Attribution Statistics 2024 – Everything You Need to Know

Are you looking to add Attribution to your arsenal of tools? Maybe for your business or personal use only, whatever it is – it’s always a good idea to know more about the most important Attribution statistics of 2024.

My team and I scanned the entire web and collected all the most useful Attribution stats on this page. You don’t need to check any other resource on the web for any Attribution statistics. All are here only 🙂

How much of an impact will Attribution have on your day-to-day? or the day-to-day of your business? Should you invest in Attribution? We will answer all your Attribution related questions here.

Please read the page carefully and don’t miss any word. 🙂

Best Attribution Statistics

☰ Use “CTRL+F” to quickly find statistics. There are total 123 Attribution Statistics on this page 🙂

Attribution Market Statistics

  • 76% of all marketers say they currently have, or will have in the next 12 months, the capability to use marketing attribution . [0]
  • 75% of companies are using a multi touch attribution model to measure marketing performance. [0]
  • 41% of marketers are most commonly using the “last touch” method for their online attribution . [0]
  • 44% say that a “firsttouch” model is more useful for measuring digital campaigns . [0]
  • 84% of marketers are confident that marketing impacts revenue and sales. [0]
  • While the majority of marketers are satisfied with the value of their campaigns, only 60% are confident they’re able to demonstrate ROI. [0]
  • Optimising the customer journey across multiple touch points is viewed “very important” by 71% of marketers. [0]
  • 59.4% agree that sales and marketing alignment is the main goal of marketing attribution Sales and marketing alignment , or smarketing, is possibly the greatest opportunity for successful marketing attribution. [0]
  • Attribution across your multitude of marketing channels can provide efficiency gains of 15. [0]
  • The average conversion rate for email marketing is 3.9%. [0]
  • 100% of marketers say they have trouble tracking television or radio ads. [0]
  • 62% of marketers are failing to attribute revenue to inbound calls. [0]
  • 53.3% say a minimal understanding is the main challenge of effective marketing attribution. [0]
  • 53% of marketers are struggling to track and attribute live chat conversions. [0]
  • 42% of marketers report attribution manually using spreadsheets . [0]
  • During a survey, nearly 70% of marketers said they use a CRM to store key lead generation data. [0]
  • Only 39% of companies are carrying out attribution on ‘all or most’ of their marketing activities . [0]
  • 56% of marketers believe attribution is important, while a further 33% believe it’s nothing short of critical. [1]
  • Inbound marketing budget are cut 12% more for companies that don’t calculate ROI. [1]
  • 12% say data driven marketing that focuses on the individual is. [1]
  • the single most important opportunit 8% say multichannel marketing is the single most important opportunity. [1]
  • 42% report attribution manually using spreadsheets 39% expect to use an average of 6 or more channels over two years 78% of marketers plan to adapt of increase their use of cross channel attribution 30% plan on changing attribution model in the next 6 months. [1]
  • Leading marketers are 31% more likely than mainstream marketers to have increased investment in technologies that make site experiences faster. [2]
  • The top 100 most mature marketers on the maturity curve are 4X as likely to exceed business goals, increase market share, and increase revenue as the 100 least mature marketers. [2]
  • A Bayesian regression model indicates a threshold response to anthropogenic warming, with a greatly increased chance of recruitment failure for FAR ≥ 0.98 (Bayesian R2 = 0.35 [95% CI 0.16–0.51]; Fig. 4b). [3]

