Time For Alternative Attribution Models

“Data Driven Thinking” is written by members of the media community and containing fresh ideas on the digital revolution in media.

Today’s column is written by Ronald Paul, CEO at Quisma, a European performance marketing agency and a subsidiary of WPP Group’s GroupM.

When working in online advertising, if you try and evaluate individual channels as effective or non effective you are in danger of losing efficiency and money.

It is much more important to look at “the big picture” and to analyse how and when consumers use individual channels in the different phases of the purchasing process.

Both in advertising research as well as in practice it is increasingly recognized that individual advertising channels should not be examined separately. Users interact with brands and products via multiple channels – driven by growing multimedia consumer behaviour – creating numerous contact points.

Longer running display campaigns, for example, can be used to draw attention to new products, whereas specific search queries often come at the end of a purchasing process. Under the last cookie wins model, search queries are attributed more conversions than they are actually responsible for. This illustrates that this method does not adequately evaluate the effectiveness and the efficiency of marketing measures.

Individual attribution models are more effective because they are more precise. By placing a different emphasis on the touch points, other channels will inevitably be rewarded as well, thereby changing their share and contribution to the overall sales. By using individual attribution models, advertisers can find out which channels initiated the purchase, which ones prepared the purchase and which ones profited from the purchase.

First Cookie Wins

Obviously, if we’re looking for the opposite of the “last cookie wins” we have to look at the “first cookie wins” model where the sale will be attributed to the first ad a user clicked on, but what is the difference?

Even if the buyer clicked on several ads, the conversion will only be attributed to the first click. This method allows you to identify those advertising channels that drew the user’s attention to the product in the first place. The focus therefore lies on awareness channels, such as display advertising or SEA generic (that is buying keywords that are associated with the brand rather than actual brand names), resulting in lower CPOs and a higher share in the overall sales.

The following charts represent a hypothetical example of different conversion attribution models and their effects on conversions and costs, starting with first cookie wins vs. last cookie wins.

The table below compares two attribution methods, last cookie wins and first cookie wins. Under the last cookie wins model the conversion is attributed to the last “touchpoint” with the ad creative and vice versa. The table shows the difference between two hypothetical scenarios and shows which conversions (per channel) and costs (per channel) arise under the different attribution scenarios.


While under the last cookie wins model retargeting is considered one of the two best channels, under the first cookie wins model retargeting is less efficient. The same applies for brand search. Display advertising and Search generic gain sales and efficiency under the first cookie wins model, which underlines their awareness generating effect.

This is all interesting reading, but can be very confusing for the client – But which should an advertiser choose? Even if you have data about one’s advertising activities available, conducting the most difficult calculations, and having large information about the consumer behaviour, you will never find a clear answer to this question due to the fact that even consumers cannot clearly say why they bought a product from a specific provider.

If every product buy could be attributed to the first click or the first recommendation, only the first cookie wins model would be applicable. But consumers change their mind, they meet new providers and products while deciding which product to buy, and they are also strongly influenced by their direct environment. A consumer does not determine his or her opinion after one impression. On the other hand, you can also argue against the last cookie wins model. How would a channel, such as retargeting, receive 100% conversion, if the user didn’t even have the chance to see a retargeting banner without upstream channels (i.e. affiliate or SEA generic)? Also SEA brand clicks are always the result of brand awareness, created by other advertising channels and PR.

Therefore, it is recommended that advertisers use the uniform distribution model, where every click on an ad during a customer journey receives the same conversion share. This method allows you not to limit yourself to one individual channel, but instead look at the interaction of all channels. This should be the basis for a more efficient budget allocation. Other models, such as the uniform distribution or completely individual distributions should only be used in exceptional cases.

The second table, shown below, compares the last cookies wins model with the equal attribution of conversions. The latter attaches equal importance to each channel within the customer journey. The table shows how the different models affect conversions (per channel) and costs (per channel).


The uniformly distributed allocation model is the best compromise between the first cookie wins and the last cookie wins model, since it also takes those channels into account that appear in the middle of the purchase decision process. Initiating and spreading channels, such as display advertising and Search generic, benefit from the uniformly distributed evaluation compared to the last cookie wins model, while the efficiency of absorbing channels, such as retargeting and SEA brand, is downgraded.

This knowledge helps advertisers determine the effectiveness and efficiency of individual channels, and to allocate online marketing budgets based on these results.

The matrix below shows how efficient and effective each channel is, the cost per order of the different cookie scenarios are shown in relation to their share of the overall sales percentage. Therefore it is possible to evaluate each channel according to its efficiency (cost per order) and effectiveness (percentage in relation to overall sales) within the different cookie scenarios.


You can see that in the uniformly distributed attribution model display advertising is responsible for many sales, but at relatively high costs compared to affiliate. Shifting budget from display to affiliate would allow generating conversions way more cost efficiently, increasing also the share of affiliate sales.

At the same time you could relocate a significant share of the SEA generic budget to display and affiliate. Since all three channels address the user at similar points, adverse effects are hardly expected.

