Celebrus and Pega Always-On Insights
Static data means static experiences. Tap into your customer’s behavior in the moment – so you can deliver relevant, engaging, human experiences.
How We Do It
Combine the power of a real-time customer data platform with AI-powered decisioning
Why Pega and Celebrus?
Your customers are interacting with you more than ever on digital channels. But it’s still challenging to turn that data into deep insights that power enterprise decisioning strategies – compliantly, cost-effectively, and in real-time.
With Celebrus’ Customer Data Platform (CDP) and the Pega Customer Decision Hub™ you’ll use first-party data signals, AI-powered decisioning, and next best experiences to adapt instantly, engage with empathy, and build long-lasting relationships that are optimized for customer value.
Sense your customer’s needs
Celebrus uses advanced machine learning to identify every customer and instantly contextualize their journey. The Celebrus CDP captures customers’ behavioral signals – no tagging required – all while being compliant with PII & GDPR regulations. This data is curated and seamlessly transferred to the Pega Customer Decision Hub in real-time, providing insight around an individual’s propensities, behavior, and needs.
Determine the next best experience
Using those insights, Pega’s always-on brain gains a real-time view of the customer. The brain then prioritizes engagement options and recommends a “next best experience” comprised of the best action, treatment, and channel for each individual. These engagements are hyper-personalized and adapted to a customer’s unique needs.
Unify your customer’s journey, effortlessly
Once the right action is calculated, you’ll deliver these messages to customers through connected channels. These experiences can engage customers on a one-to-one level on owned digital properties or ad platforms. The result? Outstanding experiences, greater than ever customer loyalty, higher NPS scores, and increased conversion and retention.
Frequently Asked Questions
A Customer Data Platform (CDP) is a software application built to support marketing and CX use cases, by unifying a company’s customer data from marketing and other channels. CDPs optimize the timing and targeting of messages, offers and customer engagement activities, and enable the analysis of individual-level customer behavior over time.
Organizations turn to CDPs to help them:
- Activate the massive amount of data they have – collected from customers, channels, and lines of business across the organization
- Operationalize analytics and AI - to analyze customer interactions, predict behaviors, and generate value-add recommendations
- Rationalize their technologies - consolidate 1000’s of applications into a manageable suite of “core” solutions
- Humanize their customer relationships - move from sales-based interactions, to more relevant, value-add experiences
Per the CDP Institute:
- Ingesting all types of customer data
- Using identity matching, unify into a single customer profile
- Analyze, segment, and produce new insights from the data
- Activate that data and insights
Some CDPs have Identity Matching capabilities, which include deterministic matching (via emails and other hard keys), and probabilistic matching (by likelihood) of devices to an individual with an ID.
Customer Decision Hub helps clients get the most out of their CDP implementation by:
- Providing industry data model with best-practice recommendations for model performance, business rules, compliance, and triggers
- Populating key data elements (including aggregates) into customer profiles – reducing implementation cycles & time to value
- Adaptive models evaluate your data attributes with every decision, using or discarding them based on their value to decisioning
- Leveraging best-practice framework to streamline integrations between Customer Decision Hub and Customer Data Platforms
A Pega White Paper
Responsible AI: great power requires greater accountability
The benefits of AI are well established, but organizations using AI-driven systems must be accountable for their actions. Beyond the financial and legal risk of “AI gone wrong”, there is a moral obligation to improve AI.
Learn how responsible AI can set higher standards, reduce bias, and promote empathy.