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5 Ways eCommerce Brands Can Use Artificial Intelligence for Black Friday

Black Friday is a treasure trove for e-retailers who see it as the perfect opportunity to attract more customers and drive higher sales. Here’s how your eCommerce brand can use artificial intelligence (AI) to get the most out of the Black Friday:

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A Quick Guide to Forecasting Sales Using Artificial Intelligence in eCommerce

AI can scan large volumes of real-time and historical data, to identify the most lucrative sales leads. Learn how artificial intelligence is shaping sales in eCommerce.

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How Deep Learning Is Revolutionizing e-Commerce Today

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:

  1. 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 predictive pricing for the same product.

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.

  1. 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.

Recommendation algorithms of Amazon and Netflix.

Source

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 developed its own virtual personal shopper using the IBM Watson.

Source

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.

  1. 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.

  1. 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.

  1. 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.

Reasons for cart abandonment during checkout.

Source: Baymard

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.

Wrapping up

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.

Thankfully, you can leverage deep learning for your e-Commerce business with Datoin. Try the automated ML platform for eCommerce today!

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3 Effective Ways to do e-Commerce Marketing Using Artificial Intelligence

Today, e-Commerce brands are relying heavily on using artificial intelligence (AI) to provide a personalized shopping experience to their customers. Here’s how. 

What makes an e-Commerce business thrive? Indeed, there is no one clear answer. The success depends on multiple factors such as excellent products, impeccable customer service, efficient marketing, and so on.

As per a Statista report, over 1.92 billion consumers will participate in eCommerce activities globally by 2019. The number is expected to increase to more than two billion by 2021.

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5 Use Cases of ML in ECommerce with ROI

Machine learning is the science that makes the critical decision for ECommerce today. If you are a small and medium-size ECommerce company, then you might have been avoiding machine learning until now. A crucial question for most ECommerce business owners is what to expect in return if you begin using machine learning? Machine learning models have significantly evolved today. Evolution of ML helps us get a clearer ROI perspective from ML and AI solution investment. There are five specific use cases that you are already addressing in your ECommerce business which can be better addressed by ML. You are likely to drive incremental business from each of these use cases with the use of ML.

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Benefits of AI: Rule Based Vs ML in ECommerce

There is confusion in the minds of technologists around artificial intelligence (AI) and machine learning (ML). This confusion is because most practitioners come from the experience of engineering and not data science. In this article, we focus on understanding what it means to run an AI-based ECommerce in real sense.

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Automated AI Experiments: A Key to Success in ECommerce

Automated AI is the holy grail for everyone who doesn’t know a thing about AI and machine learning. Take a closer look at the history of washing machines. The simple items such as washing clothes have been through more than a century of evolution. Washing clothes at once would be the longest endeavor of the everyday. With its automation, the human has availed itself liberty to do more innovative things. AI in ECommerceis going through a similar journey, albeit much faster than washing machines.

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What is AI Driven AI in ECommerce

There are two kinds of people who are at the center of ECommerce company’s operations. They are technology experts and those who are good at the domain of the particular ECommerce line of business. As ECommerce businesses evolved over the last decade ECommerce companies started seeing the value of data. To leverage the value of data to its full potential ECommerce, companies need to have data science experts on their team. Data scientists are expensive and also take more time to demonstrate results than acceptable in a typical ECommerce SMB culture. So the critical question has been how to adopt data-driven decision making in ECommerce without having the data science skillset?

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