis of the set of gaps between the plurality of customer experiences and service provider experiences for optimizing a first set of comparison values for reducing the set of gaps between the plurality of customer experiences and service provider experiences, wherein each of the comparison values is determined based on a comparison between a set of service ratings provided by the plurality of customers and received by the plurality of service providers respectively.

6. The system of claim 1, wherein the one or more hardware processors are further configured to compute a second set of comparison values corresponding to the plurality of customer experiences based upon an analysis of the plurality of total score values, the plurality of pre-defined threshold scores and the second set of quantitative values to optimize the set of quality values, wherein the second set of comparison values are computed to identify a potential score to be optimized from amongst the set of scores corresponding to each of the plurality of experience indicators for determining a quality level of the customer for the service based upon the plurality of customer and service provider experiences.

7. The system of claim 1, wherein the one or more hardware processors are further configured to determine a third set of quantitative values closest to the plurality of pre-defined threshold scores by performing a mapping of the plurality of total score values with the set of a pre-defined quality values to optimize the first set of comparison values, wherein the mapping is performed by taking an aggregate of the first set of scores and performing a comparison of the plurality of total score values with the set of a pre-defined quality values.

8. The system of claim 1, wherein the one or more hardware processors are further configured to optimize the second set of comparison values by assigning a first set of weightages to each of the plurality of customer experience indicators by performing an analysis of the plurality of total score values, the plurality of pre-defined threshold scores and the first and the second set of quantitative values for computing a second set of weightages to identify one or more weightage values to be optimized, wherein the first and the second set of weightages comprises one or more weightages assigned to each of the plurality of customer experience indicators.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEMS AND METHODS FOR MEASUREMENT AND DEPLOYMENT OF CUSTOMER EXPERIENCES

Applicant:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD
The present application generally relates to measurement and deployment of customer experiences. More particularly, the present application relates to systems and methods for measurement and deployment of customer experiences.

BACKGROUND
Understanding a customer’s need and satisfaction level are essential in a service industry, especially for a customer service organization. Such understanding gives an insight of customer experience while availing a service offered to the customer. In the service industry, there may be different types of domain-specific services offered to the customer. While availing the service, the customer may have to come across different channels of sub-services related to the service. Thus, the travel path or journey of the customer made while availing the services are important to understand customer’s view about the services. The existing tools/techniques available for capturing such customer experience are domain-specific and have limitations in providing in-depth analysis. Further, understanding the working of such tools/techniques becomes difficult for the service providers or users when implemented in different domains. Thus, the process becomes time consuming and tedious for the users using such tools/techniques. Further, capturing customer’s experience data across different sub-services related to the service using these tools and consolidating such data on a common platform to understand the customer’s experience is another concern. Analysis of customer’s experience data individually over different platforms may not be worthwhile and lead to an increase in internal computing/processing time for these tools.
A Customer Experience (CE) Framework is one of a business framework which measures and analyzes a customer perception towards a service provided by a service provider. The analysis of the customer perception helps in changing or improving internal business strategies to improve customer satisfaction and experience. Generally, the service provider presents a specification as standard operating procedures to the customers for receiving their perceptions for the service. On the contrary, the customers have their own specifications based on their need-driven expectations for changes to their process-inputs. Misalignment between the service provider specification and the customer-specification for the service process leads to dissatisfaction, even when the process goes exactly as it was designed. Thus, it is challenge for the service provider to understand and address individual customer needs. For understanding the customer needs, the service provider captures customer’s view or perceptions, for the service provided, from different sources. However, due to qualitative nature of the perception, it is practically difficult to understand and have a standard unit of scale for measuring the perceptions received from different customers. The perceptions received changes over time and situation. For example, two people may perceive same things differently and also the same person may perceive same thing differently at different times. Thus, there exist gaps between what the customer expects and what the service provider delivers.
Hence, there exists a need for a technology which provides for an accurate quantification and representation of customer’s view or perceptions, for the service provided, from different sources with an equal level of an accurate computation and representation of the customer’s view or perceptions from the service provider’s point of view in order to reduce the gaps with an ultimate objective of optimizing the level of customer satisfaction and revenue potential of a business.

SUMMARY
The following presents a simplified summary of some embodiments of the disclosure in order to provide a basic understanding of the embodiments. This summary is not an extensive overview of the embodiments. It is not intended to identify key/critical elements of the embodiments or to delineate the scope of the embodiments. Its sole purpose is to present some embodiments in a simplified form as a prelude to the more detailed description that is presented below.
