Apply modern data science techniques to establish unbiased indicators that provide predictive insights for dynamic pricing, customer segmentation, product recommendations, and behavioral analytics.
- Create a modern data warehouse that unifies
customer data, website analytics, and enriched 3rd
party consumer data.
- Apply data science algorithms to discover the
predictive indicators the underlie customer behavior
and optimal engagement processes.
- Integrate these insights in real-time to capitalize on
predicted customer behavior.
- Improve customer targeting and segmentation
- Personalized product recommendations
- Onsite behavioral analytics
- Increase repurchases with loyalty programs
- Higher margins with dynamic pricing
- Advanced marketing analytics
Unified Customer Insights
Machine learning provides comprehensive predictions across the customer life cycle.
Influencing customer behavior is based on finding similarities
in different customer types. Identifying specific systematic
groups of customers can make marketing more precise.
Machine Learning is excellent at finding patterns in large sets
Behavioral Analysis is a system to help marketers better
understand how and why buyers act, as well as make more
accurate predictions based on that information. Analysis of
raw event data provides a more complete insight that
exposes opportunities to influence performance indicators.
A well trained chatbot can reduce customer engagement
costs while increasing customer convenience. Conversational
marketing has been proven to increase conversion rates
while driving specific user behavior that is trackable. Chatbots
can be deployed at scale to engage customers in real-time.
Targeted and specific automated recommendations increase
cross-product conversion. A couple popular recommendation
models are derived from user-specific content-based data or
collaborative filtering, which identifies similarity between
groups of customers that made different purchases.