The challenges of affiliation

• The distribution of affiliates into sub-programs by traffic categories and by objectives
• Commissioning rules in coherence with the company’s margin and types of affiliates
• Effective and automated tools for analysis and control

The main objective is clear: to generate more orders for the same or lower investment.

The distribution of affiliates into sub-programs

In general, the analysis of affiliate performance is made at two levels:

• the programme as a whole
• the individual performance of affiliates

If we had to draw a parallel with the SEA, it would be like :

• an analysis of the Google Adwords source as a whole (including brand and retargeting)
• an analysis of the performance of each keyword

It is clear that an intermediate level of analysis by campaign is missing. First of all, it allows to distinguish between generic, brand and retargeting campaigns. But it also makes it possible to distinguish campaigns by product type or product category.

To get back to affiliation, it is necessary to increase the analytical granularity of a program by segmenting it into several subprograms.
The best way to do this is to create subprograms by type of affiliate: affinity, cashback, promo-codes, shopping guides, etc.

It is essential to have the ability to define KPIs for each sub-program, in order to be able to organize affiliates. It is therefore useless to go into too much segmentation, which would create more confusion than it would provide solutions.

Here is an example of defining objectives and KPIs for segmentation into 4 sub-programs: Content & Bloggers, Cashback, Coupons and Shopping Guides.

a) Content & Bloggers

• Objective: to initiate conversion paths
• KPI : CPO First click < CPO Last click, new visits and new customers > 50%

b) Cashback

• Objective: finalize the conversion paths
• KPI: conversion rate > 10%, bounce rate < 30%

c) Coupons

• Objective: finalize the conversion paths
• KPI: conversion rate > 10%, bounce rate < 30%

d) Shopping Guides

• Objective: to generate spontaneous orders
• KPI: CPO First click ≈ CPO Last click, conversion rate ≈ Google shopping, new visits > 40%

The performance of each sub-program will then be analyzed independently of the others. This will avoid equating a valuable Blogger that generates content and positions itself in conversion path initiation and a Coupon affiliate that positions itself at the end of the conversion path.

Let’s now look at how to use subprograms to structure your commission rules.

Commission rules

It is possible to use many criteria to calculate the commission of its affiliates. Here are the three main criteria:

• by sub-program: content & bloggers, cashback, promo-codes, shopping guides
• by type of customer: new or old customer, B2C or B2B
• by product category: high margin or low margin products

First of all, the commission rules must be defined according to the subprogram and the type of affiliate.
Then, an adjustment work is to be carried out to introduce the product categories, according to the associated margins and the advertiser’s sales objectives.
For example, when launching a new product category, it is advisable to set up commissions specific to that category in order to promote it.

Here are the commissioning indicators that we suggest for the subprograms used in the examples above.

a) Content & Bloggers

• “High” commission for a new customer
• “Average” commission for an existing customer

The high commission for a new customer must motivate the Affiliate to send “fresh” traffic, which involves renewing its audience through good organic positioning or developing its followers’ base.

Content affiliates & Bloggers are often penalized by last click deduplication models. It is important to adopt a different deduplication for this group of affiliates in order to remunerate their contribution to the initiation of the conversion path.
First click payment mechanisms are not very common, but post-click deduplication is a good compromise, with a sufficiently long cookie period, for example 15 days.
The implementation of post-click deduplication is relatively simple, the control is more complex. It is necessary to use a tool to control the correct assignment of commissions.

b) Cashback

• “Average” commission for all types of clients

The commission remains the same regardless of the criteria in order to avoid the multiplication of customer complaints about the cashback they have received. It is sometimes difficult for a customer to accept a high cashback on his first order and then a low cashback on his second order, since he is no longer considered as a new customer.
And this could lead the customer to create an account for each order, that would affect performance tracking for the advertiser.

c) Coupons

• “Average” commission for a new customer
• “Low” commission for an existing customer

Unlike cashback, the value of commissions has no influence on the traffic or transformation of coupons sites. It is relevant to make savings by keeping commissions relatively low.
We recommend to pay half the commission of cashback, since cashback pays almost half of its commissions to its users.
Finally, we recommend to transfer the savings made to the proposed coupons in order to increase their attractiveness and efficiency.

d) Shopping Guides

• “High” commission for a new customer
• “Average” commission for an existing customer

Unlike Content affiliates & Bloggers, Shoppings Guides can be deduplicated at the last click, as they have a greater impact than Content affiliates & Bloggers at the end of the customer journey. The high remuneration is a way to compensate for the lack of remuneration for their contribution to the initiation of conversion paths.

It is important to regularly check that all affiliates in a sub-program comply with the defined KPIs. If this is not the case, they must be moved to the corresponding subprogram or stopped. It should be noted that some affiliates do not hesitate to position themselves in a sub-program that is not their own in order to obtain higher commissions.

Performance analysis

Advertisers often have difficulty analyzing the performance indicators of each affiliate.
Their analytical tool does not allow them to have this level of granularity and they fall back on the analysis tools offered by their traffic providers.

Affiliate platforms use the data they have collected themselves to pay affiliates. These are still third party data that are subject to a more or less significant error rate – usually due to a bad configuration of the advertiser’s tag management tool, or simple malfunctions of the affiliate platform tracker.
This comment is also valid for all other traffic channels (retargeting, SEA, SMA, …).

The same applies to the correct application of deduplication (Post-click, Last click). It must be controlled on the basis of first party data – those collected by the advertiser via an analytical solution.
Otherwise there will always be a gap between the performance identified by the advertiser and that communicated by its partners, regardless of the channel.

To see this, simply compare the number of orders assigned to the affiliation channel by your analytical solution and those identified by the affiliation platform over the same period. The differential is often greater than 10%.

Fraud control

Advertisers are often powerless against technical fraud levers, such as cookie stuffing, hidden toolbars and any other forms of fraud or error that results in excessive assignment of orders to some affiliates.
It is also important to control forbidden traffic (SEA brand, pop-under, click-under,…), as some affiliates do not hesitate to use them.

Affiliate platforms often demand an anti-fraud analysis of advertisers’ programs. However, it is difficult to be judge and jury. In this context, a second look is necessary. And it seems obvious that it must rely on First party data – those collected by the advertiser via its analytical solution.

An anti-fraud solution is needed to protect against these abuses. There are two types of tools to control affiliate traffic depending on its destination:

• Mobile application: the tools will mainly target fake downloads, click dropping, robots, wrong attribution.
• Website (desktop or mobile): the tools will mainly target cookie stuffing, hidden toolbars, pop-under, click-under and wrong attribution.