I'm admittedly new to access so forgive me if there is a simple solution. I have had difficulty searching for an answer as I'm not sure how this needs to be worded...
I have a database containing hundreds of thousands of invoices. I have a query that searches for invoices of the same amount on the same day by the same vendor (with different invoice numbers).
I group by this criteria as well as a count > 1 to display possible duplicates. I'd like to see each record displayed, but it only shows the first invoice number since if I were to group by invoice, the count would be 1 and nothing would get pulled..
I'm sure there is a better way of doing this to achieve the results that I want,. But I am at a loss. If there is further information required to assist, I'll provide what I can.
Thank you.
I have a dataset with a list of customers and their product preferences. Basically, it is a simple CSV with a column called "CUSTOMER" and 5 other columns called "PRODUCT_WANTED_A", "PRODUCT_WANTED_B" and so on.
I asked these customers if they were interested to know more about a particular product, and answers could be simply YES or NO (1 or 0 in the dataset). The dataset can be downloaded here. Obviously, there will be customers with many different interests, based on the mix of their YES or NO in these 5 columns.
My goal is to understand which customers are similar to others in such interests. This will help me manage an agenda of product presentations and, in each meeting, I would like to understand the best grouping for it. I started with a hierarchical plot like this:
customer_list <- read.csv("customers_products_wanted.csv", sep=",", header = TRUE)
customer.hclust <- hclust(dist(customers_list))
plot(customer.hclust, customer_list$CUSTOMER)
library(rect.hclust)
rect.clust(customer.hplot,5)
This is the plot I got, asking for 5 clusters:
Tried the same, but with 10 clusters:
Question 1: I know it's always hard to tell, but looking at the charts and dataset, what would be your 'cut' to group customers? 5? 10?
I was reviewing the results, and in the same group, I had CUSTOMER112 with 1,0,1,0,1 as their preferences together with CUSTOMER 110 (1,1,1,1,1), CUSTOMER106 (1,1,1,1,0) and so on. The "distance" can be right, but in a given group I have customers with some relevant differences in their preferences.
Question 2: I don't know if it's a case of total ignorance about clustering, the code I used or even the dataset. Based on your experience, what would be your approach for the best clustering in this case?
Any comments will be highly appreciated. As you see, I did some efforts, but still in doubt.
Thanks a lot!
Ricardo
All answers were important, but #Ben video recommendation and #Samuel Tan advice on breaking the customers into grids, I found a good way to handle it.
The video gave me a lot of insights about "noisy" variables in hierarchical clustering, and the grid recommendation helped me think on what the data is really trying to tell me.
That said, a basic data cleaning process eliminated all customers with no interests in any products (this is obvious, but I didn't pay attention to it at first). Then, I ignored customers with a specific interest (single product). It was done because these customers wouldn't need to attend the workshop series I'm planning (they just want to listen about one product).
Evaluating all the others, interested in more than one product, I realized the product mix could point me to a better classification. From there, I grouped customers into 3 clusters: integration opportunities (2 or 3 products), convergence opportunities (4 products) and transformation opportunities (all products).
Now it's clear to me which customers I should focus on for my workshops, and plan my post-workshop sales campaigns leveraging materials that target each customer group (integration, convergence, transformation).
Thanks for all the advices!
Ricardo
I need some help with the next question:
I have a select that I want to show but the result is shown in several pages because the select contains over 100 rows.
I want to show two or three tables in the same page with these 100 rows but I don't know how to do it
I appreciate any kind of help.
Thanks in advance
There is no feature exists as such in Pentaho Report Designer.
JIRA new feature ticket is there whose status is still
Unresolved
http://jira.pentaho.com/browse/PRD-2107
According to the ticket it has been moved to Backlog to be developed in Version 6.0.0
I'm looking to make a modification in the logic where an invoice is made from a Sales Order.
In other words, as soon as the Sales Order status changes to "Invoiced", I want to be able to add some additional code there that updates something else.
I'm having a lot of trouble finding the class where this is performed. Can somebody point me in the right direction? It would be greatly appreciated.
Thanks.
The classes you are looking for are
\Classes\SalesFormLetter_Invoice - For interacting with SalesEditLines
\Classes\SalesInvoiceJournalCreate - Creating journal data
\Classes\SalesInvoiceJournalPost - Posting a journal
\Classes\SalesInvoiceJournalPrint - Printing
\Classes\SalesFormletterParmDataInvoice - Creating parm data
The specific method you probably want is \Classes\SalesInvoiceJournalPost\postJournalPost, which is executed after the journal has posted from \Classes\FormletterService\run#91
Refer to this whitepaper for some more information:
http://technet.microsoft.com/en-us/library/hh272871.aspx
I have a client who has suggested laying out a long list of categories in a custom order. The order is to be decided by them based on product items they sell the most etc.
I tend to disagree and feel that people browsing the internet prefer to search lists of categories that are in alphabetical order or sorted by something they can take reference of such as a date.
I would like to know others thoughts on this and it would be appreciated if anyone could point me in the direction of any open source surveys that have been taken in this area.
Thanks
Ben
What a silly stance to take regarding a simple customer request. Allow for both orderings, and other ones too. There is no survey that will demonstrate that the client is wrong as they are - by definition - correct.
Code that allows for different orderings has greater utility anyway, and real user data will be able to show them which - if either - should be the default.