Machine Learning algorithms can hugely impact the sector of e-commerce by categorizing and recommending products for search queries based upon the information provided in the product details and descriptions (i.e. size, color, shape, manufacturer, etc.).
However, for the algorithms to continue evolving, and to ensure that only the most relevant search results are being seen by shoppers, a human element must exist within the process.
Human-in-the-loop machine learning (HITL) merges machine learning with the irreplaceable element of human intelligence to build an important sector of artificial intelligence. Human-in-the-loop machine learning is a cyclical process, both beginning and ending with human knowledge.
A typical HITL process looks like –
Human expert creates data model
Machine learning algorithm learns to make decisions based on data model
Human expert fine tunes model and adjusts for outlier costs
Human expert tests model
With the addition of human intelligence to the machine learning process, shoppers see more accurate search results. These improved search results lead to a better customer experience, and ultimately, higher conversion rates and revenues.
HITL TO DRIVE IMPROVED CUSTOMER EXPERIENCE AND CONVERSION
Better customer experience and higher conversion rates result when a company gains a better understanding of its customers. Since the search engine algorithm communicates directly with the shoppers, calibrations to improve accuracy are key.
When examining query intent (the result the searcher was after when he/she plugged a phrase into a search engine) the crucial question to be asked is, “How do recent searches by shoppers compare to the results the searcher was given?”
In an e-commerce HITL process, shoppers search history is analyzed and graded, based on the data model that has previously been created, in comparison to the results that buyer would have seen.
For example, if a shopper searched “denim for men”, the human analyzing the search history will determine how relevant the results given are. A grade will then be given which will train the algorithm to move that specific result up or down in the search results. These adjustments, made by human intelligence, are crucial to the growth of the algorithm.
The next layer of analyzing search history is relevance comparison, or, the comparison between two search results that were given for the same search query. The goal of relevance comparison is to make sure the most relevant search results appear closer to the top of the search results page. Human analysis to determine which of these top search results is most relevant to the query is crucial and is an ongoing process while the algorithm is being honed.
Product images are a key factor in driving conversion in e-commerce. The most important factors in image classification are:
• Classification (including full product image, product detail image, lifestyle image).
For example, an electronics supplier has provided multiple photos of a television they are selling. Each image needs to be categorized based on quality (i.e. which photos are high enough quality to be displayed) and then classified by type of image.
Here is how images are classified
A picture of the television in a furnished room, on a TV stand, helps the buyer visualize the size and scope of the television. This type of image would be classified as a lifestyle image.
A picture of the back or side of the television, showing the number of HDMI inputs and any other A/V connections gives further detail into the mechanisms the television offers. This type of image would be classified as a product detail image.
A standalone photo of the television against a solid-colored background, without any objects for scale and without specific details of the inner workings of the television would be categorized as a full product image.
In the e-commerce sphere, product data that comes from suppliers is often incorrect or inconsistent and may not meet the standards of the online retailer. Since consistent and accurate product information leads to higher conversion rates, it’s important to identify unique characteristics for each item, including:
Including each of these characteristics ensures that search queries will pull up correct items and yield higher conversion rates.
Furthermore, it’s important to examine and re-categorize products that have been flagged as incorrect by shoppers. HITL prioritizes quality control and makes it a more efficient part of e-commerce.
USER GENERATED CONTENT MANAGEMENT IN E-COMMERCE
Within e-commerce, user-generated content management (UGCM) almost exclusively refers to user-posted reviews on e-commerce sites.
Many shoppers will go to a product’s review section before making the decision to purchase a product. Having a review section, and having it be easily accessible to shoppers to both write and read reviews, is in the best interest of the company since it shows honesty and transparency.
In the interest of making the review section a positive, secure space where shoppers who previously bought a product will be motivated to leave reviews, machine learning algorithms should be able to identify:
Human annotation is valuable at this stage of the process as well since human knowledge can help further train the algorithm to identify inappropriate content. For example, an algorithm can be trained to identify a single explicit word, but it may not be able to identify a string of words that could be inappropriate (i.e. a phrase that does not include an expletive but could still form an explicit or abusive phrase when typed together).
Human training of the algorithm ensures a more secure review space and a better customer experience, both contributing to increased conversion rates. The upgraded integrity of the customer review section gives shoppers more confidence when potentially buying a product and should encourage those who have already purchased to leave a review.
IMPLEMENT HITL FOR HIGHER CONVERSION RATES
E-commerce is a data-intensive sector. With online shopping gaining more popularity, the amount of data generated every day in the form of text, image, and video is massive. And this data can unlock valuable insights into the drivers of purchase.
Human-in-the-loop machine learning leads to higher conversion rates by continually improving the ease of search for customers, categorizing images and products better, and making product reviews safer and more useful.
On one hand, HITL enables e-retailers to proactively reach out to shoppers and offer intelligent product recommendations. On the other hand, it helps shoppers make informed purchase decisions.
Each step in the HITL process boosts shoppers overall experience and will not only lead them to purchasing more quickly but will also help them become return customers.