Attribution (marketing)

Last updated February 11, 2024

In marketing, attribution, also known as multi-touch attribution, is the identification of a set of user actions ("events" or "touchpoints") that contribute to a desired outcome, and then the assignment of a value to each of these events.^[1]^[2] Marketing attribution provides a level of understanding of what combination of events in what particular order influence individuals to engage in a desired behavior, typically referred to as a conversion.^[1]^[2]

History

The roots of marketing attribution can be traced to the psychological theory of attribution.^[2]^[3] By most accounts, the current application of attribution theory in marketing was spurred by the transition of advertising spending from traditional, offline ads to digital media and the expansion of data available through digital channels such as paid and organic search, display, and email marketing.^[2]^[4]

Concept

The purpose of marketing attribution is to quantify the influence each advertising impression has on a consumer's decision to make a purchase decision, or convert.^[4] Visibility into what influences the audience, when and to what extent, allows marketers to optimize media spend for conversions and compare the value of different marketing channels, including paid and organic search, email, affiliate marketing, display ads, social media and more.^[2] Understanding the entire conversion path across the whole marketing mix diminishes the accuracy challenge of analyzing data from siloed channels. Typically, attribution data is used by marketers to plan future ad campaigns and inform the performance of previous campaigns by analyzing which media placements (ads) were the most cost-effective and influential as determined by metrics such as return on ad spend (ROAS) or cost per lead (CPL).^[2]

Attribution models

Resulting from the disruption created by the rapid growth of online advertising over the last ten years, marketing organizations have access to significantly more data to track effectiveness and ROI. This change has impacted how marketers measure the effectiveness of advertisements, as well as the development of new metrics such as cost per click (CPC), Cost per thousand impressions (CPM), Cost per action/acquisition (CPA) and click-through conversion. Additionally, multiple attribution models have evolved over time as the proliferation of digital devices and tremendous growth in data available have pushed the development of attribution technology.

Single Source Attribution (also Single Touch Attribution) models assign all the credit to one event, such as the last click, the first click or the last channel to show an ad (post view). Simple or last-click attribution is widely considered as less accurate than alternative forms of attribution as it fails to account for all contributing factors that led to a desired outcome.^[2]^[5]
Fractional Attribution includes equal weights, time decay, customer credit, and multi-touch / curve models.^[2]^[4] Equal weight models give the same amount of credit to the events, customer credit uses past experience and sometimes simply guesswork to allocate credit, and the multi-touch assigns various credit to across all the touchpoints in the buyer journey at set amounts.^[5]
Algorithmic or Probabilistic Attribution uses statistical modeling and machine learning techniques to derive probability of conversion across all marketing touchpoints which can then be used to weight the value of each touchpoint preceding the conversion.^[5]^[6] Also known as Data Driven Attribution Google's Doubleclick and Analytics 360 use sophisticated algorithms to analyze all of the different paths in your account (both non-converting and converting) to figure out which touchpoints help the most with conversions.^[7] Algorithmic attribution analyzes both converting and non-converting paths across all channels to determine probability of conversion.^[4]^[6] With a probability assigned to each touchpoint, the touchpoint weights can be aggregated by a dimension of that touchpoint (channel, placement, creative, etc.) to determine a total weight for that dimension.
Customer Driven Attribution models are developed through the collection of zero-party data and then use projective analytics to provide a full view attribution picture. This method of attribution was developed with the belief that responses from customers should be the strongest weighted data point in an attribution calculation. It's an attempt to simplify attribution and go back to the basics.

Constructing an algorithmic attribution model

Binary classification methods from statistics and machine learning can be used to build appropriate models. However, an important element of the models is model interpretability; therefore, logistic regression is often appropriate due to the ease of interpreting model coefficients.

