December 11, 2018 Last updated December 8th, 2018 454 Reads share

How AI Is Intruding Into the Commerce World

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Shopping has clearly outgrown its traditional definition, morphing into a sophisticated customer journey. Modern consumers expect retail to offer them more than just the act of a purchase. In pursuit of exquisite experiences, AI-enabled products and services are fitting in extremely well. 

New Retail, introduced by Alibaba founder Jack Ma, is gaining momentum as an amalgamation of the best practices of AI. It empowers customers to acquire the most out of each retail excursion as if they were assisted by the most skillful personal adviser. For the retailers, this means profit maximization and higher competitiveness. For the industry – an increased focus on AI.

A vision of the next gen retail integrates the format online + offline shopping with smart logistics and big data capabilities. Every single act of a buyer’s behavior becomes more precise and fulfilling by virtue of topical retail issues, proclaimed, again, by Alibaba:

  • a complete digitization;
  • omnichannelization;
  • platformization;
  • entertainmentization.

Is the audience ready for such a feature-packed agenda? A recent Salesforce shopper research is a resounding yes. Reporting that nearly one-third of consumers desire artificial intelligence and relevant technologies that shape modern shopping today. While Millennials and other young shoppers tend to be more open-minded towards innovation than other generations, there are plenty of technophiles in each age group category. And many of them are already looking for one-of-a-kind retail offers.

 

AI in action

Where there’s demand, there’s supply. Artificial Intelligence has overstepped the boundaries of sci-fi, as retail elephants are eagerly seizing the opportunity with substantive novelties:

  • Amazon Go store has recently introduced its customers to a new shopping style with no checkout is necessary. Myriads of cameras and sensors are used to identify customers and track their purchases. Payments are effected automatically when goods leave the store.

 

  • Image, a New Zealand AI-company, has suggested something quite similar to the preceding subject. They designed a shopping cart that identifies every product placed in it. Therefore, there’s no need to scan packages and wait in line.

 

  • Intelligent human-computer interaction systems, also known as chatbots, do not just attend basic clients’ requests, but resolve more serious issues and handle complaints. Alibaba’s brainchild, Alice, is an example of a digital assistant that has learned the ropes of natural language understanding. An AI-powered Alibaba’s e-commerce bot deals with millions of queries daily, in particular in English.

 

  • Through WeChat Pay, and big data analysis sellers accumulate information related to their target audiences needs and preferences. Aside from that, handy apps allow a well-balanced fusion of online and offline experiences.

 

  • AI processes the collected data to personalize prices and display offers to suit a specific client. Price management significantly increases the chances for a successful sale.   

 

  • Machine learning and AI reduce the delivery time for both non-grocery items and foods. Logistic platforms, such as FarEye, efficiently leverage certain data points to predict delivery time windows, manage order density and choose an optimal route.

 

  • Virtual storefronts display information tailored to the individual preferences of the shoppers. At the same time, product search has become as accurate and precise as never before.

 

This list is far from being comprehensive.

Artificial intelligence provides brands with an unprecedented information about customers so that the retailers know their customers even better than they know themselves. E-commerce is, therefore, evolving into E-commerce, embracing all the phases of the customer journey. From the search of the product – to its door to door delivery, and even further.

Alibaba

Alibaba is employing manifold machine-learning methods, teaching computers how to analyze aggregated data, reach conclusions and recognize meaningful patterns. Overall, the process resembles the way we gather information about the outer world and learn from it. Artificial neural networks grow wiser and thicker with every new fragment of data they are being fed.

In fact, today Alibaba’s websites (including Taobao Marketplace and Tmall.com) have built up a 500-million-user audience with millions of daily visits. Such an impressive audience compiles a treasure trove of valuable information, such as:

  • shopping habits;
  • payment history;
  • personal preferences and search style;
  • demographics, etc.

 

Drawing their conclusions from big data, Alibaba has developed a number of AI specialties:

  • E-commerce Brain – software predicting customer’s desires and future purchases. Instead of focusing on recent shopping activity (as most competitors do), it opts for the big picture, analyzing bookmarks, comments, searches, and other value variables.

