As consumer habits continue to shift, a distinctive, tailored customer experience goes hand-in-hand with revenue growth.  SparkBeyond Discovery enables you to personalize the entire customer journey by accurately microsegmenting your customer base and suggesting highly accurate sell/cross-sell opportunities that match each profile.  

To generate hyper-personalized recommendations, SparkBeyond Discovery analyzes internal data (such as customer service interaction, browsing history, product reviews, purchases, redemptions, and offers) and seamlessly aligns it with external sources, including market and social media trends, mobility, traffic flow and seasonality.  

The platform automatically tests thousands of hypotheses, identifying the best cross-sell and upsell opportunities (many of them novel), and the right mix of messaging, to match each customer’s preferred channel and device.  Predictive models constantly crunch new data, generating results that are powerfully visualized, ready for marketing and communications teams to action.  

Instead of creating marketing messages that don’t meet consumers’ specific needs, an American & Swiss multinational financial services corporation looked to use AI analytics to boost its upsell wins. The platform connected multiple datasets (including GDPR-compliant external datasets) and discovered 100+ million potential drivers behind customer acquisition, cross-sell and upsell behavior patterns. 

Outcomes included:

• Improved target response rate, with 7x greater returns from each campaign
• 4+ million CHF in potential bottom-line value for the business
• 15x ROI in 5 months
  

Up-sells and cross-sells are easy wins with personalized campaigns, based on granular microsegments. SparkBeyond’s external data network complements incomplete data profiles, exponentially increasing the accuracy of recommended up-sell/cross-sell opportunities.

Our transparent AI allows all departments to benefit from interpretable insights for retention and marketing campaigns, customer service inquiries, and identification of negative customer experience trends.