Every ecommerce site has the opportunity to sell. So, why do we see $300 Billion in lost revenue in the US market alone? It’s simple. Bad search experiences, irrelevant shopping journeys, and unrelated recommendations can cost you more than you think.

Let's take a look at it. Let's say a shopper in a cold region lands on your website and searches for "coats". They will expect to find cozy winter coats that can keep them warm. If instead they see a listing page filled with lightweight clothing that is more suitable for warmer climates, you are not personalizing the experience based on the shopper's data and are missing out on sales.

But why does this disconnect happen?

Complications in personalizing shopper experiences

Limited shopper data

Many ecommerce stores struggle to gather comprehensive and accurate shopper data due to inadequate technology, limited resources for data collection, or infrastructure limitations. And without sufficient data, it's challenging to understand consumer preferences and behavior effectively.

Lack of real-time insights

Legacy platforms might provide shopper data, but it's often outdated due to changing shopper preferences. And without timely data, it’s nearly impossible to respond to shopper needs promptly.

Complexity of personalization algorithms

Implementing effective personalization algorithms requires sophisticated machine learning and AI capabilities. Many ecommerce stores lack the expertise or resources to develop and maintain advanced algorithms that can analyze vast amounts of data and generate personalized recommendations.

Scalability challenges

As ecommerce businesses grow and acquire more shoppers, scaling personalization efforts becomes increasingly challenging. Manual approaches to personalization can’t accommodate the growing volume of shopper data and interactions.

Balancing automation with human touch

While automation is crucial for timely delivery of personalized experiences at scale, ecommerce stores must also strike the right balance between automated recommendations and the merchandiser’s business expertise.

Privacy and data security concerns

With increasing scrutiny over data privacy and regulations such as GDPR and CCPA, ecommerce stores must navigate complex compliance requirements while leveraging shopper data for personalization. Balancing personalization with data privacy and security considerations can be a significant challenge.

Fragmented data source

Shopper data is often dispersed across various systems and platforms, such as CRM systems, email marketing tools, and social media platforms. Integrating and centralizing data to create a unified view of the shopper can be complex and time-consuming.

So, what can you do?

Netcore Unbxd Advanced Merchandising

A data-driven approach The linchpin of Netcore Unbxd AI lies in data. Our data collection spans categories, products, shopper behaviors, past interactions, and purchases. We also observe positive signals and success metrics, such as clicks, views, add-to-cart actions, ratings and reviews, signups, purchases, and other data, including returns, product availability, and competitor pricing.

A balanced AI-human relationship Another crucial differentiator of Netcore Unbxd from other search and category merchandising platforms is,

• Our AI saves merchandisers from redundant manual tasks by automating operations that do not require expertise.
• We understand that lacking the human touch can lead to apathy in strategy execution. So, we enable merchandisers to pitch their business expertise for an optimal experience.

The ability to personalize experiences real-time The preferences of shoppers change rapidly. Our AI models are constantly collecting new information and using it to adapt merchandising strategies as and when shoppers are in session, responding to changes in buying behavior, inventory levels, and pricing dynamics.

Overcome data limitations In situations where comprehensive data might not be readily available, such as during the introduction of a new catalog or the initiation of a new user session, our fallback solutions become invaluable. You can configure a blend of rule-based manual interventions and automated processes, ensuring that each shopper receives personalized recommendations tailored to their individual preferences.

Furthermore, we facilitate the generation of a unique user ID for new shoppers, enabling us to track their shopping behaviors and promptly enhance their shopping experience based on their interactions with the platform. This approach ensures that even in scenarios where complete data is not immediately accessible, we can still provide personalized and enriching shopping experiences right from the start.

Today, where the time you have to impress the shoppers is very less, higher average order value and increased revenue hinges on the ability to craft unique shopper journeys that resonate with individual preferences and needs. However, the landscape is filled with challenges, from data limitations to the complexities of personalization algorithms and the ever-present concerns surrounding privacy and security.

At Netcore Unbxd, we recognize these hurdles and have engineered the perfect solution to overcome them. Our Advanced Merchandising platform leverages a data-driven approach, drawing insights from shopper behavior, interactions, and success metrics. With a balanced AI-human relationship, we empower merchandisers to infuse their expertise into the optimization process, ensuring a harmonious blend of automation and strategic insight.

So, if you're looking to increase the profitability of your ecommerce business and unlock its full potential, it's time to explore Netcore Unbxd’s AI-powered Search and Recommendations.