#marketing attribution

[peterbordes]

(via The difference between attribution and intent driven advertising on the open web)

Attribution and intent are related but distinct concepts in marketing measurement. Attribution focuses on assigning credit for conversions or sales to specific marketing touchpoints along the customer journey. It aims to understand which ads, channels, or campaigns influenced a customer’s purchase decision. Common attribution models include last-click, first-click, linear, time decay, and data-driven models.

[aianddata]

A Beginner’s Guide to Channel Attribution Modeling in Marketing

Introduction

In a typical ‘from think to buy’ customer journey, a customer goes through multiple touch points before zeroing in on the final product to buy. This is even more prominent in the case of e-commerce sales. It is relatively easier to track which are the different touch points the customer has encountered before making the final purchase.

As marketing moves more and more towards the consumer driven side of things, identifying the right channels to target customers has become critical for companies. This helps companies optimise their marketing spend and target the right customers in the right places.

More often than not, companies usually invest in the last channel which customers encounter before making the final purchase. However, this may not always be the right approach. There are multiple channels preceding that channel which eventually drive the customer conversion. The underlying concept to study this behavior is known as ‘multi-channel attribution modeling.’

In this article, we look at what channel attribution is and how it ties into the concept of Markov chains. We’ll also take a case study of an e-commerce company to understand how this concept works, both theoretically and practically (using R).

Table of Contents

What is Channel Attribution?

Markov Chains

Removal Effect

Case Study of an E-Commerce Company

Implementation in R

What is Channel Attribution?

Google Analytics offers a standard set of rules for attribution modeling. As per Google, “An attribution model is the rule, or set of rules, that determines how credit for sales and conversions is assigned to touchpoints in conversion paths. For example, the Last Interaction model in Analytics assigns 100% credit to the final touchpoints (i.e., clicks) that immediately precede sales or conversions. In contrast, the First Interaction model assigns 100% credit to touchpoints that initiate conversion paths.”

We will see the last interaction model and first interaction model later in this article. Before that, let’s take a small example and understand channel attribution a little further. Let’s say we have a transition diagram as shown below:

In the above scenario, a customer can either start their journey through channel ‘C1’ or channel ‘C2’. The probability of starting with either C1 or C2 is 50% (or 0.5) each. Let’s calculate the overall probability of conversion first and then go further to see the effect of each of the channels.

P(conversion) = P(C1 -> C2 -> C3 -> Conversion) + P(C2 -> C3 -> Conversion)

= 0.5*0.5*1*0.6 + 0.5*1*0.6 = 0.15 + 0.3 = 0.45

Markov Chains

Markov chains is a process which maps the movement and gives a probability distribution, for moving from one state to another state. A Markov Chain is defined by three properties:

State space – set of all the states in which process could potentially exist

Transition operator –the probability of moving from one state to other state

Current state probability distribution – probability distribution of being in any one of the states at the start of the process

We know the stages through which we can pass, the probability of moving from each of the paths and we know the current state. This looks similar to Markov chains, doesn’t it?

Removal Effect

This is, in fact, an application of a Markov chains. We will come back to this later; let’s stick to our example for now. If we were to figure out what is the contribution of channel 1 in our customer’s journey from start to end conversion, we will use the principle of removal effect. Removal effect principle says that if we want to find the contribution of each channel in the customer journey, we can do so by removing each channel and see how many conversions are happening without that channel being in place.

For example, let’s assume we have to calculate the contribution of channel C1. We will remove the channel C1 from the model and see how many conversions are happening without C1 in the picture, viz-a-viz total conversion when all the channels are intact. Let’s calculate for channel C1:

P(Conversion after removing C1) = P(C2 -> C3 -> Convert)

= 0.5*1*0.6

= 0.3

30% customer interactions can be converted without channel C1 being in place; while with C1 intact, 45% interactions can be converted. So, the removal effect of C1 is

0.3/0.45 = 0.666.

The removal effect of C2 and C3 is 1 (you may try calculating it, but think intuitively. If we were to remove either C2 or C3, will we be able to complete any conversion?).

This is a very useful application of Markov chains. In the above case, all the channels – C1, C2, C3 (at different stages) – are called transition states; while the probability of moving from one channel to another channel is called transition probability.

Customer journey, which is a sequence of channels, can be considered as a chain in a directed Markov graph where each vertex is a state (channel/touch-point), and each edge represents transition probability of moving from one state to another. Since the probability of reaching a state depends only on the previous state, it can be considered as a memory-less Markov chain.

