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RETAIN

From One-Time Buyers to Lifelong Customers: Maximizing Retention Through AI-Driven Personalization

Leverage real-time and dynamic personalization strategies to foster loyalty, increase repeat purchases, and maximize customer lifetime value.

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Retail Challenges

Building lasting customer relationships requires more than just transactions, yet generic promotions, limited engagement strategies, and outdated segmentation prevent retailers from driving true loyalty and repeat purchases.

Irrelevancy of Personalized Offers

Personalized offers continue to miss the mark with customers. Despite 70% of customers having received a personalized offer in the last 30 days, 56% of customers find offers irrelevant to their needs. This results in flat or decreasing basket sizes.

This stems from retailers using a segment-based approach and a preset offers catalogue to personalize the offers to an individual customer. A common approach used by retailers to personalize offers to customers is as follows:

Ignoring Engagement Beyond Transactions

Retailers only reward purchases, and overlook other valuable interactions, such as which products they viewed, abandoned carts, product searches, etc.

Over-Reliance on Broad Customer Segments

Using broad segments leads to generalized strategies that ignore individual customer behaviors, resulting in missed opportunities for targeted engagement and relevance.

Ineffectiveness of Mass Promotions

Mass promotions fail to account for each customer’s purchasing behavior and product-level price sensitivities, often leading to lower return on investment and ineffective use of marketing dollars.
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BeansOS at a Glance
Witness the powerful results BeansOS has delivered for leading retailers. By leveraging data-driven insights, BeansOS drives real, measurable impact in personalizing offers, refining product recommendations, and enhancing customer engagement.
+$4 Billion
2024 Client Web Sales

+300 Million

Personalized recommendations every week

150 ms

Average API response time

Smarter Retention, Stronger Loyalty

The BeansOS Differentiators

Granularity

Customer-Level Insights

BeansOS forgoes the use of any customer segmentation in its AI and ML models in favor of modeling data at the individual customer level.  Preferences, purchasing behavior, product-level sensitivities and purchase probability, and response to offers and product recommendations are analyzed at the individual customer level.

Product-Level Variability

Tailored to Individual Preferences

With an extensive product catalog, it’s crucial to understand how each customer’s preferences and needs vary across individual products. BeansOS identifies these variations to make more accurate recommendations that resonate with each customer’s unique tastes.

Offer Scalability

Unleashing Endless Experimentation

BeansOS enables experimentation with every allowable product x discount combination. By continuously analyzing these interactions, we uncover hidden opportunities for cross-selling and switching, ensuring that every recommendation aligns with each customer’s preferences and maximizes retention.

Sequential Choice Behavior

Understanding the Customer Journey

BeansOS tracks customer behaviors in real time, accounting for the timing and sequence of each action. Doing so allows for the evaluation of how prior decisions influence future ones, enabling BeansOS to deliver relevant recommendations at the perfect moment in a customer’s journey.

Product-Level Willingness To Pay

Aligning Recommendations to Each Customer’s Perceived Value
Understanding that a customer’s willingness to pay varies by product, BeansOS tailors each personalized recommendation to reflect this variability. Doing so allows BeansOS to maximize conversions while staying aligned with each customer’s perceived value of a product.
Multi-Layered AI and ML Models
Precision and Adaptability

Our suite of AI and ML models—including gradient boosted trees, Bayesian networks, multi-armed bandits, , and neural networks—works in tandem to evaluate each individual customer’s behaviors and decisions, continuously optimizing to achieve the desired business objective.

Adaptive Model Selection

Continuously Optimizing for Success
BeansOS deploys a range of models for each retailer, constantly monitoring their performance. Our system automatically selects and adapts to the best-performing model, ensuring the most accurate predictions and recommendations are made at all times.

Recommendation Efficiency

Evolving with the Customer

BeansOS fine-tunes product and offer recommendations continuously, adapting to changes in customer needs and behaviors. This dynamic approach ensures that each recommendation remains relevant, efficient, and impactful, leading to higher customer retention over time.

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Stronger in Unity

Retain becomes even more impactful when combined with the other Growth Drivers enabled by BeansOS, all seamlessly integrated into one powerful operating system built for personalized recommendations.

Discover

Identify, build, and analyze audiences

Acquire

Acquire new customers

Engage

Reach customers at critical moments

Maximize

Maximize gross profits

Build Lasting Loyalty.
Transform transactions into relationships. Strengthen retention by delivering personalized recommendations that meet each customer’s needs and preferences.