Behavioral model

Suppose observed advertising data are $\{(X_{i},A_{i},Y_{i})\}_{i=1}^{n}$ where

$X\in \mathbb {R}$ covariates
$A\in \{0,1\}$ consumer saw ad or not
$Y\in \{0,1\}$ conversion: binary response to the ad

Consumer choice model

$u(x,a)=\mathbb {E} (Y|X=x,A=a)$ $\forall X\in \mathbb {R}$ covariates and $\forall A$ ads

$u=\sum _{k}A\beta ^{k}\psi (x)+\epsilon$

Covariates, $X$ , generally include different characteristics about the ad served (creative, size, campaign, marketing tactic, etc.) and descriptive data about the consumer who saw the ad (geographic location, device type, OS type, etc.). ^[8]

Utility theory

$y_{i}^{*}={\underset {y_{i}}{\max }}{\bigl (}\mathbb {E} [u_{i}]{\bigr )}$

$Pr(y=1|x)=Pr(u_{1}>u_{0})$

$=1/[1+e^{\sum _{k}A\beta ^{k}\psi (x)}]$ ^[9]

Counterfactual procedure

An important feature of the modeling approach is estimating the potential outcome of consumers supposing that they were not exposed to an ad. Because marketing is not a controlled experiment, it is helpful to derive potential outcomes in order to understand the true effect of marketing.

Mean outcome if all consumers saw the same advertisement is given by

$\mu _{a}=\mathbb {E} Y^{*}(a)$

$=\mathbb {E} \{\mathbb {E} (Y|X,A=a)\}$

A marketer is often interested in understanding the 'base', or the likelihood that a consumer will convert without being influenced by marketing. This allows the marketer to understand the true effectiveness of the marketing plan. The total number of conversions minus the 'base' conversions will give an accurate view of the number of conversions driven by marketing. The 'base' estimate can be approximated using the derived logistic function and using potential outcomes.

${\text{Base}}={\frac {\text{Predicted Conversions Without Observed Marketing}}{\text{Predicted Conversions With Observed Marketing}}}$ $={\frac {\mathbb {E} \{\mathbb {E} (Y|X,A=0)\}}{\mathbb {E} \{\mathbb {E} (Y|X,A=1)\}}}$

Once the base is derived, the incremental effect of marketing can be understood to be the lift over the 'base' for each ad supposing the others were not seen in the potential outcome. This lift over the base is often used as the weight for that characteristic inside the attribution model.

${\text{Attribution Weight}}=$ $={\frac {\mathbb {E} \{\mathbb {E} (Y|X,A=1)\}-\mathbb {E} \{\mathbb {E} (Y|X,A=0)\}}{\mathbb {E} \{\mathbb {E} (Y|X,A=1)\}}}$

With the weights constructed, the marketer can know the true proportion of conversions driven by different marketing channels or tactics.

Marketing mix and attribution models

Depending on the company's marketing mix, they may use different types of attribution to track their marketing channels:

Interactive Attribution refers to the measurement of digital channels only, while cross-channel attribution refers to the measurement of both online and offline channels.^[6]
Account based attribution refers to measuring and attributing credit to companies as a whole rather than individual people and is often used in B2B marketing.^[10]

Benefits and challenges of marketing attribution

Marketing attribution is a tool that provides companies with information about the effectiveness of their strategies.^[11]^[12] However, like any tool, marketing attribution has its pros and cons.

Advantages

Improved decision-making process. Marketing attribution provides companies with information about the effectiveness of their various marketing channels and campaigns. It provides data-driven decision making, allowing companies to allocate resources more efficiently.^[13]^[14]
Return on investment. One of the major benefits of marketing attribution is the ability to maximize return on investment (ROI).^[15] Attribution provides a 15-30% improvement in investment performance.^[16]
Precision targeting. With attribution data, companies can target their marketing efforts more precisely. They can tailor messages, content, and offers to specific customer segments, increasing the relevance of their marketing materials and, in turn, conversion rates.

Challenges

Without a clear understanding of the pros and cons of each model, attribution reports are difficult to structure and customize to meet an organization's goals.
There is no such thing as a perfect strategy. Determining marketing attribution is not a perfect science, and trial and error methods are necessary.
Ever since GDPR, CCPA and other privacy laws were enacted, analytics data has become more and more unreliable every year.^[17]^[18]
For organizations that rely on web visitors signing up to be tracked, attribution modeling suffers due to the inability to track every touchpoint.^[19]

Related Research Articles

In economics, utility is a measure of the satisfaction that a certain person has from a certain state of the world. Over time, the term has been used in two different meanings.

