Did you know that returning customers spend three times more money than new ones? Here you will find the tools for fostering their loyalty.

Find the resources you need to amplify your e-commerce and cultivate a base of recurring clients.


 Did you know that 58% of users are willing to abandon an e-commerce platform if they have a poor experience? Poor user experience and lack of trust might be affecting your site's sales conversions. So, what are the key points that e-commerce businesses should consider in order to enhance or safeguard their service?

1. Ensure an excellent user experience

It's no surprise that all websites, particularly e-commerce platforms, should prioritize user-centric designs. 58% of users stop purchasing on e-commerce if they had a bad experience while using it. So ensuring a positive experience for consumers won’t only attract new customers, but it will also retain returning customers, who on average spend up to three times more. 

Globally, 90% of consumers consider customer service pivotal when choosing a brand. That’s why all processes, from pre-sales to post-sales, should deliver personalized attention to users during every stage: registration, cancellations, and inquiries. This approach ensures a trustworthy and satisfying experience.

2. Offer an organized and updated catalog

During the product or service selection phase, online stores should function as user-friendly maps, with organized and simplified access to everything. Achieving this requires a meticulous product cataloging process.

Investing in product cataloging enhances the user experience by increasing on-site retention rates, thereby boosting the likelihood of successful sales. This entails not only having well-structured categories in a comprehensive menu, but also ensuring that consumers can locate products by using search words or phrases.

3. Recover abandoned carts

In the first quarter of 2021 the rate of abandoned carts reached 59%, a figure that the e-commerce industry is keenly focused on. While enhancing the user experience can positively impact conversion rates, users may still leave their carts without completing the purchase for various reasons. How can you recover these potential sales? An effective strategy involves email marketing: sales originating from abandoned cart recovery emails constitute around 13%.

Keys to boost your online sales.

Maximize the potential of your e-commerce.

 the quality of a brand's customer service significantly influences their purchasing decisions.

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 For 90% of consumers worldwide, 

We know that the success of online purchases is inherently tied to consumer confidence in the website—ensuring that both the online environment and transactions remain secure. Consequently, a pivotal factor involves the implementation of identity validation mechanisms, combining automation with human intervention to prevent fraud and to uphold the security norms specific to each marketplace.


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 Trust and site security

Arbusta Resource Download our whitepaper “Commerce: 7 keys to boost your online sales” here.

At Arbusta, we offer you a variety of solutions that will enhance your e-commerce and make your users' experience quick and effective from start to finish.


**1. Catalog management.** We optimize the content and the presentation of the products on your platform, so that customers can easily find what they're looking for.

**2. Digital back-office operation.** We make sure that every detail of your store works as it should.

**3. Meta’s conversion API.** We implement Meta’s conversion API to optimize the attribution and ROI of your campaigns.

You can rely on our Commerce services

At Arbusta, we provide you with agile and scalable solutions to help your business grow.

Discover what your e-commerce needs in order for it to become your top seller.


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How to boost your ecommerce

Machine Learning is a way to apply AI to systems, allowing them to learn and improve automatically from experiences, eliminating the need for explicit programming. It revolves around the development of computer programs capable of accessing data and independently learning from it. The primary objective is to enable computers to learn and make decisions without human intervention, thereby adapting their actions accordingly. In today's business landscape, this technique is pivotal for any company or enterprise striving for digital transformation.

Recent studies reveal that 29% of global developers have actively worked on AI/Machine Learning software in the past year. It's projected that 25% of Fortune 500 companies will incorporate essential AI components, such as text analysis and machine learning, into their robotic process automation (RPA) endeavors, paving the way for new applications of intelligent process automation (IPA). Additionally, analysts predict that by 2021, about 15% of customer experience applications will offer hyper-personalized services, combining diverse data sources and reinforcement learning algorithms.

