Personalized Recommendations in e-Commerce

Enhanced customer journey

For the e-Commerce industry, personalized recommendations have become essential rather than just desirable. Recommendation engine brings major advantage in attracting customers as well as increasing business revenue.

For a long time, the term “personalized recommendations” was mostly associated to marketing departments. However, it is now clear that customer contact is not limited to marketeers only, but extends to the entire enterprise.

In e-commerce, personalized recommender engines refer to the practice of using data and algorithms to suggest relevant products or services that are likely to be of interest to individual customers. Additionally, these recommendations are typically based on a customer’s past purchase history, browsing behavior, demographics, and other data points. Consequently, by analyzing this data, recommender systems are predicting current interests and preferences. As a result, it is effectively enhancing the shopping experience and fostering lasting relationships with the customers. Recommendations can be delivered through various channels, such as email, on-site product recommendations, or through mobile apps.

There are several types of personalized recommendations that eCommerce businesses can use, including:

  • Product recommendations based on past purchases or abandoned carts;
  • Recommendations based on browsing behavior;
  • Recommendations based on customer preferences or demographics;
  • Recommendations based on voluntarily entered personal information, for example shoe size.

How can businesses benefit from personalized recommendations?

Gain customer insights

People’s needs and interests change over time due to life events like marriage, having children, or moving. Incomplete data, above all, makes it impossible to maintain up-to-date profiles of your customers. Ultimately, sharing this data can help you to maximize your ROI without wasting marketing spend. In fact, better understanding of customers and their preferences leads to more accurate marketing strategies, product development, and other business decisions.

Increase customer engagement

When customers receive relevant recommendations, they may be more likely to spend more time on the business’s website or app. This can lead not only to increased engagement and brand awareness, but also to higher customer satisfaction and loyalty.

Enhance customer experience and loyalty

By providing relevant recommendations, businesses can demonstrate that they truly understand their customers’ needs and preferences. When customers receive relevant recommendations, they are generally more likely to feel satisfied with their shopping experience and build stronger relationships with the brand. Customers may be more inclined to return to the business in the future, and their loyalty increases.

Boost sales

Personalized recommendations can encourage customers to make additional purchases, leading to increased revenue for the business. After all, when a customer receives a personalized recommendation for a product or service that aligns with their interests or previous purchases, they are more likely to make a purchase.

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How is data connectivity related to personalized recommendations in eCommerce?

Data connectivity plays a vital role in personalized recommendations in e-commerce. E-commerce websites collect vast amounts of data about user behavior, including browsing history, search queries, purchase history, and clickstream data. Subsequently, this data is then analyzed to generate personalized recommendations that are tailored to each user’s unique interests and preferences. Furthermore, a strong data connectivity infrastructure ensures that the data is collected and analyzed in real-time, without a delay. Moreover, data connectivity enables e-commerce websites to gather data from multiple sources, including third-party data sources such as social media and customer reviews.

Choose the right strategy and go for it

In conclusion, it is crucial to select the right strategy and execute it effectively. Displaying the inadequate or irrelevant product, at the wrong time and in the wrong place, can cause the loss of interest and disappointment among users. Meeting the individual’s needs would, on the other hands, results in a greater satisfaction along the onsite journey.
All in all, it is highly important to make sure the recommender system is developed with the goal to keep users engaged, simplify decision-making, and ultimately increase the conversion rates. At Factor Blue, we are here to help you achieve these goals.

Our awesome clients

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This is our roadmap:

Understanding your data landscape

The first thing we will do is analyze the current applications and software that is in use. Thereupon, together we will create a technical overview of all the processes.

Put the data in the right gear

What will follow is detailed advice about how the dataflow structure should be built in the most efficient way. The goal is to create the most desired situation that will fit your company's goals.

Collect project requirements

Once we have collected all the needed dataflows and connections, thereby we deliver a technical report in order to start the integration process.

Form the team

Based on the project scope, we will firstly form the team that will work on the integrations. Afterward, the technical team lead will take care of the tasks together with the team members. Besides, we will follow the scrum principles.

Reviewing and adjusting

Integration and data are constantly evolving processes. Therefore, we collaborate and reflect regularly, ensuring everyone involved stays satisfied and productive. Striving for the perfect lap.

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