 

  • Ali Xiaomi (Ali Assistant) – an AI-driven virtual assistant able to cope with both written texts and spoken queries. Aside from being a powerful problem-solving tool functioning as a customer service specialist, it is also capable of helping clients look for specific products. In general, Ali Assistant handles up to 95% of requests.

 

  • Pervasive personalization that allows precise calibration of virtual storefronts. The algorithm is designed to suggest items that might spark the interest of a specific customer, building the pool upon previous buying activity on the platform and elsewhere.

 

  • Ali Smart Supply Chain uses artificial intelligence to investigate ever-changing consumer interest and predict the demand. At the same time, ASSC enables elaborate coordination with suppliers. A smart approach to supplier search has already resulted in sales increase and reduction of time to the customer throughout China.

 

  • Cainiao – Alibaba’s affiliate in Logistics, employing state-of-the-art tools in order to find the shortest destination path. Over two-thirds of parcels deliveries in China are now analyzed through the network to ensure clients’ satisfaction.

Alibaba is working aggressively to stay ahead of the curve, and AI-enabled solutions splendidly suit this purpose. The list of their most recent projects includes face-recognition called to secure electronic payments. With these AI developments and more yet to come. The Three Laws of Robotics are relevant as ever since robots are becoming more and more integrated into different branches of trade. In fact, Jack Ma and other top range fear that AI will soon rebel and threaten workplaces.

 

Why AI needs a human touch

People created Artificial Intelligence to serve human needs. Today, customers’ expectations are at their highest, thus AI is fulfilling its role underway towards the most personalized experiences ever. However, these experiences are impossible to understand and embrace without emotional intelligence in the first place. This area is far from being thoroughly examined by machines, and that is a major shortcoming questioning AI self-sustainability. There are more reasons why AI cannot fully replace humans, such as:

  • AI is highly rational, and people are not. Consequently, the conclusions drawn from the analysis of human behavior won’t satisfy all the planning needs;
  • robots have a limited capability of working with contexts, analyzing complex lexical units, exposed to regional and/or emotional influences;
  • at the moment, AI lacks imaginative capabilities to deal with non-standard situations, when creative thinking is necessary;
  • customers are not sure how to feel about AI at the moment. 35% claim they are comfortable with emerging technologies, but another 17% admit having problems with it. Just over one third reported no specific feelings towards the subject. Once customers put AI-aided technologies to test, at least two times, their attitude shifts towards positive.

 

These general principles might be analyzed again and again to identify potential isolated incidents. For instance, customer service without a human touch might become vulnerable and ineffective, since pragmatic solutions that the AI producers are unlikely to make a strong appeal to a versatile human nature.

 

Why retailers need AI

The retail ecosystem is evolving, bringing new challenges and opportunities. While manual solutions have clearly lost their battle to AI, human intelligence has not. In fact, it takes advantage of digital robotic talents, creating a new retail environment in order to:

  • see the situation through the lens of the customer and deliver unique personalized experiences;
  • meet the expectations of today’s hard-driving clients;
  • increase sales and hit financial targets, while significantly reducing the amount of effort and investment;
  • grow the business and expand into a new market.

 

The value of AI is gaining traction, and neural networks are another shining example of how rapidly the world is changing. They do not need voluminous databases since their learning process is response-based. Relying on this principle, the latest version of AlphaGo Zero, the computer program that plays the Chinese game of Go, is now entirely self-taught. Having mastered the skill in just an hour, the program did not analyze third-party data but played against itself. As a result, the product of human intelligence has already defeated the world champion.

 

Once neural networks learn how to play pricing strategies, no category manager will ever compare. The process has commenced. Of course, the first to start using new algorithms will have a tremendous advantage. Eventually, the process will become strictly regulated and taken under control. But the world will definitely witness a couple of unexpected turns. Big potential brings big surprises, and they are already quite beneficial for retailers.

 businessman hands Elements of this image furnished by NASA stock image

Nikolay Savin

Nikolay Savin

Nikolay Savin is Head of Product at Competera, where he helps businesses achieve better results through the merge of data, machine learning, and retail best practices.

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