Customer Experience Measurement – Past data or Future (Predicted) data

In the last couple of blogs, we discussed about the three key questions an organization needs to answer to bring in clarity in its CX improvement program as well as assessing and enhancing benefits from the same. These are, 

1. Should Customer experience be measured as stand-alone value or it should be measured in terms of  rate of change in the CX measure? 
2. Should we measure using Direct Customer Feedback or through Indirect (derived) feedback? 
3. Should we use past data or future (Predicted) data?

We deliberated first two questions in the earlier blogs and will discuss the third one in this blog. Refer following links to read through-          https://personalandprofessionalexcellence.blogspot.com/2019/11/customer-experience-measurement-stand.html,

https://personalandprofessionalexcellence.blogspot.com/2019/11/customer-experience-measurement-direct.html

This blog talks about the third question regarding usage of “past” or “future data (predictions)” in order to bring about business improvement using CX. Before we get into the discussion around data and its state, let us look at the typical process of CX measurement and how it helps in business improvements.

Following diagram depicts the process in simple terms.

Organization assesses customer journey for a specific aspect of business and identifies areas where an intervention for improving CX is necessary. It then designs and implements the intervention based on the findings from journey assessment as well as its understanding of customer preferences & behavior. Feedback from the customer is collected through survey / interviews using online / in-person mechanism. If the feedback is positive i.e. CX is good, then organization continues the journey to reap benefits of the intervention. However, if the feedback is mixed or not encouraging or there is no impact at all; organization identifies shortcomings/misses during design / implementation of the intervention and subsequently defines a tweak that would be necessary to address the same. This tweaked intervention is again implemented, and the same cycle follows. If the mistake is such that it cannot be corrected by smaller tweaks in the intervention, then organization abandons the initiative as early as possible to contain the damage it might be creating. The cycle from assessing the feedback till implementation of tweaked intervention easily takes 2-4 months. If the right fix requires more than one tweak, the delay in getting the desired results increases proportionately.  

As we know, CX (as well as related revenue impact) follows a lifecycle similar to product lifecycle as shown in the following diagram. Refer following link to understand detailed discussion around CX lifecycle.   https://personalandprofessionalexcellence.blogspot.com/2019/07/cx-life-cycle-management-for-continuous.html  Once the CX intervention is correctly designed and implemented, it starts showing positive results on experience as well as revenue as shown in the diagram. However, if the intervention is not appropriately designed and customers do not embrace it as expected, the tweak cycle kicks in. After one or two tweaks the CX journey settles in. In such cases the graph for CX shifts to the right (Blue Line in the diagram below) creating a period of uncertainty from the start of first intervention to the implementation of intervention after tweak/s. 

During this period, CX may go down or waiver or may not change at all. The company is busy in finding the right tweak and can be vulnerable for attacks from competition. There are chances that it may lose revenue as well as goodwill with customer base.

There are two challenges faced by company in this endeavor. First one is feedback cycle takes significant time delaying the tweak that might be required to be implemented.  Process of getting feedback from customer has inherent limitations in terms of how frequently you can go to customer and ask their feedback coupled with no guarantee that customer will provide feedback when reached through modes like e-mail or online survey. Organization is pushed into uncertainty zone and there is no easy way to reduce this time. This limitation could be eliminated to some extent by creating multiple sets of customer segment with similar characteristics and reaching out to them at higher frequency; however, the process becomes complicated and does not help reducing uncertainty period by significant proportion.

Second challenge is that we really do not know when the CX lifecycle attains maturity. The trajectory is known but the time period for each zone like growth or maturity is not known and it changes with the specific intervention and its impact. It could be a steep curve up and steep down or it could be slow up and steep downward or it could be steep up and slow down. It is critical for a company to understand the stage of lifecycle CX is passing through. If the CX is in upward climb, organization continues to reap in benefits from the intervention whereas if it is on the downward slope, the organization starts reducing the benefits that it receives through this intervention and may start losing revenue. Organizations wish to elongate the growth stage as long as possible and would like to be in maturity stage forever. However, in reality this does not happen, the impact of the CX intervention weans away at some point of time and CX graph enters in to decline phase. It is critical for organization to plan another tweak or new intervention right in time so that its impact starts showing up by the time the impact from earlier intervention gets in to decline mode. The organization struggles to find this point, it cannot estimate the time it is going to take for any intervention to reach this maturity/decline inflection point (CX fatigue). It is also difficult to measure the CX level continuously to track its progress and estimate the next intervention point leaving organization vulnerable.  

