- Видео 96
- Просмотров 206 712
Ye Hu
США
Добавлен 4 дек 2014
yehu.info/
Excel Table Basics
Basic operations of an Excel data table
RUclips users who are not students of UH may download the data file using the following Patreon link (subscription required):
www.patreon.com/posts/data-files-for-71155184
RUclips users who are not students of UH may download the data file using the following Patreon link (subscription required):
www.patreon.com/posts/data-files-for-71155184
Просмотров: 543
Видео
Using regression for prediction & performance measure using ROC (receiver operating characteristic)
Просмотров 1086 месяцев назад
Using regression for prediction & performance measure using ROC (receiver operating characteristic)
Using Excel solver to find optimal warehouse locations (updated)
Просмотров 2,3 тыс.11 месяцев назад
Where is the optimal warehouse location to minimize shipping cost? Why is Lexington, KY an appealing logistic location? RUclips users who are not students of UH may download the data file using the following Patreon link (subscription required): www.patreon.com/posts/data-files-for-72826747
Using Excel evolutionary solver to find the shortest distance for a traveling salesperson (updated)
Просмотров 1,4 тыс.11 месяцев назад
What is the optimal route for a business trip of multiple cities? RUclips users who are not students of UH may download the data file using the following Patreon link (subscription required): www.patreon.com/posts/data-files-for-72826747
Using Excel to Segment Customer in an RFM Framework (updated)
Просмотров 2,2 тыс.Год назад
Method: widely adopted RFM (Recency, Frequency, Monetary) framework for customer relationship management and targeting. RUclips users who are not students of UH may download the data file using the following Patreon link (subscription required): www.patreon.com/posts/data-files-for-72016448
Using pivot table and chart to summarize Major League Baseball (MLB) players birth months (updated)
Просмотров 998Год назад
Excel covered: MONTH, DAY, Pivot Table, Column Chart, Scatter Chart RUclips users who are not students of UH may download the data file using the following Patreon link (subscription required): www.patreon.com/posts/data-files-for-71515312
Using the stand-alone CASA monitor for your exam
Просмотров 1,7 тыс.Год назад
Using the stand-alone CASA monitor for your exam
Using Excel's goal seek to price ad at a target ROI
Просмотров 1,6 тыс.Год назад
Excel function used: goal seek RUclips users who are not students of UH may download the data file using the following Patreon link (subscription required): www.patreon.com/posts/data-files-for-71223065
Optimize salesforce effort allocation
Просмотров 2 тыс.Год назад
Using Excel's solver function demonstrate how to optimize salesforce time allocation. RUclips users who are not students of UH may download the data file using the following Patreon link (subscription required): www.patreon.com/posts/three-excel-data-75231638
Oil Drilling Regression
Просмотров 1,9 тыс.Год назад
Using Excel's regression function demonstrate how to analyze performance of oil drilling rigs and interpret the results. RUclips users who are not students of UH may download the data file using the following Patreon link (subscription required): www.patreon.com/posts/three-excel-data-75231638
Pricing movie tickets
Просмотров 2,4 тыс.Год назад
Using Excel's solver function demonstrate how to optimize a movie theater's ticket price. RUclips users who are not students of UH may download the data file using the following Patreon link (subscription required): www.patreon.com/posts/three-excel-data-75231638
Set up a conjoint analysis in conjointly
Просмотров 2,2 тыс.Год назад
Set up a conjoint analysis in conjointly
Marketing Analytics: New Product Design and Conjoint Analysis
Просмотров 2,2 тыс.Год назад
Marketing Analytics: New Product Design and Conjoint Analysis
Using Excel to run opening box office regression
Просмотров 3,3 тыс.Год назад
Using Excel to run opening box office regression
Marketing Analytics: Predicting Customer Behavior Using Regression
Просмотров 2,5 тыс.Год назад
Marketing Analytics: Predicting Customer Behavior Using Regression
Analyzing experiment data using t-tests
Просмотров 1,4 тыс.Год назад
Analyzing experiment data using t-tests
Marketing Analytics: Promotion and Experiments
Просмотров 1,2 тыс.Год назад
Marketing Analytics: Promotion and Experiments
Using Excel to map Costco and Sam's Club stores in each state
Просмотров 1,3 тыс.Год назад
Using Excel to map Costco and Sam's Club stores in each state
Using Excel evolutionary solver to find the shortest distance for a traveling salesperson
Просмотров 3 тыс.Год назад
Using Excel evolutionary solver to find the shortest distance for a traveling salesperson
Using Excel solver to optimize salesforce effort in detailing
Просмотров 1,7 тыс.Год назад
Using Excel solver to optimize salesforce effort in detailing
Using Excel solver to find optimal warehouse locations
Просмотров 8 тыс.Год назад
Using Excel solver to find optimal warehouse locations
Marketing Analytics: Place Analytics
Просмотров 1,1 тыс.Год назад
Marketing Analytics: Place Analytics
Using solver for product tie-in pricing: Razors and Blades
Просмотров 2,1 тыс.Год назад
Using solver for product tie-in pricing: Razors and Blades
Using Trendline on a scatter chart to find the demand curve
Просмотров 1,7 тыс.Год назад
Using Trendline on a scatter chart to find the demand curve
Thank you for the great educational video! I want to ask a question about the RFM chart. If the number of years a customer has been with us falls below one and becomes a decimal (indicating the customer has been with us for less than a year), when we divide it by the number of purchases, it results in a large number. What should we do in this case? One solution I thought of is to round decimal numbers up to one. Is that correct?
