Individual ConversioN Attribution
December / 2012
Efficiency Boost for Your Online Campaigns
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1. Introduction In advertising research, as well as in practice, there is widespread acknowledgement that in online marketing individual advertising channels should not be evaluated in isolation. Driven by increasing multimediadriven consumer behaviour, the interaction between users and brands or products takes place across several channels, creating numerous touch points with the advertising mediums. Therefore it would be shortsighted to simply evaluate the effectiveness of individual channels. Analysing how and when consumers use respective channels during the purchasing journey allows for consideration of the “bigger picture”. This examination moves away from focusing on the individual channel and puts the customer conversion as a starting point instead. In the long run, attribution models are geared towards helping to determine the advertising channels’ contribution to a customer conversion. While far-reaching display campaigns can be used to call attention to new products, specific search queries often appear towards the end of a decision-making process. The “last-cookie-wins” model, which is frequently employed, attributes final search queries more conversions than they realistically deserve. This is why this principle is fundamentally unsuitable for accurately aluating marketing measures’ efficiency and effectiveness.
Alternative approaches are often the most effective form of analysis and are equally important to advertisers, publishers, and agencies. Since different attribution models lead to different CPOs, every advertiser needs to ask: which model is best for my individual case?
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2. Common attribution models Conventional attribution models attribute conversions to the individual channels depending on the order of their touch points with the advertising mediums:
Last Cookie-wins-Modell
100%
100%
75%
The last-cookie-wins model attributes all conversion to the last touch point within a customer journey.
50% 25% 0%
First Cookie-wins-Modell
100% 75%
The first-cookie-wins model attributes all conversion to the first touch point within a customer journey.
100%
50% 25% 0%
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Uniformly distributed attribution model
50% 25%
25%
25%
25%
25%
0%
This model equally distributes the shares of each conversion to all touch points. If, for instance, a user was addressed at four different touch points, each touch point would get 25 percent of the conversion success.
Decreasing model
50% 25%
35%
30%
20%
15%
0%
According to this model, the importance of the touch points decreases with time. The closer a touch point to a conversion, the less credit it receives. Each touch point’s importance is individually determined.
Increasing model
50% 25%
15%
20%
35%
30%
0%
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The importance increases with time. The closer a touch point to a conversion, the more credit it receives. Each touch point’s importance is individually determined.
U-shaped model
50% 25%
40%
40% 40%
10% 10%
10%
40%
10%
0%
The u-shaped model places more importance on the first and on the last touch point than on all the others in between. The first touch point because it attracts the users’ attention, and the last one because it concludes the transaction. All the other touch points can be evaluated equally low. Each touch point’s importance is also individually determined.
The different attribution scenarios can have a serious impact on the evaluation of the individual channels’ efficiency:
Touch point 1:
Touch point 2:
Touch point 3:
Touch point 4:
Click on a display banner (banner viewing with click)
Click on an affiliate partner (banner viewing with click)
SEA with click on the product
URL input with sale
According to the last-cookie-wins model the display advertising and affiliate marketing channels would get nothing at all, whereas the first-cookie-wins model would attribute the entire conversion to display advertising. The uniformly distributed attribution model, on the other hand, would attribute 33.3 percent of the conversion to each touch point.
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3. Which attribution model should I use? If a customer’s decision-making process is particularly short, attribution models only play a subordinate role. For instance, when someone is buying a book, 95% of all customer journeys consist of only one touch point. In this case, the results of the different attribution models differ only slightly. In the travel industry the picture looks completely different. The user’s decision-making process can take up to several weeks. They use a wide range of online services, such as blogs, portals, price comparison websites, etc. to inform themselves. They also make contact with numerous advertising mediums and formats. In such cases, the attribution model has a substantial impact on the budget and therefore on the overall performance of a campaign. Being based on certain assumptions and being of static nature, the above-mentioned models have significantweak spots. Models, which emphasize the final touch points (last-cookie, increasing model) assume that advertising measures at the end of a decision-making process have the biggest influence on the performance. Whereas models which emphasize the initial touch points (first-cookie, decreasing model) suggest a strong branding-effect at the beginning of the decision-making process, therefore being a decisive factor for the users. The u-shaped model and the uniformly distributed attribution model are a mix of the other models. Moreover, these models assume that certain channels are always used at certain moments during the decision-making process – for example a brand search comes always at the end of the process. This is not necessarily wrong, but it is shortsighted as when evaluating the marketing channels’ effectiveness there are also many other factors that play an important role, such as type of interaction (click/view), type-, size-, and position of the advertising medium, and many more.
One solution to this problem would be an individual model, adapted to the respective advertiser. It is dynamic and not restricted to the order of the touch points. Instead, it provides a holistic view regarding the factors mentioned above.
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4. The individual attribution model QUISMA’s individual attribution model is an evaluation pattern, which, unlike general allocation formulas, determines the exact contribution of each online marketing channel to every successful journey (conversion). Based on these values, the conversions will be divided and distributed proportionally among the respective touch points. To determine an advertising channel’s contribution we look at several factors:
Main factor
• Channel efficiency
Calculating the individual channel’s contribution to the generated conversions.
To determine a channel’s efficiency we use modeling that takes the entire user journey into account, including those journeys that did not lead to a transaction. This way path-to-conversions as well as path-to-non-conversions are taken into consideration for the end result.
Other factors
• Type of interaction: Different emphasis on the type of interaction whether it is a view or a click
• Touch points: Different emphasis on the touch points whether it is an introducer, influencer, or closer
• Advertising medium quality: Different emphasis depending on the
quality of the advertising medium. In display advertising, for example, you have a different emphasis depending on if it is a stand alone banner, moving image, wallpaper, etc. In search engine advertising you have a different emphasis depending on the keyword quality, display position, etc.