Attribution Latest Statistics

  • Estimated spawning stok biomass from age strutured population models with 95% CI (for pollok) and ± 2SD (for od). [3]
  • [95% CI 0.22–0.53], see Table 1 for detailed results on model selection and SI for parameter estimates). [3]
  • Beach seines were conducted during four years with FAR values ≥ 0.98, indicating SST anomalies that would have been highly unlikely in the preindustrial ocean. [3]
  • in all four of these years . [3]
  • [95% CI 19–89 fish set−1], and 2 fish set−1 at FAR ≥ 0.98. [3]
  • Estimated od reruitment from stok assessment model (1977–2016; 2017–2020 values derived from beah seines). [3]
  • As a second source of cod recruitment information, we consider the time series of estimated recruitment from the age structured stock assessment model, which begins in 1977. [3]
  • Values for 2017–2020 are poorly supported by data in the model, so we estimated these values from the beach seine data . [3]
  • Time series lodings nd 95% CI. [3]
  • Shared trend in variaility, with 95% CI. [3]
  • Estimated age 0 pollock recruitment from stock assessment model as a function of FAR predicted values and 80%/90%/95% CI. [3]
  • Red line indicates predicted FAR value, with grey ribbons indicating 80/90/95% CI due to model uncertainty. [3]
  • Predicted cod and pollock recruitment ased on historical oserved FAR values and current/future decades under. [3]
  • FAR projections median values with 95% CI. [3]
  • The 38–88% decline in median, non log transformed recruitment projected for the 2020s would likely be sufficient to ensure a reduced likelihood of recovery or increased likelihood of population declines. [3]
  • The differences in expected recruitment under historical and forward looking perspectives are statements of probability , and the 95% credible intervals for recruitment in the 2020s include the historical mean values. [3]
  • However, the strong expected climate trend in this system is likely to overwhelm any beneficial effects due to climate variability 60 Field sampling and stock assessment models. [3]
  • While age0 cod preferentially occupy nearshore habitats that are well sampled by beach seines, we were concerned that age 0 pollock are more likely to be offshore and thus poorly sampled by seines. [3]
  • Abundance and biomass of pollock at age are estimated from 16 m below the sea surface to 0.5 m above the seafloor. [3]
  • We estimated field values of pollock recruitment with a DFA model fit in the R package MARSS, using an error structure with different time series variances and no covariance 63. [3]
  • (95% credible intervals indicating at least 89% of the risk was human induced; Fig. [3]
  • First, we estimated annual abundance for beach seines using a ZINB model that included a day of year effect and random effects for sampling location. [3]
  • Second, we predicted recruitment for 2017–2020 using a linear regression model that was fit using only the years of overlap between assessment recruitment and model estimated CPUE. [3]
  • 53.5% say that the last touch attribution model is somewhat effective. [0]
  • Data suggests that industrial (7.4%), B2B services (5.9%), finance (5.8%) and professional services (5.1%) come out on top for having the highest average conversion rates, whereas real estate (1.3%) and automotive (0.8%). [0]
  • The average conversion rate for organic search is 5.0%. [0]
  • Professional services (12.3%), industrial (8.5%) and B2B services (7.0%) sweep the board for having the highest conversion rates, whereas B2B tech (1.0%), B2B eCommerce (3.5%) and travel (3.5%). [0]
  • The average conversion rate for paid search is 3.6%. [0]
  • Professional services (7.0%) came out on top for having the highest average conversion rate, closely followed by agency (6.6%) and finance (6.0%). [0]
  • automotive (1.3%), real estate (1.5%) and B2C eCommerce (1.8%). [0]
  • The average conversion rate for referral is 4.1%. [0]
  • Interestingly, travel (9.5%). [0]
  • Finance (7.1%) and healthcare (7.1%) came in second, while automotive (1.3%) and real estate (1.5%). [0]
  • The average conversion rate for social media is 1.9%. [0]
  • Performance is low across the board, although we found that professional services (4.0%), healthcare (3.1%) and automotive (2.9%). [0]
  • In fact, TV ad spending in the US is set to bounce back this year and increase by 33.1% to $2.85 billion. [0]
  • 70% of businesses are now struggling to act on the insights they gain from attribution . [0]
  • For example, reporting that “Google Ads generated £100k revenue” is a lot more effective than “we increased conversions by 50%”. [0]
  • In fact, phone calls are 10 15 times more likely to convert than inbound web leads. [0]
  • 59.6% ranked “generating qualified leads” the top lead generation challenge. [0]
  • This figure has climbed from 31% in the last year. [0]
  • Attribution provides efficiency gains of 15. [1]
  • 67% of shoppers regularly use more than one channel to make purchases. [1]
  • 59% say creating a culture of measurement and accuracy is the biggest attribution challenge 22% say they believe they’re using the right attribution model. [1]
  • 30% say the reason fro choosing their current attribution model is ease of implementation and setup. [1]
  • 77% say they believe they’re not using the right attribution models, or they don’t know. [1]
  • 60% don’t action the insights they get from attribution. [1]
  • 24% of UK companies is very confident that their agencies carry out attribution impartially. [1]
  • 56% say campaign tracking/tagging is a big attribution challenge. [1]
  • 56% data validation/normalisation is a big attribution challenge. [1]
  • But, just 23% use these methods. [1]
  • The suggested campaign budget reallocations from SMX®’s optimizer can routinely find ROI gains of 40% plus in the B2B space through informed reasonable reallocations. [4]
  • On average, retailers are investing less than 1% of their revenue on personalization, while bestin class retailers are investing 30% more. [2]
  • Stone and Allen formally introduced the concepts of fraction attributable risk and risk ratio and proposed how they might be estimated given available tools. [5]
  • In this context, one often uses a block maximum approach, blocking by year, or a peaksover threshold approach that only uses observations over a high threshold, such as the 99th percentile of the observations. [5]
