$collection = $this->_itemCollectionFactory->create()->getCollection();
$collection->getSelect()->columns(array('total_orders' => new \Zend_Db_Expr('COUNT(order_id)')))
->columns(array('total_qty' => new \Zend_Db_Expr('ROUND(SUM(qty_ordered))')))
->group('sku');
$collection->getSelect()->limit(3);
$collection->addFieldToFilter('store_id', 2);
Through this code i can get the total quantity and total number of orders of all sales rep. But i need only those total quantity and total orders which were done by the Sales Rep og Logged in Sale Manager.
As you mentioned in your question you only want to get the order info based on a certain sales rep. To do that you need to somehow find a unique identify for the currently logged in sales rep and add a filter for that .
For example:
$collection = $this->_itemCollectionFactory->create()->getCollection();
$collection->getSelect()->columns(array('total_orders' => new \Zend_Db_Expr('COUNT(order_id)')))
->columns(array('total_qty' => new \Zend_Db_Expr('ROUND(SUM(qty_ordered))')))
->group('sku');
$collection->getSelect()->limit(3);
$collection->addFieldToFilter('store_id', 2);
$collection->addFieldToFilter('sales_rep_identifier', $loggedInSalesRepIdentifier);
The label sales_rep_identifier and the value $loggedInSalesRepIdentifier are just examples, you may have to adjust the format in which the data is stored if it doesn't currently have a field to check natively what sales rep did it, and adjust the values there accordingly
If you're using some kind of prebuilt system then maybe there are other labels for the identifier you could use, without more information on the specific database structure it's impossible to answer exactly, but essentially you need to filter by the unique sales rep identifier, and if that doesn't exist now in the database it should somehow but added
this might be a stupid question but I'm blanking at the moment and can't find the answer in google.
I have the following example table
customer
bought
A
food
B
food,drink
C
drink
D
drink
now how do I calculate the percentage of customers that bought food/drink over total customers? what would be the best way to calculate this?
solution
% food = #customers who bought food / #total unique customers = 2 / 4 = 50%
% drink = #customers who bought drink / #total unique customers = 3 / 4 = 75%
the problem here is the total % exceeds 100%
solution - count customer B twice, once for drink and once for food
% food = #customers who bought food / #total customers = 2 / 5 = 40%
% drink = #customers who bought drink / #total customers = 3 / 5 = 60%
the total is 100% but is this the correct way to calc % in this case?
The issue is obviously that one customer can buy both food and drink and I'm not sure how to handle this case. Any help would be appreciated. Thank you!
UPDATE:
thanks for the answer. It makes sense to remove altogether the customers who bought both products. But now I'm wondering what happens if there are more than 2 categories?
Example as follows, now we have an additional product (ice cream) in the mix
customer
bought
A
food
B
food,drink
C
drink
D
drink
E
drink,ice cream
F
ice cream
G
food,drink,ice cream
I guess with the same logic we can remove customer G since they bought all the products? And how should we handle customer E and B?
I think that what you are looking for is simply to eliminate the number of customers that purchased both products.
If you are looking for unique combinations of customers who purchased only a specific product then:
3 unique customers bought a single unique item
1/3 food => 33%
2/3 drink => 66%
The ones who bought both products, don't matter in these calculations since they add the same percentage to both cases.
EDIT:
I used excel to help me out. I hope that is fine
I added your data into an excel sheet and created a crosstable pivot.
I've set the Customers as columns and the Products as rows, in order to see what each individual customer has purchased via the values field Count of Product
I've changed the count of Product into the formula for % of Total
in order to show me for each product, the percentage split between the customers, so for example if customer B bought both drink and food he will be shown as 50% in both corresponding rows. If he bought all 3 then 33%
The grand total column, will contains the final percentage for each product row item. Excel calculates it the same way as states in some comments, it counts the products total instead of the customers total, which makes sense since that is your data set being consumed, and not the customers themselves.
If we swap the rows and columns around and do the same calculations, we see individual percentages for each customer (how much % of product they each individually purchased) and we can sum them up for each product. The result is the same
In a survey of 100 people, I am asking each person to choose between product A and product B. I ask each person this question 3 times, but each time I present a different set of products. Say, first time, Person 1 is asked to choose between 'Phone 1' and 'Phone 2', given certain attributes of each phone. The second time the choice is again 'Phone 1' vs. 'Phone 2', but a different set of attributes for each phone.
A person is presented three attributes associated with the two phone alternatives every time the question is asked. So, each time between Phone 1 and Phone 2, the attributes of the phone such as cost, memory and camera pixels are presented so that user can choose which set of attributes is most attractive, Phone 1's or Phone 2's.
Overall, 3*100 = 300 responses; 3 responses per person. Each time the attributes cost, memory and camera pixels presented and user asked to choose the feature set they prefer.
My goal is to analyze how users value features of a phone vs. cost of the phone.
In this scenario, can I use a MNL - even though each time I asked the person a question, I only presented two choices ? My understanding is that MNL is sued when (a) there are multiple choices and (b) the choice options do not change across observations, i.e. each person is asked to choose between multiple products, say A, B, C and A, B, C do not change across observations.