• Time lag between the touch points: If, for instance, the same interaction occurs several times within a given period of time, it counts as only one interaction.
• Journey type: Different emphasis on the journey whether it is a sales or a lead journey, if both are present.
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If we have this information available, we can create a scoring model to evaluate the journeys. The following example will show you how such a scoring model works. We will look at five journeys, which only include the channels display (banner advertising) and SEA. To simplify the process we assume that only channel efficiency has an impact on the model.
Figure 1
With the last-cookie-wins method, SEA would receive three conversions in this example, and banner advertising would receive two (see fig. 1, last touch points before the conversion). Let’s say the channel efficiency analysis leads to a 70% contribution being attributed to the channel SEA, and a 30% contribution to the channel banner advertising. Then we would have the following results for the individual customer journeys.
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Example scenario to determine the individual channel’s shares in the conversions.
Figure 2
For the first journey in figure 2, the conversion would be distributed between banner advertising and search engine advertising. Banner advertising would get credit for 56.25% of the conversion, and search engine advertising for 43.75%. For the second journey, banner advertising would receive 17.65%, and search engine advertising contributed to 82.35% to the conversion. If you add up each channel’s contribution throughout all journeys, you receive its share in the total number of conversions. This means, for the five conversions, SEA is attributed 3,261 shares, and banner advertising 1,739 shares. (As a reminder: according to “last-cookie-wins” the distribution would be 3:2.). The resulting CPOs, which differ greatly from the last-cookie-wins attribution, will be used to optimally allocate the budget.
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Transferring the model to existing journeys and determining the channels’ contributions within the journeys.
QUISMA’s attribution model is an individual evaluation pattern that displays the efficiency and effectiveness of the separate channels – for every journey, for every touch point, and for the overall view.
This model can also be refined with business factors. If these values are known for the journeys, the budget allocation can be complemented with additional parameters. In this case, not only do you monitor the situation up until the conversion, but you calculate which conversions are the most profitable for the company. The following points should be included into your calculations:
Business factors
• Sales target
Expanding the model depending on the sales the journeys generated.
• Customer scoring
Further fine-tuning of the model depending on the advertisers’ customer evaluation. Optimising the journeys in accordance with the customer lifetime value and the customer equity.
Many advertisers have various products in their product line with different contribution margins and different sales figures. Amazon, for example, sells socks and washing machines. Naturally, the generated contribution margins for these two products differ greatly. Such information can be integrated into the journey evaluation. In the attribution model you can place more emphasis on channels, or channel combinations that lead to higher sales. In this case, the prime concern is not anymore the conversion, but the generated contribution margin per journey.
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5. Retail case study The individual attribution model was implemented, for instance, to create an evaluation pattern for the retail sector, which included the customer journey data of five retail companies. The model was calculated based on the respective channel efficiency, the type of interaction (view or click), and the touch point position (introducer, influencer, or closer). The individual channel’s sales shares were calculated based on these parameters and compared with sales shares of the last-cookie-wins method.
Figure 3
Sales distribution according to the last-cookie-wins attribution
Figure 4
Sales distribution according to the individual attribution
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In a direct comparison there are notable changes in the different channels’ sales shares. With the new attribution, SEA brand, retargeting, affiliate marketing, as well as product and price search engines get less credit for the conversion than they had with the last-cookiewins scenario. On the other hand, the shares of the channels SEO generic, display advertising, and SEA generic increased. This also leads to different CPOs: The channels’ overall performance
Channel
Overall Costs
SEA Brand
Sales
(Last Cookie)
CPO
(Last Cookie)
Sales
(Individual))
CPO
(Individual))
CPO
Modification
1.020.000 €
699.682
1,46 €
611.224
1,67 €
14,47%
SEO Generic
610.000 €
140.748
4,33 €
185.787
3,28 €
-24,24%
Retargeting
756.000 €
178.524
4,23 €
147.466
5,13 €
21,06%
1.629.000 €
236.794
6,88 €
195.592
8,33 €
21,07%
Display Advertising
1.076.000 €
44.892
23,97 €
95.887
11,22 €
-53,18%
SEA Generic
3.603.000 €
199.965
18,02 €
279.951
12,87 €
-28,57%
Product and Price Search Engines
2.470.00 €
173.249
14,26 €
157.947
15,64€
9,69%
11.164.000 €
1.673.854
6,67€
1.673.854
Affiliate Marketing
Overall
6,67 €
Based on the CPOs you can now redistribute the marketing budget in order to increase its efficiency. However, you need to keep an eye on the channels’ specific features. Some channels are not infinitely expandable or have a sharply rising marginal cost curve (e.g. SEA brand, retargeting).
For our retail scenario, the budget redistribution strategy would look the following: Budget reduction
Budget increase
SEA generic, product and price search engines
SEA brand, SEO generic, retargeting, affiliate marketing, and display advertising
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You need to keep in mind that the budget redistribution is a holistic process and that you should approach the optimal budget distribution step by step. With small budgets shifts you continuously optimise the overall conversion output.
6. Conclusion
Developing individual attribution models is an extremely important practice when looking to make a business ase and give reliable evidence of the return on investment of all online marketing channels employed. Attribution has been proven to be the only way to integrate channel efficiency, the type of interaction, and the advertising medium quality into a successful and useable performance evaluation. A model that does not focus on touch point positions can render a valid evaluation of the online marketing channels’ efficiency and effectiveness redundant. Re-evaluating the focus of measurability allows better distribution of budgets, leading to significant increases to sales and turnover.
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