Mental accounting is a model of consumer behaviour developed by Richard Thaler that attempts to describe the process whereby people code, categorize and evaluate economic outcomes. Mental accounting incorporates the economic concepts of prospect theory and transactional utility theory to evaluate how people create distinctions between their financial resources in the form of mental accounts, which in turn impacts the buyer decision process and reaction to economic outcomes. People are presumed to make mental accounts as a self control strategy to manage and keep track of their spending and resources. People budget money into mental accounts for savings or expense categories. People also are assumed to make mental accounts to facilitate savings for larger purposes. Mental accounting can result in people demonstrating greater loss aversion for certain mental accounts, resulting in cognitive bias that incentivizes systematic departures from consumer rationality. Through increased understanding of mental accounting differences in decision making based on different resources, and different reactions based on similar outcomes can be greater understood.

In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment. Intuitively, IVs are used when an explanatory variable of interest is correlated with the error term (endogenous), in which case ordinary least squares and ANOVA give biased results. A valid instrument induces changes in the explanatory variable but has no independent effect on the dependent variable and is not correlated with the error term, allowing a researcher to uncover the causal effect of the explanatory variable on the dependent variable.

Cost per action (CPA), also sometimes misconstrued in marketing environments as cost per acquisition, is an online advertising measurement and pricing model referring to a specified action, for example, a sale, click, or form submit.

In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters. A Poisson regression model is sometimes known as a log-linear model, especially when used to model contingency tables.

In online marketing, a landing page, sometimes known as a "lead capture page", "single property page", "static page", "squeeze page" or a "destination page", is a single web page that appears in response to clicking on a search engine optimized search result, marketing promotion, marketing email or an online advertisement. The landing page will usually display directed sales copy that is a logical extension of the advertisement, search result or link. Landing pages are used for lead generation. The actions that a visitor takes on a landing page are what determine an advertiser's conversion rate. A landing page may be part of a microsite or a single page within an organization's main web site.

In statistics, binomial regression is a regression analysis technique in which the response has a binomial distribution: it is the number of successes in a series of independent Bernoulli trials, where each trial has probability of success . In binomial regression, the probability of a success is related to explanatory variables: the corresponding concept in ordinary regression is to relate the mean value of the unobserved response to explanatory variables.

Digital marketing is the component of marketing that uses the Internet and online-based digital technologies such as desktop computers, mobile phones, and other digital media and platforms to promote products and services. Its development during the 1990s and 2000s changed the way brands and businesses use technology for marketing. As digital platforms became increasingly incorporated into marketing plans and everyday life, and as people increasingly used digital devices instead of visiting physical shops, digital marketing campaigns have become prevalent, employing combinations of search engine optimization (SEO), search engine marketing (SEM), content marketing, influencer marketing, content automation, campaign marketing, data-driven marketing, e-commerce marketing, social media marketing, social media optimization, e-mail direct marketing, display advertising, e-books, and optical disks and games have become commonplace. Digital marketing extends to non-Internet channels that provide digital media, such as television, mobile phones, callbacks, and on-hold mobile ringtones. The extension to non-Internet channels differentiates digital marketing from online marketing.

A touchpoint can be defined as any way consumers can interact with a business organization, whether it be person-to-person, through a website, an app or any form of communication. When consumers come in contact with these touchpoints it gives them the opportunity to compare their prior perceptions of the business and form an opinion.

In statistics, principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). More specifically, PCR is used for estimating the unknown regression coefficients in a standard linear regression model.

Customer experience is the totality of cognitive, affective, sensory, and behavioral customer responses during all stages of the consumption process including pre-purchase, consumption, and post-purchase stages.

In statistics and machine learning, lasso is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model. The lasso method assumes that the coefficients of the linear model are sparse, meaning that few of them are non-zero. It was originally introduced in geophysics, and later by Robert Tibshirani, who coined the term.

A user journey is the experiences a person has when interacting with something, typically software. This idea is generally used by those involved with user experience design, web design, user-centered design, or anyone else focusing on how users interact with software experiences. It is often used as a shorthand for the overall user experience and set of actions that one can take in software or other virtual experiences.

