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icicle.techeCommerce has always been a disruptor. First it rocked the brick & mortar industry, forcing physical retailers to rethink the way they do business. Then eCommerce brands continued to reinvent the game, riding on the back of technological developments like eWallets and mobile apps - all to keep the customer engaged and delighted. This constant evolution has made the eCommerce field of play extremely interesting to watch, and very lucrative for those with their fingers on the pulse.
Now, artificial intelligence (AI) and machine learning (ML) are set to change the game again. Servion Global Solutions predicts that by 2025, 95% of all customer interactions will be managed by AI technologies[1].
In fact, machine learning developers are already making huge waves, especially in the big players and market leaders: Amazon, Shopify, Rakuten, Alibaba, and so on. AI-enabled revenues in eCommerce is expected to reach $36.8 billion worldwide by 2025[2].
Even proprietary eCommerce platforms are getting in on the game. Magento and WooCommerce are offering better personalization and product recommendations using machine learning algorithms. And in 2019, Shopify launched their AI-powered eCommerce fulfillment network.
As innovations in eCommerce development continue to proceed at a breakneck pace, we can expect to see AI & ML continue to have a significant impact on eCommerce sales. This article takes a look at how companies are already leveraging AI & ML, and what kind of picture this paints for the future.
Quick side-note on definitions:
Broadly speaking, AI refers to a computer’s ability to imitate human logic, and make decisions like a person would.
ML is a subset of AI, and refers to the way in which a computer can “learn” logical rules without being specifically programmed for a particular task.
In essence, AI uses ML to take a set of data and figure out how to work with it. But for the purpose of this article, AI and ML can be used interchangeably.
How eCommerce Developers can Leverage AI and ML to Impact Sales
The thing about AI is that it relies on (and indeed activates the potential of) your data. The economic impact of using this data manifests as:
- Enhancing products: Using customer feedback to make products better, or analysing trends to create new products.
- Making more informed decisions business decisions:Accurate forecasting based on real-time data, demand analysis, etc.
- Optimizing processes and operations: Get the most out of your delivery and warehouse ops.
- Better, more personalized customer service: We are now in the age of personalisation 2.0 or “hyper-personalisation”.
- Identifying new markets: Through building of buyer personas, advanced market research, etc.
- Automating workflows: Both digital and physical.
- Smarter advertising, marketing, targeting.
- Prevention of fraud.
Literally any kind of set of data can be used to extract business-impacting insights. Just some of the data sources that are relevant to eCommerce businesses are:
- Reviews (customer reviews and professional reviews)
- Call centre and customer support transcripts
- Natural language (as in voice data, for example)
- Purchase and search history
- Operations and logistics
- Social media
- Site usage analytics
- Wearable technology
Customers are constantly creating rich data sets as they interact with eCommerce brands. Artificial intelligence developers thus have a lot to work with.
These data sources are an absolute gold mine for companies, and the challenge in eCommerce development is to find a way to turn them into insights.
Applications of AI and ML in eCommerce
eCommerce development is taking great advantage of AI and ML. Let’s dig a little deeper into their applications in eCommerce, with some examples of companies that are doing it with success.
Customers Want Intelligent Product Recommendations
Smarter recommendations leads to more sales, especially for larger retailers with millions, perhaps hundreds of millions of items. In fact, AI-enabled personalized product recommendations for online shoppers are increasing conversion rates by 915%[3].
Amazon has proof, stating that product upselling and cross-selling is responsible for an impressive 35% of its overall revenues[4].
Amazon uses a combination of Collaborative Filtering and Next-in-Sequence models to make predictions on products that customers may need next.
Alibaba is also using AI algorithms to drive smart product and search recommendations by analyzing customer browsing and interactions with the website.
Using AI to Provide Visual Search, a More Natural User Experience
One of the more exciting aspects of AI is the enablement of visual search, using images. It gives the customer a much better – and more natural – way to search for products.
With visual search, if a customer sees a dress she likes, she doesn’t have to try to painstakingly describe it, or even know the brand. All she has to do is take a picture, and intelligent image searching technology will pull up the same or similar products.
Amazon has dubbed their visual search product StyleSnap. Recently, an article in usmagazine.com demonstrated a use case for this feature that strongly mimicked natural customer behavior. It used an innocuous Instagram photo of Jennifer Aniston to find near-exact matches to the top she was wearing in the photo.
Notice that the photo wasn’t anything special, nor did it show much of what she was wearing. Yet StyleSnap delivered a reasonable selection of choices. Real users will not wait for an ideal photo, they will act impulsively based on what they see in the moment, and AI can handle this behavior.
AI Assistant for Customer Service to Boost Sales
Sometimes customers just need a little help to make a purchase. Artificial intelligence developers have a brilliant solution for this: automated sales reps. They come in the form of chatbots, virtual assistants, and even voice-command assistants.
Virtual assistants show great promise in helping customers find what they want. The North Face is using IBM’s AI solution called Watson to enable online shoppers to discover their perfect jacket.
Users answer questions like “where and when will you be using your jacket?” through voice input, and the AI software scans through hundreds of products to find perfect matches based on real-time customer input and its own research, like weather conditions in the local area. The more variables the assistant has to work with, the more customized a solution it can provide.