Case Study of an E-Commerce Company

Let’s take a real-life case study and see how we can implement channel attribution modeling.

An e-commerce company conducted a survey and collected data from its customers. This can be considered as representative population. In the survey, the company collected data about the various touch points where customers visit before finally purchasing the product on its website.

In total, there are 19 channels where customers can encounter the product or the product advertisement. After the 19 channels, there are three more cases:

#20 – customer has decided which device to buy;

#21 – customer has made the final purchase, and;

#22 – customer hasn’t decided yet.

The overall categories of channels are as below:CategoryChannel Website (1,2,3) Company’s website or competitor’s website Research Reports (4,5,6,7,8) Industry Advisory Research Reports Online/Reviews (9,10) Organic Searches, Forums Price Comparison (11) Aggregators Friends (12,13) Social Network Expert (14) Expert online or offline Retail Stores (15,16,17) Physical Stores Misc. (18,19) Others such as Promotional Campaigns at various location

Now, we need to help the e-commerce company in identifying the right strategy for investing in marketing channels. Which channels should be focused on? Which channels should the company invest in? We’ll figure this out using R in the following section. To complement such advanced analytics, companies often engage AI consulting firms to integrate predictive capabilities into their business processes.

Implementation using R

Let’s move ahead and try the implementation in R and check the results. You can download the dataset here and follow along as we go. #Install the libraries install.packages("ChannelAttribution") install.packages("ggplot2") install.packages("reshape") install.packages("dplyr") install.packages("plyr") install.packages("reshape2") install.packages("markovchain") install.packages("plotly") #Load the libraries library("ChannelAttribution") library("ggplot2") library("reshape") library("dplyr") library("plyr") library("reshape2") library("markovchain") library("plotly") #Read the data into R > channel = read.csv("Channel_attribution.csv", header = T) > head(channel)

Output:R05A.01R05A.02R05A.03R05A.04…..R05A.18R05A.19R05A.20 16 4 3 5 �� NA NA NA 2 1 9 10   NA NA NA 9 13 20 16   NA NA NA 8 15 20 21   NA NA NA 16 9 13 20   NA NA NA 1 11 8 4   NA NA NA

We will do some data processing to bring it to a stage where we can use it as an input in the model. Then, we will identify which customer journeys have gone to the final conversion (in our case, all the journeys have reached final conversion state).

We will create a variable ‘path’ in a specific format which can be fed as an input to the model. Also, we will find out the total occurrences of each path using the ‘dplyr’ package. > for(row in 1:nrow(channel)) {   if(21 %in% channel[row,]){channel$convert[row] = 1} } > column = colnames(channel) > channel$path = do.call(paste, c(channel[column], sep = " > ")) > head(channel$path) [1] "16 > 4 > 3 > 5 > 10 > 8 > 6 > 8 > 13 > 20 > 21 > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > 1"     [2] "2 > 1 > 9 > 10 > 1 > 4 > 3 > 21 > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > 1"     [3] "9 > 13 > 20 > 16 > 15 > 21 > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > 1" [4] "8 > 15 > 20 > 21 > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > 1" [5] "16 > 9 > 13 > 20 > 21 > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > 1" [6] "1 > 11 > 8 > 4 > 9 > 21 > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > NA > 1" > for(row in 1:nrow(channel)) {   channel$path[row] = strsplit(channel$path[row], " > 21")[[1]][1] } > channel_fin = channel[,c(23,22)] > channel_fin = ddply(channel_fin,~path,summarise, conversion= sum(convert)) > head(channel_fin)

Output:pathconversion 1 > 1 > 1 > 20 1 1 > 1 > 12 > 12 1 1 > 1 > 14 > 13 > 12 > 20 1 1 > 1 > 3 > 13 > 3 > 20 1 1 > 1 > 3 > 17 > 17 1 > 1 > 6 > 1 > 12 > 20 > 12 1 > Data = channel_fin > head(Data)

Output:pathconversion 1 > 1 > 1 > 20 1 1 > 1 > 12 > 12 1 1 > 1 > 14 > 13 > 12 > 20 1 1 > 1 > 3 > 13 > 3 > 20 1 1 > 1 > 3 > 17 > 17 1 1 > 1 > 6 > 1 > 12 > 20 > 12 1

Now, we will create a heuristic model and a Markov model, combine the two, and then check the final results. > H <- heuristic_models(Data, 'path', 'conversion', var_value='conversion') > H