Systems and methods of the present disclosure enable measuring and deploying of customer experiences. In an embodiment of the present disclosure, there is provided a method for measurement and deployment of customer experiences, the method comprising: defining, by one or more hardware processors, a plurality of experience indicators corresponding to a plurality of customers and service providers; mapping, by the one or more hardware processors, a set of a pre-defined quality values corresponding to the plurality of customer experience indicators for obtaining a first set of quantitative values indicating a plurality of services experienced by the plurality of customers; computing, by the one or more hardware processors, a first set of scores that are unique and corresponding to each of the plurality of experience indicators for obtaining a second set of quantitative values relevant to the plurality of customer experience indicators, wherein the second set of quantitative values indicate a set of gaps between a plurality of customer experiences and service provider experiences; based on the first set of scores and the second set of quantitative values performing: (i) computing, by the one or more hardware processors, a plurality of total score values corresponding and unique to a plurality of pre-defined threshold scores, wherein each of the total score value is a summation of the first set of scores corresponding to each of the plurality of experience indicators, and wherein each of the pre-defined threshold score is a summation of a set of pre-defined scores corresponding to each of the plurality of customer experience indicators; and (ii) determining, by the one or more hardware processors, a third set of quantitative values based upon the plurality of total score values closest to the plurality of pre-defined threshold scores for optimizing a set of quality values for reducing the set of gaps between the plurality of customer experiences and service provider experiences, wherein the set of quality values indicate a plurality of quality level of customer experiences; determining, by the one or more hardware processors, a second set of scores based upon an analysis of the set of gaps between the plurality of customer experiences and service provider experiences for optimizing a first set of comparison values for reducing the set of gaps between the plurality of customer experiences and service provider experiences, wherein each of the comparison values is determined based on a comparison between a set of service ratings provided by the plurality of customers and received by the plurality of service providers; computing a second set of comparison values corresponding to the plurality of customer experiences optimizing the set of quality values based upon an analysis of the plurality of total score values, the plurality of pre-defined threshold scores and the second set of quantitative values, wherein the second set of comparison values are computed for identifying a potential score to be optimized from amongst the set of scores corresponding to each of the plurality of experience indicators for determining a quality level of the customer for the service based upon the plurality of customer experiences and service provider experiences; determining a third set of quantitative values closest to the plurality of pre-defined threshold scores by performing a mapping of the plurality of total score values with the set of a pre-defined quality values for optimizing the first set of comparison values, wherein the mapping is performed by taking an aggregate of the first set of scores and performing a comparison of the plurality of total score values with the set of a pre-defined quality values; and optimizing the second set of comparison values by assigning a first set of weightages to each of the plurality of customer experience indicators by performing an analysis of the plurality of total score values, the plurality of pre-defined threshold scores and the first and the second set of quantitative values for computing a second set of weightages to identify one or more weightage values to be optimized, wherein the first and the second set of weightages comprises one or more weightages assigned to each of the plurality of customer experience indicators.
In an embodiment of the present disclosure, there is provided a system for measurement and deployment of customer experiences, the system comprising one or more processors; one or more data storage devices operatively coupled to the one or more processors and configured to store instructions configured for execution by the one or more processors to: define, by one or more hardware processors, a plurality of experience indicators corresponding to a plurality of customers and service providers; map, by the one or more hardware processors, a set of a pre-defined quality values corresponding to the plurality of customer experience indicators for obtaining a first set of quantitative values indicating a plurality of services experienced by the plurality of customers; compute, by the one or more hardware processors, a first set of scores that are unique and corresponding to each of the plurality of experience indicators for obtaining a second set of quantitative values relevant to the plurality of customer experience indicators, wherein the second set of quantitative values indicate a set of gaps between a plurality of customer experiences and service provider experiences; based on the first set of scores and the second set of quantitative values performing: (i) compute, by the one or more hardware processors, a plurality of total score values corresponding and unique to a plurality of pre-defined threshold scores, wherein each of the total score value is a summation of the first set of scores corresponding to each of the plurality of experience indicators, and wherein each of the pre-defined threshold score is a summation of a set of pre-defined scores corresponding to each of the plurality of customer experience indicators; and (ii) determine, by the one or more hardware processors, a third set of quantitative values based upon the plurality of total score values closest to the plurality of pre-defined threshold scores for optimizing a set of quality values for reducing the set of gaps between the plurality of customer experiences and service provider experiences, wherein the set of quality values indicate a plurality of quality level of customer experiences; determine, by the one or more hardware processors, a second set of scores based upon an analysis of the set of gaps between the plurality of customer experiences and service provider experiences for optimizing a first set of comparison values for reducing the set of gaps between the plurality of customer experiences and service provider experiences, wherein each of the comparison values is determined based on a comparison between a set of service ratings provided by the plurality of customers and received by the plurality of service providers; compute a second set of comparison values corresponding to the