However, you need to be careful when allocating to absorbing channels, i.e. SEA brand and retargeting. It may seem logical to invest more into these channels, since their CPOs are considerably low compared to other advertising instruments. But traffic in these channels is often limited; it therefore happens frequently that the budget cannot be completely allocated. Also, it would lead to cuts in awareness generating channels, causing a drop in image and brand awareness.

To sum up, a multi-channel analysis together with the uniformly distributed attribution model has the big advantage of taking into account every channel in the customer journey by simply placing equal importance on each channel. This allows advertisers to decide how to allocate your budget more efficiently. It would be advisable to record CPO modifications, at least on a monthly basis, to reach the budget optimum.

Follow QUISMA (@twisma) and AdExchanger (@adexchanger) on Twitter.

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  1. Charles III

    Interesting article! I’ve some questions:

    1) uniform distribution model = Multi-Touch Attribution as Google Analytics?
    2) Why into second table total sales between “Last Cookie” and “Uniformly distributed” don’t match? (55.000 vs 54.951). How are they calculated?
    3) Very interesting the graph, where Display post-click on last-cookie vision is efficient and effective. These data are based just on US market?


    • When it comes to uniform distribution companies like Google & Quisma consider every touchpoint equally which allows them to see each variable’s contribution to overall sales. The difference between Google is that they can also include search views into their model. Search visits and views are generated when a user enters a keyword or sees an ad without clicking on it. Logically only Google is able to track those views as they do so with their Google Conversion Tracking Tool. Google, however, focuses on the search channel, while QUISMA applies a more holistic approach looking at every marketing channel. The number is rounded up for SEA brand but it is still the same amount. The numbers we used were hypothetical to show a possible scenario how conversions are distributed under the last cookie wins and uniform distribution model. Does that answer your question? Cheers, Ronald

  2. All good stuff, Ronald, but just as you feel last ad click isn’t good enough, I feel that pre-built weighting models aren’t good enough either. We use machine-learning algorithms that identify incremental lifts across the board for every media touch point (and variables of touch points) to build out very accurate models for every data set…the models even change from month to month as the campaign variables change. As well, machine-learning has an advantage that it can ingest new data streams and incorporate them on the fly to understand their impact (if any) to the attribution modeling.

    I’m glad you’re promoting the use of attribution but I fundamentally believe that squeezing campaign data into pre-built models is just not the way to go.

    • Hi Josh,
      I completely agree that models based on machine-learning algorithms show the outcome of advertising activities. They can predict sales based on the investments made. If one invests 100,000 dollars in Search and 200,000 in Display the model will predict sales based on these investments. The result is, however, like a “black box” as the model doesn’t allow to differentiate between the contributions of each channel which is why it falls short to answer the question what happens when budget is shifted from Display to Search and vice versa. When thinking of a solution how to approach conversion attribution of advertising activities (online and offline) we had considered machine-learning algorithms too, but In the end we decided to use the regression analysis as to take all media channels into account. Cheers, Ronald

      • Nicolas

        Hi Ronald,

        Just to make sure I understand, are you saying that you came to the conclusion that the uniform distribution model is best based on the regression analysis performed? I agree with other commenters that a machine-learning or regression-based approach is better than pre-weighted models, and in fact I believe that with the right analytics team in place, those models don’t have to be a black box. Great read!

      • When I mentioned the black box, I was referring solely to machine-learning algorithms not to regression analysis. Machine-learning systems can calculate something without being able to explain of what the result is made up. That is why I used the example with the two budgets for Search and Display.

        In order to allocate budgets it is of upmost importance to know each channel’s individual contribution to the outcome or overall sales. One must know exactly the influence of every single advertising activity on the conversions to be able to allocate budgets as efficient and effective as possible.

        Certainly regression analysis models are able to show you how to allocate your budget, especially when it comes to allocating offline and online spending. If you only focus on online channels other automated models are a better choice (regression models must be calculated individually and manually while attribution is automated). This is where conversion attribution comes into play.

        Based on sector and client traffic data it has to be decided which individual attribution model works best for the client. This is by no means an arbitrary decision but rather the result of weighting individual touchpoints based on statistical analysis, studies, and experience. This way we were able to provide clients with recommendations that helped them to significantly raise their sales and revenues in the past.

  3. Great article!

    Another question, for display, are you counting post view conversions or just post click?



    • Hey Nathan,
      we`re measuring and modeling post view data and post click data to understand the full value of the costumer journey and the influence of each media channel. Cheers, Ronald

      • Thanks Ronald,

        Surely the issue with doing this is that a lot of display banners are not seen (up to 70%). Wouldn’t that completely skew the figures?

      • In online marketing Display Advertising is still one of the best channels to support brand awareness and draw users’ attention to a product or service. Our analyses of marketing activities repeatedly revealed that users that had been presented with ad banners, under the post view or post click model, were more likely to convert than others.

        Furthermore experience has shown that those users afterwards look for the products or services in search channels (be it organic search or paid listings). Years of data collection have shown that advertisers that invest more in Display campaigns will increase conversions and hence sales and revenue. This is why we also consider post-view activities in our models.

  4. Christian

    Hi Ronald,

    Great piece! I think there’s a small mistake in the matrix: you guys mixed up the small color blocks next to “display”. With a uniform attribution modeling in place display should be valued as more effective and actually much more efficient than with a last click modelling, right?