Output:channel_namefirst_touch_conversions…..linear_touch_conversionslinear_touch_value 1 130   73.773661 73.773661 20 0   473.998171 473.998171 12 75   76.127863 76.127863 14 34   56.335744 56.335744 13 320   204.039552 204.039552 3 168   117.609677 117.609677 17 31   76.583847 76.583847 6 50   54.707124 54.707124 8 56   53.677862 53.677862 10 547   211.822393 211.822393 11 66   107.109048 107.109048 16 111   156.049086 156.049086 2 199   94.111668 94.111668 4 231   250.784033 250.784033 7 26   33.435991 33.435991 5 62   74.900402 74.900402 9 250   194.07169 194.07169 15 22   65.159225 65.159225 18 4   5.026587 5.026587 19 10   12.676375 12.676375 > M <- markov_model(Data, 'path', 'conversion', var_value='conversion', order = 1)> M

Output:channel_nametotal_conversiontotal_conversion_value 1 82.482961 82.482961 20 432.40615 432.40615 12 83.942587 83.942587 14 63.08676 63.08676 13 195.751556 195.751556 3 122.973752 122.973752 17 83.866724 83.866724 6 63.280828 63.280828 8 61.016115 61.016115 10 209.035208 209.035208 11 118.563707 118.563707 16 158.692238 158.692238 2 98.067199 98.067199 4 223.709091 223.709091 7 41.919248 41.919248 5 81.865473 81.865473 9 179.483376 179.483376 15 70.360777 70.360777 18 5.950827 5.950827 19 15.545424 15.545424

Before going further, let’s first understand what a few of the terms we’ve seen above mean.

First Touch Conversion: The conversion happening through the channel when that channel is the first touch point for a customer. 100% credit is given to the first touch point.

Last Touch Conversion: The conversion happening through the channel when that channel is the last touch point for a customer. 100% credit is given to the last touch point.

Linear Touch Conversion: All channels/touch points are given equal credit in the conversion.

Getting back to the R code, let’s merge the two models and represent the output in a visually appealing manner which is easier to understand. # Merges the two data frames on the "channel_name" column. R <- merge(H, M, by='channel_name') # Select only relevant columns R1 <- R[, (colnames(R) %in %c('channel_name', 'first_touch_conversions', 'last_touch_conversions', 'linear_touch_conversions', 'total_conversion'))] # Transforms the dataset into a data frame that ggplot2 can use to plot the outcomes R1 <- melt(R1, id='channel_name') # Plot the total conversions ggplot(R1, aes(channel_name, value, fill = variable)) +   geom_bar(stat='identity', position='dodge') +   ggtitle('TOTAL CONVERSIONS') +   theme(axis.title.x = element_text(vjust = -2)) +   theme(axis.title.y = element_text(vjust = +2)) +   theme(title = element_text(size = 16)) +   theme(plot.title=element_text(size = 20)) +   ylab("")

The scenario is clearly visible from the above graph. From the first touch conversion perspective, channel 10, channel 13, channel 2, channel 4 and channel 9 are quite important; while from the last touch perspective, channel 20 is the most important (in our case, it should be because the customer has decided which product to buy). In terms of linear touch conversion, channel 20, channel 4 and channel 9 are coming out to be important. From the total conversions perspective, channel 10, 13, 20, 4 and 9 are quite important.

End Notes

In the above chart we have been able to figure out which are the important channels for us to focus on and which can be discarded or ignored. This case gives us a very good insight into the application of Markov chain models in the customer analytics space. E-commerce companies can now confidently create their marketing strategy and distribute their marketing budget using data driven insights. Our team of experienced AI consulting firms can assist you in transforming these insights into impactful business outcomes.

Read more: Tableau Sales Dashboard Performance | Anomaly Detection | Text Mining Tips | Machine Learning with SVM

[infoanalysishub]

Why Brand Advertising Is Crucial for Paid Media Success in 2025

Discover why brand advertising is essential for improving paid media performance. Learn how brand awareness reduces CPA, boosts search efficiency, and drives long-term growth. Why Brand Advertising Matters for Paid Media Performance As digital marketing evolves amid rising costs and murky attribution, many performance marketers are facing a sobering truth: the old playbook for pay-per-click…

[blinkmarketing]

Why Marketing Attribution is Important

Understanding which campaigns and channels work is vital to the business. Here are just a few reasons to continuously measure marketing impact.

