In Adobe Analytics, three concepts appear again and again: props, eVars, and events. They are the foundation for collecting and analysing data in the platform, but it is not always obvious how they differ or when each one should be used.
Understanding the role of each variable and, most importantly, its persistence is essential if you want to get more value from your reports. In this article, I’ll go over what each one is, their main characteristics, and some practical use cases, before comparing them more clearly at the end.
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Props (Traffic Variables)
Props in Adobe Analytics are variables known as traffic variables. Their main purpose is to capture information about what happens in a specific hit, such as a page view or an interaction. One key characteristic is that they do not persist: the value assigned to a prop only exists for that specific hit and disappears on the next one.
This means props are useful when you want to analyse traffic from an immediate, hit-level perspective, without following the user beyond the page or interaction where the value was collected. For example, if you want to know which page category a user is viewing, such as “Men > T-shirts” or “Women > Shoes”, capturing that value in a prop is enough. The value will be tied to that specific page, but if the user moves to another section, the prop does not retain the previous value.
Because they have no memory, props are not practical for conversion analysis. If the goal is to connect an initial interaction with a later outcome, a prop will not work because it cannot receive credit for something that happens afterwards. That is why, when you need to understand how a campaign, an internal search, or a banner influenced a purchase, eVars are usually the better choice. Props are better reserved for cases where only the immediate context matters.
Another common use case is capturing technical or navigation data, such as the page name, device type, or browser used. This kind of information changes constantly and does not need to stay associated with the user across the journey, which makes it a good fit for the temporary nature of a prop.
Although props were used heavily in the early days of Adobe Analytics, eVars are now usually prioritised in most implementations because they offer greater flexibility and support deeper analysis. Even so, props still make sense in specific situations, especially when what you need is an immediate snapshot of traffic or when you want faster hit-level processing for certain types of data.
eVars (Conversion Variables)
eVars are known as conversion variables. Unlike props, they are not limited to the hit in which they are sent, because they can persist over time. This means a value captured in an eVar can stay active for an entire visit, for several days, or even until a specific event occurs, depending on how it is configured.
Because of that persistence, eVars make it possible to analyse the relationship between an initial action and a later outcome. For example, if a user reaches the site through an email campaign, that information can be stored in an eVar and kept until the user completes a purchase. That way, the conversion can be attributed to the original campaign.
eVars offer several configuration options that make them very flexible. One of the most important is expiration, which determines how long the value remains active: it can expire at the end of the visit, after a number of days, when a specific event happens, or never. You can also define the allocation method, which controls what happens if the same eVar receives multiple values: keep the first value, replace it with the most recent one, or distribute credit linearly.
A common example of an eVar is internal search tracking. Imagine a user searches for “trainers” on the site and, after browsing a few pages, ends up making a purchase. If that keyword was stored in an eVar with expiration set to the purchase event, you can connect the conversion to the search term used. The same logic applies to banners, product recommendations, or user segmentation.
Working with eVars requires a bit more care than working with props. Because they persist, it is important not to overuse them or leave them active longer than necessary, as that can lead to incorrect attribution. It is also good practice to document what each eVar measures and reset it if it is reused for a different purpose, so old and new data do not get mixed together.
In practice, eVars are at the core of any conversion analysis in Adobe Analytics. They help answer key business questions such as: Which campaign generated the most revenue? Which internal searches lead to a purchase? Which categories convert best? That ability to connect cause and effect is what makes them one of the most powerful variable types in the platform.
Events
Events, or success events, in Adobe Analytics are metrics used to record success actions or measurable interactions on the website or app. Unlike props or eVars, they are not variables used to capture contextual information. Instead, they represent something measurable that happened, such as a completed purchase, a banner click, a document download, or a video play.
Events can be configured as simple counters, where only the occurrence of the action is counted, or as numeric and currency events, where a value is assigned, such as the amount of a sale or the time spent watching a video. This flexibility makes it possible both to measure how often certain actions happen and to quantify their business impact. It is also possible to define how they are counted: every time they occur, once per visit, or serialised by ID.
An important characteristic of events is that they can be combined with eVars or props to provide context. For example, you can record a purchase event and associate it with an eVar that stores the user’s originating campaign, allowing you to analyse which traffic sources generate the most conversions. Without that link, events would only be isolated numbers with no information about what caused them.
When working with events, it helps to think in terms of business objectives. Naming them clearly and consistently avoids confusion and makes reports easier to interpret. It is also worth avoiding duplicates and reviewing them periodically to make sure each event is still measuring what it is supposed to measure, especially as campaigns, products, or site features evolve.
In short, events are the measurement points that show what is happening on the site or app. On their own, they do not tell the full story, but when combined with eVars and props they make it possible to understand not only what happened, but also why and under what conditions.
Comparison: props, eVars, and events
Although props, eVars, and events coexist in Adobe Analytics, each one serves a different purpose, and understanding those differences is essential if you want to implement an effective measurement plan.
| Variable | What does it measure? | Persistence | Level | Examples | Common uses |
|---|---|---|---|---|---|
| Props | Hit-level information | Does not persist | Hit | Page name, category, device type | Analysing traffic and visited sections |
| eVars | Conversion information and the causes behind actions | Persists depending on configuration: visit, event, days, or never | Visit or user | Source campaign, internal search, banner viewed | Tracking cause and effect, attributing conversions, analysing the user journey |
| Events | Measurable actions or outcomes | Not applicable | Hit / event | Purchase, download, button click | Measuring key results and KPIs, and combining them with eVars and props for context |
In the end, props, eVars, and events are three essential components of Adobe Analytics, each with its own purpose and limitations. Props are used to capture immediate hit-level information, eVars make it possible to link initial actions to later outcomes thanks to their persistence, and events record success metrics or specific interactions. Understanding these differences is key to building a clear and useful measurement plan.
When these three elements are used together correctly, they provide a much more complete view of user behaviour, from navigation through to conversion. Reviewing and documenting how each prop, eVar, and event is used helps keep the data accurate and makes reporting much easier to interpret.