plurality of customer experiences based upon an analysis of the plurality of total score values, the plurality of pre-defined threshold scores and the second set of quantitative values to optimize the set of quality values, wherein the second set of comparison values are computed to identify a potential score to be optimized from amongst the set of scores corresponding to each of the plurality of experience indicators for determining a quality level of the customer for the service based upon the plurality of customer experiences and service provider experiences; determine a third set of quantitative values closest to the plurality of pre-defined threshold scores by performing a mapping of the plurality of total score values with the set of a pre-defined quality values to optimize the first set of comparison values, wherein the mapping is performed by taking an aggregate of the first set of scores and performing a comparison of the plurality of total score values with the set of a pre-defined quality values; and optimize the second set of comparison values by assigning a first set of weightages to each of the plurality of customer experience indicators by performing an analysis of the plurality of total score values, the plurality of pre-defined threshold scores and the first and the second set of quantitative values for computing a second set of weightages to identify one or more weightage values to be optimized, wherein the first and the second set of weightages comprises one or more weightages assigned to each of the plurality of customer experience indicators.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
Fig. 1 illustrates a block diagram of a system for measurement and deployment of customer experiences according to an embodiment of the present disclosure; and
Fig. 2 is a flowchart illustrating the steps involved for the measurement and deployment of customer experiences according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS
The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The embodiments of the present disclosure provides systems and methods for measurement and deployment of customer experiences. A Customer Experience (CE) framework is one of a business framework which measures and analyzes a customer perception towards a service provided by a service provider. The traditional methods for the measurement of the customer experiences comprises (but not limited to) management of customer experiences or perceptions, analysis of customer values, loyalty monitoring and measuring customer satisfaction and the deployment may comprise of (but not limited to) a pictorial or graphical representation of one or more processes identified and prioritized, defining measureable objectives and mapping of the customers journey. However while measuring and defining the customers perception, the traditional systems and methods tend underestimate the cultural implications and fail in largest part due to their inability to manage their social implications. Many traditional systems and methods approach the customer experiences objectives without a change management plan, recognize change management symptoms early or respond to these challenges in a timely and deliberate way. Further, due to qualitative nature of the perception, it is practically difficult to understand and have a standard unit of scale for measuring the perceptions received from different customers. The perceptions received changes over time and situation. For example, two people may perceive same things differently and also the same person may perceive same thing differently at different times. Thus, there exist gaps between what the customer expects and what the service provider delivers. Hence, there exists a need for a technology which provides for an accurate quantification and representation of customer’s view or perceptions, for the service provided, from different sources with an equal level of an accurate computation and representation of the customer’s view or perceptions from the service provider’s point of view in order to reduce the gaps with an ultimate objective of optimizing the level of customer satisfaction and revenue potential of a business.
Referring now to the drawings, and more particularly to FIG. 1 through FIG. 2, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
FIG. 1 illustrates an exemplary block diagram of a system 100 for the measurement and deployment of customer experiences. In an embodiment, the system 100 includes one or more processors 104, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more processors 104. The one or more processors 104 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.
The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.
The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.
FIG. 2, with reference to FIG. 1, illustrates an exemplary flow diagram of a method for the measurement and deployment of customer experience according to an embodiment of the present disclosure. In an embodiment the system 100 comprises one or more data storage devices of the memory 102 operatively coupled to the one or more hardware processors 104 and is configured to store instructions for execution of steps of the method by the one or more processors 104. The steps of the method of the present disclosure will now be explained with reference to the components of the system 100 as depicted in FIG. 1 and the flow diagram. In the embodiments of the present disclosure, the hardware processors 104 when configured the instructions performs one or more methodologies described herein.
In an embodiment of the present disclosure, at step 201, the one or more hardware processors 104 define a plurality of experience indicators (C_Xis) corresponding to a plurality of customers and service providers. Referring to Table 1 below, the plurality of C_Xis are indicated in Column B. These C_Xis are based upon capabilities, knowledge, willingness etc. of the plurality of customers. Similarly C_Xis may be defined for a plurality of touch-points. It may be noted that defining the plurality of C_Xis comprises assigning one or more of the plurality of C_Xis defined to one or more of the plurality of touch-points. The plurality of touch-points comprises a start point, an entering point, a leaving point, and an exit point. Further, the plurality of touch-points may indicate an interaction-point being interacted by the customer while availing the service. According to embodiments of the present disclosure, the one or more stages for the service may comprise “customer mining stage”, “assessing customer experience stage”, and “capturing customer experience stage”. Corresponding to these stages, one or more touch-points identified may comprise “start point”, “entering point”, “leaving point”, and “exit point”. It may be noted that the scope of the present disclosure does not restrict defining the C_Xis indicated in column B of table 1 below and the present disclosure may permit a different C_Xis to be defined at the plurality of touch-points corresponding to the plurality of customers and service providers.