Optimize Marketing Spend

Enhanced Customer Journey Insights

Improved ROI Measurement

Data-Driven Decision Making

Cross-Channel Understanding and Strategy Development

Competitive Advantage

Future Planning and Forecasting

As marketers are challenged to “do more with less,” measuring the effectiveness of marketing campaigns has never been more critical. Marketers invest in analytics tools that promise to reveal attribution details and hire people to manage the analysis. 

Find more about marketing channel attribution

[mktgendeavor]

CTV Advertising: Targeting, Engagement, and Measurable Results

Television is no longer just about branding. Superior targeting with CFO-friendly attribution is now a reality. #CTV #performancemarketing #digitalmedia

The Purr in Performance Marketing In the jungle of digital marketing, Connected TV (CTV) has emerged as a powerful and effective channel for advertisers. I remember the first pitch I heard (many years ago) from my local cable system, ironically. Digital TV, as it was being called, would give advertisers the ability to target their television commercials to customers by household zip codes,…

[horse-breed-a-day]

Horse breed of the day: Irish Hunter/Irish Sport Horse

Height: 15-17 hh

Common coat colors: Grey, black, chestnut, and various roans

Place of origin: Ireland

[gingermintpepper]

I have finished the book. It was not a very good book at all. My preliminary thoughts are as follows:

Prose is the best part about it but also one of the worst. Evocative and lovely language was used for what I can only describe as manipulating the reader. In a different book by a different writer, perhaps that would be the mark of a good unreliable narrator but this is not that kind of book and the writer, I fear, is not clever enough for that kind of narrative.

The characters - I do not think of them. Trapped as we are in Alcestis' head, she is a passive, dull thing who I do not know any better at the end of the book than I did at the start. I couldn't distinguish where her voice ended and where the author's began and it is through that haziness that all other characters are observed. Admetus is an objectively phenomenal husband but his kindness and regard is made bitter because he loves Apollo not her. Apollo is no more than a ghost haunting them both. Hippothoe and Phylomache exist as images of what Alcestis could be - a woman who dies of illness or children - and Heracles is awkward and small, quiet and passive in the way everything is in this book. Persephone and Hades are perhaps the only characters of some interest in here, but Persephone is a volatile mess of power masquerading as empowerment while Hades, like all the men in this book really, is small and passive and really only becomes worth something when out of Persephone's massive shadow.

The romance - What romance? There is no romance in this book. Alcestis imagines that what occurs between Admetus and Apollo is a romance but we do not get to see that story. The obvious regard and kindness Admetus holds for Alcestis would be romance in any other book - but in this it is described with so much apathy and loathing so as to make Admetus' actions seem unremarkable and lacking. As for Persephone and Alcestis; Alcestis wants nothing to do with her until Persephone forces her. She assaults her, like Hades assaulted her, while speaking the words of that tale into Alcestis' flesh while Alcestis rejects her and somehow this becomes the catalyst of their 'love'. There is no romance in this book.

The queer themes - I am not sure how one makes a story so easily given to a queer imagining biphobic, lesbophobic and not poly but it sure did that. Alcestis' ideas about love and sexuality and how it might possibly have different forms and fashions is unchanged from start to finish and homosexuality, for all that it is what she blames her ruined marriage on, is never explored past a few fleeting, derogatory descriptions and quickly brushed over allusions.

The Apollo Thing - Listen to me so carefully. Apollo is only in a single scene of this book. One single scene and it is the wedding scene where Admetus calls upon him to save them from the poisonouse snakes. Every other mention of him in this book is in passing, an offhanded mention of a person praying to a god, or with the underlying scorn and anger of a wife thinking of her husband's mistress. Apollo is not even there when Alcestis dies. He wasn't even responsible for Heracles eventually going down to the Underworld to retrieve her. If the author could've erased him from the wedding scene too, I'm sure she would've invented a way to do it. Apollo has no relationship with Alcestis, we do not get to see the nature of his relationship with Admetus and every other opportunity which existed to show him on their side was neatly and entirely erased. Thanatos, naturally, is completely absent from this novel.

In short, this was entirely unpleasant from essentially start to finish. I was very excited when I started this book and saw the quality of the prose and also the ambivalent character sketched of its gods but things went so rapidly and extremely downhill that I am left wondering how it was possibly flubbed that badly.

Regarding this book's feminism, I will dedicate a separate post to that entirely. To this book's credit, it did not have the superficial girlboss feminism that many of its contemporaries tend to champion but to its complete and utter detriment, the feminism it champions is perhaps the most insidious kind. The sort that excludes the disenfranchised, the impoverished and the normal woman. This book's feminism is for the privileged and the powerful and it is a deeply upsetting thing.

