Prediction Based on multi Variables

[esabzi]

Hi All,

Im wondering if someone can help me, im trying to build a revenue prediction model based on marketing spend, daily users and daily number of people who actually make purchase. so basically i would like to predict my future daily users and people who will make purchase and also if i increase my marketing spend i can get more users.

i hope this make some sense i can provide the data in excel file ?

[Iris]

Hi @esabzi

this sounds like a really interesting use case. You can find a lot of example on the KNIME Hub hub.knime.com for learning how to solve this in KNIME.
I can also highly recommend our free e-learning course.

[esabzi]

Hi, can you please send me the link for the example you think will help me? i know how to use knime but i dont know how to predict if i want to have manual way to adjust the numbers.

[mlauber71]

These discussions might give you further insights and also contain links to some workflows. Maybe the Rossmann Kaggle case comes quite close to your question. Prediction of future sales are always a challenge since you will have to put into data the drivers that would influence your future sales like holidays and moving holidays (like easter), marketing campaigns and so on.

Recursive Row Loop for Sales Forecasting

I would like to point you to a few resources where they tried to predict future sales at a german retail chain on Kaggle. Maybe you might get some useful insights. One thing to consider might be special things like (regional) holidays or events like easter that will not be at the same date every year, and the holidays might not only affect the day itself but the days before and after. You might want to take that into account. Trying to predict future sales on Kaggle Interview with winner I…

Prediction for a new year

The data of the restaurant challenge is hosted at Kaggle: (you might need to register an account) And the KNIME workflow can be downloaded from the Examples Server https://nodepit.com/download/workflow/public-server.knime.com%3A80%2F04_Analytics%2F15_H2O_Machine_Learning%2F07_Customer_prediction_with_H2O https://nodepit.com/workflow/public-server.knime.com%3A80%2F04_Analytics%2F15_H2O_Machine_Learning%2F07_Customer_prediction_with_H2O It could be that for your specific problem you would…

Prediction model

There is an example of the prediction of the number of customers to a restaurant using the powerful h2o.ai nodes of KNIME. Maybe you can adapt that: And also as @HansS mentioned there are lots of nodes and example around. Below are a few links about models for numeric targets that might be helpful. Also you might check out specific time series themes (I do not have a collection of links about that right now).

Predictive analytics for Sales

I can think of a few examples that might show you the possible directions This site has an introduction to model building with KNIME and uses an example that could be similar to your question: Concerning the predictions you want you will have to think about how to set up the analysis and prediction. Would you make a prediction for every single product and dealer and what role does the time play in your analysis. Sales within a month or a week. Then the quality of a model will very much de…

Forecasting sales per customer for the next 360 days

@ScottF provided us with simple workflow that shows how you would set up a regression model and do an evaluation using the RMSE ( Root-mean-square deviation) => the lower the better. The KNIME numeric scorer node has you covered there. You could switch out the type of model used: We had a discussion about that here: And also further information about ways to determine the quality of a model (mostly 1/0 but check out the link). I have to see if I have an example with a regression targe…

Evaluate a Linear Regression model

Typically you would use the Scorer nodes. Then there are a few entries dealing with numeric targets, you might want to check them. Regression models (numeric Target) predict how many future visitors a restaurant will receive (with H2O.ai)