TABLE 1
S. No (A) Customer Experience Indicators
C_Xis (B) Weightages (C) Set of values (1-10)
(D) First set of scores / Second set of quantitative values (E) Pre-defined threshold scores (F) Set of quality values (G) Second set of scores (H)
1 Capabilities 10% 4.25 0.43 1

0.49 -0.07
2 Knowledge 10% 4.50 0.45 1 -0.04
3 Willingness 20% 3.75 0.75 2 0.26
4 Attitude 30% 3.50 1.05 3 0.56
5 Tolerance 10% 4.50 0.45 1 -0.04
6 Channel Accessibility 10% 4.00 0.40 1 -0.09
7 Cost of Sales 5% 3.75 0.19 0.5 -0.30
8 Average Handling Time 5% 4.25 0.21 0.5 -0.28
100% 3.925 10

According to an embodiment of the present disclosure, at step 202, the one or more hardware processors 104 map a set of pre-defined quality values corresponding to the plurality of customer experience indicators for obtaining a first set of quantitative values indicating a plurality of services experienced by the plurality of customers. Referring to table 2, the set of pre-defined quality values may be obtained by identifying a set of bands in terms of percentage to be assigned to each of the customer experience level and perception and are indicated in column A.
According to an embodiment of the present disclosure, the one or more hardware processors 104 may then map the set of pre-defined quality values with the C_Xis in table 1 above to obtain a set of quantitative values mentioned in column B in table 2 below. The set of quantitative values indicate (or comprise) a plurality of services experienced by the plurality of customers in quantitative terms. Similarly, the mapping of the set of a pre-defined quality values with the C_Xis may be performed to obtain the set of quantitative values indicating plurality of services experienced by the plurality of customers at each of the plurality of touch-points. Further, the set of quantitative values also indicate the experiences of the plurality of customers in non-quantitative terms as indicated in column C to table 2 below. Finally, the perceptions of the plurality of customers are mentioned in column D which indicate expressions corresponding to the plurality of services experienced by the plurality of customers. For example, for a delightful and luxurious experience, a customer may say “wow”. If the customer gets more than desired experience he or she may say “nice” or “great”. It may be noted that the scope of the present disclosure does not restricts the set of pre-defined quality values or the perceptions to below range or to a set of five bands only and may comprise defining a set of different bands (of different ranges) and the corresponding first sets of quantitative values or a plurality of different perceptions.
TABLE 2
Pre-defined quality values (A) First set of quantitative values (B) Experience level (C) Perception (D)
51%-100% 1 Delight Wow to have
61%-80% 2 Luxury Wow to have
41%-60% 3 More than desired Nice to have
21%-40% 4 Competitive Must
0%-20% 5 Acceptable Must

According to an embodiment of the present disclosure, at step 203, the one or more hardware processors 104 compute a first set of scores corresponding and unique to each of the C_Xis for obtaining a second set of quantitative values relevant to the C_Xis, wherein the second set of quantitative values indicate a set of gaps between a plurality of customer experiences and service provider experiences. Referring to table 1 above, the first set of scores are indicated in column E, wherein each of the score corresponding and unique to each of the C_Xis in column B may be computed using below formula:
C2×D2
For example, for a C_Xis “capabilities” the first set of scores may be obtained in column E of table 1 as 10%×4.25=0.425 or 0.43 (rounded-off). Similarly, for each of the C_Xis the first set of scores have been obtained in table 1. It may be noted that a set of values in column D of table 1 may be computed by obtaining a difference between a value of highest level of quality expected by one or more customers (rated on a scale of 1 to 10) from the plurality of customers and a value of where the one or more customers may be expected to be at delight (rated on a scale of 1 to 10). Therefore, for the C_Xis “capabilities” if the value of highest level of quality expected by the one or more customers is 10 and the value of where the one or more customers may be expected to be more than satisfied is 5.75, it indicates a gap of 4.25 or 4.3 (rounded off). The first set of scores are computed to obtain the second of quantitative values indicating a set of gaps between the plurality of customer experiences and service provider experiences corresponding and unique to each of the C_Xis. The set of gaps comprises of one or more gaps, wherein the one or more gaps may be obtained by a comparison of one or more services experienced by the plurality of customers and the one or more services provided by the plurality of service providers. The set of gaps are computed to determine the difference between the satisfaction or higher than the satisfaction level (like delight) of the one or more customers from the services received by them and the satisfaction or higher level of the one or more customers according to one or more service providers from amongst the plurality of service providers for the services provided by them.
