Oracle Unity Customer Data Platform provides many out-of-the-box AI/machine learning (ML) models to create more predictive customer experiences.
Deliver differentiated CX by applying industry context to your data with Oracle Unity Customer Data Platform, using AI/ML models along with industry-specific data models.
The account lead scoring model is a predictive, ready-to-use data science model that scores B2B accounts on their likelihood of conversion using their profile, revenue, behavior data, and engagement pattern. The scores identify the propensity for accounts to make purchases.
The contact lead scoring model is a predictive, ready-to-use data science model that scores contacts on their likelihood of conversion using their profile, revenue, behavior data, and engagement pattern.
The model generates lead score values with lead score timestamps for every contact. It helps to determine the contacts who are active in different levels of the sales funnel and their potential to make purchases, enabling you to precisely target customer segments and effectively align sales and marketing strategies.
The customer lifetime value (CLV) model is a ready-to-use data science model that estimates a customer's value over a specific time period. This prediction is based on multiple touchpoints, including customer profile data, past transaction history, and the transaction's monetary value and frequency.
Business users can customize the CLV model to give their customers three, six, or twelve months of lifetime value.
The campaign revenue attribution models are ready-to-use data science models that help you determine the success of campaigns by analyzing the touchpoints that lead to sales and conversions. There are two types of campaign revenue attribution models.
Each model considers all the touchpoints that contributed to the conversion of the campaign.
The recency, frequency, and monetary (RFM) model is a ready-to-use data science model that generates numerical scores for recency, frequency, and monetary values based on event and transaction data. With it, you can segregate customers into various personas and then target them with the most relevant messaging.
The RFM model uses the following characteristics to measure engagement and purchase behavior:
Each characteristic is represented by a score between one and five: one is the least recent, least frequent, or lowest purchase value and five is the most recent, most frequent, or highest purchase value.
The model uses the following personas to indicate the value of each customer.
The churn propensity model is a ready-to-use data science model that scores and measures the likelihood of a customer churn based on their transactional and behavioral patterns.
It identifies the customers more likely to churn, giving marketers insight into which customers may want to be targeted with specific campaigns or messaging to retain them.
The engagement propensity model measures a customer's likelihood to engage with emails (open, click, subscribe, or unsubscribe) based on their past interactions.
This ready-to-use model predicts the likelihood of customers buying a specific product based on historical interactions and customer profile data.
The model enables you to identify which customers are most likely to buy a specific product by looking at the propensity score for customer and product combinations.
Gain insights that wouldn't otherwise be available to your company for improved decision-making.
The repurchase propensity model gauges the likelihood of customers repurchasing specific products. Repurchase propensity scores are calculated based on past customer transactions and demographic and behavioral data.
The next best action model is a ready-to-use data science model that predicts customer needs and recommends the most relevant actions for every customer based on sales and transaction patterns.
The model uses customer profile data, customer engagement, product catalog data, and purchases to generate the top five recommended actions for the customer. You can use these recommendations to determine the most relevant action for a specific customer.
Oracle Unity’s next best offer model is a ready-to-use data science model that predicts customer needs and recommends the most relevant offers for every customer based on sales and transaction patterns.
The model uses customer profile, customer engagement, product catalog, and purchases data to generate recommendations. It enables users to choose from top recommendations on offers tied to various products or services. Users can use these recommendations to determine the most relevant offers to send to specific customers.
The next best promotion model is a ready-to-use data science model that uses customers’ historical product purchases to determine the price a customer is willing to pay for a particular product. Leveraging this model enables you to intelligently personalize the pricing of products for your customers.
The campaign recommender model is a ready-to-use data science model that identifies the most effective campaign to be sent for every customer based on customer's past engagement and conversion trends across different campaigns.
The model uses various timeframes (three months, one year, and three years) to rank recurring and one-time B2C campaigns for every customer in any instance based on the likelihood of conversions.
This ready-to-use data science model recommends the best marketing channel for customers based on historical interactions data.
The channel recommender model ranks engagement channels for every customer in any instance based on the likelihood of conversions. You get insights into which channels drive revenue and can find opportunities to increase revenue by distributing spend across channels with high conversion rates.
The following channels are assessed:
This ready-to-use data science model classifies customers into different levels of message fatigue based on their profile and engagement levels.
The fatigue segmentation model helps prevent customer fatigue by offering insights into the number of campaigns and messages that need to be sent to each customer profile.
It measures the message fatigue of every customer profile based on the customer's engagement, history of campaigns received and opened, and most importantly, the persona of customer profile. You determine and control the optimal number of messages to send to each customer profile to avoid fatigue.
The send time optimization model is a ready-to-use data science model that determines the optimal time to send campaign emails to customers based on past email behavior.
For example, the model would trigger sending campaign emails before customers typically check their inboxes. As a result, the message would appear at the top of the customer's inbox, ensuring that the email is most likely to be seen and opened.
Learn how Oracle Unity Customer Data Platform can help you.