I have Customer ID, Product ID, and Industry columns. For example:
**CustomerID ProductID Industry**
1 A Entertainment
1 B Entertainment
2 A Entertainment
3 A Entertainment
4 A Entertainment
4 B Entertainment
4 A Cleaning
I have a grid set up with industry for my columns and Product ID for my rows.
For example,
Entertainment Cleaning
A
B
I'm trying to find the % of customers in a specific industry purchasing each product.
In other words the (number of unique customers by Product ID)/(Unique customers in each industry).
The numerator is: COUNTD([Customer ID])
The denominator is throwing me off. I'm not sure how to get unique customers in a segment.
In the example above, product A has 4 unique customers in the Entertainment industry, and Entertainment has 4 unique customers for all products, so the calculation for product A would 4/4 or 100%.
But product B would be 2 unique customers / 4 total unique customers in Entertainment or 50%.
Does anyone know how I create the calculation for the denominator.
COUNTD([Customer ID])/???
Related
I am doing campaign analysis and the objective of campaigns is to convert lead into customer. I want to see how many leads are exposed to how many campaigns in terms of percentages before they convert to customer.
Below is the sample data where there are four unique lead.
abc has seen three campaigns, efg has seen two campaigns and so on and so forth
I want to show in pie chart may be something like below in tableau where out of 4 leads 2 leads has seen 1 campaign each so 50%, 1 lead has seen 2 campaigns so 25% and 1 lead has seen 3 campaign so another 25%
First of all you have to find how many campaigns have been exposed for each Lead, Count Campaign:
{ FIXED [Lead id] : COUNT([Campaign])}
Since you want to breakdown your pie with this value, you need to convert this calculated field into a dimension.
In order to calculate the % you're looking for, you need just an additional step; Metric:
COUNTD([Lead id]) / attr({ FIXED : COUNTD([Lead id])})
Doing so you're going to compare each value to the total distinct value of your Lead (4)
Is there any API that allows to query the number of ratings from the Apple App Store?
I know that http://itunes.apple.com/rss/customerreviews... can be used to get a list of reviews. But since a rating (selecting stars 1-5) does not require to write a review (additional text added to the rating), the number of reviews is not the same as the number of ratings. An app could have hundreds of ratings without having a single review.
I know that one can use http://itunes.apple.com/lookup?id=SomeID to get the total number of ratings and the average rating of the current version, e.g. "100 ratings with an average of 4 stars".
But I am looking for a solution to get the number of different rating, e.g.
60 x 5 stars
20 x 4 stars
10 x 3 stars
7 x 2 stars
3 x 1 stars
Is there any (more or less) official way of getting these concrete star ratings without reverse engineering the iTunes protocol and without using any paid third party APIs?
I have retail transactions data set. some of the attributes are CUSTID, BILL_DT, ITEM_Desc, VALUE. I want to classify the custid as churn y or n. Should i use the days between last purchase date till now as a criteria to classify? Can i say anything beyond 180 days that customer has churned? What is the criteria which the big retailers like costco, walmart uses?
Thanks,
From the transaction data, you could extract history consisting of a sequence of the pair (time elapsed since last transaction, amount spent in current transaction). If you know which customers have actually churned, you could build a predictive model using the Markov Chain classifier.
https://pkghosh.wordpress.com/2015/07/06/customer-conversion-prediction-with-markov-chain-classifier/
In Paypal I want to give a discount for total quantity ordered of multiple items. For example 3 pcs of Item 1 and 2 pieces of Item 2 qualify for a 10% discount because a total of 5 pieces were ordered
Why don't you call their support? They will give you simple, fast and correct solution.
I want to find the inter annotator agreement for few annotators.
Annotators annotates few categories (out of 10 categories) for each subjects.
For e.g. there are 3 annotator , 10 categories and 100 subjects .
I am aware about http://en.wikipedia.org/wiki/Cohen's_kappa (For two annotators) and http://en.wikipedia.org/wiki/Fleiss%27_kappa (for more than two annotators) inter annotator agreement but I realized that they may not work if user annotates more than one category for any subject.
Do anyone has any idea for determining inter annotation agreement in this scenario.
Thanks
i had to do this several years back. i cant recall how exactly i did it(i dont have code anymore) but i have a worked example to report to my professor. i was dealing with annotation of comments and have 56 categories and 4 annotators.
note:at the time i need a way to detect where annotators most disagree so that after each annotation session they can focus on why they disagree and set out reasonable rules to maximize this statistic. it worked well for that purpose
Let's assume A-D are annotators and 1-5 are categories. This is a possible scenario.
A B C D Probability of agreement
1 X X X X 4/4
2 X X X 3/4
3 X X 2/4
4 X 1/4
5
A tags this comment as 1,2,3,4 B->1,2,3, and so forth.
For each category the probability of agreement is calculated.
Which is then divided by the number of unique categories tagged for that particular comment.
Therefore for the example comment, we have 10/16 as annotator's agreement. This is a value between 0 and 1.
if this doesnt work for you then (http://www.mitpressjournals.org/doi/pdf/10.1162/coli.07-034-R2) pg-567, which was referenced by pg-587 case study.
Compute agreement on a per-label basis. If you treat one of the annotators as the gold standard, you can then compute recall and precision on label assignments. Another option is label overlap, which would be the proportion of subjects where either annotator assigned a category where the both assigned it (intersection over union).