What is the expected output when a marketing specialist uses Customer AI to generate custom propensity scores?

The expected output when a marketing specialist uses Customer AI to generate custom propensity scores is to provide customizable options for unique use cases. This capability allows marketers to tailor their propensity models based on specific business needs and customer behavior patterns.

By generating custom propensity scores, the marketing specialist can better predict how likely individual customers are to engage with certain products or campaigns, allowing for more precise targeting and optimization of marketing efforts. This customization is key because it enables organizations to apply models that align directly with their strategic goals, be it identifying high-value customers or predicting cross-sell opportunities.

The other options do not align closely with the core purpose of generating custom propensity scores. For instance, maximizing advertising reach focuses more on broad audience engagement rather than individual customer insights. Monthly sales forecasts relate to predictability of sales performance over time rather than immediate customer engagement behavior. Creating generic customer profiles lacks the specificity that custom propensity scores provide, as those scores are designed to capture unique behavioral patterns rather than a one-size-fits-all profile.