An attribution model is a rule or set of rules, that determines how credit for sales and conversions is assigned to touchpoints in conversion paths.
For example, the Last Interaction model assigns 100% credit to the final touchpoints (i.e., clicks) that immediately precede sales or conversions. In contrast, the First Interaction model assigns 100% credit to touchpoints that initiate conversion paths

In attribution models, there is no correct or wrong one. It depends from the business and the way the company operates on the web. Most companies use the Last Interaction model for simplicity purposes.

The First Interaction model attributes 100% of the conversion value to the first channel with which the customer interacted.

This model is appropriate if you run ads or campaigns to create initial awareness. For example, if your brand is not well known, you may place a premium on the keywords or channels that first exposed customers to the brand.

The Last Interaction model attributes 100% of the conversion value to the last channel with which the customer interacted before buying or converting.

If your ads and campaigns are designed to attract people at the moment of purchase, or your business is primarily transactional with a sales cycle that does not involve a consideration phase, the Last Interaction model may be appropriate.

The Last Non-Direct Click model ignores direct traffic and attributes 100% of the conversion value to the last channel that the customer clicked through from before buying or converting. Analytics uses this model by default when attributing conversion value in non-Multi-Channel Funnels reports. When it’s useful: Because the Last Non-Direct Click model is the default model used for non-Multi-Channel Funnels reports, it provides a useful benchmark to compare with results from other models. In addition, if you consider direct traffic to be from customers who have already been won through a different channel, then you may wish to filter out direct traffic and focus on the last marketing activity before conversion.

Lead forensics is a term that refers to the use of digital forensics techniques to gather and analyze data from leads, or potential sources of information, in a digital auditing or investigation. This may involve examining electronic devices or other digital media to extract and analyze data such as emails, website visits, campaigns used, IPs, text messages, or other types of communication that could provide leads or clues about the nature of the source. Lead forensics is often used in digital audits or analyses, corporate fraud investigations, and cybersecurity incidents, among others.

Overall, lead forensics is a key tool that is used to gather and analyze digital evidence in a variety of contexts, helping auditors to solve attribution, identify wrongdoing, and hold individuals accountable for their actions.

Attribution refers to the process of identifying the source or origin of an action or event. In the context of digital forensics, attribution refers to the process of identifying the individuals or organizations responsible for collecting leads or other types of digital incidents such as a GDPR-related investigation.

CCPA and GDPR both have ways for users to ask to recall all the information collected for them. Very often we utilize lead forensics approaches to answer the question “why I’m getting this message”.

The Linear model gives equal credit to each channel interaction on the way to conversion.

This model is useful if your campaigns are designed to maintain contact and awareness with the customer throughout the entire sales cycle. In this case, each touchpoint is equally important during the consideration process.

The Position Based model allows you to create a hybrid of the Last Interaction and First Interaction models. Instead of giving all the credit to either the first or last interaction, you can split the credit between them. One common scenario is to assign 40% credit each to the first interaction and last interaction and assign 20% credit to the interactions in the middle.

If you most value touchpoints that introduced customers to your brand and final touchpoints that resulted in sales, use the Position Based model.

If the sales cycle involves only a short consideration phase, the Time Decay model may be appropriate. This model is based on the concept of exponential decay and most heavily credits the touchpoints that occurred nearest to the time of conversion.

The Time Decay model has a default half-life of 7 days, meaning that a touchpoint occurring 7 days prior to a conversion will receive 1/2 the credit of a touchpoint that occurs on the day of conversion. Similarly, a touchpoint occurring 14 days prior will receive 1/4 the credit of a day-of-conversion touchpoint. The exponential decay continues within your lookback window (default of 30 days).

If you run one-day or two-day promotion campaigns, you may wish to give more credit to interactions during the days of the promotion. In this case, interactions that occurred one week before have only a small value as compared to touchpoints near the conversion.