Can we overcome these challenges? – YES we can !!!

The problem could be solved if the organization uses future (predicted) data. Organization can construct a model using indirect feedback mechanism as described in the earlier blog. This model will enable organization to obtain the exact stage of CX at desired frequency like daily. It can also go to a hourly frequency if we have the right model with right parameters and technological capability to manipulate the data collected through these points.

If organization creates a model using various data points from different interventions which depict the life cycle pattern for an initiative and build a capability to self-learn; this could create a tremendous intelligence for the organization to predict what is going to happen in near as well as distant future and when it should be ready for next intervention. The customer behavior is unpredictable and CX movement may not be same as what is predicted or there could be a significant campaign run by competition which is impacting the CX behavior; the organization can get a sense of these movements from the capture of indirect feedback without really waiting for physical feedback and can adjust the projections accordingly. This generates new predictions for organization to re-calibrate their actions on real time basis. The predictions are dependent on the AI/ML based self-learning model. The closer it reflects the reality as well as its ability of mid course correction determines the effectiveness.

In summary

• Mapping the CX value v/s Rate of change of CX can provide the status of CX initiative and direction that an organization should take going forward
• The indirect method to capture CX using parameters available in the organizations business systems wins over the direct data collection mechanisms
• Predicted data can provide organization with inputs which are early and granular than the past data collected from the customers
• The success of the CX backed business improvement depends largely on ability to create the algorithm which could represent the customer behavior using the parameters available for us in the system as well as one for predicting the CX lifecycle. AI/ML can enable organizations to achieve this and can help move ahead of curve in continuously improving CX as well as improving the growth rate for the business.

Customer Experience Measurement – Direct v/s Indirect

In my last blog, we discussed about three key questions an organization needs to answer to bring in clarity in their CX improvement program as well as assessing the benefits from the same. These are,

1. Should Customer experience be measured as stand-alone value or it should be measured in terms of “rate of change” in the CX measure?
2. Should we measure CX using Direct Customer Feedback or through Indirect (Derived) feedback?
3. Should we use past data or future (predicted) data?

We deliberated on whether we should measure the CX in a stand-alone manner or using “rate of change” in my earlier blog and concluded that measuring the “rate of change” will benefit the organization more. It will also help to assess the stage of the CX lifecycle the initiative is passing through and take the appropriate action. You can read the same using this URL--  https://personalandprofessionalexcellence.blogspot.com/2019/11/customer-experience-measurement-stand.html

In this blog, I am going to discuss the second Question i.e. Should we measure CX through Direct Customer Feedback or through Indirect (derived) feedback? First let us understand what we mean by Direct as well as Indirect

Direct Feedback: When a feedback about customer experience is obtained directly from a customer through any kind of Q&A mechanism, it is called a direct feedback. The mechanism could be a physical survey, online survey, meet & greet meetings, telephone, home visits, etc.  We ask customer to rate the product or service and their rating decides the CX level.

Indirect (Derived) Feedback: Indirect Customer feedback is calculated by establishing a relationship between multiple parameters, other than answers from customer and available through customer transactions. Multiple parameters like sale of a specific product before and after the initiative is launched or changes in the traffic on website or customer behavior on website or customer response in various geographies, etc. which are available with the organization can be used to create an algorithm which could represent the pattern of CX behavior and used for measuring the status of CX at any point of time. 

