The 360 Degree Customer View
Draw a more accurate and vibrant picture of your customer by blending behavioral analytics with traditional and unstructured data sources. 360 degree modeling yields a much richer rendering of your customer.
- Create in-depth personas
- Map the digital footprint across mobile and online
- Understand key purchase drivers
- Influence customer behavior with deeper insights
- Discover distinct new microsegments
Micro Segmentation + Behavioral Analytics
Machine learning techniques can identify patterns and behaviors as they emerge, moving you beyond a static snapshot of a few customer segments. Dynamic micro segmentation discovers personas and archetypes that you weren’t even aware of.
- Segment customers based on patterns and behaviors
- Identify the best campaigns for individual customers
- Build a behavior-driven customer experience
- Discover new user personas and archetypes
Customer Engagement Strategies
Catching the attention of your prospects is part art, part technology. You need to reach them on their turf, with something they want, and a way to get it. Analytics can help break through the noise to connect and inspire action.
- Create microsegments to personalize the experience
- Communicate via preferred channels at the best times
- Identify offers and content that solve real pain points
- Understand loyalty and repurchase drivers
- Predict what customers will need next
Customer Acquisition Analytics
Learn what consumers buy, why they buy it, and when. Then delve into the journey of building a strong healthy customer base.
- Create hyper-personalized offers to maximize conversions
- Predict intent to buy with social listening
- Learn which buying journeys convert most effectively
- Analyze past campaigns to make better offers
- Track prospect awareness, consideration and intent
Customer Lifetime Value (CLV) Modeling
Predictive and behavioral analytics allow businesses to quantify and forecast the value of individual customers across time, product lines, segments and even channels. CLV models help global brands design programs that accelerate monetization.
- Allocate marketing spend more strategically
- Customize programs to specific buying groups
- Identify hidden high potential prospects
- Refine upsell and cross sell offers
- Synch communications with life transitions
Customer Retention and Win Back
Losing a customer is costly, so early churn prediction and prevention are worth the investment. Pinpoint churn risk factors with predictive models to prevent churn. And when a customer does leave, use data to help bring that revenue back where it belongs.
- Identify customers exhibiting high risk of churn
- Segment retention offers based on predicted customer value
- Detect dissatisfaction patterns to mitigate exposure
- Analyze existing data to discover new win back offers
- Profile lost customers with high win back potential
Customer Satisfaction Analysis
Data can play an active part in boosting customer satisfaction. Go beyond simply measuring current customer satisfaction (CSAT). Analytics discovers new drivers of positive customer satisfaction to help nurture healthy customer relationships.
- Analyze incoming customer data to ensure positive experiences
- Develop services based on customer needs
- Find and fix the root causes of poor customer feedback
- Measure the impact of interactions on each customer’s satisfaction score
- Leverage loyalty program data to find CSAT drivers