Lastly, I would like to thank @superkooku , @konu-d and @waterlinkedgirl for cheering me on through this absolute torment. I would not have finished this without them. Take that as you will.

If you are interested in the tale of Admetus and Alcestis or just want to read a retelling, I urge you to just read @reawakened-revenant (CiCiRose on ao3)'s God of the Golden Bow series. It is captivating, enthralling, impeccably researched and so utterly submersed in passion, love and care that it is breathtaking to read. It is a personal favourite of mine and the standard to which I hold all other Admetus and Alcestis retellings.

With all that done, I am going to stare at a wall and contemplate the horrors now. ✌🏾

[antisocialxconstruct]

was there ever actually any proof that socmed sites suppress posts with words like "commission" or "shop" in them, or is that just the boogeyman we all collectively invented to explain away the fact that people simply do not interact as much with posts where you ask them for money

[manmishra]

🚀 Explore how AI can transform your B2B marketing strategy! Discover actionable tactics to enhance buyer engagement and create personalized experiences. Dive into AI-driven buyer-centric strategies today! #B2BMarketing #AI #BuyerEngagement #DigitalMarketing

[taamlok]

i'm not gonna lie the whole "trick weekes is playing 4d chess with us" argument about every fucking thing about taash is getting old, they've written more than just solas

[quibbs126]

Okay because my mind’s still on the subject, I have a bit of a weird question, but I just want an answer

So like, my favorite type of drama in series is usually familial drama, most often ones between parents and their children. And also usually, those dramas that I love are specifically between father and son

And now I’m wondering, what are parent-child dramas/dynamics that involve a female character usually like? As in, mother-son, father-daughter, or even mother-daughter? And additionally, are they as interesting as father-son dynamics? Because I feel like I don’t see nearly enough of them, or at least, they never give me that same amount of intrigue or they just aren’t as focused on

By which I mean specifically in my context, would they be interesting in an action based series? I have a particular taste, namely that I like my series (mostly this applies to shows, I’m a bit more flexible with games) to be more action focused, or at least have some fantastical elements to it, or else I don’t care. I’m not interested in soap opera kind of drama, I want something that would fit in an action based show

Sorry, again this is probably a weird request (and to be honest I really don’t expect answers, posts of mine like these usually get little to no attention), but it’s on my mind. I feel like I see so many interesting father-son dynamics and conflicts, but I never see one that involves a woman, at least not when it comes to parent-child dynamics, and not ones nearly as interesting

[arctic-hands]

Why is it so hard to find a viable digital copy or a cheap physical copy of a text that is two thousand years past copyright–and even accounting for its most popular English translation it's still public domain now–without paying at least thirty dollars for it

[aimarketresearch]

Variable Reluctance Market Size, Share, Trends, Opportunities, Key Drivers and Growth Prospectus

[blinkmarketing]

B2B Marketing Attribution for better strategies!

Customer journeys are getting more complex every day. With growing digital footprints, marketers and growth leaders use more advanced strategies to learn whether their efforts drive significant outcomes. B2B marketers must also deal with buying committees, long sales cycles, and complicated sales processes.

B2B marketers find measuring the conversion of a lead into a deal challenging with the available marketing analytics.

They need marketing attribution that collects, joins, and models all relevant B2B customer data from across the martech stack, empowering teams to build and repeat success.

Read the comprehensive guide off B2B marketing attribution

[analyticsliv]

Data driven attribution employs real-time data and machine learning to assign value to each touchpoint in the customer journey. This, in turn, helps brands make intelligent marketing decisions, reduce wasteful ad spends, and enhance their Conversion Rate Optimization Services.

[daneiljames213]

4 types of Salesforce Marketing Attribution

VALiNTRY360’s Salesforce Marketing Attribution solutions provide businesses with powerful insights into which marketing efforts drive real results. By leveraging Salesforce’s robust CRM and automation tools, we help you accurately track every customer interaction across channels—from first touch to final conversion. Our customized attribution models, including first-touch, last-touch, and multi-touch strategies, offer a clear view of campaign effectiveness, enabling data-driven decisions and optimized marketing spend. With VALiNTRY360, you gain full visibility into your marketing ROI and can align efforts more closely with sales outcomes, boosting overall performance and growth.

 For more info visit us https://valintry360.com/b2b-marketing-attribution-solutions