This article takes only 1 minute to read
By Kamil Bartocha on May 4, 2015
(ccby nancynance https://www.flickr.com/photos/nancynance)
Customer segmentation is the process of splitting your customer database into smaller groups. By focusing on specific customer types, you will maximize customer lifetime value and better understand who they are and what they need. Typically customers differ in terms of:
 products they are interested in,
 marketing channels they interact with (e.g. offline media like TV and press, social networks etc.),
 the maximum amount they can pay for a product (willingness to pay),
 types of promotions and benefits they expect (discounts, free shipping),
 buying patterns and frequency.
Click here to continue reading “Why You Should Have a Customer Segmentation”…
Posted in CRM  Tagged customer segmentation, segmentation 
This article takes only less than a minute to read
By Kamil Bartocha on May 3, 2015
I published this first on LinkedIn and received very positive feedback. If you like it too, go to the original post here and click on thumbs up or share on Twitter. Thanks!
 Data is never clean.
 You will spend most of your time cleaning and preparing data.
 95% of tasks do not require deep learning.
 In 90% of cases generalized linear regression will do the trick.
 Big Data is just a tool.
 You should embrace the Bayesian approach.
 No one cares how you did it.
 Academia and business are two different worlds.
 Presentation is key – be a master of Power Point.
 All models are false, but some are useful.
 There is no fully automated Data Science. You need to get your hands dirty.
Posted in Data 
This article takes only 2 minutes to read
By Kamil Bartocha on April 21, 2015
A Marketing Mix Model is a powerful tool in the hands of a CMO or a digital strategists. It can answer key questions like:
 What is my return of investment in digital marketing?
 When should I invest in specific channels?
 How should I allocate my budget?
 What is causing the uplift or downfall of my metrics?
 How will my numbers look like if I double my investment in Google?
Digital marketing channels are great for marketing mix modeling because there is often a direct relationship between cost and effect (visits, sales etc.). Things are not that easy with offline media like TV, press and outdoor. Parameters in the model often become insignificant or indicate no impact of offline campaigns.
The main reason behind this phenomenon is that the regression model fails to capture the long term effect of offline media like TV. When you see a cool ad on the TV you do not rush immediately to your PC or use your mobile phone to navigate to the website (BTW: this might be changing with second screen interaction). Click here to continue reading “Marketing Mix Modeling with Adstock for Offline Media”…
Posted in Modeling and forecasting, R  Tagged adstock, marketing mix modelling 
This article takes only less than a minute to read
By Kamil Bartocha on April 12, 2015
RFM Segmentation is the easiest and most frequently used form of database segmentation. It is based on three key metrics: Recency, Frequency and Monetary Value of customer activity. RFM is often used with transactional history in ecommerce, but can also work for Social Media interactions, online gaming or discussion boards. Based on calculated segments a marketer can prepare crosssell, upsell, retention and reactivation capampaigns. This deck provides a simple introduction to the RFM Segmentation methodology.
Posted in CRM, Data 
This article takes only 3 minutes to read
By Kamil Bartocha on February 8, 2015
Imagine you are playing the Heads or Tails game and count a successful conversion (win) when the coin lands Heads. A fair coin will land 50% of the time Heads and 50% of the time Tails. If you were playing a game with only a single coin toss in each round, you would have expected to see a fairly equal number of Heads and Tails in the long run. For example, after 10 rounds:
In a more complex game, you may want to toss multiple coins in each round. For example, 3 rounds and 10 coin tosses in each:

TTTHHHHHHT,HHHTHTHHHT,HHTHHTHHHT 
Count the number of Heads (wins/conversions) in each round. In the example above it is 6, 7 and 7 successes respectively. The probability of observing \(x\) successes (Heads) in \(n\) trials (tosses) when the probability of Heads is \(p\) can be calculated with the binomial distribution:
\[P(x,n,p) = {n \choose x} p^x (1p)^{nx}\]
It can be easily calculated in Excel. For example, to calculate the probability of 3 successes in 10 trials with 0.5 probability of success use this formula:

=BINOM.DIST(3, 10, 0.5, FALSE) 
The result is 11% chance of seeing 3 Heads.
Conversion Rates (and Clickthrough Rates etc.) can be easily modeled with a coin toss game. Each impression of an ad will be a coin toss and each click a will be a success (coin landing Heads). The probability of landing Heads is the Conversion Rate.
Click here to continue reading “How to Calculate Confidence Intervals for Conversion Rate”…
Posted in Modeling and forecasting, Statistics  Tagged binomial distribution, confidence intervals, conversion rate 
This article takes only 3 minutes to read
By Kamil Bartocha on January 10, 2015
(ccsa) 401(K) 2014 – https://www.flickr.com/photos/68751915@N05/
Customer behavior models are central to a successful CRM campaign in ecommerce. Let’s look at the very basic question of "How many orders is this customer expected to make?". We start with basic building blocks.
Continuous buying behavior
Our model will be in a continuous setting, where customers are free to make a purchase at any time. Standard scenarios in ecommerce and retail are for products like groceries, books, movies, hotel rooms etc.
When you look at your order history database, customers might appear to be buying randomly. A deeper analysis will usually reveal a constant average purchase rate that is specific to individual customers – one book approximately every 20 days, one movie every weekend, groceries every 58 days etc. If all purchases are independent and follow a constant average rate, then customer’s buying behavior can be modeled with a Poisson Process. It counts the number of events (orders) and the time that passes between them.
Click here to continue reading “Modeling Buying Behavior with Negative Binomial Distribution in Stan”…
Posted in CRM, Modeling and forecasting, R  Tagged bayesian, customer analytics, nbd, stan 
This article takes only 2 minutes to read
By Kamil Bartocha on December 13, 2014
(ccby) TaxCredits.net
Estimating Customer Lifetime Value allows you to say how much you can spend on customer acquisition and retention. This is especially important for subscriptionbased products like games, online services, information marketing, loyalty programes etc. In this article we will use a classical approach to estimate CLV with a simple equation and customer cohort analysis. There are more accurate probabilistic models available and I will look into them in following posts. If you are just starting with CLV calculation for your business, I strongly recommend going for the simple solution first and then becoming sophisticated as necessary. Click here to continue reading “Customer Lifetime Value for Subscription Products”…
Posted in Uncategorized 
This article takes only 2 minutes to read
By Krzysztof Osiewalski on November 29, 2014
Be faster! (c) www.shoppingcartdiagnostics.com
In this article we give some hints on how to use your machine in most efficient way while programming in R and when this can be achieved.
Click here to continue reading “Unleash the power of your multicore CPU with R”…
Posted in R, Statistics  Tagged parallel, R 
This article takes only less than a minute to read
By Krzysztof Zawadzki on November 29, 2014
(c) MarketingDistillery.com
“I think datascientist is a sexed up term for a statistician…. Statistics is a branch of science. Data scientist is slightly redundant in some way and people shouldn’t berate the term statistician.”
Nate Silver (applied statistician)
Click here to continue reading “Is Data Science a buzzword? Modern Data Scientist defined”…
Posted in Data, Statistics  Tagged data science, data scientist, modern data scientist 
This article takes only 1 minute to read
By Krzysztof Zawadzki on November 25, 2014
Recently I have compiled top 10 web analtyics presentations from SlideShare. Have a look below, there are a few interesting ones.
Click here to continue reading “Top 10 Web Analytics presentations on SlideShare”…
Posted in Links  Tagged top 10, web analytics 