The growing digital economy gives users many choices on different sales platforms. This leads to the customer's purchasing journey becoming much more complicated. So how can brands determine which touchpoints are meaningful in creating conversions in the customer's purchasing journey?
The number of touchpoints between a prospect and a brand before a conversion or sale can range from 5 to 50. With so many touchpoints, the consumer journey mapping seamlessly across channels and devices, online and offline, is also becoming more complex than ever.
Therefore, Marketers must always find ways to optimize advertising campaigns and measure effectiveness. The most important element of this process is to accurately identify the attribution source (the source that generates conversions from the last touchpoint). By analyzing and identifying the source of app installs or purchases. You can determine the effectiveness of different marketing activities and decide on the next step.
Attribution helps you measure and evaluate the impact of individual touchpoints, so you can optimize what’s not working or redirect budget to channels that are performing better.
Benefits of adopting multi-touch attribution (MTA) model
-
Reflecting real-world journeys on mobile
The user journey on a mobile app is complex. A user doesn’t simply decide to install an app by viewing a single ad. For a user to achieve a conversion, they often go through a long journey that includes multiple ads across multiple channels. In fact, internal research from Airbridge shows that over 30% conversions are made after three or more touchpoints. Therefore, using multi-touch attribution can better reflect how users think and act in real life.
-
Budget Optimization
Understanding how each channel contributes can help advertisers better allocate their ad budgets to optimize ad performance. It is now clear that real-world users interact with multiple touchpoints. Therefore, considering only one touchpoint as contributing to a conversion can overstate the performance of some channels while underestimating others. Multi-touch attribution can identify how each channel contributes and provide advertisers with an objective view, helping them allocate their budgets more effectively.
-
Provide detailed data to advertisers
The mobile app industry is becoming increasingly competitive as new channels emerge and users become more wary of advertising messages. As a result, advertisers will need deeper data to optimize their advertising systems. Multi-touch attribution can provide advertisers with accurate and detailed data on which channels their ads perform best and how to optimize their advertising strategies.
Attribution: Measuring ad performance
Attribution is the process of crediting the last touchpoint that results in a conversion in the customer journey. Based on the contribution of each touchpoint, performance advertising is calculated. There are many cases in the market where advertising channels and agencies disagree on attribution results – advertising channels claim that their ads contributed to the conversion, while advertising agencies claim that it was thanks to their ads.
Single-touch attribution – Single-touch attribution
Single-touch attribution is the simplest method, in which all conversions are credited to a single touchpoint. There are two main variations of this model, including first-touch attribution and last-touch attribution.
First-touch attribution assigns all conversions to the first touchpoint that occurs in the user's journey. This method assumes and credits the conversion to the first ad that introduces the service to the user.
In the examples below, the touchpoints come from multiple channels. Additionally, app installs are the only action considered when considering conversions.
Last-touch attribution (LTA) credits conversions to the last touchpoint in the user journey. This method assumes and credits conversions to ads viewed and clicked by users right before installing the app. LTA is the most popular model today due to its simplicity and ease of understanding.
Multi-touch Attribution
Multi-touch attribution (MTA) attributes conversions to different touchpoints throughout the user journey. This type of attribution is built on the premise that it is unfair to attribute all conversion efforts to a single touchpoint. When implementing multi-touch attribution, it is important to maintain a consistent approach to conversion attribution. MTA can be divided into two types depending on whether they are set up as rule-based or data-driven.
Understanding multi-touch attribution
| Classification standards | Principle | |||
|---|---|---|---|---|
| Multi-point distribution | Measure performance based on predefined rules | Rule-based multi-point allocation | All touch points recorded results equally | Linear model |
| Touchpoints closer to conversion behavior are more recognized | Time-decay model | |||
| Performance measurement based on data models | Data-driven multi-touch attribution | Shapley value | ||
| Markov chain | ||||
| Performance measurement with due regard to causes | Incrementality | |||
With the rule-based model, conversions are distributed according to pre-defined rules. Advertisers decide on the attribution rules that best suit the nature of their service. The rule-based MTA model can be divided into two types: linear and time decay.