The foundation of Machine Learning rests upon the confidence in systems' ability to learn from data, discern patterns, and make decisions with minimal human intervention. However, the journey begins with algorithm training, a critical aspect of the Machine Learning process. This process, for example, is integral in developing virtual assistants or chatbots, commonly found in advanced e-commerce websites.

These chatbots also use Natural Language Processing (NLP) techniques to dissect, comprehend, and interpret human language, facilitating the annotation processes. These techniques also extend to text recognition, enabling the extraction and analysis of information from image-based documents.


Machine Learning

In the realm of machine learning, the developers are responsible for crafting systems or models equipped to address single or multiple questions. This model construction hinges on a process called "training." The core objective of this training process is to develop a model that is precise and capable of delivering accurate answers most of the time. The model needs to be “trained” by explicitly providing it with correct answers. In other words, in order to train a model, it is necessary to gather data that will help establish connections between data patterns and accurate responses, facilitating in this way predictions based on the acquired training.

The first step is to collect data that will be used to feed into the model. While quality is crucial, quantity also holds value. An automated data collection technique frequently employed is web scraping, which sources data from diverse outlets.

Following data collection,one must prepare the data by mixing it and ensuring that correlations don't bias the answers in a specific direction. Once again, human intervention plays a pivotal role in this phase.


How are machines trained under the HITL paradigm?

The Human in the Loop (HITL) paradigm involves integrating human feedback during the training phase (modeling and simulation) of machine learning algorithms. What's the purpose behind HITL? This approach has emerged as the optimal route to train more accurate models, as individuals test, refine, and continually feed the data in real time, constructively and virtually. 

Given that machine-powered systems haven't yet achieved the desired levels of precision, human intervention is vital during the training process to enhance the precision of machine learning models. In simple terms, HITL describes the process that occurs when a machine or computer system cannot yield an answer to a problem and a person must intervene. 

At Arbusta, we embrace the HITL paradigm to ensure the quality and accuracy of our machine learning models. Our approach centers on fostering collaboration between humans and machines, leading to superior outcomes that cater to our clients' needs.

But, what is HITL? And during what part of the process is it essential?

Data scientists typically divide their data into three categories: training data, cross-validation data, and testing data.

**1. Training Data:** In the machine learning training process, training data serves to enable the machine to recognize patterns. This data contributes to training and adjusting the model's criteria, with the objective of minimizing bias and enhancing performance.

**2. Cross-validation data:** they are used to ensure higher accuracy and efficiency of the algorithm during machine training. It's used for hyperparameter tuning and for enhancing the model's capacity for generalization.

**3. Testing data:** it is instrumental in evaluating the performance of the trained model when predicting new responses. This data offers an impartial assessment of the final model, as they weren't used during the training phase, providing insight into how the model may perform new data.

All these data components coexist within the model, emphasizing the importance of ensuring that validation and testing data stem from the same distribution, and that the same training data isn't used for evaluation. This measure prevents the machine from memorizing responses and facilitates a more precise evaluation. A model achieving an accuracy rate of 90% or higher instills a high level of confidence in its responses.
Upon completion of training and evaluation phases, the model is primed to answer questions and make assumptions in context.


Final adjustment: If the accuracy of prediction percentages falls short, it is necessary to adjust or reconfigure the parameters (also known as hyperparameters). However, it's equally important to establish the criteria for what a sufficiently accurate model is, as obsessing over parameter adjustments could be time-consuming.

The Implementation of Artificial Intelligence and Machine Learning presents a lot of opportunities and advantages for businesses. At Arbusta, we are ready to assist you in maximizing these technologies and tailoring them to meet your distinct needs. Feel free to reach out to us at hola@arbusta.net to start a conversation. Explore the potential of training models through data with our Data Services.


Phases of Machine Learning: Training data, cross-validation, and testing

Artificial Intelligence (AI) and Machine Learning (ML) strategies have gained substantial traction in IT investments, driven even further by the COVID-19 pandemic. These technologies have matured significantly within the areas of e-commerce, healthcare, finance, and education.

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