Following table provides a comparison between the two methods on certain criteria which can throw some light on pros and cons of both the methods

	Direct Feedback	Indirect (Derived) Feedback
Customer Involvement	Real feedback through multiple means of interviewing / Survey	Feedback interpretation depends on the definition of relationship between the parameters measured and customer experience. Experimentation is required to build the relationship between multiple parameters and CX
Ease of gathering information	Process of gathering & analyzing feedback is predominantly manual or semi-automatic. Thus, requires longer time	No special information gathering is required. Existing information is used. So, process could be completely automatic
Frequency for measurement of CX	Limitation on how frequently you can go to a customer and reluctance of customer to give feedback frequently.	No limitations and every transaction could be used for calculating CX. The measurement could be near real time.
Coverage – Customers	Due to inherent nature of the process, there is a limitation on how many customers could be contacted and how many really respond. Many times unhappy customers voice their feedback and happy customer do not respond creating incorrect picture.	No limitations, feedback calculation can consider every transaction
Coverage – Bias in selection of respondents	Due to limitations on reaching customers, there is a possibility of introducing a bias in selection of customers to provide feedback and thus influencing the CX	No bias as entire data can be used to give real picture.
Coverage – Time period	Due to longer frequency, the information gathered does not represent the experience for the whole period but typically for the events / transactions just before the survey.	As the data is used continuously, a true overall picture for the respective time period can be achieved
Coverage - Customers / Non-Customers	Typically, the customers who have bought products / services are part of this exercise and customers who have not bought the products / services are not. As a result, we tend to miss out on the experience traits which have made some customer to go away from our products / services	The indirect mode enables us to compile data for those who have not bought the product / services through some of the parameters like "Returns", "Selection of items and removing from shopping cart without a purchase", "Complaints", "Replacements", "footfalls v/s sales", "termination of services", etc. This information is available and could be used for building CX model.
Response time for CX feedback	Understanding CX, deciding action based on it, implementing the same and measuring the impact is a very long process and could easily take 2-3 quarters if not more. It takes time to detect negative trend.	The CX could be measured on a continuous basis. The frequency could be daily or better depending on the algorithm and data collection & crunching capability. Company can get the trend in CX as it is taking place and empowers it to tweak the CX intervention based on the same real time. Negative trend could be tracked early.

Based on the above table, it is evident that indirect method wins the game. Only challenge here is to get the algorithm right. Unless we get the algorithm right, all the measurement is of no use and can lead us to wrong direction. So if the organization can use new techniques in AI/ML to create a self learning algorithm using the past data along with experimentation; organization can really go ahead of curve in continuously improving CX.

Ref :    

•  CX Life cycle management for continuous improvement in Customer Experience and Revenue/Customer https://personalandprofessionalexcellence.blogspot.com/2019/07/cx-life-cycle-management-for-continuous.html
• AI for early detection of Customer Experience Fatigue and effective extension of CX lifecycle https://personalandprofessionalexcellence.blogspot.com/2019/08/ai-for-early-detection-of-customer.html

 

Customer Experience Measurement – Stand Alone v/s Rate of Change

Customer has become king again and is in a position to demand much more beyond better pricing and service from sellers. Sellers are also obliging in multiple unique ways to differentiate themselves in the eyes of customer. Several experiments are being made and significant dollars are set aside for this purpose. The ultimate motive of this push is to improve revenue from existing streams as well as add new revenue streams for the future. The success of the initiatives is measured in terms of how much the needle has moved.

There are two steps in this process; first step is, to define how we would measure customer experience and second step is, how do we connect this CX measure to Revenue movement. This kind of rigor is relatively new to CX and multiple options are being tried by organizations to connect it to revenue. It is not perfected yet, however learning from each experiment is improving the clarity.

An organization will need to answer these three questions to bring in clarity in their CX program as well as measuring benefits from the same.

1. Should Customer experience be measured as stand alone value or it should be measured in terms of rate of change in the CX measure?
2. Should we measure using Direct Customer Feedback or through Indirect (derived) feedback?
3. Should we use past data or future (predicted) data?

I have analyzed first question in this blog and identified possible way forward for companies.

Stand Alone or Rate of Change:

There are a few popular metrics to measure CX today; prominent being NPS and CSAT. Companies have devised ways to capture the NPS / CSAT score at definite intervals and use the feedback from each survey to create next round of initiatives. Some of the companies have built the necessary rigor in to their processes and collect the measurement at regular frequency. However, they still face some challenges like,

• The score is influenced many times by specific performance just before the survey and does not represent the entire period between two surveys 
• The highest frequency of such survey is Quarterly, but Half yearly or yearly is preferred due to investment of time and efforts in executing one cycle for company and customer
• Chances of bias in selection of respondents. Detractors or customers who did not make a purchase are missed out depriving the comprehensive CX level.

