Infosearch provides accurate annotation labelling services for ML, AI. Contact Infosearch for outsourcing annotation labelling services. This article is a case study on how a retail brand improved its AI model through effective annotation labelling.
Background
A well-known retail firm, “RetailCo”, was experiencing
difficulties in improving their recommendation system implemented with AI. The
idea of the company was to enhance the ways of making proper product
recommendations for its online customers to enhance sales and satisfaction
levels. The recommendation system that utilised machine learning had to be more
intelligent and learn user trends and patterns. To do this RetailCo
resolved to acquire efficient annotation labelling to improve the quality of training
data.
Problem Statement
RetailCo's recommendation system was underperforming,
leading to:
Inaccurate Recommendations: The customers were getting
exposed to products irrelevant to them or less relevant, or low relevance to
them.
Low Conversion Rates: Specifically, this meant that
recommendations were not aligned with customers’ preferences, and this skewed
the conversion numbers.
High Cart Abandonment Rates: It has been observed that
customers were getting frustrated and dropping out of the carts simply because
they were not interested on being exposed to or sometimes even offended by
irrelevant product recommendations.
To solve these problems, RetailCo required accurate
annotated data that can enable adequate retraining and fine-tuning of the AI
solutions across the firm.
Solution: Effective Annotation Labelling
To achieve the goal, RetailCo adopted a structured approach
of AI model for annotation labelling. Here’s how they achieved it:
1. Saying Specific Goals and Annotating Standards
Goal Setting: Objectives for the annotation project were
defined with RetailCo as follows: The goal is to increase the efficiency of the
proposed products depending on users’ activities and choices.
Guidelines Development: Very precise requirements for
annotation were developed, explaining how clicks, purchases, or browsing can be
labeled, as well as the types of products, and the kinds of users.
2. How to Choose the Correct Annotation Tools and Services
Tool Selection: Advanced tools for annotation assisting in
large-scale data processing were selected by RetailCo, and tools for tag
assignment, categorization, and quality assurance were implemented.
Service Provider: A specialized annotation service provider
with adequate knowledge in dealing with retail and e-commerce data was selected
to collaborate with the company.
3. Training and Onboarding Annotators
Annotator Training: The annotators went through an extensive
training process in adherence to the RetailCo guidelines and in particular the
methods for labelling the user behaviours and product data correctly.
Quality Assurance: To standardise the process of annotation,
a team of experts to supervise the work of consolidating the annotated
references.
4. Implementing Quality Control Measures
Regular Audits: It was the practice of RetailCo to conduct
constant audits of the annotated data to check on the accuracy and standardization
of data.
Feedback Mechanism: There was the creation of feedback where the
annotators could be informed and apply these remarks in enhancing labelling practices subsequently.
5. Using Annotated Data in AI Model Training
Data Integration: The annotated data was fed into the
pipeline which trains the actual model with good quality relevant data to learn
the users’ preferences and usage behaviour.
Model Fine-Tuning: RetailCo employed the improved dataset to
refine its recommendation based on other elaborate patterns and users’ data.
6. Monitoring and Iteration
Performance Monitoring: The new recommendation system
performance was measured through statistical indicators such as
recommendation efficacy rate, conversion rate, and cart abandonment rates.
Continuous Improvement: RetailCo also provided knowledge
on how he improved the guidelines and training data in terms of performance
results and users’ feedback.
Results
The implementation of effective annotation labelling led to
significant improvements in RetailCo’s AI-driven recommendation system:
1. Increased Recommendation Accuracy
Personalized Recommendations: The consequences witnessed are
an enhanced capability of providing customers with products that they are
interested in through offering closely matched recommendations.
2. Higher Conversion Rates
Sales Growth: Conversion rates were felt to have improved
significantly since customers were more willing to buy the products which the
recommendation system recommended.
3. Reduced Cart Abandonment
Engaging Suggestions: Combined with product suggestions that
are most pertinent to the consumer, cart abandonment rate trends receded
improving the general flow of shopping.
4. Enhanced Customer Satisfaction
Positive Feedback: Recommendations were more helpful and
accurate because the surveys were completed in the earlier stage and did not
take much time to ask at the end of our sales conversation, which received
positive reactions from customers and built our brand loyalty.
5. Efficient Data Utilization
Optimized Processes: Through annotating data, it made it
easier for RetailCo to reduce the time taken in performing machine learning
model training and development when doing enhancements as well as incorporating
new features.
Conclusion
Through proper funding in annotation labelling, RetailCo was
able to improve its AI recommendation system, which in return increased its recommendation rate, conversion rates and overall customer satisfaction. This
particular case shows that accurate annotation is essential for developing and
implementing machine learning solutions and achieving organisational
objectives. These include well-defined objectives, guidelines as well as
adherence to quality standards in the annotation process that were hallmarked
as a strength of RetailCo.