Call tracking software records information about incoming telephone calls, and in some regions even the conversation. Call tracking is a technology which can enable the pay per call, pay per minute or pay per lead business model, allowing the tracking of phone calls to be associated with performance-based advertising such as Google AdWords, SEO Services, Display and Electronic Direct Marketing, and supplying additional analytic information about the phone calls themselves. Call tracking is a method of performance review for advertising and/or staff. It is based on the technological possibility of measuring the behavior of callers and is thus the equivalent in telephony to the conversion tracking used on the internet. Via different channels, both procedures offer the opportunity of clearly assigning a customer response to a specific advertising medium.

Inverse probability weighting is a statistical technique for estimating quantities related to a population other than the one from which the data was collected. Study designs with a disparate sampling population and population of target inference are common in application. There may be prohibitive factors barring researchers from directly sampling from the target population such as cost, time, or ethical concerns. A solution to this problem is to use an alternate design strategy, e.g. stratified sampling. Weighting, when correctly applied, can potentially improve the efficiency and reduce the bias of unweighted estimators.

In electronic commerce, conversion marketing is marketing with the intention of increasing conversions—that is, site visitors who are paying customers.

Marketing automation refers to software platforms and technologies designed for marketing departments and organizations to more effectively market on multiple channels online and automate repetitive tasks.

Functional regression is a version of regression analysis when responses or covariates include functional data. Functional regression models can be classified into four types depending on whether the responses or covariates are functional or scalar: (i) scalar responses with functional covariates, (ii) functional responses with scalar covariates, (iii) functional responses with functional covariates, and (iv) scalar or functional responses with functional and scalar covariates. In addition, functional regression models can be linear, partially linear, or nonlinear. In particular, functional polynomial models, functional single and multiple index models and functional additive models are three special cases of functional nonlinear models.

Google Attribution is a monitoring program developed by Internet advertising company Alphabet Inc. launched in 2017. It has to link with a Google Analytics view that is associated with a registered AdWords or DoubleClick Search account. An attribution model is set of rules, that shows how credit for sales and conversions are allocated to touchpoints in conversion paths. For example, the Last Interaction model in Analytics assigns 100% credit to final touchpoints that immediately precede sales or conversion. In contrast, the First Interaction model assigns 100% credit to touchpoints that initiate conversion paths.

Data-driven marketing is a process used by marketers to gain insights and identify trends about consumers and how they behave — what they buy, the effectiveness of ads, and how they browse. Modern solutions rely on big data strategies and collect information about consumer interactions and engagements to generate predictions about future behaviors. This kind of analysis involves understanding the data that is already present, the data that can be acquired, and how to organize, analyze, and apply that data to better marketing efforts. The intended goal is generally to enhance and personalize the customer experience. The market research allows for a comprehensive study of preferences.

References

1 2 Benjamin Dick (August 1, 2016). "Digital Attribution Primer 2.0" (PDF). IAB.com. Retrieved April 30, 2019.
1 2 3 4 5 6 7 8 Stephanie Miller (February 6, 2013). "Digital Marketing Attribution.Digital Marketing Attribution". DMNews.com. Retrieved March 25, 2013.
↑ Kartik Hosanagar (July 2012). "Attribution: Who gets the Credit for a New Customer?". The Wharton School. Retrieved March 25, 2013.
1 2 3 4 Yair Halevi (October 10, 2012). "The problem with click-based attribution". iMediaConnection.com. Retrieved March 25, 2013.
1 2 3 Tina Moffett (April 30, 2012). "The Forrester Wave: Cross-Channel Attribution Providers". Forrester Research. Archived from the original on April 13, 2013. Retrieved March 22, 2013.
1 2 3 David Raab (July 1, 2011). "Marketing Attribution Beyond the Last Click". Information-Management.com. Retrieved March 25, 2013.
↑ Broadbent, Andrew J. (1918-01-10). Perfect attribution modeling and how to attain this marketing nirvana. TNW.
↑ Lancaster, Kelvin J. (1966-01-01). "A New Approach to Consumer Theory". Journal of Political Economy. 74 (2): 132–157. doi:10.1086/259131. S2CID 222425622.
↑ McFadden, D. (1972-01-01). "CONDITIONAL LOGIT ANALYSIS OF QUALITATIVE CHOICE BEHAVIOR". Working Paper Institute of Urban and Regional (199/).
↑ "Why Your Demand Team Can't Ignore Account Based Attribution". www.bizible.com. Retrieved 2016-01-11.
↑ "What is Marketing Attribution?". www.deskera.com. Retrieved 2023-12-09.
↑ "Marketing Attribution Models: Types & 2023 Best Options". businessyield.com. Retrieved 2023-12-09.
↑ "Benefits of marketing attribution model in business". www.optimics.cz. Retrieved 2023-12-09.
↑ "Marketing Attribution Model: A Primer for Content Marketers". contentmarketinginstitute.com. Retrieved 2023-12-09.
↑ "The Ultimate Guide To Marketing Attribution In 2024". growify.ai. Retrieved 2023-12-09.
↑ "Attribution Analysis: Definition and How It's Used for Portfolios". www.investopedia.com. Retrieved 2023-12-09.
↑ "GDPR, CCPA and beyond: Changes in data privacy laws and enforcement risks to monitor in 2019". www.dataprotectionreport.com. Retrieved 2023-12-09.
↑ "Data Protection in Transition: GDPR, CCPA and Comparable Data Protection Laws". www.dfinsolutions.com. Retrieved 2023-12-09.
↑ "Touchpoints and attribution reports" (PDF). www.zoho.com. Retrieved 2023-12-09.