These challenges create limitations in terms of how fast one can respond, how comprehensively one can understand the trend as well as how to measure the actual impact on revenue movement.

If we want to understand the customer experience, how it is getting impacted by a specific initiative as well as how it is impacting revenue with minimal delay, observation of Rate of Change in CX is the answer. It provides us with the direction of impact and extent of impact together, which is critical to take the next course of action. Knowledge about success or failure of the initiative helps organization to make a kill or stop the losses, so faster is better. If the trend of CX is positive and rate of change is increasing, the initiative is working and impact is increasing exponentially. If the rate of change is zero, initiative has no impact on CX and mostly will not have any impact on revenue. Declining rate of change signifies decline in impact and possible revenue reduction.

The model in the adjacent figure depicts the status and revenue impact of a CX initiative through out its lifecycle. Typically, a positive burst in terms of increasing CX is seen at the time of introduction of new initiative and the relative impact reduces as the initiative matures. Slowly the impact is nullified and then ventures into negative territory. Once we map our initiative to the quadrant, we can determine our next steps.

Success of this model depends on frequency of capturing CX measure. Faster the capture, better the results.

With the current metrics used i.e. NPS or CSAT, it is almost impossible to capture the customer feedback at higher frequency due to the nature and mechanism of executing such initiatives. However, with the success of experimentation with AI/ML, certain algorithm-based metrics could be created using the data on inputs and outputs available with the company. This metric value could be calculated on daily basis or segment basis or geo basis to see how it is moving to plot it in the right quadrant.

Ref :    

• 1CX Life cycle management for continuous improvement in Customer Experience and Revenue/Customer https://personalandprofessionalexcellence.blogspot.com/2019/07/cx-life-cycle-management-for-continuous.html
• AAI for early detection of Customer Experience Fatigue and effective extension of CX lifecycle https://personalandprofessionalexcellence.blogspot.com/2019/08/ai-for-early-detection-of-customer.html

 

Predictable Customer (Internal) Experience Improvement- CIO Perspective

In my earlier blog, Predictability in Customer Experience Improvement – A Perspective for Grocery Retail  Industry, 

https://personalandprofessionalexcellence.blogspot.com/2019/07/predictability-in-customer-experience.html?_sm_au_=iVV4Rtj4p0NLfDVr, 

We deliberated on how do we use the differentiated-needs-pyramid to understand the needs of different set of customers for a Grocery chain and use it to define the initiatives and create a predictable improvement in the customer experience.

In this blog, we are going to apply the same technique to understand the internal customers for an organization unit and how we can create predictable improvement in their experience through planned initiatives. I have picked IT department as an example, which serves business; enables them to perform their functions effectively and achieve desired business goals. Business team is customer for IT team and IT team strives to provide a superlative customer experience to the business teams through their actions and initiatives. CIO being leader of IT unit can use this differentiated-needs-pyramid to understand his/her customer better and start planning on various initiatives to move the experience to higher level. The customer in this case, “Business Team”, is not a single block with common needs across, but have multiple segments with each one having unique needs as well as expectations from IT department. Combination of how CIO and team satisfies them and reinforces specific level again and again through multiple actions across these segments determines the experience perceived by customers. In this blog, I have considered two segments within the business team i.e. “Business Leaders” and “Business End users” to give further clarity.

The differentiated needs pyramid which has five levels starting from physiological needs to safety needs ultimately self-actualization, gets translated in to pyramid shown in the adjacent fig. The levels get translated as,  

1.     Availability of IT Support

2.  Comprehensive Coverage and Secure Landscape

3.   Business Feels that IT dept understands its needs well

4.    Business is proud of its IT

5.    Business Feels Nirvana

Each level is interpreted by different segments differently e.g. Business End users are predominantly concerned about the product / application they are using and if they are getting what they need from that application, they express greater satisfaction. Their experience does not change if the application used by some of their peers is not as effective/ useful giving them hard time. Whereas leaders would look at entire gamut of applications supporting their business and any one not servicing the needs will create a negative experience for them. Therefore, an action of fixing a problem in one application, could result into significant improvement of experience in a section of customers, no change for a section of customers and marginal change for another set of customers. Similarly Cost of IT is very important for leadership and not so for the Business End user community. Ease of use is more important to End user community than the leadership team, thus delivering a differentiated impact on customer experience after every action is taken.