  • For the sake of illustration we will discuss a 90% confidence interval here, but the ideas are the same for other levels of confidence. [5]
  • The basic principle of a 90% confidence interval is that it is a random interval that should include the true value of the RR in 90% of the datasets that we might observe/collect. [5]
  • Our goal is to use a procedure for calculating a confidence interval that produces intervals as short as possible while still having the specified coverage probability of 90%. [5]
  • Given knowledge of this sampling distribution, we can analytically derive a 90% confidence interval for μ as plus/minus 1.64 times the standard error, , without needing to use the bootstrap. [5]
  • If we are happy to assume normality, we can use the empirical standard deviation of the values as the standard error estimate, which we denote , to form a 90% confidence interval in the usual way, •. [5]
  • This gives the following 90% interval, involving the 5th and 95th percentiles of the values, •. [5]
  • Let and be the 5th and 95th percentiles of the values. [5]
  • Adjusted percentile bootstrap interval this approach seeks to improve upon the percentile interval by estimating a transformation that brings the sampling distribution closer to normality. [5]
  • More generally than just in this context of estimating the RR, the percentile bootstrap method is known to perform poorly in practice. [5]
  • Thus we focus on 95% one. [5]
  • We see that for the LR, basic bootstrap, and bootstrap t intervals, the intervals fail to include the true value at least 95% of the time. [5]
  • In contrast, the other methods are overly conservative (the intervals include the true value more than 95% of the time). [5]
  • While values higher than 95% may sound appealing, they provide conservative results with intervals that are overly long and thereby increased uncertainty in estimating the RR. [5]
  • Coverage probability of 95% lower confidence bound for for various methods and values of RR and. [5]
  • Coverage probability of 95% upper confidence bound for for various methods and values of RR and. [5]
  • In general these results are similar to those for , but for we see that coverage probability is generally closer to 95%, as we would expect with increasing sample size. [5]
  • Rupp and Mote performed a probabilistic event attribution analysis, but with counterfactual conditions estimated from previous years with similar anomalous temperature patterns in the Pacific Ocean. [5]
  • When using EVA we set the threshold, u, as the 90th percentile for temperature of the values being analyzed. [5]
  • For precipitation we consider the 20th percentile. [5]
  • Using the likelihood ratiobased confidence interval, we have a one sided 95% confidence interval of , while using the Koopman method we have ; both are quite uncertain because of how extreme the event is. [5]
  • The RR estimate is still , with and , but the 95% one sided CI using the likelihood ratio approach is , providing strong evidence for a large RR. [5]
  • Note that we report a twosided 90% CI by considering two one. [5]
  • RR estimates and 90% CI for a variety of event definitions based on binomial count approach. [5]
  • For the actual event, an anomaly value of 0.40 (i.e., 40% as much precipitation as the historical mean). [5]
  • Estimated risk ratio and 90% confidence intervals for both binomial counts and EVA for various definitions of the event in terms of March–August precipitation anomaly over Texas. [5]
  • Zwiers F Overestimated global warming over the past 20 years. [6]
  • Taking account of the information in Table 2, a better error rate estimate is 3.7%. [7]
  • [for example] what would be the effect on annual incidence of cancer in the United States of reducing by 10% the medical use of x. [7]
  • This study showed that it was very likely (greater than 90% chance). [8]
  • The second flaw, it is argued, is that standard event attribution approaches require significance testing in which the null hypothesis of no human influence must first be ruled out at a sufficiently high significance level (typically 5%). [8]
  • 2015) suggest that such a testing based approach is “conservative” and argue that an approach in which event likelihoods are estimated via Bayesian methods would be better both empirically and ethically. [8]
  • Bellprat O, Doblas Reyes F Attribution of extreme weather and climate events overestimated by unreliable climate simulations. [8]
  • genotypeC. jejuni and C. coli which are the dominant species associated with approximately 80% and 15% of illnesses respectively [15]. [9]
  • The total number of unique genotypes from all isolates is 348, with 36% of genotypes found among human cases. [9]
  • Approximately 8% of individuals in the Manawatu dataset have no information about the location, which we assume are missing at random. [9]
  • Posterior attribution probabilityPosterior attribution of human cases of campylobacteriosis with 80% credible intervals for each source is illustrated for each rurality grade in figure 2. [9]
  • Posterior mean attribution of human cases with 80% credible intervals for source poultry, ruminants, water and others over the rurality scales from highly rural areas to main urban areas. [9]
  • To illustrate this, figure 3 shows the probability of source given a selection of four genotypes, assuming a priori that each source was equally likely. [9]
  • Posterior mean attribution of human cases during 2005–2007 and 2008–2014 with 80% credible intervals for poultry, ruminants, water and other sources over the rurality scales from highly rural areas to main urban areas. [9]
  • However, water is known as a key source of outbreaks of campylobacteriosis, such as the large outbreak in Havelock North, New Zealand in 2016 where an estimated 5500 out of 14 000 residents became ill [29]. [9]
  • Posterior attribution of human cases of campylobacteriosis with 80% credible intervals for each source is illustrated for each rurality grade in figure 2. [9]
  • During a survey, 37 percent of respondents stated that first interaction was the most used attribution model. [10]
  • Available to download in PNG, PDF, XLS format 33% off until Jun 30th. [10]

