Marketers identify personalization opportunities for real time engagement based on data-driven understanding of customer behavior.
Data scientists get a set of rigorous and sophisticated algorithms out-of-box, while retaining the flexibility to explore data and manage custom algorithms.
IT acquires a customer data warehouse and methods to process structured and unstructured data.
Channels – online, stores, marketplaces
Devices – mobile, tablets, laptops
Interactions – purchase, post, like, feedback, service
Next best action
Retention drivers
Up sell & cross sell opportunities
Campaign lift
Channel performance
Segment productivity
Consumes data from different sources – transaction systems, channels, touch-points and other unstructured sources
Structures the data along merchandise, customers, marketing events
Enables slice-dice of data to understand context – purchase, preference, triggers, root-cause, history
Historic patterns
Response behavior
Contextual interactions
Stores
Online
Mobile commerce
Segment customers by demography, purchase behavior, campaing responses and channel prefernces
Score customers by purchase behavior
Identify preffered and non-obvious affinity between products and brands
Forecast value of every customer
Identify customer who are most likey to churn
Predict the likelihood of a customer behavior like response to campaign, brand and category adoption
Packages all must-have customer analytic algorithms in an easy to consume way
Natively translates marketing hypothesis into models to make it suitable for use by marketers
Manages multiple models concurrently to support diverse marketing programs
Helps understanding of customers profiles, purchase behavior and Engagement Preferences
Enables build of micro-segments using combinations of customer attributes, algorithm outputs and product & purchase preferences
Simplifies tracking of campaign lift, channel effectiveness and marketing ROI and bring accountability and agility to campaigns
Serves unique needs of marketing and business stakeholders
Prepares targeted list of consumers for marketing campaigns
Instantaneously consumes outputs of algorithms for contextual analysis
Enables the use of discovery workbench to perform root cause analysis12
Leverage customer data model and single view of customer to carve out a slice of clean data for custom analysis
Use easy to build model workflows to experiment with the data and various modeling techniques to build effective models.
Seamlessly import exisiting models using PMML, score customers and publish results
Marketers identify personalization opportunities for real time engagement based on data-driven understanding of customer behavior.
Pre-built connectors and defined processes drive faster self service data loading into a built for analytics marketing data model. Unify data from external and enterprise systems.
Architected for use by business users, with ample self service and on-demand help to accelerate adoption. First analytical view in 2 days.
Data No PII (Personal Identifiable Info), Encrypted Blocks, Secure Data in motion and rest
Application Secure design and deployment principles, multi-factor authentication
Network Hardened Network, Secure Access, VPN, Multi-layer Authentication
Facilities Physical and environmental security with multi-factor access controls
Process Standards-based Adaptive Operational Process
Expand discovery range, drive category and brand penetration based on a holistic understanding of customer lifestyles, stages, and behaviors.
Identify customers at risk and prevent attrition with automated retention campaigns with the right mix of offers and channels.
Encourage your customers with targeted offers (cross-sell) based on past purchase insights to maximize basket value in every trip.