According to an embodiment of the present disclosure, at step 204, the one or more hardware processors 104 compute a plurality of total score values corresponding and unique to a plurality of pre-defined threshold scores, wherein each of the total score value is a summation of the first set of scores (obtained in step 203) corresponding to each of the plurality of experience indicators, and wherein each of the pre-defined threshold score is a summation of a set of pre-defined scores corresponding to each of the plurality of customer experience indicators. For example, referring to table 1 above, the summation of the first set of scores results in a total score value of 3.925 for one of the touch-point. Referring to table 1 again, the pre-defined threshold scores are indicated in column F. The pre-defined threshold scores may be computed based upon the maximum score indicating a highest level of customer satisfaction in the range of 1 to 10 (where 10 indicates the highest positive feedback from one or more of the plurality of customers) corresponding to each of the C_Xis. The pre-defined threshold scores may vary with any variations in the set of weightages in column C. Based upon the maximum score indicating a highest level of customer satisfaction for the C_Xis “capabilities” the pre-defined threshold score (as indicated in column F of table 1) may be computed as:
C1×10=1
Further, the pre-defined threshold scores may be computed for each of the plurality of touch-points. Similarly, the plurality of total score values corresponding and unique to the plurality of pre-defined threshold scores may be obtained by aggregating the first set of scores corresponding to each of the plurality of experience indicators for the plurality of touch-points. Further, the pre-defined threshold scores may also be determined for the plurality of touch-points. It may be noted that the pre-defined threshold scores and the total score values will be unique for each of the C_Xis defined for each of the touch-points based upon the set of weightages that may be assigned to each of the C_Xis for the plurality of touch-points in table 1. Depending upon customer requirements, geography and other factors, the set of weightages that may be assigned to each of the C_Xis may be different at each of the touch-points. For example, at one of the touch-point in a different geographical location like Chennai, cost of sales may be assigned less weightage, for example, 2%, as the customer may prefer less spending and may need more varieties of silk clothes. The C_Xis “knowledge” may be assigned higher set of weightage. Thus the set of weightages, the pre-defined threshold scores the total score values may be unique at each of the plurality of touch-points.
Further, the plurality of total score values and the corresponding unique plurality of pre-defined threshold scores may be computed to reduce a set of gap values between the customer and the service provider and thus improving the level of customer satisfaction. It may be noted that the scope of the present disclosure does not restricts computation of the pre-defined threshold scores and the plurality of total score values based upon the C_Xis indicated in table 1 only. At each of the plurality of touch-points, a plurality of other C_Xis may be considered with a different set of weightages depending upon a plurality of factors like customer’s preference and geographical location for obtaining the pre-defined threshold scores and the plurality of total score values to improve the level of customer satisfaction.
According to an embodiment of the present disclosure, at step 204, the one or more hardware processors 104 further determine of a third set of quantitative values based upon the plurality of total score values closest to the plurality of pre-defined threshold scores for optimizing a set of quality values for optimizing a set of quality values for reducing the gaps between the plurality of customer experiences and service provider experiences, wherein the set of quality values indicate a plurality of quality level of customer experiences. Referring to table 1 above, the third set of quantitative value for the touch-point T1 may be computed as the summation of the first set of scores computed for each of the C_Xis for the touch-point T1 as an aggregate of 0.43+0.45+0.75+1.05+0.45+0.40+0.19+0.21=3.925. Similarly, the third set of quantitative values for each of the plurality of touch-points from T2 to Tn may be obtained based upon the plurality of total score values (where each of the total score value may be obtained as an aggregate of the first set of scores computed for each of the C_Xis) for the touch-points T2 to Tn. Referring to table 3 below, the third set of quantitative values are indicated in column C. The pre-defined threshold score for the touch-point T1 (as computed in step 204) is 10. Similarly, the pre-defined threshold score may be obtained for each of the plurality of touch-points.
Referring again to table 1 above, the computation of the set of quality values may now be considered in detail. Each quality value from the set of quality values may be obtained by taking a mean of the total score value (obtained as the summation of the first set of scores). For example, for the touch-point T1, the total score value is 3.925 and hence the quality value may be computed based upon the total score value and the total number of C_Xis for T1. The quality value for the touch-point T1 is 3.925÷8=0.49 indicated in column G of table 1. Similarly, the set of quality values may be obtained by computing the quality value of each of the plurality of touch-points. Thus, for touch-points T2 to Tn, the quality values corresponding and unique to each of the touch-points may be computed as the total score values for each of the touch-points (T2….Tn) divided by total number of C_Xis in each of the touch-points.
