Our secure training data annotation platform combined with our human-in-the-loop experts make it easy to manage your training data pipeline at scale.
In the last few years, many businesses have pivoted to include the use of AI in automating processes. This negation of human input allows for the focus to be back on the consumer. Alongside the improvement of inventory management, there are several ways in which AI can positively impact the retail sector, including efficiency in sales processes, personalization of products, implementation of chatbots, bridging the gap between personalization & privacy, and more. Crucial to the implementation of these processes is accuracy. This has been well illustrated by Elon Musk, who relayed an anecdote in his fireside chat with Salman Khan of Tesla’s past accuracy failures in inventory management. In this scenario, one of Tesla’s suppliers had failed to deliver USB cables. This simple $3 item brought the entire Tesla production line to a standstill as the team literally has to drive to every electronic store in the Bay Area to try obtain USB cables for the car’s computer system.
“Good inventory management revolves around a single contradiction: keeping enough stock in the warehouse to ensure the business keeps moving but not enough stock to drain its limited cash reserves.”
- Remi AI
AI applications now being utilized to avoid similar mistakes and irregularities include time series prediction and reinforcement learning systems. A great example of this has been seen at e-commerce giant Amazon, who currently uses both RL and time series prediction models for their calculation of supplier backorders, warehouse optimization and stock levels across different locations.
Personalized shopping and product recommendation also see great advancement through the use of AI. It’s been suggested that Amazon has a product recommendation system so robust that it was responsible for 35% of the company's revenue to date. This is not an anomaly in the industry, with several reports suggesting that recommender systems have been seen to both increase average order value by 369% and conversion rate by 288%. These numbers are echoed by Salesforce, that studied the effect of recommender systems in e-commerce, showing that shoppers are 4.5x more likely to add items to their cart, and 4.5x more likely to complete their purchase when utilizing accurate AI-led recommendation systems for products.
Shocked? It gets better. The recommendation systems alone bring in huge amounts of business, but predictions suggest that this could be increased even more when combined with image recognition. In a world in which we are regularly influenced by the style trends of celebrities and bloggers, there is always demand for worn or promoted products. With the advancements in image recognition, computer vision and computation power, the average consumer is able to find their desired item through an image on their phone, just minutes from sighting to buying, further increasing conversion rates and organizational revenue.
This method is currently used at Alibaba. The company created a powerful machine-learning service: Taobao’s image-based product search. An Alibaba shopper can take a photo of an item in a shop window and immediately be steered to a page where they can purchase it. Alibaba processes a billion images like this a day. The tool also uses ML algorithms in its retail stores, operating under the brand Hema, [for autonomous checkout]. Cameras installed throughout the glitzy new supermarkets track shoppers around the store and identify the products they take off the shelves.
Learn how Walmart Labs works with our team to make sure its machine learning model is set up for success.
While the previously mentioned algorithms and techniques will make huge advancements in the retail and e-commerce sectors, we should understand that none of the above is possible without huge swathes of data. Data collection is important, but these data sets must be also endowed with the contextual information that computers need in order to learn statistical associations between components of that data set and their meaning to human beings. As with any AI application, there must not only be a great amount of data, but that data must also be of a high quality. Here we speak of garbage in, garbage out: if your AI training dataset is “garbage,” the resulting algorithm will also be sub-par.
Access our checklist on how to ensure quality training data for ML models.
Your training data, especially for a complex environment like retail, should be consistent, accurate, unique, cleaned and tested. Any errors present will compromise your data and therefore void any use of it in developing your AI application.
The development of AI has had a huge impact on e-commerce and manufacturing. In an e-commerce environment, high levels of stock tracking and product availability through AI, if done correctly, can lead to an increased level of customer satisfaction and conversion rates (revenue!). Recent studies suggest that customers factor a brand’s product knowledge and process transparency in their purchasing process, increasing the level of trust in a brand. Simply put, consumers need to trust the data that retail organisations present to them; be it on stock levels, product availability, or other facets of data driven information. If they don’t, consumers will gradually abandon systems and brands. This is also seen through the recent consumer trend of primarily using category or search-based navigation on their chosen supplier website. Negative product recommendations or recall due to unstructured or poorly labeled data, can again lead to consumers looking elsewhere or leaving a website without purchasing.
Now for the challenges. For years, retailers have overestimated the cleanliness of their data, or using data with too many holes, errors and inconsistencies. This leads to poor results across the board, or force organizations to create or purchase a completely new cleaned and trained dataset at great expense.
Complexity issues with AI, especially in retail, is another pain point. There are hundreds of ways to describe an item. E-commerce platforms have to classify tens of thousands of products according to specific dimensions. Getting the labels of each product's dimensions correctly and making them visible to the customer who’s highly likely to purchase—and maintaining this database—can be an overwhelming project.
Finally, a big barrier to AI adoption for retailers is data security, according to KPMG’s “Living in an AI World 2020 Report.” 70% of retailers said perceived threats involving consumer data security and privacy may slow AI adoption. 90% agreed that their companies need to be responsible for implementing a code of ethics.
Despite concerns, 80% of retail insiders say AI technology is already regularly being used to alleviate customer service issues. 86% believe AI has the potential to significantly improve organizational efficiencies.
The first step to build a training data pipeline for your e-commerce business is to select relevant data. Remove images that don’t meet your specific requirements and apply ML to detect elements important to your use case to get the most value out of your data.
Choose your taxonomy wisely. Create relevant labels and attributes based on your use case. As described in the previous section, having multiple labels per each dimension of one product can sometimes be quite complex to manage. Create a tree to clearly highlight the hierarchy between class and attributes for your labeling partner. Relevant meta-data labels are particularly important to help contextualize and categorize your dataset, making it easier to identify gaps and quickly refresh datasets for new and unique products.
Iterate on your instructions to remove subjectivity. Your quality rubric and training requirements should cover as many examples as possible, so there’s zero room for your labeling partner to misinterpret labeling instructions. A trusted training data partner can help identify edge cases and recommend annotation best practices to improve your initial training guidelines. Digging deep to understand why a result is high quality is a step toward calibrating instructions that are specific, measured and objective.
Data annotation can be time-consuming, but it’s important to balance labeling speed with efficiency and quality. Poor quality data labeling leads to flawed AI systems — establishing a quality assurance process helps reliably produce quality AI training data. A few best practices we’ve put in place for our clients include automated QA checks, advanced quality analytics reports and other gold tasks to meet and exceed quality SLAs.
Don’t forget to maintain your models. You probably constantly add new brands and new products to your inventory. That’s why it’s important to evaluate the content of your dataset on a regular basis: identify new data for which your model is uncertain and identify biases in your model compared to real world data.
Maintain the quality and accuracy of your AI training data with data labeling solutions and training data strategy from Sama.