For e-retailers, deep learning offers immense opportunities to increase conversion rates and improve the brand image through positive customer experience. Let us learn how.
According to a 2018 U.S. Census Bureau report, the e-Commerce industry has shown impressive growth in the past one decade. The online revenues have steadily eaten into an additional 1.5% of the yearly retail sales for the past several years.
Tech giant Amazon is projected to make up half of all eCommerce sales in the US by 2021. Statista predicts the global e-Commerce sales will amount to $ 4.88 trillion; thereby, showcasing a yearly growth of 20%.
Artificial intelligence (AI) and machine learning (ML) have played a pivotal role in impacting how consumers shop and how e-retailers interact with the users. These two technologies have been instrumental in offering online e-retailers the ability to personalize their interactions with prospects and customers to provide them with an improved shopping experience.
As we prepare to enter 2020, a new buzzword is making rounds in the e-Commerce industry, i.e., deep learning.
What is deep learning?
An advanced branch of ML, deep learning helps discover and track buyer journey online to predict and anticipate user actions. Thereby, brands can make relevant suggestions to consumers on digital mediums even before they ask for anything.
Since deep learning algorithms refer to vast amounts of data simultaneously, e-retailers can have a clear picture of the type of product info consumers search for before making a purchase. Let us study in detail how deep learning influences e-Commerce today:
- Pricing optimization
Dynamic pricing is a data-driven process. To drive sales and maximize their profits, e-Commerce companies often change prices on-the-go based on user demand, vendor supply, competitors, seasons and more.
Due to the price fluctuations, a pricing engine can be created to take into consideration many factors such as account current trends, product abandonment rates, buyer information, and competitor prices.
Amazon’s dynamic pricing for the same product.
Deep learning algorithms can combine this information with customer behavior to determine the discount to offer on particular products. Thus, increasing the chances of e-retailers to make a sale without hurting the margins.
Dynamic pricing is particularly helpful for businesses that sell tens of thousands of products. Since humans can’t do this task with full coverage manually, deep learning saves the day by optimizing pricing on the catalog of products.
- Product recommendations
When one comes across ‘product recommendations,’ it is hard not to think of how Amazon and Netflix leverages machine learning-driven recommendation system to gain a competitive edge in the industry. By analyzing buyer data from different channels, deep learning algorithms can help e-retailers suggest relevant products that consumers would be more inclined to purchase.
Alternatively, activewear and outdoor sports gear retailer, The North Face developed its own virtual personal shopper using the IBM Watson platform. The technology uses customers’ shopping needs, online/offline queries, and travel plans to recommend items that meet their requirements which are suited to the locations where the customers plan to wear them.
The North Face takes into account the weather forecast of the places where their customers are based. Product recommendations enable e-retailers to remind their customers of an item that they may want or need but had forgotten about. Or to nudge them to buy something they were not planning on buying in the first place.
- Fraud protection
Fraud detection has been a significant challenge for most domains, including banking, finance, and retail. The e-Commerce industry is no exception, either. Most buyers, especially first-time ones, have the impression that shopping online is not safe enough.
Therefore, e-Commerce businesses can’t afford to cut corners when it comes to taking measures to prevent fraud. Thankfully, deep learning analyzes all the transactions performed on the e-Commerce platform and creates an algorithm that proactively detects a faulty purchase.
For example: if an account adds three credit cards simultaneously, and two of them are rejected, then the algorithm will instantly highlight that anomaly, thereby enabling the retailer to verify it is a fraud.
- Customer support
Support-focused tools driven by deep learning are slowly growing famous due to their successful applications across many domains, including e-Commerce
Deep learning, for example, enables chatbots to stimulate interaction with a potential customer and resolve simple queries. The chatbots are trained to learn when they should ask for more information, the use of specific responses based on the situation and when they should direct the conversation to a human agent.
With 15% of customer service interactions to be managed by AI by 2020 as per Gartner, the opportunities presented by deep learning seem exciting indeed. Additionally, deep learning can be used to analyze data that pours in from support tickets and turn them into actionable insights.
Deep learning algorithms can work in real-time to analyze data for determining the overall customer satisfaction score and to deliver better buying experiences accordingly.
- Cart abandonment reduction
Cart abandonment is one of the biggest challenges faced by the e-Commerce industry. According to a recent Baymard study, the average shopping cart abandonment rate across various industries is 69.57%! Online shoppers abandon a cart due to reasons such as a complicated checkout process, hidden shipping costs and unsatisfactory return policy.
Often, consumers browse through the website without intending to buy the products. That’s where deep learning algorithms can add value. E-retailers can execute retargeting campaigns to reach out to users who have previously abandoned a cart, made a purchase or simply browsed through the website.
Deep learning makes it possible to study the visitors’ browsing history, the steps they undertook on the website and whether they converted or interacted in the past. Based on this data, retailers can predict the recommendations most likely to work when attempting to convert a potential customer with similar habits and profile details.
For example: for some, a coupon code with limited validity will be enough, while for others, offering free shipping will do the trick.
For e-retailers, deep learning offers immense opportunities to increase conversion rates, retain existing customers and improve the brand image through positive customer experience.
The technology not only serves the customers well through targeted, personalized marketing but also offers them related products that they are likely to be interested in.