According to an embodiment of the present disclosure, at step 205, the one or more hardware processor 104 may then determine a second set of scores to optimize a first of comparison values for reducing the set of gaps between the plurality of customer experiences and service provider experiences. Referring to table 1 again, the second set of scores (indicated in column H) may be obtained by obtained by obtaining a difference of the each of the first set of scores and the mean of the aggregate of the first set of scores. Thus, the second set of score for the C_Xis “capabilities” may be obtained as 0.425 (0r 0.43 rounded off) -0.49 =-0.065 or (-0.07 rounded off). Similarly, the second set of scores corresponding and unique to each of the C_Xis may be obtained for each of the plurality of touch-points.
The first set of comparison values (indicated below and not shown in any of the tables) may then be determined based upon a comparison and an analysis of the mean of the aggregate of the first set of scores and each of the first set of scores. Therefore, referring to table 1 again for the touch-point T1, first set of comparison values may be derived as those having the score (from amongst the first set of scores) less than or equal to the mean and those having the score greater than the mean. The first set of comparison values may then be obtained and distinguished as:
for the C_Xis ”willingness” and “attitude” 0.75 and 1.05 (having the score greater than the mean); and
for the remaining C_Xis 0.43, 0.45, 0.45, 0.40, 0.19 and 0.21 (having the score less than the mean).
The first set of comparison values may then need to be optimized for reducing the gaps between the plurality of customer experiences and service provider experiences. The optimization of the first set of comparison values may be performed by comparing and analyzing each of the first set of comparison values with the first set of scores in column E of the table 1. Referring to table 1 again, if the quality value or the mean 0.49, it may be noted that the two C_Xis that is “willingness” and “attitude” in column E have the score greater than 0.49. Therefore, the two C_Xis are performing better and need no optimization. However, the remaining C_Xis have the score less than 0.49 with the C_Xis “cost of sales” and “average handling time” having the scores of 0.19 and 0.21 respectively. Therefore, the C_Xis having less score than the mean need further improvement reducing the set of gaps between the plurality of customer experiences and service provider experiences for the touch-point T1. Further, the C_Xis “cost of sales” and “average handling time” may need significant improvement by the service provider to optimize the scores and thereby improve the customer experiences at the touch-point T1.
Similarly, for each of the plurality of touch-points, the first set of comparison values may be computed and identified for optimization to reduce the set of gaps between the plurality of customer experiences and service provider experiences. The optimization of the first set of comparison values may further enhance return on investment by improving the customer experiences. It may however be noted that the scope of the present disclosure does not restricts the computation of the first set of comparison values and their optimization based upon above method or parameters only. It may consider a plurality of other parameters and methods or combinations of one or more methods thereof for the computation and optimization of the first set of comparison values to reduce the set of gaps.
TABLE 3
Touch-points (A) Process (B) Third set of quantitative values (C) Service provider expected values (D) Second set of comparison values (E) C_Xis to be optimized (F)
T1 P1, P3, P7 3.9 (39)% 5 1.1 1, 2, 5, 6, 7, 8
T2 P2, P4, P5 4.5 (45%) 6 1.5 1, 2, 3,4, 7
T3 P6 7.5 (75%) 9 1.5 4, 5, 6, 7
….. ….. …… …. ….. ……
…… …… …… ….. ….. ……
Tn P5, P8 6.5 (65%) 9 2.5 6, 8

According to an embodiment of the present disclosure, determining the third set of quantitative values further comprises performing a mapping of the plurality of total score values with the set of a pre-defined quality values for optimizing the first set of comparison values. The mapping may be performed by taking an aggregate of the first set of scores or the third set of quantitative values, converting each of the aggregate of the first set of scores to a percentage score and then performing a comparison of the plurality of total score values (obtained as the aggregate of the first set of scores for the plurality of touch-points) with the set of a pre-defined quality values. Referring to table 1 above, the aggregate of the first set of scores is 3.925 and the equivalent percentage score will be 39%.
Similarly, for each of the plurality of touch-points the aggregate of the first set of scores and the equivalent percentage score may be computed. For the touch-point T2, T3…Tn the aggregate of the first set of scores and the equivalent percentage scores will be 45%, 75% and 65% respectively. Further, the one or more hardware processors 104 may perform the mapping (by comparing and analyzing) of the plurality of total score values with the set of a pre-defined quality values in table 2. For example, for the touch-point T1, the aggregate of the first set of scores and the equivalent percentage score of 39% may be mapped the pre-defined quality value of 21-40% (column A of table 2). Based upon the mapping, it may be observed that experience level (service experienced) of the one or more customers is “competitive” and thus there is a scope of further improvement at that touch-point T1. Therefore, the one or more service providers may further identify which of the C_Xis it may improve upon, based upon the optimization of the one or more comparison values from amongst the first set of comparison values. For example, if the C_Xis “cost of sales” and “average handling time” were earlier identified for the optimization in the step 204 above, based upon the mapping performed it may be observed that the for obtaining the higher experience level (like “more than desired” or “luxury”), the C_Xis “channel accessibility” and “capabilities” have the scores less than the mean of 0.49 and thus may the next C_Xis to improve upon.