Another aspect is interpretation of the level by segment. Let us take example of level 4, “Business is proud of IT”. For Leadership segment, it means they get recognized in their peer groups for using specific technology, products or something their IT team has enabled to do which has enhanced their business or made it more effective. The end user community will be proud of their IT if they get to work in the latest technology and opportunity to learn the latest and greatest in their area.

Following diagram shows the interpretation of the needs for two segments of the internal customers i.e. Business Leaders and Business End users for your reference

In real life, Business End user segment could be split in to some more like Finance End Users, End users on field (sales/Delivery/Service), End users in Manufacturing plant, etc. each group has a unique perspective on the IT support they need. Many more segments could be added.

There is a significant pull on every organization to go digital, implement IOT/AI/ML based solutions to address new business challenges. Let us take a situation where an organization wants to implement a customer facing application using Image recognition technology. It also decides to implement an automation solution in the warehouses so that it can create some funds which could be used for the new project. Let us examine the impact of these actions on customer experience for various segments 

1   Business Leadership – For this segment, IT is creating a futuristic solution for their business problem and has a potential to increase revenue. Coupled with it, IT is also working on another project which could bring in savings -- the action is hitting level 4 and 5 in positive way.

2     End User community in warehouse – This community has no connection with customer facing application and hence that intervention does not have any impact on their customer experience; Where as they are directly affected by the warehouse automation project and there is a possibility of negative impact due retrenchment possibility.

3   End User community in other departments like Finance, sales, etc. – No impact

Now, CIO can identify the impact on each customer segments he /she is servicing and map them together. The big picture would clearly signal if there are any proactive steps necessary to maintain the experience level as it is or is there a need to create another parallel initiative to maintain the experience at the same level in specific customer segment. This structured approach will help CIOs manage their customers well and maintain their experience in a continuous improving cycle.

Another way to use this approach is to map the experience levels of each customer segment at any time and then plan specific actions to improve specific customer experiences at specific levels for specific segments. Periodic mapping and measurement of customer experience for various segments will help identify right interventions, reduce wastage and create predictable improvement in Customer experience.

Ref : Customer Experience Improvement using Maslow’s theory   https://personalandprofessionalexcellence.blogspot.com/2018/03/customer-experience-improvement-using.html

AI for early detection of Customer Experience Fatigue and effective extension of CX lifecycle

As we discussed in my previous blog, CX Lifecycle Management for continuous improvement in Customer Experience and revenue/customer, 

 https://personalandprofessionalexcellence.blogspot.com/2019/07/cx-life-cycle-management-for-continuous.html?_sm_au_=iVVWwvDnr4MNHqqF,  Customer experience follows life cycle similar to product life cycle and we can extend this lifecycle through right interventions at right time. We also discussed that time for intervention is critical and determined by the time when Customer Experience Fatigue sets in. Just to refresh, Customer Experience Fatigue is defined as the time and stage when the experience, which once thrilled customers does not excite them anymore and they start looking for something more, something different. This provokes customers to try out a different experience offered by competitors and company start losing customers along with associated revenue.  The fig. below depicts the CX lifecycle and the stage where Customer Experience Fatigue Kicks in.

It is critical for any company to identify this point in the lifecycle of their CX initiative. Earlier the detection, higher the time available for the company to design and introduce new initiative for extension the life cycle. Any time before position “A” would be a good time. “B” is the tipping point where the decline starts. If company is not able to introduce the intervention before this, reviving the life cycle starts becoming difficult. Once it crosses “C”, the efforts for reviving the life cycle grow exponential and slowly it becomes impossible to revive the life cycle and company sees disproportionate decline in customers as well as revenue.

Critical questions that come to mind are,

·         Can we detect this fatigue?

·         Can we predict it much before it sets in?

·         Do we know reasons for the fatigue which we can address in the next intervention?

