Did you know that 68% of shoppers would not return to a site that provided a poor search experience?1
Not only that, people who use site search on e-commerce sites are more likely to purchase products, with a conversion rate of 50% higher than average2. Today, anyone can search anything on Google and expect relevant results based on location and preferences within milliseconds!
Once the customer enters a keyword such as “dove,” the search engine will crawl through a plethora of internet information. But how does it know if the desired result for the query is a soap, a chocolate bar, or a bird?
This is why fine-tuning search relevance is crucial for your business.
Search relevance is a measurement of how closely related a returned result is to its query3. When search results match user intent, you’ve achieved relevance. Good search algorithms understand the context of the user’s queries and deliver relevant results.
The process starts with collecting ground truth data that the search algorithm can learn from to make correct decisions. This training data is used to train algorithms on contextual information such as natural language text and user intent.
Training data created from search query categorization teaches algorithms the indicators that help distinguish between related queries. Learning all possible iterations of this is the challenge companies face without proper training through data labeling of their datasets.
The accuracy of training data directly determines the accuracy of the trained model. Thus, it is critical to put great effort and investment into the collection and annotation of high-quality data.
That’s where human intervention comes into play.
Natural Language Processing (NLP) is the branch of artificial intelligence that helps search engines understand and respond to user queries. In order for NLP-based search algorithms to improve, they need to be trained with large sets of natural language data enhanced with metadata on user intent, named entities, sentiment, and more.
As such, it’s most beneficial to implement a human-in-the-loop approach to building and refining AI-based search systems. In this workflow, human evaluators rate whether specific inquiries are providing relevant search results. Human evaluators judge search results, giving critical feedback and new training data to the algorithm. A diverse base of evaluators (demographically, linguistically, among others.) helps the algorithm perform better across markets.
As a result, the majority of time allocated in machine learning projects is dedicated to data collection and annotation. Based on a report by analytics firm Cognilytica, ¼ of total developer time is spent on acquiring labeled data. Some companies have plenty of raw data but lack the resources to support large-scale data labeling or lack an effective process to ensure accuracy and quality4.
There are several types of services that data labeling companies offer to improve search relevance. The most popular is the evaluation of search results, wherein evaluators determine whether or not a particular search result is relevant to a search query.
In the past, relevance was based on the frequency of the appearance of keywords on a webpage. Now instead, search relevance puts greater emphasis on accuracy and context. Search result evaluation can be done through the following:
The evaluator’s assessment of the search results is then used as an input for the search engine’s algorithm, bringing in more accurate results on top of the list.
Another type is ads evaluation. Like search result evaluation, evaluators are tasked to assess the relevance of advertisements based on what users are searching. Ads are known to have an impact on customer search experience; which is why evaluators also cross-check the appearance of the ads with the corresponding landing page.
Other types of search evaluation tasks such as auto-fill evaluation and recommendation evaluation—where related results are predicted and suggested by the search engine based on the search query—are also geared towards improving search experience.
Organizations must adopt the most optimal data labeling practices to minimize the labeling cost and time. This significant step will result in an efficient data labeling process and develop accurate datasets to train high-quality models.
Here are some of the best practices for data labeling that we recommend:
Given that data annotators must work on repetitive and time-consuming tasks, in-house teams struggle to allocate sufficient staff and meet the high demands. Additionally, the lack of domain knowledge will only lead to inaccurate models. Thus, human evaluators should be carefully pre-screened and qualified, and should work within strict guidelines to ensure high-quality training data.
Data labeling projects can be overly complicated and costly, which is why it’s essential to design workflows to capture high-quality, diverse training data. Without explicit goals and careful planning, your data pipeline can easily become messy.
Data problems trickle down to model development, potentially causing issues in the model’s ability to make predictions. To ensure you only collect meaningful data, it’s necessary to break down complex labeling projects into a series of simple tasks with crystal clear instructions for labelers.
In addition, the data collected should be diverse, capturing all user demographics, languages, geographic locations, and more. Ensuring the quality and diversity of data is an integral part of the project design in AI data operations. Failure to spot and anticipate issues in the training data will lead to operational inefficiencies, financial and legal repercussions, and even public scrutiny. Apart from analyzing the integrity of the data, it’s also crucial to revisit and analyze the first trained model. More often than not, the first model formed is suboptimal, and finding the right design and the right combination of parameters and tools can provide additional precision.
If a company is to outsource its AI development, one must vet every effort to find the most qualified partner for the task at hand and be convinced you can rely on the people you end up working with. Here are some of the most vital points worth considering:
Ensuring the quality and diversity of data is an integral part of the project design in AI data operations.
Quality training data is the foundation of any successful AI model. With 10+ years of experience in delivering data labeling services, TaskUs has the human capital and process expertise to build that very foundation.
Data quality is our main obsession and this permeates throughout the whole Search Evaluation process. Combined with best-in-class labeling tools and industry standard security protocols–optimal and accurate search relevance for your business is just a call away.
Ready to get started? Click here to learn more about our data labeling services and get in touch with our team today.