artificial intelligence for e-commerce conversion rates

How AI can fuel e-commerce conversion rates

What constitutes a successful e-commerce business?

As is the case with everything, there’s no defined answer. While there are many well-known industry best practices of selling online successfully, such as providing excellent customer service and delivering intelligent marketing—in this day and age’s e-commerce market where competition is rife, such practices are a given. What then should businesses do to separate them from the crowd and boost their conversion rates?

Cue artificial intelligence, or simply AI.

Recent advancements in machine learning and natural language programming have seen AI become a disruptive force across industries, and e-commerce has perhaps been the most affected. When it comes to e-commerce, AI has become integral to selling online successfully. In fact, a Gartner study has predicted it to be so prevalent by next year that 85% of customer interactions by online businesses will be serviced through AI technology.

Most notably, the technology is being leveraged to complement the most fundamental business objective: improve conversions. By embracing this technology and using it as a key part of marketing and sales, e-commerce businesses are seeing excellent returns. Here, we’ll discuss a few of those noteworthy ways that technology is revolutionizing online selling.

1. Allowing visual internal search with AI

AI is being used to enable several of the world’s most advanced technological solutions. Its application in the e-commerce industry has seen a turn for the better as far as customer-centricity is concerned. AI has made browsers and search engines more intelligent in an important use case by enabling visual internal search.

Rather than expecting humans to think like them (like textual search does), visual search analyses pictures based on optic cues as well as metadata. With the help of AI, visual search gives the most accurate results possible depending on commonalities, such as color or a specific style.

This contributes to a more frictionless and satisfactory retail experience.

As per recent studies, the worldwide visual search market is predicted to surpass $14,727m by 2023 at a CAGR of 9% by 2023. These numbers, coupled with the rising use of social media for product discovery, indicate that merchants and businesses must improve visual content filtering by AI to make sure that they are ready for it in the future.

stylesnap ecommerce example

Take, for example, StyleSnap on the Amazon app, which is the e-commerce giant’s foray into the visual search market. The feature allows customers to upload images where deep learning algorithms are used to identify and detect the various products in the uploaded image and then categorize those products into classes, boosting the store’s conversion rate progressively.

2. Setting up chatbots for customer support assistance

Conversational chatbots have long been an integral part of customer care. The usefulness of these bots is building an emerging and substantial presence in customer service, and it is anticipated that by 2025, AI will fuel 85% of all consumer interactions.

By implementing a chatbot in your e-commerce store, you can easily provide prompt assistance to your clients. These bots can easily answer more than 80% of routinely asked questions while also reducing expenses and response time. Furthermore, for the consumer, this means low effort and instant gratification, attributes around which today’s customer experience is built.

sephora chatbot

Source: chatbotguide.org

Sephora, for instance, utilizes chatbots to help shoppers locate goods that match their tastes and interests. Consumers can scan a picture or product to see a list of matching products. They can tell the bot which features or attributes they want after selecting a product category from a menu. Then, the bot responds to links to the relevant product pages.

3. Leveraging consumer behavior data

Previously, businesses had to make educated guesses as to which items and experiences their customers would enjoy. But today, due to an overabundance of customer data and AI algorithms, this exercise in speculation is no longer necessary.

Whenever a user enters a search term, machine learning algorithms analyze prior search history and behavior to identify products that are most relevant to the query. This then means that ML algorithms no longer have to depend on text-matching and can instead rely exclusively on past behavior learned through machine learning.

This could include the most accurate results for a specific search query – as well as those with the highest probability to convert. Consumers can start finding what they want thanks to a plethora of rich data that can be used to produce appropriate search results, even when the query phrase is not the exact description of the metadata of the desired product.

4. Tailored recommendations system for higher chances of conversion

According to a Barilliance study, tailored recommendations produced more than 31% of e-commerce income. Customers have often expressed a desire for relevance. In fact, according to another study, 53% of respondents believe that e-commerce websites that customize and personalize experiences are providing a valuable service.

Machine learning especially is the foundation for individualized product recommendations. Generally, AI systems operate by constantly testing two or more different variants of recommendation systems to determine the more appealing products to consumers.

If the algorithm notices that consumers of a specific profile convert more for product A than for product B, it begins recommending product A.

ASOS product recommendation engine

Source: ASOS

The e-commerce app ASOS has a similar product recommendation system on its website. This system is powered by an algorithm that collects suggestions on the basis of the previous purchase history of the customer along with object and color matches from similar products.

5. Creating dynamic headlines by leveraging AI

Predictive algorithms can discover the product page designs and headlines that perform the best for consumer conversion. This is done through machine learning algorithms that constantly A/B test the product pages on the basis of comparing multiple font sizes, headline variations, and button styles to ascertain the ones that produce the best results.

The on-site searching feature is a logical extension of a well-designed e-commerce website. AI-powered website customization provides your consumers what they are seeking, whether that be suggesting seasonal products at the correct time or delivering a focused set of recommendations to clients seeking the latest accessories.

AI tools can be used on subcategory sites or to enhance product sites.

Customers can identify goods they like or add products to their shopping cart in the form of upsells by delivering improved product page recommendations.

ralph lauren complete the look artificial intelligence

Source: Ralph Lauren

Ralph Lauren, for instance, has a button on their e-commerce site that prompts consumers to “Complete the Look” by proposing additions that complement the products buyers are looking for along with showing the model in the complete ensemble. This way, Ralph Lauren can sell an entire wardrobe rather than simply a few pieces of apparel.

6. Filtering fake reviews with AI

False reviews that have no basis in reality have bubbled away under the surface of e-commerce and online stores for years. Did you know that as many as 80% of American consumers check reviews of products before they think about making a purchase? What’s more, research also proves that customers are more easily convinced by simple star ratings than they are by what reviewers actually write about a product.

Negative and fake product reviews are a major issue in the e-commerce business as they serve to deter and harm the reputation of a brand while also having a direct impact on visitors’ purchasing decisions. According to Dimensional Research, 90% of customers believe that favorable internet evaluations about a product impact their purchasing choice, while 86% believe that unfavorable ratings reduce the chances of a user to complete the purchase.

There are many people, bots, or competitors who attempt to create bogus reviews in order to influence customer decisions on an e-commerce website. AI can filter such reviews and resolve this problem. Such intelligent systems make use of language processing algorithms to identify atypical and abnormal patterns in writing style, text, or formatting.

For instance, a group of researchers at the University of Chicago sometime around 2017 developed a machine learning system, which happened to be a deep neural network and depended upon a dataset of more than three million Yelp reviews of restaurants to filter out fake reviews.

In much the same way, e-commerce websites and consumer-centric businesses can utilize AI to not only grasp the general customer sentiment felt toward their brand but also use this knowledge to train AI systems accordingly to identify and eliminate fake reviews.

Filtering false reviews altogether from this equation then serves to illuminate and simplify the general perception of customers about a brand, and not what any malicious actors or third-party influencers attempt to falsely generate.

Conclusion

Artificial intelligence and machine learning have the potential to improve the performance of e-commerce platforms. They can also help make relevant recommendations regarding pro services and products in an efficient manner, which can increase conversion rates.


Author Bio
Dhruv Mehta

Dhruv Mehta is a Digital Marketing Professional who works at Acquire and provides solutions in the digital era. In his free time, he loves to write on tech and marketing. He is a frequent contributor to Tweak Your Biz. Connect with him on Twitter or LinkedIn.

Dhruv Mehta, Acquire.io

Cover photo by Rock’n Roll Monkey on Unsplash