The answer is YES for all the three questions. I believe that the recent developments in AI & Machine Learning can help us to detect this fatigue earlier than it sets in. Let us look at two scenarios, first a physical intervention at the store location and second, an online intervention through the website carried out by the company to improve their customer experience and see how AI could be used to predict CX fatigue before it sets in.

Instore Intervention: Let us assume that a Retail / Grocery store introduces a “Creche for pets” at its stores so that the customers can leave their pets at this creche and do their shopping without any worries or time limits. To sweeten this initiative, the stores does not charge any money for this service for all their loyalty customers for certain time (could be little more than the average time taken by a customer to shop in the store so that he/she can do some small errands beyond grocery shopping also) and charge fees beyond this time. Customers who do not have loyalty card, either can create one on the spot or can pay a deeply discounted fee per hour.

The idea here is that the customers can spend time leisurely in the store and can browse through additional isles/ products and thus buying something which he/she does not buy normally increasing the revenue. This initiative will also attract new customers to the store and generate additional revenue. The incentivization through Loyalty cards (which is typically free) will help us collect/analyze the data about customers, their buying patterns, choices, other habits like typical basket of customer during every trip and many more. 

Proof of the pudding is in eating, and ultimate reason for introduction of intervention is to increase revenue. So, if revenue behavior can be modelled, one can predict the future behavior, thus give an early indication of fatigue. The model could be built using input parameters like footfalls in pet creche, existing v/s new customers, time spent in the shop by customers using pet creche or otherwise, shopping basket of loyal customers using pet creche and before pet creche was introduced,  Shopping preferences and time spent in different aisles,  age, gender, race, fitness, etc. obtained from loyalty and computer vision through multiple cameras in the store. Overall revenue can be considered as output. The system can create Neural Networks to build the relationship between various input variables described above & Revenue and tweak it continuously as it learns. It will help predicting the revenue trend. If this model is complemented with Information from social media interactions, the model could also factor in the impact of social media chatter on the experience and ultimately on revenue.  

Website intervention: Let us assume that a retailer wants to introduce an online only clothing range for adults and young adults (male and female) and creates a feature on the website using computer vision so that the customer can try out a dress virtually. With click of button, they can see the image of them wearing the selected dress as if they are trying out the dress and looking themselves in the mirror. Company also wants to provide a special service at a charge to alter the dress to some extent and create a super fit dress for the customer at much cheaper price than a tailored one. Silver lining could be added through a discount for trying the dress virtually before buying it through the site.

This will eliminate the hesitation in the minds of customer of online buying without trying it out as well as almost eliminate the hassle involved in return/exchange. A BIG relief for customer and lots of savings for the company. Expectation is that after a big splash in the media and social media regarding this intervention, it is expected that existing customers will try this new feature and buy some more cloths. It will also attract new customers who are attracted to the convenience and features provided by this intervention and buy new cloths. Revenue will show a positive movement.

Similar to earlier example neural networks could be created using inputs like cloth purchases after using the feature and without using the feature, customers purchasing cloths using this feature v/s those otherwise, Customers extending their purchases beyond cloths to other items available on the web site, age, race, gender, region, number of trials before a buy, etc. and out put as revenue from online cloth sales. This will help predict the revenue behavior in future to identify the fatigue in advance.

Once the company is able to predict the CX fatigue, they can get another intervention ready for introduction at an appropriate time. Company actually can build another algorithm using results of multiple interventions and their impacts vis a vis customer characteristic to identify the features that the next intervention should contain or if the intervention has certain features, how will it impact the revenue.

In summary, Customer experience has a life cycle of Introduction-Growth-Maturity-Decline. Every intervention created by company will flow through these phases and will stop giving any results in due course. An injection of new intervention at right time can restart the life cycle and enable companies to maintain higher customer experience as well as higher revenue per customer. AI can be very useful in predicting the intervention point early through tracking of CX fatigue as well as defining features for next intervention. Thus, company can continuously extend the CX lifecycle and maintain positive trend in revenue.   