Multi-touch attribution models
Linear Attribution Model
The linear model allocates conversion results equally to each touchpoint, assuming that all ads contributed equally to the user's conversion.
Time-decay attribution
The time-decay attribution model works on a different principle: touchpoints closer to the conversion process have a greater impact.
As a result, this model credits more of the touchpoints that occur closer to the conversion. By gradually reducing the contribution of earlier touchpoints, advertisers can set conversion attribution for each touchpoint to match the nature of their service.
In the above example, we apply the allocation ratio for the touch points TP 1: TP 2: TP 3: TP 4: TP 5 as 1: 2: 3: 4: 5 respectively. However, the models may vary depending on the specific situation.
Shapley attribution model – Shapley attribution
The Shapley value was originally designed to distribute rewards among contributors based on their individual contributions. In this context, touchpoints represent contributors, and the number of conversions corresponds to the reward. Each touchpoint receives a certain percentage of the total conversions based on its contribution.
32 possible user journeys include: (No TP), (TP 1), (TP 2), (TP 3), (TP 4), (TP 5), (TP 1, TP 2), (TP 1, TP 3), …, (TP 1, TP 2, TP 3, TP 4, TP 5)
Markov attribution model
Markov chains simplify a sequence of events to evaluate the performance of touchpoints. Markov chains allow you to describe the probability of (i) transition from one touchpoint to the next and (ii) the occurrence of transitions in a unified graph.
Both the Shapley value and the Markov chain assume situations where a touchpoint does not occur to measure ad performance. However, the Markov chain determines conversion outcomes based on the probability between touchpoints and conversions, while the Shapley value uses real-world metrics like app installs to determine attribution.
For example, 40% total users start their journey through TP 1 touchpoint and 50% of them will encounter TP 5 later. Of those who encounter TP 5, 30% eventually successfully convert. This results in a probability that 6% of the TP 1 – TP 5 journey could result in a conversion. Second, of the group that experienced TP 1, 30% continue to TP 2. Of those who encounter TP 2, 60% convert. This results in a probability of 7.2% of the TP 1 – TP 2 journey resulting in a conversion. In total, the distribution of TP 1 will be aggregated to 13.2%.
The difference between the 2 models LTA and MTA
|
LTA |
MTA |
|
| Accuracy | Comes with the risk of overestimating or underestimating certain touchpoints | Provides a more complete and accurate view of the true impact of each touchpoint on conversions |
| Complexity | Simple and easy to understand, easy to do | Requires more data and analysis, harder to implement |
| Application | Provides clear answers to the question of which touchpoints lead to conversions, making it easy for businesses to take action and optimize marketing efforts | Provides deeper insight into the user journey, which can make it more difficult to identify specific areas for improvement |
| Meaning in mobile | Failing to capture the fragmented nature of the user journey, making it difficult to accurately measure performance | Multiple devices can use a lot of data, but there can be disagreements about how credit is allocated |
Why MTA model should be focused by businesses
The MTA nature records all touch points and applies the last touch principle to allocation. Therefore, the MTA Model provides an overall view and covers the entire channel, providing detailed information about the user journey, increasing investment efficiency.
So you should care about multi-touch attribution because:
- Tells you which campaigns deliver the best return on ad spend (ROAS) or return on investment (ROI).
- Tells you which channels generate the most conversions, leads, and revenue.
- Helps you spend your marketing budget wisely.
- Helps you predict what's coming so you can make adjustments to your marketing tactics in real time.
When it comes to multi-touch attribution, there is no one-size-fits-all approach. The attribution model you choose will depend on the specific KPIs for your app and campaign. For example, if you measure app downloads, a time-decay model might be best. However, if you measure LTV, you may need a model that includes multiple touchpoints, even after the conversion, like a full path model.
Before deciding which model to use, it is important to identify your KPIs and then choose the most appropriate model. You should also test different models and see which one best fits your strategy. If one model does not yield results, try another until you get the information you need.
Finally, compare the results between the models and see where you can optimize to improve performance.