In the scenario described above, the two choices varied across the three times the same person was asked the question ? If not MNL, should I rather create a binary logit model given that user only had to choose between two options when the question was asked (even though he was asked the question three times)? If I can use binary logit, should I be concerned that the choice set of products change across observations ? or should I let the attributes defined in each of the rows address the differences in product choices across observations.
I have setup the data as follows (thinking I can do MNL but may be I should set it up differently and use another modeling approach?):
I am working on designing and analyzing similar survey but mine is related to transportation. I am at the beginning level and I am still new to the whole concept, however I will give you an advice and reference maybe it is helpful.
First point: I have come cross 3 models as following from a useful video on YouTube:
MNL refers to Multinomial Logit Model. MNL is used with
alternative-invariant regressors (for example salary of participant
in the survey, or his/her gender …).
Conditional logit model is used with alternative-invariant (gender,
salary, education level …) and alternative-variant regressors (cost
of the product, memory, camera pixel …)
Mixed logit model which uses random parameters. It is also used with
alternative-invariant (gender, salary, education level …) and
alternative-variant regressors (cost of the product, memory, camera
pixel …)
Note regarding alternative-invariant and alternative-variant regressors:
The gender of person participating in the survey will NOT vary between Product A or Product P, so it is alternative-invariant regressor. While price of product could vary between Product A and Product B so it is called alternative-variant regressors.
Based on above I assume you need to use conditional logit model or mixed logit model.
For me I couldn’t find a special function in R for the conditional logit model or mixed logit model. The same mlogit function is used, refer to the examples below for the help of mlogit package:
a pure "multinomial model"
summary(mlogit(mode ~ 0 | income, data = Fish))
a pure "conditional" model
summary(mlogit(mode ~ price + catch, data = Fish))
a "mixed" model
m <- mlogit(mode ~ price+ catch | income, data = Fish)
summary(m)
same model with charter as the reference level
m <- mlogit(mode ~ price+ catch | income, data = Fish, reflevel = "charter")
From the examples above, I think (but NOT sure) that in the Manual of mlogit package, they refer to mixed logit when you used both alternative-invariant and alternative-variant regressors. While conditional model when you have only alternative-variant regressors. On the other hand, multinomial model when you have only multinomial alternative-invariant regressors.
Second point: There is something called “panel data” when you are asking the same person to choose one product for each choice-set. Same person here means that in your model you are taking into consideration, the gender, the salary, the education level … which they will stay the same for the same person. Check this: https://en.wikipedia.org/wiki/Panel_data
To use panel techniques please refer to help in mlogit package in R. I am quoting from it the following:
“panel only relevant if rpar is not NULL and if the data are repeated observations of the same unit ; if TRUE, the mixed-logit model is estimated using panel techniques”
So in my understanding, if you want to use the panel techniques you have to use random draws because panel will be true and rpar will not be NULL.
Moreover, for example about using the panel data, please refer to the below example from “Estimation of multinomial logit models in R : The mlogit Packages” by Yves Croissant
data("Train", package = "mlogit")
Tr <- mlogit.data(Train, shape = "wide", varying = 4:11, choice = "choice", sep = "_", opposite = c("price", "time", "change", "comfort"), alt.levels=c("A", "B"), id.var ="id")
Train.ml <- mlogit(choice ~ price + time + change + comfort, Tr)
Train.mxlc <- mlogit(choice ~ price + time + change + comfort, Tr, panel = TRUE, rpar = c(time = "cn", change = "n", comfort = "ln"), correlation = TRUE, R = 100, halton = NA)
Train.mxlu <- update(Train.mxlc, correlation = FALSE)
I hope that is useful to you.
Currently I'm developing a vb.net program which is based on 'Movie Ticket Purchasing System'. Right now I'm trying to develop a code where, once the cinema is closed for the day, the system will calculate the total amount of collections in both movie ticket purchases and snack/drink purchases.
Let's say that:
TicketPrice - total price of movie tickets purchased
SnackPrice - total price of snacks/drinks purchased
TotalCinemaPrice - TicketPrice + SnackPrice
So, "TotalDayRevenue", for example, will be the total number of tickets, snacks and drinks sold throughout the day.
Also, I know that 'For Loop' will be used in order to get this kind of value, but I don't know how to efficiently, and properly write the code.
Help is much appreciated, thank you.
First create a variable to store the total day revenue:
double totalAmount = 0;
Then get the amount of tickets and snacks you've sold and multiply that with the price. Then add it to the total day revenue:
totalAmount += amountOfTicketsSold * priceForOneTicket;
totalAmount += amountOfSnacksSold * priceForOneSnack;
Kind regards
So I have a knowledge base of these three tables
% order(customer_name, item_name, quantity).
% customer(name, credit_rating).
% item(name, quantity_in_stock).
I am supposed to find all valid orders. Valid orders are defined as valid customers with a good credit rating (rating above 500), the item the customer is ordering should exist in the stock and the order quantity must be less than that of the quantity_in_stock.
I already figured out for good rating, its just customer(X, Rating), Rating >=500. But I cannot figure out how to combine a good customer rating, quantity and then comparing that to the quantity_in_stock.