According to an embodiment of the present disclosure, the one or more hardware processors 104 may further compute a second set of comparison values corresponding to the plurality of customer experiences based upon an analysis of the plurality of total score values, the plurality of pre-defined threshold scores and the second set of quantitative values for optimizing the set of quality values. The second set of comparison values are computed for identifying a potential score to be optimized from amongst the set of scores corresponding to each of the plurality of experience indicators for determining a quality level of the customer for the service based upon the plurality of customer experiences and service provider experiences. Referring to table 3 above, the third set of quantitative values obtained based upon the plurality of total score values indicated in column C may be compared and analyzed with a set of service provider expected values in column D.
The set of service provider expected values comprises of a pre-defined value (in the scale of 1 to 10, where 10 indicates a highest level of customer satisfaction) at which the one or more service providers may expect the level of customer satisfaction (or a higher level than the satisfaction level) to be on the basis of the services provided. Referring to table 3 column D again, the set of service provider expected values for the touch-points T1, T2, T3….Tn are 5, 6, 9….9 respectively. It may be noted that the set of service provider expected values (in column D of table 3 above) may be computed based upon the first set of scores. The first set of scores (obtained in the same manner as indicated in step 203) may further be aggregated to obtain the set of service provider expected values indicated in column D of the table 3 above.
Referring to table 4 below, computation of the service provider expected values may now be considered in detail. First, the set of values (column D) may be obtained in the same manner as the set of values in table 1 above (by obtaining a difference between a value of highest level of quality expected by one or more customers (rated on a scale of 1 to 10) from the plurality of customers and a value of where the one or more customers may be expected to be at delight (rated on a scale of 1 to 10). For example, for the C_Xis “capabilities”, the first set of score may be obtained as 20%×3.75=0.75 and the corresponding pre-defined threshold score may be obtained as 20%×10=2. Similarly, for the C_Xis “attitude” and “channel accessibility”, the first set of score and the pre-defined threshold scores have been obtained as 0.90 (the first set of score), 2 (the pre-defined threshold score) and 1.25 (the first set of score) and 5 (the pre-defined threshold score) respectively. The first set of scores may then be aggregated to obtain the service provider expected value for one of the touch-point from amongst the plurality of touch-points. Thus, for the touch-point T1 the set of service provider expected value may be obtained as an aggregate of the first set of scores in column E i.e. 5. Similarly, the set of service provider expected values may be obtained for the plurality of touch-points. It may be noted that the scope of the present disclosure does not restricts computation of the set of service provider expected values based upon the first set of scores only and may further comprise including a plurality of other values or scores obtained by a plurality of other methods or any combination of one or more methods thereof for computing the set of service provider expected values.
TABLE 4
S. No (A) Customer Experience Indicators
C_Xis (B) Weightages (C) Set of values (1-10)
(D) First set of scores (E) Pre-defined threshold scores (F)
1 Capabilities 20% 3.75 0.75 2
2 Knowledge 10% 5.50 0.55 1
3 Willingness 5% 4.50 0.23 0.5
4 Attitude 20% 4.50 0.90 2
5 Tolerance 20% 4.75 0.95 2
6 Channel Accessibility 5% 5.00 1.25 0.5
7 Cost of Sales 10% 1.80 0.18 1
8 Average Handling Time 10% 1.90 0.19 1
100% 5.00 10

Further, each value from the set of service provider expected values may then be compared and analyzed with the third set of quantitative values and a difference may then be obtained to arrive at the second set of comparison values in column E. Therefore, for the touch-point T1, if the one or more service provider expected the level of customer satisfaction (or a higher level than the satisfaction level) to be at value 5 but the level of customer satisfaction is at 3.9 value, the comparison value of the gap is 1.1 (which needs to be optimized). Similarly, for the touch-points T2, T3……Tn, the gaps are 1.5, 1.5……..2.5 respectively.
Referring to table 3 above again, Based upon the second set of comparison values for each of the plurality of touch-points it may be noted that there are corresponding C_Xis to be optimized or improved. Referring to table 3 above again, for example, the second set of comparison values for the plurality of touch-points T1, T2, T3……..Tn are 1.1, 1.5, 1.5…..2.5 respectively. The second set of comparison values may then be arranged in an ascending order in the form of scores to identify a least of the comparison value to be optimized. Therefore, one or more service providers may first identify the touch-point T1 with the score of 1.1 to be optimized. For the touch-point T1, the one or more service providers may then identify the one or more C_Xis to improve upon for optimizing the score of 1.1. For example, referring to table 3 above, the C_Xis 1, 2, 5, 6, 7 and 8 may need to be improved and for the touch-point T3 the C_Xis 4, 5, 6 and 7 may need improvement. It may be noted that the present disclosure does not restricts the C_Xis to be identified and used for computation purposes at each of the plurality of touch-points to above only. The customer or the service providers may identify any number and kind of the C_Xis for computing and reducing the gaps between the one or more customers and the one or more service providers.