Mofet, Tina. "The Forrester Wave: Cross-Channel Attribution Providers (November 7, 2014)". Archived from the original on July 9, 2015. Retrieved July 8, 2015.

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[A1-1] 1 2 Benjamin Dick (August 1, 2016). "Digital Attribution Primer 2.0" (PDF). IAB.com. Retrieved April 30, 2019.

[A2-2] 1 2 3 4 5 6 7 8 Stephanie Miller (February 6, 2013). "Digital Marketing Attribution.Digital Marketing Attribution". DMNews.com. Retrieved March 25, 2013.

[A3-3] Kartik Hosanagar (July 2012). "Attribution: Who gets the Credit for a New Customer?". The Wharton School. Retrieved March 25, 2013.

[A4-4] 1 2 3 4 Yair Halevi (October 10, 2012). "The problem with click-based attribution". iMediaConnection.com. Retrieved March 25, 2013.

[A5-5] 1 2 3 Tina Moffett (April 30, 2012). "The Forrester Wave: Cross-Channel Attribution Providers". Forrester Research. Archived from the original on April 13, 2013. Retrieved March 22, 2013.

[A6-6] 1 2 3 David Raab (July 1, 2011). "Marketing Attribution Beyond the Last Click". Information-Management.com. Retrieved March 25, 2013.

[7] Broadbent, Andrew J. (1918-01-10). Perfect attribution modeling and how to attain this marketing nirvana. TNW.

[8] Lancaster, Kelvin J. (1966-01-01). "A New Approach to Consumer Theory". Journal of Political Economy. 74 (2): 132–157. doi:10.1086/259131. S2CID 222425622.

[9] McFadden, D. (1972-01-01). "CONDITIONAL LOGIT ANALYSIS OF QUALITATIVE CHOICE BEHAVIOR". Working Paper Institute of Urban and Regional (199/).

[10] "Why Your Demand Team Can't Ignore Account Based Attribution". www.bizible.com. Retrieved 2016-01-11.

[11] "What is Marketing Attribution?". www.deskera.com. Retrieved 2023-12-09.

[12] "Marketing Attribution Models: Types & 2023 Best Options". businessyield.com. Retrieved 2023-12-09.

[13] "Benefits of marketing attribution model in business". www.optimics.cz. Retrieved 2023-12-09.

[14] "Marketing Attribution Model: A Primer for Content Marketers". contentmarketinginstitute.com. Retrieved 2023-12-09.

[15] "The Ultimate Guide To Marketing Attribution In 2024". growify.ai. Retrieved 2023-12-09.

[16] "Attribution Analysis: Definition and How It's Used for Portfolios". www.investopedia.com. Retrieved 2023-12-09.

[17] "GDPR, CCPA and beyond: Changes in data privacy laws and enforcement risks to monitor in 2019". www.dataprotectionreport.com. Retrieved 2023-12-09.

[18] "Data Protection in Transition: GDPR, CCPA and Comparable Data Protection Laws". www.dfinsolutions.com. Retrieved 2023-12-09.

[19] "Touchpoints and attribution reports" (PDF). www.zoho.com. Retrieved 2023-12-09.

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