Recommendation Engines

This generates a "relevance problem."

The more content you have, the more you don't have. Too much content doesn't mean a good customer experience. If customers don't discover it intelligently, they may have difficulty finding it.

Common Challenges Include:

• When there are too many choices to choose from.
• Large catalogs or catalogs that change quickly.
• Poor or inadequate product or content discoverability.
• Generic customer experiences.
• Weak search relevance.
• Limited personalization.
• Missed cross-sell opportunities.
• Missed upsell opportunities.
• Low engagement.
• Static marketing experiences.
• Fragmented customer information.
• Manual merchandising and targeting.
• Issues with acting upon behavioral information.
• The lack of ability to personalize experiences at scale.
• Lack of real-time customer context.

Available data can be used to help organizations create more relevant digital experiences through the use of recommendation systems.

The goal is not just to display more suggestions. It's about providing better recommendations to the right user at the right time, in the right place.

Before investing large amounts of business development resources, ZechionAI can help businesses assess the potential for recommendations, readiness of the data, architecture needs, and business goals.

We assist in deciding on the right solution for the problem; it can be either simple rules-based, traditional machine learning model, hybrid architecture, or more advanced recommendation system.

Typical Use Cases

• An existing recommendation system evaluation
• Creating a new personalization platform
• Recognizing high-value recommendation opportunities
• Implementing legacy replacement logic in a modern way
• Seeing the larger picture of personalization. Thinking bigger about personalization.
• Creating an AI/ML recommendation roadmap

Business Value

The goal is to not invest in unnecessary technical complexity, but rather develop a recommender capability that is focused on measurable business requirements.

ZechionAI creates recommendation engines for:

Examples include:

Integration Considerations

Recommendation systems might need to integrate with:

ZechionAI builds the recommendations architecture based on the client's catalog structure, traffic, business rules, data availability, and user experience.

ZechionAI creates recommendation systems for:

Systems can evaluate:

Example Experiences

A recommendation system can be used with:

The goal is to enhance discovery while meeting relevance, freshness, diversity, and business needs.

ZechionAI can create recommendation systems based on suitable behavioral indicators like:

These signals can be captured and used for personalizing experiences across websites, applications, CRM systems, customer portals, marketing systems, and more.

Example

A SaaS user who is using a certain feature may be suggested additional features, integrations, learning material, or a suitable plan.

If a retail customer continues to click through a category, they might get a more targeted product feed.

Content may be delivered to a user that is related to their current interest.

Personalisation needs to be developed with proper privacy, data-use, security, governance, and consent aspects in mind.

The best advice might be a course of action.

ZechionAI works on next-best-action and next-best-offer systems to analyze the context of the customer, business rules, eligibility, timing, and behavioral signals.

Signals May Include

Such systems can power sales, marketing, customer success, service, and customer retention processes.

In regulated environments, recommendations are recommended to be based on applicable eligibility, privacy, compliance, governance, and human-review requirements.

ZechionAI builds the recommender's capabilities for:

Recommendation models can use customer behavior, product relationships, account attributes, purchase history, and business rules, as well as just relying on generic promotions.

Example

A software platform may suggest that, by looking at the behavior of existing customers, they might want to add a module that is beneficial for them.

A retailer could suggest other items as a recommendation because of the customer's current basket.

A B2B provider may identify services that are relevant to an account that is already in existence.

The recommendation should be relevant to the customer's situation and not just focused on maximizing promotional exposure.

There is a possibility that the recommendation from yesterday's activity may not be suitable for the current session of the customer.

ZechionAI creates real-time and context-aware recommendation architectures that can adapt to shifting signals.

The context for the current page or application.

Supporting Architecture

Different solutions can be considered if needed, including:

All organizations are not equally demanding in terms of recommendations in milliseconds. ZechionAI plans the latency and infrastructure based on the real requirements of the business.

Answers answer a specific question. It might be possible to use a recommendation system to decide what to discover before, during, or after that query.

With the help of ZechionAI, organizations can enhance:

The two types of signals are query signals and behavioral signals.

For instance, if there are two users looking for the same product category, and their behavior and preferences in the past are significant, they may get different rankings of the results.

The recommendations on a retail website are different in requirements than the ones used in healthcare, financial services, government, or enterprise software.

ZechionAI considers:

This allows for the possibility that recommendation technology is put in use to help the organization's actual workflows, and not to take a "one size fits all" approach to personalization.

ZechionAI can create recommendation functionality to integrate with:

Depending on the needs of the architecture, the solutions may be one of the following:

Recommendation logic can be broken down into reusable enterprise services instead of being tightly coupled to one particular app.

ZechionAI enables companies to constantly assess the relevance, usefulness, and alignment of recommendations with business goals.

Analytics & Testing Can Include:

Where appropriate, conduct multivariate testing.

For various applications:

Business Metrics

The top metrics will vary based on the business objective. The media company might be more interested in consumption, and the e-commerce company in discovery or conversion.

Post-deployment operations, monitoring, and improvement processes can be set up using ZechionAI.

The recommendation system must be testable and scalable with the change of data, customer behavior, products, business rules, and models.