Today, personalized user experiences have moved from being a nice-to-have, to a non-negotiable demand. To the point where customers are often put off by generic recommendations. In fact, around 71% express frustration at impersonal experiences. As businesses realize the immense opportunity that lies within the space, the obvious question arises – how does one maximize yields via personalized recommendations? 

This curiosity has given rise to an industry of recommendation engines that help businesses upsell, cross-sell, and drive favorable business outcomes. But recommendation engines aren’t straightforward. Due to modern consumer demands and a slew of technical limitations, there’s often a sizable gap between what a customer wants and the reality on their screens – oftentimes generic, clunky, and irrelevant suggestions. 

Challenges with Recommendations

Data Privacy: While personalization is non-negotiable, customers also simultaneously demand data security and integrity. But often organizations struggle to maintain the sanctity of data integrity, as most recommendation engines demand the user data be moved outside of the company’s firewall or to the cloud. 

Cold starts: Businesses also struggle with handling new users – the ‘cold start’ problem. Often this can result in irrelevant or generic recommendations which can do more harm than good.

Business constraints: Many recommendation engines don’t have provisions to accommodate for business constraints i.e. critical industry- or regulatory-specific exceptions. This results in recommendations that are impractical at best or unlawful at worst. 

Accessibility: Several recommendation engines are advanced and require a high degree of expertise. Often data scientists need to step in to glean insights, raising the barrier to entry even higher. 

These challenges were more than mere observations, they were core to our strategy when we set out to solve the challenges surrounding generic recommendations.

Enter Generic Recommendation Engine (GRE)

GRE is our take on the modern recommendation engine. It’s an industry-agnostic ensemble of AI and ML algorithms that accurately generates relevant recommendations. A pragmatic and scalable accelerator, emphasizing security, efficiency, and user-friendliness – it checks all the boxes for modern enterprises. What does it do differently? 

• In a departure from the general notion of recommendation engines, GRE is adept at handling B2B use cases just as effectively as B2C ones.
• It can deal with a wide variety of datasets including interaction data, product information as well as user data. 
• Addressing the industry’s sacrosanct need for user privacy, GRE operates securely within a company’s firewall, offering end-to-end deployment in the client’s environment.
• It also addresses the aforementioned cold start issue by dedicating a constituent model that primarily works on product information, not needing any customer information to generate engaging recommendations. 
• It lets users adjust hyperparameters for their specific use cases, thus allowing for recommendations that account for the various business constraints across industries.  
• GRE doesn’t need data science expertise on the client side. The system facilitates easy deployment, integration, and maintenance, streamlining the overall UX.

SDG: The GenAI Catalyst

GRE’s capabilities are further enhanced by our in-house synthetic data generator (SDG) – a GenAI-powered accelerator for when there’s inadequate data. It works in tandem with GRE for accurately recognizing complex patterns leveraging schema and metadata to fill the gaps and ensure appropriate recommendations at all times. 

Most real-world datasets are non-linear, meaning many ML algorithms struggle to learn the patterns within. The GenAI model used in GRE, with its array of complex and comprehensive learning parameters, remains resilient in the face of non-linear data – understanding even the subtlest patterns and behaviors of customers.

For instance, a new customer only has a tiny set of invoice/ transaction data available. In such cases, a conventional AI model won’t be able to successfully learn their purchase patterns and behaviors, resulting in a low-quality recommendation. With SDG, however, organizations can create synthetic invoice data for said customer. This would mimic their behavior, enabling the model to learn better. 

SDG can execute row-level augmentation of existing data, meaning all the distributional properties and relationships of the original data are conserved. For accurate trend analysis and forecasting, it works with time series data generation to stay ahead of the curve. 

GRE + SDG in action

Once implemented, how this translates looks different across industries. The common thread being a positive impact on the bottom line. For financial industries, GRE and SDG help with cross-selling and upselling. Thereafter, businesses can offer recommendations for marketing campaigns specific to the products. Similarly, in the retail and manufacturing domain, better recommendations can help price/discount products competitively to better penetrate categories. It speaks to the breadth of the use cases GRE and SDG can be deployed within. Regardless of whether it’s a B2B or B2C application, they remain unfazed, a testament to their generic nature.  

Manifesting Business Growth

A robust recommendation engine isn’t just a tool; it’s a catalyst for business growth. In an increasingly consumer-centric world, embracing Generative AI to maximize customer engagement becomes essential. As businesses navigate this new era, those who hesitate risk falling behind. With advanced recommendation engines like GRE, businesses gain actionable insights that fuel growth strategies and drive informed decision-making. It’s not just about recommendations; it’s about leveraging every opportunity to make a tangible business impact.