Marketing doesn’t have to be chaotic, and having a strategy is key. But how can an advertiser be sure that this or that approach is a winning one? Analytical tools and measurements can help clear up which touchpoints work well and which should be altered for a better ROI. Attribution is one of the parameters crucial for measuring the effectiveness of marketing efforts.
What is attribution?
Marketing attribution is a tool that helps advertisers analyze the effectiveness of marketing campaigns in general, and certain tactics in particular. It allows you to discover which user actions led to a purchase. This way, marketers can adjust their campaigns to strengthen the points that have the most impact on the users’ decision to buy. They can make changes to their strategies, both to satisfy customers’ desires and improve their marketing ROI. Apart from that, attribution implies the following benefits:
- spendings optimization: by determining the strong and weak points a marketer can use budgets effectively
- better targeting: attribution data makes it easy to understand the preferences and needs of users
- improved communication: a certain attribution model can help enhance the messages addressed to the users
In order to use attribution to the best advantage and deliver the right messages at the proper time, it is necessary to consider the different types of attribution models.
There are first-touch and last-touch attribution models. The first-touch model implies that a customer makes a purchase after the first ad, while in the last-touch model, the potential buyer is converted after the last advertisement.
This type is the most accurate method of evaluating user activity in terms of touchpoints that lead to a purchase and there are four key varieties that are most commonly used by marketers.
Linear: This is where credit is given to every touchpoint in the customer journey regarding its contribution to a conversion or sale. If, for example, someone watching a CTV-based ad clicks on a display ad, and then makes a purchase after receiving an email, follow-up message, etc., then each of these customer interactions (aka touchpoints) receives equal credit for the final sale. This approach provides a good view of the customers’ buying process but fails to accurately point out which touchpoint worked most effectively.
U-shaped: This format assigns a higher weight to two specific touchpoints in the customer journey, usually the first and last touchpoint, but it also provides credit by tracking results in the middle of the process hence the ‘U shape.’ Most often, the first and final touchpoints are given a percentage rating of 40% each, while any other touchpoints at the lower section of the u-shaped dip can accumulate 20% in total between them. It provides a more comprehensive view of the customer purchasing process than the linear model but it still doesn’t provide as complex an overview as other models.
W-shaped: As the name suggests, this form of multi-touch attribution shares much in common with the u-shaped format, but instead of only weighting the first and final points equally, w-shaped attribution also focuses on the opportunity creation touchpoint (a specific interaction that leads to the identification of a sales opportunity). The way it works in CTV attribution is this; Imagine someone is watching CTV content and sees an ad, they then click on it, provide their personal info for an e-brochure, receive a follow-up email, and finally, receive another follow-up message. Under the w-shaped format for CTV, the original ad, the brochure, and the final follow-up are weighted most heavily at 30% each, and the other points account for 10% between them.
Time decay: Finally, and fittingly given its more ominous name, time decay CTV attribution recognizes that touchpoints occurring closer to the conversion event are typically more impactful in driving that conversion, and more recent interactions are weighted more heavily. If a CTV viewer views an ad, clicks on it, receives an email, etc, and then makes a purchase after viewing a retargeting ad, then that will be weighed most heavily. As a result, this format provides an accurate reflection of the diminishing power of interactions that are chronologically older.
What is CTV attribution?
How are TV ad campaigns measured? Linear TV offers probabilistic attribution that usually lacks information and accuracy. Advertisers receive standard reports with approximate information provided by Nielsen. Such an approach may give some demographic data, but won’t let marketers find out what caused the viewers’ impressions and which particular actions provoked conversion. In order to study that effect in detail, additional research is required – which implies more time and expenses. CTV advertisers, on the other hand, can get detailed measurements by relying on tracking pixels, SDK integrations, and specific software to analyze viewer engagement levels and understand the behavior highlighting the customer actions that resulted in conversions.
Benefits of CTV attribution:
- better understanding of TV commercial performance in terms of frequency and efficiency
- detailed information on TV consumption and viewer behavior, including preferred networks, genres, and watch time
- selecting the partners that perform best according to KPI and are the most cost-effective
- new audience discovery
- custom target optimization to make future advertising campaigns even more effective
CTV attribution allows for estimating brand health and raising brand awareness by finding out if the marketing campaign is effective against Key Performance Indicators. It helps analyze the traffic and the online conversions by relying on first-party CRM data, third-party tracking pixels, and specific Software Development Kits.
How CTV attribution works
First, let’s find out how attribution works in general. By utilizing data from first and third parties, a marketer defines the target and addresses the Identity Resolution Provider, which in its turn sends the target list to inventory sources (MVPDs, OTT publishers, and devices) and Demand Side Platforms (DSPs).
The ad exposure data (from ad servers, publisher logs, ACR) and the outcome data (based on online and offline traffic, brand health surveys) is then sent to the Attribution Report Solution Provider which conducts a study, taking into account the KPIs and criteria defined beforehand. After the report is ready, it is sent to the inventory source to guarantee quality before it reaches the marketer.
This workflow changes when applying the attribution definition to a CTV environment. The video ad is shown to a household according to targeting parameters set by the advertiser: demographics, geographic data, viewer interests, and behavior. Third parties collect the exposure data, such as user and device ID, IP address, authentication details (if there are any), etc. The more details that are available for analysis, the more precisely the exposure data will be matched to the household.
CTV attribution tools and software
Various tools are available to analyze the viewer journey within a CTV channel, understand how to invest in advertising properly, how to conduct campaigns effectively, define which traffic source works better, and adjust video ad strategies to achieve the best results. Using a certain attribution platform, a marketer can track viewer visits and actions, when they change focus and switch from one category to another, what led a user to subscription, and how much is spent and earned per customer. In other words, an advertiser receives fully transparent deep analytics of all the in-app events in one place.
It’s also worth mentioning that it doesn’t take much time and effort to make use of attribution software, since everything is pre-developed. For example, an advertiser or an agency just need to sign up for this or that attribution method, integrate the SDK, and create necessary tags and events – they’ll then have access to detailed reports with all the analytics. There is no need to code anything from scratch to access the crucial data that will help make any ad campaign more efficient.
It is hard to imagine a marketer or an agency running a CTV advertising campaign not being aware of the impact it has on their audience, brand awareness, and conversion rates. It isn’t an exaggeration to say that a marketing strategy implemented blind, with no analytics, is a total waste of time and money – especially today when advanced attribution tools are available. CTV measurement is developing and improving by leaps and bounds.
Though CTV attribution is not yet perfect, numerous advanced attribution tools and software are taking measurement to a new level. They help advertisers gain a deeper understanding of viewers’ behavior, especially by highlighting the strong and weak points of every advertising campaign, as well as optimize media spend and, as a result, increase conversions.
Nick Platonenko, CEO at VlogBox