It would make more sense plug in the median purchases per year for new customers, since you have very little information about them and may as well treat them as an "average customer." Without established purchasing frequency, directly dividing the number of purchases by "years" of new customers can be misleading. Rounding it up to one is still arbitrary.
@@YeHu4ps The question that arises here is that a customer may not have reached a full year of lifetime yet, but within that short period, they might have made several purchases, thus creating significant value for the organization. Naturally, if they continue this behavior, they could become one of the loyal customers. Now, if we consider the average, we might miss out on the unique behavior of such customers.
Muchas gracias 🙏🏻🙏🏻🙏🏻
Amazing
This is amazing info, this will help me in my business
bahahaha best teacher ive had. Great intro.
What is the point of Calculating the adjustment rating for Gabriel? If you could just sum product her actual ratings with the similarities. And if you wanted to find what the adjustment was you could just subtract the prediction from the mean. I get that it shows how much weight each movie had on the adjustment...but why is that even important? Once you have a predicted rating you can just use that as the basis of a recommendation.
You can also look up the values with Index(match)
First i would like to say thank you so much for your videos, though im having issues with this one given exactly the same steps CLV per customer is coming out as 270.2 and beginning of year as 310.7 and the 2 dimensional table to include for the interest rate is being stuck on showing the same 15% interest rate values for every column so basically just the single dimensional table but treating every interest rate column as if it was 15%. Any correction or help with this would be greatly appreciated.
Hello Ye why we use this formula
Is this for user-user or item-item collaboration?
@@giselleruiz7531 This is user based collaborative filtering.
how to calculate hoy many warehouses i need for next deploy the location analysys?
I wish you were my professor! Such a precise and clear examples!
Thank you! I just got hired as a Marketing Analyst and this Course will surely help me a lot!
Thanks so much for the video. Can you give me a hint how to apply the video to solve my problem? We have a headquarters' location fixed and we want to find optimal location for several branches.
It sounds like the "branches" are like the "warehouses" in this video. The specific setup depends on what you are trying to optimize.
This video deserves more views and your channel deserves much more subscribers. Thank you so much
Wow, thank you!
How did you pull the original data? I am having trouble finding a place to get the statistics from.
Can this strategy be used in a retail business
Yes, when you sell multiple brands in the same product category. A common example is store brands vs. national brands.
@@YeHu4ps Thanks for the info
Great video. This is what I needed for my sales calls to show ROI on investment. Cheers!
This helped me so much thank you!
Is there any way for me to download the data file aside from patreon? I don't have Patreon, but I would like a copy of the data file.
what is total number of shipments sir , without fixing warehouse how can determine total no of shipments
Warehouse location has nothing to do with shipment. Imagine Amazon, shipments are how many products Amazon sells in each region. An optimal warehouse location would minimize the total cost of shipments.
how do you determine the demand equation?
ruclips.net/video/PHU6pWPpb6w/видео.htmlsi=5QeVHAurILd3xfBw
Hello, thank you for your detailed explanations! What about if there are existing warehouses in the network, and you’re trying to solve for where the next warehouse should be?
In the two warehouses case, fix the location of one of the warehouses (existing). Use both warehouses to calculate the total shipping cost, and then minimize the cost by solving for only the location of the second warehouse.
Got it. Thank you so much!
i need the xls file. ...can you help me..?
It seems kind of pointless to create a bundle when there's no discount involved. What's the point, just buy two separate tickets.
The kind of people who "just buy two separate tickets" have high WTP for both shows. Bundling prompt people who are lukewarm to one show and like the other to buy the bundle. They would likely otherwise only buy one ticket. The point of the bundle is to separate people with different WTP structure.
If I was to hypothetically calculate the "cost" of said DC(s) I can use a cost per mile value on top of the distance × #shipments? Also, I could extrapolate this out to 3, 4, 5, DCs by updating new min distance columns, and changing the solver for new line of lat/long?
Yes, you can factor in the costs of shipping. And yes, you can extrapolate this to more warehouses, similar to what the video does from one warehouse to two warehouses.
Thank you a lot for this video! Just one question, if I have the perfect segment. how to chose/shift or create a specific table for the specific segment? Thank you again.
Whether date( column c ) here is last purchase date ?
Date of the specific transaction for the specific customer.
Very useful . Thanks you
This was really helpful, thank you very much ❤
Hello Ye, Thank you very much for sharing this simplified process of conducting an RFM analysis with us at no cost. I now have a better understanding of the process and feel confident to solve my assignments and even more optimistic about acing my finals. Also, I feel closer to being an Excel power user just by learning from you. Thank you very much, I am delighted because I feel intelligent knowing how to conduct an RFM analysis.
2x speed
good info thanks a lot
Thanks for the info!!
i always suck in this, thanks for video
Never knew how to do this, thanks
I like how your educating your student and people how aren't your student. You give them access to data file through Patreon. That's a very cool model that you created.
Thanks a lot- a comment from two yrs later
So useful! Nice!
Thanks!
Great Video - let's do it
Nice vid solid info!
Very nice and informative too. Nice work.
Helpful stuff! (Don’t sub to this channel please!)
Coool video my friend thank you for sharing ❤😊
I definitely needed to brush up on my excel skills. Thank you for this 🔥🔥
big brain
🧠
excellent video. looking forward to watching more, so keep up the great work!
great timing I was looking for a tool to help advise on next warehouse location.. thanks Ye Hu, great work..