I know you want to use Attribution Software, thus we made this list of best Attribution Software. We also wrote about how to learn Attribution Software and how to install Attribution Software. Recently we wrote how to uninstall Attribution Software for newbie users. Don’t forgot to check latest Attribution statistics of 2024.


  1. ruleranalytics –
  2. leadsrx –
  3. thinkwithgoogle –
  4. nature –
  5. datasciencecentral –
  6. sciencedirect –
  7. springer –
  8. tandfonline –
  9. springer –
  10. royalsocietypublishing –
  11. statista –

How Useful is Attribution

One of the key benefits of attribution is its ability to provide insights into the holistic customer journey. In today’s complex digital environment, customers are interacting with brands across multiple touchpoints before making a purchase decision. Attribution allows marketers to connect the dots between these touchpoints and understand how each interaction influenced the customer’s path to conversion. This deeper understanding of the customer journey enables marketers to deliver more personalized and targeted messaging that resonates with their audience.

Additionally, attribution helps marketers allocate their budget more effectively by identifying high-performing channels and optimizing for better ROI. By analyzing the impact of each touchpoint on the customer journey, marketers can identify which channels are driving conversions and which are underperforming. This data-driven approach allows them to reallocate budget to the most effective channels and tactics, maximizing their marketing efforts and driving better results.

Attribution also helps in identifying the true value of each marketing touchpoint. In a multi-channel marketing strategy, different touchpoints play different roles in influencing the customer’s decision-making process. Attribution helps marketers attribute value to each touchpoint based on its impact on the customer journey, shedding light on the interactions that are most crucial in driving conversions. This understanding allows marketers to focus their efforts on the most impactful touchpoints and optimize their campaigns for better results.

While attribution is a valuable tool for marketers, it is important to acknowledge its limitations. Attribution models are not foolproof and can be subject to biases and inaccuracies. In a complex and interconnected digital ecosystem, it can be challenging to accurately attribute credit to each touchpoint, leading to discrepancies in the data. Marketers must approach attribution with a critical eye and take into account the limitations of their chosen model to ensure they are making informed decisions based on reliable data.

In conclusion, attribution is a valuable tool for marketers in understanding the customer journey, optimizing their marketing strategy, and driving better results. By properly attributing credit to each touchpoint, marketers can gain valuable insights into the effectiveness of their campaigns and make informed decisions about their budget allocations. While attribution is not without its limitations, it remains an essential component of a data-driven marketing strategy in today’s digital age.

In Conclusion

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