Dive into this enlightening case study revealing how a health insurance company revolutionized their marketing approach by implementing Next Best Action (NBA) strategies. By leveraging customer profile mapping, deep learning algorithms, and real-time interaction management, they achieved remarkable results. Discover how their predictive analytics and personalized recommendations led to a staggering 120% increase in conversion rates for age-in prospects and a potential $110 million in incremental sales across all business units.
A primary Health Insurance Provider serving over 200 countries and territories was looking for the best combination of offers that they could send to customers around their 60th birthday, with the goal of maximizing sign-up rates.
The client faced a variety of technical and marketing challenges:
· They needed to raise conversion rates for targeted marketing campaigns without robust analytical support
· Their current choice of marketing campaigns provides only marginal improvements
· Existing marketing decisions do not consider the interactions between different actions in the history of outreaches to prospects
The Client essentially needed a way to improve and revolutionize targeted marketing, and Blend360 offered exactly that. Blend360moved towards creating customer profile mapping between the customer’s interactions, transactions, and demographics. To drive this, Blend360 utilized:
1) Customer Journey Analytics
· This served to predict personalized conversion likelihood for each interaction
2) Deep Learning (LSTM)
· This allowed for real-time interaction management, from campaign-based marketing to trigger-based marketing
Blend360 also developed a Next Best Action (NBA) Engine for scoring and recommendation, helping the Client to better understand their next steps.
Through Blend360’s profile mapping solutions, the Client was able to drive significant increments in conversation rates. We could see a vast increase in incremental sales if adopted in all business units. The NBA Engine also ensures that the Client can make clear decisions based on easily viewable data.
Through customer journey analytics and deep learning, the Client was able to achieve their goals of maximizing their sign-up rates.
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