According to an embodiment of the present disclosure, the optimizing the second set of comparison values may comprise assigning a first set of weightages to each of the plurality of customer experience indicators by performing an analysis of the plurality of total score values, the plurality of pre-defined threshold scores and the first and second set of quantitative values for computing a second set of weightages to identify one or more weightage values to be optimized, wherein the first and second set of weightages comprises one or more weightages assigned to each of the plurality of customer experience indicators. Based upon the analysis of the plurality of total score values, the plurality of pre-defined threshold scores and the first and second set of quantitative values computed, the one or more of the C_Xi may be identified for improvement. Further, based upon the C_Xis improved, the first set of weightages may be assigned to each of the plurality of customer experience indicators.
The second set of weightages may then be assigned to identify one or more weightage values to be optimized. For example, if the C_Xis “knowledge” had assigned weightage of 10% at the touch-point T1, the C_Xis”knowledge” may have been identified as the C_Xis to be improved upon. Further, at the touch-point T2, the C_Xis “knowledge” may be given the first set of weightage of 20%, that is an extra weightage, as it was identified as one of the C_Xis that needed improvement. Similarly, at the touch-point T3, the C_Xi “knowledge” may then be assigned the second set of weightage as 25% or 7% depending upon whether the C_Xis “knowledge” facilitated reduction in the gap between the one or more customer and the one or more service providers at the touch-point T2. Still further, if the C_Xis “knowledge” needs no further improvement, at the touch-point T4, the one or more service providers may identify another least of the weightages from the set of weightages assigned to each of the C_Xis. For example, if the C_Xis “knowledge” needs no further improvement at the touch-point T4, another of the C_Xis like “channel of accessibility” which may need further optimization may be identified and weightage value may be optimized accordingly by reducing the weightage value of the C_Xis “knowledge”. The present disclosure thus facilitates computing the second set of weightages to identify one or more weightage values to be optimized based upon the first set of weightages assigned to each of the C_Xis.
It may be noted that defining the plurality of customer indicators, mapping of the set of pre-defined quality values, computing the first set of scores that are unique and corresponding to each of the plurality of experience indicators for obtaining the second set of quantitative values relevant to the plurality of customer experience indicators, computing the plurality of pre-defined threshold scores, computation of the plurality of total score values corresponding and unique to a plurality of pre-defined threshold scores and determining the second set of scores for reducing the gaps between customer and service provider experiences are stored in the memory 102 of the system 100.
According to an embodiment of the present disclosure, an example of how the present disclosure facilitates reducing the gaps between the one or more customer and the one or more service providers and thereby improve the productivity may now be considered. The working of the technique may be explained with an example of an apparel manufacturing company. For example, a consumer may be more conscious about the apparel fitting the body so that it not only makes one look appealing but also elegant. A garment manufacturing company needs to constantly evolve in its styling, shape, colour, fittings, cloth feel and stretch and shade. There could be more parameters which may be required to be dynamically adjusted based on the customer’s preferences. To continuously address the customer’s needs and demands the manufacturing plants may need to be dynamically customized too. The technique of analyzing the gaps thus enables the manufacturing plant to configure itself dynamically and produce more effective jeans. The technique collects the inputs from the customer based on the various parameters such as styling, shape, colour, fittings, cloth feel and stretch and shade. The inputs are constantly fed into a centralized system. The centralized system then slices the data and segregates the data logically as per the logical segments. The central system also considers the data for different ages, gender, geographies as well.
The analysis produces a customized set of instructions for each unit on the assembly line. The configured parameters on the assembly unit may then be compared against the analyzed outcomes from the central system. The customization needed in the unit is sent as a set of instructions to the unit on assembly line. This improves the effectiveness of the product. Similarly the instructions from the central system are passed to each unit on the assembly line. The product at the final assemble is a product that is more effective than the regular manufacture. Because the technique enables dynamic customization most of the assembly units are computerized. This reduces the infrastructure from heavy machineries to a more compact and sophisticate computer machines. The information input cycle may repeat number of times and continuously may feed the manufacturing units. Thus the dynamic configuration of the units in manufacturing is more effective and in-turn gains more business for the company.
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, BLU-RAYs, flash drives, disks, and any other known physical storage media.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.