The next generation analytics in Salesforce Marketing Cloud refers to true causal effects measurement based on attribution, lift, and incrementality as opposed to surface-level measuring activities. Marketers can utilise these concepts in SFMC to show what truly works, minimise wastage and come up with more advanced experiments.
Why basic metrics are not enough
Open journeys, clicks, and last-Click journeys are what most teams are optimizing SFMC journeys across and these approaches, despite being useful, are largely descriptive and correlational. These metrics do not provide consistent answers to the question of what actually caused this uplift: in a world where the customer journeys were noisy; the privacy shifts; the overlap of channels, popular touchpoints like branded search or batch email tend to be over-credited.
Attribution in SFMC
Attribution is the act of giving credits to conversions to the touchpoints with the Marketing Cloud that had an impact in the conversion applying a model- first or last touch, time decay, data-driven multi-touch. The SFMC and Marketing Cloud Intelligence make it possible to define their own attribution models and integrate email, journeys, advertisements, and web analytics where a single conversion can be spread across many emails, pushes, or advertisements instead of giving all the credit to the last send. It is important to remember that attribution should be taken as an orientation on budgetary allocation and not necessarily as a clear causality even without context.
What lift and incrementality actually measure
Incrementality poses a different question, What is the number of additional conversions that that campaign or journey actually generates in comparison to nothing? Marketers forecast this using lift tests whereby a treatment group that was exposed to a message is compared to a control group that is not in SFMC or at the channel level, and then the variations in the outcomes of purchase or upgrades are measured. This lift allows background noises which may be seasonality or organic demand to be reduced and help teams to discover which journeys actually move the needle.
Bringing causal tests into Marketing Cloud
In order to go beyond the initial levels of metrics, teams have to develop experiments that are built into their SFMC programs, and not just reading dashboards retroactively. A/B control splits within Journey Builder, regional or audience-based holdouts to larger-scale promotions, and a consistent framework of incrementality that switches the segment to receiving specific automations are all common patterns. The experiments have shown over time which triggers, frequencies and the content types bring about positive incremental revenue and those that only tend to push purchases around.
Using Causal AI and advanced tooling
The Marketing Cloud data can be available to Emerging Causal AI and specialised lift tools and a lot of the intensive statistical work can be automated. Marketers do not have to set up single tests manually but provide campaign and outcome data, where causal models can then estimate impact, simulate what-if conditions and give new experiments as suggestions. This would assist in ongoing measurement of SFMC teams, as opposed to just quarterly studies and standardise methodology between email, mobile, and paid media.
A practical playbook for SFMC teams
To most of the Marketing Cloud users, transition starts with three steps, which include defining a set of business outcomes that matter to your concisely, aligning an attribution model with the current data reality, and introducing simple holdout tests to your most significant journeys. Teams can embrace attribution weights which are based on observed lift, rather than opinion, as they develop confidence with the use of causal AI services, implement geo- or audience-based incrementality tests, and rely on these tests to make decisions. Eventually, SFMC is not a channel implementation platform but a determination measuring engine, which allows marketers to spend less time arguing over reports and more on scaling programs that contribute to incremental growth.