Ref for reading:

Customer Experience Improvement using Differentiated Needs Pyramid (Maslow’s theory)

https://personalandprofessionalexcellence.blogspot.com/2018/03/customer-experience-improvement-using.html

Predictability in Customer Experience Improvement – A Perspective for Grocery Retail Industry

https://personalandprofessionalexcellence.blogspot.com/2019/07/predictability-in-customer-experience.html?_sm_au_=iVV4Rtj4p0NLfDVr

CX Life cycle management for continuous improvement in Customer Experience and Revenue/Customer

Customer Experience is like a product created for customers and it displays a lifecycle similar to product life cycle. It has the four stages of Introduction, Growth, Maturity and Decline. During Introduction phase, a new initiative is implemented by an organization which starts getting noticed by customers through the experience they get during various transactions with the company. The pace of receiving a new customer experience is slow and sporadic. People start talking about this new experience in person as well as on social media and the rate at which customer throng to get this experience increases significantly. Advertisement by the company about the change also helps picking up the rate.

This is the Growth phase during which a large section of customer are trying out this new experience. The hype creates a pull for new customers to try out the new experience and presents significant potential for the company to acquire these customers and retain them. More customers connect with company, do more and more transactions and provides a positive impact on the overall revenue as well as revenue per customer. When the initial euphoria of new experience is over, the customers start expecting this experience to be available and the speed of new customer addition slows down, the customer transaction volume remains steady. We are now in Maturity phase. This phase also keeps customer experience more or less at same level. This phase also faces a challenge in terms of competition trying to start initiatives to give same or better experience. The customers are in two minds but most of them continue. How ever some of the customers start building a customer experience fatigue i.e. the experience, which thrilled them some time ago does not excite them anymore and they start looking for something more, something different. They then start losing interest and try out a different experience offered by competitors and company start losing customers along with the revenue. The decline begins. If company does not take any action, the decline continues and company will continue to lose its customer and revenue. 

A few things that needs to be addressed by a company to grow their business. First; Growth phase should be short and rapid where the acquisition of customers as well as customer revenue occurs fast in a short period of time and second; Maturity phase should be stretched as long as possible so that company is able to maintain high level of customers and revenue over a period of time. Ideally company should be able to create another intervention at the right time so that a new life-cycle with new experience starts and impact of decline phase is arrested before it begins. Company needs to acquire mastery over two aspects

1                     Identification of time when new initiative needs to be introduced

2                     Creating a specific new initiative every time so that the cycle continues

The right time for intervention could be determined through measuring customer experience fatigue. As soon as customers provide indications of fatigue, it is time for intervention. The signs of fatigue are stagnant revenue, stagnant rate of new customer addition and introduction of similar experience initiatives by competition. If a company regularly goes through the customer numbers and revenue numbers for specific category (where the initiative was implemented) it can find the trend and catch the moment when a slight declining trend is observed. It can also watch the social media to understand chatter about its experience initiative and initiatives by competition to see if it is going to trigger the decline phase for its initiative.

How do we identify what kind of intervention to be introduced? Intervention should address the right needs which are near and dear for the specific customers who are experiencing fatigue. For which company can use the differentiated needs pyramid method, where in we can create a pyramid with differentiated needs of customer that we are trying to fulfill. We create such a pyramid for our segment of customers and map the existing initiatives against various needs. This will provide us the white spaces where company has not addressed, and competition is trying to address. Then the company can create an intervention targeting specific needs/white spaces so that the interventions are effective and provide the desired outcome of restarting the cycle. The following blogs provide detailed methodology for creating a differentiated needs pyramid for specific customer segment. 

Customer Experience Improvement using Differentiated Needs Pyramid (Maslow’s theory) --https://personalandprofessionalexcellence.blogspot.com/2018/03/customer-experience-improvement-using.html

Predictability in Customer Experience Improvement – A Perspective for Grocery Retail Industry ---https://personalandprofessionalexcellence.blogspot.com/2019/07/predictability-in-customer-experience.html?_sm_au_=iVV4Rtj4p0NLfDVr

In summary, Customer experience has a life cycle of Introduction-Growth-Maturity-Decline. Every intervention created by company will flow through these phases and will stop giving any results in due course. An injection of new intervention at right time can restart the life cycle and enable companies to maintain higher customer experience as well as higher revenue per customer.