Customer Effort Score (Score) Dynamic Dashboard Templates [Basic, standard & pro] including Key Driver Analysis. Save up to 80% time on analysis of CES Survey. Access here: selar.co/m/MSurveyPoint
Connect with me if you need support on the following services Email: msurveypoint@gmail.com Book appointment here 👉 calendar.app.google/MMasAbZPDuwCCux76 1. Survey questionnaire design 2. Online survey scripting 3. Survey data Analysis 4. Research report writing 5. Data analysis in Excel 6. Survey Dashboard in Excel 7. Survey Dashboard in Power Bi The scope of my services includes but not limited to the areas of Market Research, Customer Experience Research, Product Research, User Experience Research, Employee Engagement Surveys, NPS, CSAT & Voice of the customer, Monitoring & Evaluation, etc.
Let me know if you are on Upwork. I would be glad to assist with optimizing your Upwork profile. thank you for this video, analyzing survey data has been a grey area for me until I got a gig to train some folks so I needed to bite the bullet.
I finally found someone who in very clear manner illustrated this concept. My Question: What approach would you use to predict Customers who would return, I know there is a term for that analysis but cant seem to remember now. Ps: I sent you an email a while ago. Cheers
Thanks for the feedback. Ideally, the surveys alone only help you understand & estimate customer attitudes and their likely behaviours this could influence (E.g. A customer who feel you care about them vs competition could be more likely to repurchase or recommend others to you). To some extent surveys can help you estimate the proportion of your customers who feel positively about your brand and their intention to repurchase, However, their action to actual repurchase in the future could change and is driven by so many factors - including past purchase patterns & behaviours. So to effectively predict who would return and who is likely to churn, you will need to consolidate the survey data with the Operational data of your customers (like past purchases, amount, frequency of purchases,....etc. that) 1. Then build a model using these covariables to predict the retention probability of each customer. Note: Not all your customers will ever share their feedback. So you will need to apply the appropriate missing data approach for those who do that have survey data points. 2. Or do cohort/cluster analysis that help you identify the common behaviours of customer who are likely to return or churn. 3. Using this insights you can then tailor communications or target the right customers with the appropriate messaging or resolve the issues that could be driving their decision to churn...... and maintain what is driving retention behaviours Lastly as new customer data come in and more customer data gather, you can keep updating the model accordingly as it trained on additional data
Hi...I´m following along...at 17:34 on "Values" you left the "Top-2-box score"....should it be Bottom-2-box score....just to make sure if it was a mistake or i'm missing something...Thanks!
Here is the sample data I used in the video docs.google.com/spreadsheets/d/16iQHE29YCn6WBPiZVORZZ1emdkf2d_lC/edit?usp=sharing&ouid=117355798969062583983&rtpof=true&sd=true
@@jaunitaekuehettah1254 Hi Jaunita, Thanks for reaching out. Send me an email below with details of your requests. Please add timeline. Connect with me if you need support on the following services Email: msurveypoint@gmail.com Book appointment here 👉 calendar.app.google/MMasAbZPDuwCCux76 1. Survey questionnaire design 2. Online survey scripting 3. Survey data Analysis 4. Research report writing 5. Data analysis in Excel 6. Survey Dashboard in Excel 7. Survey Dashboard in Power Bi The scope of my services includes but not limited to the areas of Market Research, Customer Experience Research, Product Research, User Experience Research, Employee Engagement Surveys, NPS, CSAT & Voice of the customer, Monitoring & Evaluation, etc.
Customer Effort Score (Score) Dynamic Dashboard Templates [Basic, standard & pro] including Key Driver Analysis. Save up to 80% time on analysis of CES Survey.
Access here: selar.co/m/MSurveyPoint
Connect with me if you need support on the following services
Email: msurveypoint@gmail.com
Book appointment here 👉 calendar.app.google/MMasAbZPDuwCCux76
1. Survey questionnaire design
2. Online survey scripting
3. Survey data Analysis
4. Research report writing
5. Data analysis in Excel
6. Survey Dashboard in Excel
7. Survey Dashboard in Power Bi
The scope of my services includes but not limited to the areas of Market Research, Customer Experience Research, Product Research, User Experience Research, Employee Engagement Surveys, NPS, CSAT & Voice of the customer, Monitoring & Evaluation, etc.
Let me know if you are on Upwork. I would be glad to assist with optimizing your Upwork profile.
thank you for this video, analyzing survey data has been a grey area for me until I got a gig to train some folks so I needed to bite the bullet.
You are a life saver!!
I finally found someone who in very clear manner illustrated this concept.
My Question: What approach would you use to predict Customers who would return, I know there is a term for that analysis but cant seem to remember now.
Ps: I sent you an email a while ago. Cheers
Thanks for the feedback.
Ideally, the surveys alone only help you understand & estimate customer attitudes and their likely behaviours this could influence (E.g. A customer who feel you care about them vs competition could be more likely to repurchase or recommend others to you).
To some extent surveys can help you estimate the proportion of your customers who feel positively about your brand and their intention to repurchase,
However, their action to actual repurchase in the future could change and is driven by so many factors - including past purchase patterns & behaviours.
So to effectively predict who would return and who is likely to churn, you will need to consolidate the survey data with the Operational data of your customers (like past purchases, amount, frequency of purchases,....etc. that)
1. Then build a model using these covariables to predict the retention probability of each customer. Note: Not all your customers will ever share their feedback. So you will need to apply the appropriate missing data approach for those who do that have survey data points.
2. Or do cohort/cluster analysis that help you identify the common behaviours of customer who are likely to return or churn.
3. Using this insights you can then tailor communications or target the right customers with the appropriate messaging or resolve the issues that could be driving their decision to churn...... and maintain what is driving retention behaviours
Lastly as new customer data come in and more customer data gather, you can keep updating the model accordingly as it trained on additional data
Hi...I´m following along...at 17:34 on "Values" you left the "Top-2-box score"....should it be Bottom-2-box score....just to make sure if it was a mistake or i'm missing something...Thanks!
Thanks for this.
Missed that. The column label should have been Bottom-2-box too as the tittle.
Values are Bottom-2-box, anyway.
Good job mate !
Thanks Anthony.
Glade this helps.
Your support is much appreciated.
Here is the sample data I used in the video
docs.google.com/spreadsheets/d/16iQHE29YCn6WBPiZVORZZ1emdkf2d_lC/edit?usp=sharing&ouid=117355798969062583983&rtpof=true&sd=true
Hey dear I have an assignment would you be able to help me ?
@@jaunitaekuehettah1254
Hi Jaunita,
Thanks for reaching out.
Send me an email below with details of your requests. Please add timeline.
Connect with me if you need support on the following services
Email: msurveypoint@gmail.com
Book appointment here 👉 calendar.app.google/MMasAbZPDuwCCux76
1. Survey questionnaire design
2. Online survey scripting
3. Survey data Analysis
4. Research report writing
5. Data analysis in Excel
6. Survey Dashboard in Excel
7. Survey Dashboard in Power Bi
The scope of my services includes but not limited to the areas of Market Research, Customer Experience Research, Product Research, User Experience Research, Employee Engagement Surveys, NPS, CSAT & Voice of the customer, Monitoring & Evaluation, etc.