Voice-enabled services are no slouch in this category either. Amazon’s digital assistant Alexa uses NLP to learn how people talk (and Alexa is bolstered to help customers find and buy products by conversing with users).
The Google Duplex tool is developing capabilities like creating grocery lists spoken by a user and even placing orders for them.
Dynamic Pricing to Provide Ultra-Customized Discounts in Real Time
Dynamic pricing is a strategy whereby retailers change the price of the product based on supply and demand in real time.
It leverages customer data, competitive pricing data, and sales transaction data to predict when to discount, what to discount, and dynamically calculate the minimum amount of discount needed to ensure a transaction.
The result is the ability to deliver a just-in-time, tailor-made discount to an eCommerce shopper, with maximum possible profit to the brand.
Amazon is the market leader in this domain, currently seeing massive success in applying dynamic pricing. They change prices every 10 minutes, which is fifty times more than Walmart and Best Buy, and has led to a profit boost of 25%[5].
AI for Predictive Behavior Modeling to Make Better Decisions
One of the most exciting applications of AI is its ability to see into the future. With access to a variety of structured and unstructured data sources like social media, sales data, and market research, it is possible to create detailed psychographic profiles of known customers to spot emerging trends, and even predict unknown demographic profiles.
By predicting behavior, brands can cut back on wasted advertising and optimize marketing efforts, leading to increased revenue and higher profit margins.
Predictive behavior modeling can be used to make predictions on a plethora of sales related things:
- Predicting if a user will make a purchase in a specific product category in real time.
- Predicting if a user will return and what purchases they will make at certain times.
- Prediction of customer lifetime value.
- Foreseeing customer churn.
- Demand forecasting for specific product categories.
Target Corporation used predictive models based on machine learning and saw 15-30% revenue growth.
AI for Optimizing Inventory & Warehouse Management and Logistics
Efficient inventory management is all about maintaining the right level of inventory that can fulfil market demand without adding to idle stock. Optimized logistics is about reducing resource consumption in delivery and delivery time.
China’a JD.com has a strategic partnership with Siasun Robot & Automation Co Ltd., bringing AI and automation to seven logistics centers responsible for operating 209 warehouses. Reports claim that online orders doubled from 2014 to 2015 (a total of 1.26 billion), and 85% of those orders were delivered within two days[6].
Alibaba claims that smart logistics have resulted in a 10% reduction in vehicle use and a 30% reduction in travel distances. They employ a logistics affiliate, Cainiao, who uses AI to help map the most efficient delivery routes.
Filtering Fake Reviews That Can Harm Businesses
88% of customers trust online reviews as much as they trust a personal recommendation; it’s rather alarming, then, to know that experts estimate that almost 40% of Amazon reviews are fake[7].
Fake reviews have been one of the biggest problems faced by retailers. They are often published by competitors of a brand to pose as legitimate reviews in order to bring down the brand’s product ratings and tarnish the brand’s reputation.
Sometimes they are used in the opposite way, to bolster the image of a product or brand. Either way, fake eCommerce reviews can be harmful. Many brands now use AI to find and remove such spurious reviews and help customers with authentic comments on the product or brand.
Korean online retailer, 11Street, internally developed an AI robot engine to automatically filter out bogus reviews.
eBay Korea also uses an AI system to filter out reviews that consist of unrelated images or texts with vague content. For example, they crosscheck seller and reviewer information to find if there are any connections.
Fakespot is a free online tool for consumers that uses AI to detect fraudulent product reviews and third-party sellers in real-time.
AI can Solve a Variety of Complex eCommerce Problems
There really is no limit to the kind of problems that AI can solve, if applied correctly.
Take Flipkart, a leading Indian eCommerce company with a 39.5% share of the Indian eCommerce market.
In India, unlike in the western countries, postal address formats are not standardized. People follow local conventions, and often use local names rather than official ones. This can lead to major logistical issues, high costs due to misdelivered orders, and poor customer satisfaction.
Flipkart used AI and ML to tackle this tricky problem. Data scientists at Flipkart devised an address classification system, using deep learning and machine learning, which has an accuracy rate of 98%[8]. It’s playing a major role in ensuring correct and timely deliveries.
eCommerce Developers can Change the Game With AI and ML
These few examples are ways that eCommerce developers are saving or making millions of dollars for companies. But there is so much more that machine learning developers can do in the eCommerce space.
And it’s not just the heavy-hitters like Amazon or Alibaba that are getting in on the game. Even some of the most popular eCommerce platforms used by SMEs the world over are using AI and ML to better serve their customers.
In short, the sky's the limit. eCommerce growth is not slated to slow down any time soon. Indeed, the culture of delivery may have been exacerbated by the COVID-19 situation. Pretty soon, it will be more than just traditionally eCommerce businesses that will have to make use of AI and ML solutions for optimized operations, increased sales, and better customer experience.
References:
- Servion
- Tractica
- Research by MyBuys Inc.
- Amazon
- Investopedia
- Emerj
- Searchengineland
- Flipkart
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8 Ways AI and ML are Disrupting Online Shopping https://buff.ly/2QDBCFl via @icicletech
— Icicle (@icicletech) 27 August 2020