I am working with a dataset that has enough observations and ~ 10 variables, half of the variables are numeric another half of the variables are categorical with 2-3 levels (demographics) one ID variable one last variable that has sales value, 0 for no sale and bill amount for sale Using this information, I want to understand which segments of my customers to market. I am using R for code but that's not relevant here. :) I am confused about …
I have to solve two questions on the following dataset: 1. arrange customers into mutually exclusive groups.explain the clusters. 2.identify 1-1 product category association rules for each cluster, i.e. if a customer bought from this category, they are likely to buy from this category too.
I am a beginner in Data Science field, so sorry if my question is too basic. The task is to build an ad bidding model for online marketing which allows you to deliver targeted ads to the right people. A part of the given data is I don't have any additional info about the task. Is my target variable the variable spent ? And if yes which is the best method to follow to predict the target variable?
Imagine a company with multiple lines of revenues coming from diferent products, but all customer can access these different products through the same account and the same online platform. My goal is to predict the churn for each customer. Should I perform customer segmentation into clusters and build a churn prediction model for each segment? The straight foward path would be to get all relevant features for all customers and try to predict the churn for all of them. The …
Background - We pitch a product to a number of prospective customers each month via mail as a part of a campaign. Some of them don't respond, some apply and get through the application process and some don't. I have the customer engagement data for a marketing campaign conducted each month for the below months: MAR-2021 APR-2021 JUL-2021 SEP-2021 OCT-2021 NOV-2021 JAN-2022 The missing months are when we did not have any campaign. Data shows campaign date(mail sent date), some …
I'm reading about KAMA algorithm and everywhere I see it's just adopted for stocks and trading kind of things. Since the algorithm looks perfect so I was planning to use this for averaging out a 1D array. Is it possible and the right thing to do? or it's just meant to be utilized for marketing?
A dataset contains so many fields in which there is both relevant and irrelevant field. If we want to do a market campaigning using propensity scoring, which fields of the data set are relevant? How can we find which data field should be selected and which drive to the desired propensity score?
What are the ML methods one should use for subscription data comprising of visits, monthly active user, user interaction, product usage, location, source. predicting cost for our services.
I have been researching the Reinforcement Learning topic. I have been looking if this is the correct way to optimize the marketing actions of my company given that we are looking to optimize the Client's Lifetime Value. So far I have found this: The environment is the real-life o.O Agent is our company The rewards/values can be matched to the CLV itself, meaning that any action could lead to improve or worsen the CLV The actions are the possible marketing …
We're analyzing some creatives from past advertising campaigns and are looking for the best way to approach. Currently we've broken down each creative into 20+ objective characteristics, for example: CTA button (Y/N) Exclusive discount mentioned (Y/N) etc For each campaign we have two main data points: CTR and conversions. What are some possible ways to approach analyzing this data? We're looking to find out how each feature affects CTR/conversions.
I am looking into Shapley values for online marketing attribution. In recent time many articles seem to have been made on this particular approach to attribution (there are more): https://medium.com/analytics-vidhya/the-shapley-value-approach-to-multi-touch-attribution-marketing-model-e345b35f3359 And i.e.: https://blog.dataiku.com/step-up-your-marketing-attribution-with-game-theory It seems that, at least in certain cases, the result will be identical to linear attribution, so I am trying to get more information regarding whether this is to be expected / correct. The problem: The Shapley value approach for online marketing attribution in these articles seems …
I have a project and I couldn't understand what I have to do because I am new to retail analytics. They said "Our goal is to measure the effects of promotion on sales" and "Your goal is to model the effect of promotion on products and stores. In order to answer questions, you should divide products and stores into 3 clusters each. (High, Medium, Low)" I have two datasets, let's say; 1 - data.csv -> Date, Store, Product, QuantityOfSales 2 …
Hypothetically, if your company's sales had dropped significantly in 2020, what approach would you take to describe the cause? can you build a model to predict the decrease (between 2019 and 2020 for example) to visualize what the leading indicators are?
This dataset is collected by a drug making company trying to sell its drug to doctors of different specializations. The drug company has made promotional activity for its brand The promotional activity has included calls made, emails sent and faxes sent. The dataset shows how many calls have been made ('Calls Made' column), how many calls were successfully completed ('Calls Successfully Completed' column), how many emails were sent ('Emails Sent' column), how many emails were opened ('Emails Opened' column), how …
We are planning a marketing campaign to collect data and the response rates for a random sample. Total population size is 10 million and historically, response rates are low (0.5 - 0.65 %). How long do we need to run the test to collect a decent number of positive responses and how many positive responses need to aim for ? I know there are no general rules for deciding the model data size as it all depends on the attributes …
So basically I need a kind of product/ category affinity for EMI for all customers eg - Customer A is more likely to take an EMI on her insurance premium. One approach I had thought was to broadly categorize the the transactions into 3-4 categories and predict the amount( linear regression) that a customer is going to spend on each of the category in that particular month.eg It is estimated that customer A will spend huge amount on category 1 …
Does anyone know of any literature on Marketing Mix Modelling (MMM) using XGBoost? Is this a viable techniques for MMM modelling? What would the advantages/disadvantages be? I think it would be a great option due to that interactions effects are dealt with in a good way, and the model also avoids inflated values. However, I am not sure of whether XGBoost is the best model for dealing with the multi colliearity often present in MMM data. I would be happy …
The reason I ask this question is as follows: I am currently in a remote intern position. I've been asked to calculate the Customer lifetime value (past + future) for each customer based on monthly cohorts. I can choose from P/NBD, BG/NBD or BG/BB model whatever is appropriate. I came across the Gamma-Gamma spend model. Is this in any way associated with the above models? How? According to this (https://segment.com/docs/guides/warehouses/how-do-i-forecast-ltv-with-sql-and-excel-for-e-commerce/), they just use average transaction value for each customer and …
What would be appropriate models/algorithms/strategies for predicting best individual send times for marketing campaigns based on past response timestamps? Data: Given for example =========================================================== customer campaign campaign_time response_time ----------------------------------------------------------- 1 100 a 2017-01-01 06:50:01 2017-01-01 08:02:21 2 101 a 2017-01-01 06:50:01 2017-01-01 16:45:31 3 101 a 2017-01-01 06:50:01 2017-01-02 07:20:00 4 100 b 2017-01-07 06:30:21 2017-01-08 08:15:21 5 101 b 2017-01-07 06:30:21 2017-01-07 17:00:12 6 100 c 2017-01-14 06:43:55 2017-01-14 07:59:44 7 101 d 2017-01-21 14:02:01 2017-01-21 16:50:01 ----------------------------------------------------------- two …