MULTITOUCH ATTRIBUTION DOWNLOAD

Incremental Attribution: Incrementality is the measure of the lift that advertising spend provides to the conversion rate.

Models are rebuilt as often as daily, so marketers can make optimization decisions while campaigns are still in flight. Not only can marketers understand optimal budget allocation at the channel and sub-channel levels, but also the most granular levels such as keyword, placement and creative. Marketers often use this model to measure marketing efforts that have short consideration cycles, such as promotional campaigns.įractional Attribution: Fractional attribution employs machine-learning to calculate and assign fractional credit for a given success metric to the influential marketing touchpoints and dimensions (campaign, placement, publisher, creative, offer, etc.) along the consumer journey, and to inform future marketing spend allocations. Time Decay: In a time-decay model, the percentage of credit gradually builds while leading up to the last touchpoint in the consumer journey. Marketers often use this model when they want to favor the “opener” and “closer” touchpoints more heavily than “supporter” touchpoints in the middle of the consumer journey. Position Based: In a position-based model, also known as a bathtub or u-shaped model, the first and last touchpoints in the consumer journey receive a higher percentage of credit than the touchpoints in between. It’s also a good starting point for marketers who are new to multi-touch attribution and don’t have an existing understanding of the typical consumer journey across channels. Marketers often use this model to measure marketing efforts that have long consideration cycles, thus making it important to reinforce the message many times throughout the consumer journey.

These insights into past performance can also be used to inform more efficient and effective planning and optimization decisions for future campaigns, as well as those already in flight.Įven Weighting: In an even-weighting model, also known as a linear model, all touchpoints in the consumer journey receive equal credit for the KPI event.

Multi-touch attribution eliminates biases by algorithmically allocating credit to every element of every touchpoint in the consumer journey, across marketing and advertising channels and tactics, according to its influence on driving a conversion event.īy leveraging individual, user-level data across addressable channels, such as direct mail, online display and paid search, multi-touch attribution calculates and assigns credit for a key performance indicator (KPI) event to the marketing touchpoints that influenced a desired business outcome.

MULTITOUCH ATTRIBUTION DOWNLOAD

Multi-touch attribution offers a more sophisticated alternative to traditional, rules-based attribution approaches, such as first- and last-touch, which give all of the credit to the first or last marketing touchpoint before the consumer converts through a purchase, download or any other event.īy arbitrarily applying rules, these methods fail to measure the contribution of every touchpoint in the consumer journey accurately, causing marketers to make decisions based on skewed data. Multi-touch attribution is a marketing effectiveness measurement technique that takes all of the touchpoints on the consumer journey into consideration and assigns fractional credit to each so that a marketer can see how much influence each channel has on a sale.

Multitouch attribution