Artificial Intelligence in Retail is disrupting the lifestyle of each and every customer. It is not just for big retailers any more as AI adoption is swiftly increasing post covid. As online shopping was gaining momentum, the year 2020 brought in a drastic change to the retail industry with the onset of the Covid19 pandemic. The outbreak of coronavirus has resulted in the closure of many brick-and-mortar stores including popular brands not able to survive the economic effects of the pandemic.
While most stores were exploring whether to liquidate their stores or file for bankruptcy, there were some stores that managed to not only survive but fared relatively well. Retailers invested in Artificial Intelligence in the field of merchandising, e-commerce, delivery, and omnichannel experience continue to thrive better amid Covid19.
Gartner has reported that in the year 2021, 77% of retailers are surveying to adopt AI and 87% reported robotics in the warehouse to be their №1 use case. The market size of AI in Retail for the year 2020 was 2 Billion USD and it is expected to grow yearly at the rate of 30% CAGR forecasted to reach 20 Billion USD by the year 2027.
Retailers sustainable during the recession are the ones embracing agility to the changing environment centered around their customers. AI-Revolution in Retail along with the expansion of IoT transaction have shaped the Internet of Behaviors geared towards marketing aided by human psychological behavior.
AI-powered Virtual Assistants provide the assured in-store assistant shoppers look for while searching for a product. By using voice command and Natural Language Processing, Virtual Assistants are playing a major role in transforming customer experience personalized to their native language.
Virtual Assistants improve business revenue by reducing abandoned carts, increase customer satisfaction recommending products, tracking orders, and assisting in sales and billing inquiries.
With the increase in IoT devices and advancement in edge computing it is estimated that by the year 2026, the number of IoT devices will be reaching 5.7 Billion. Even though IoT was introduced 10 years back, the quick adoption by consumers will see a huge impact on the economy as stated by Mckinsey in 2015 that the economic impact from IoT will be ranging between $4 trillion to $11 trillion.
Ever-changing human behavior with the advancement of technology has contributed to the rise in visual and voice search to obtain product information before purchasing. Many retailers have jumped into the bandwagon of using IoT devices like Alexa, Siri, Google Assistant, and Cortana for e-commerce and thereby growing the number of voice searches.
Many big retail corporations are partnering to improve retail business not just for themselves but also for small mom-and-pop stores. For example, Walmart and Google have launched Walmart Voice Order by combining their technological and retail expertise.
By the year 2018, Visual search wherein customers search using digital image has gained momentum taking the retail world by storm. Gartner has predicted that early adopters implementing voice and visual search in their e-commerce will be increasing revenue by 30%.
Machine learning models predicting risky transactions and the availability of technology to handle the large volumes of data in real-time are helping businesses make smarter decisions to improve their profitability plus reputation.
Many retailers have come a long way in mitigating risk by enhancing their rule-based risk management engine with machine learning models. Companies like Walmart are externalizing their machine learning models for fraud detection and prevention aiding small businesses who cannot afford to build and manage machine learning models.
Retailers are reimagining the digital shopping experience with Augmented Reality. Consumers can now visualize before purchasing any product. AR-powered fitting rooms allow customers to visualize how they will look like while AR in furniture stores render virtual placement of furniture.
The increase in spending by 40% and the customer presence by 60% in an AR-enabled shop only proves AR is getting bigger and better in retail scene. The growth rate of AR in retail industry is projected to increase at the rate of 20% CAGR amounting to 4.6 Billion USD by 2026.
Businesses need full visibility into their inventory of products whether they are in-store or in transit. Each and every retailer is measuring their success rate around customer satisfaction using metrics like Customer Satisfaction (CSAT), Net Promoter Score (NPS), Customer Lifetime Value(CLTV). On-Demand Shipping is no longer a desired feature but rather a must in the current competitive retail trend.
Artificial Intelligence in Logistics is lessening the transportation cost and improving customer satisfaction. By predicting the optimal routes, AI is reducing product delivery delays and informing customers promptly on their desired mode of communication.
Artificial Intelligence in merchandising looks beyond traditional product categorization with machine learning models learning human behavior for better product placements increasing its sellability. By understanding and learning from consumer behavior patterns, AI-enabled merchandising empower businesses with a more agile approach in reducing out-of-stock and over-stock products by refining their stocking strategy.
The Supply Chain team usually spends a lot of time to gather data from disparate systems such as Business Intelligence and Spreadsheets scanning for patterns to improve the supply chain. Robotic Process Automation(RPA) like Cognitive Automation is enhancing the supply chain by applying Natural Language Processing(NLP), text analytics, semantic data mining, etc., improving pricing, promotion, and quality of retail service.
The technological advancements in processing big data at record speed and the availability of computing resources processing petabytes of data are revolutionizing supply chain management.
Artificial Intelligence has been playing a major role in transforming customer experience personalized to their liking. 75% of shoppers are expecting a personalized experience with 57% ready to exchange personal data for offers and discounts personalized to their shopping trends.
Product Recommendation is driving the number of sales at a record pace. Amazon increased its revenue by 30% with product recommendations. Companies like Youtube and Netflix have changed customer behavior with their recommendation engine.
Retailers like StitchFix have turned personalization into an entire business by selling not just clothes but recommending personalized styles. Starbucks app has been the most commonly used mobile payment platform until 2019 when Apple Pay took over. The success of the Starbucks app was the result of a personalized customer experience with reward points for purchasing online.
The advancement in Artificial Intelligence and the availability of powerful computing resources should have made all retailers adopt AI, but there is still a considerable lag.
Retailers trying to adopt AI usually have a team of Data Scientists working on Machine Learning Models to grow their business based on predictions. Even if the Data Scientists have built models with 100% accuracy, it is a different story in the real world. The accuracy of machine learning models decreases over time due to possible model and data drift. Machine Learning is not like software development where an application is developed and maintained, but rather an iterative process involving continuous training and deployments.
Businesses should understand that the ROI from adopting AI depends on not just building prediction models but on the continuous improvement achieved via retraining models accommodating to the ever-changing retail environment.
Retailers trying to venture into Artificial Intelligence will soon learn that it is not just building machine learning models that matters, but rather how it performs in the real world. The performance of machine learning models degrades over time due to the change in data and model. As the business grows, data and model drift happens with changes in the environment like product recategorization, changes in delivery and notification process, etc.
Educating businesses about the capabilities of AI technologies like deep learning, voice/visual search, natural language processing, neural networks, etc., and the need for IT infrastructure needed to run AI at scale are crucial to sustain and predominate the retail industry.
Advancements in Machine Learning have reached the stage where anyone can become a Citizen Data Scientist with AutoML. AutoML does the heavy lifting by automating the pipeline of converting datasets to deployable models. Understanding the domain and knowing what to expect from machine learning models, one can convert their retail data to predictions thereby managing supply and demand.
Even with AutoML, if there are no proper deployment strategies, then it defeats the purpose of building the model prediction to gain insight and improve business. Continuous Integration automates the testing phase whenever there is a change to the machine learning models and datasets. Continuous Deployment kick starts once CI completes testing. Automating CI/CD Pipelines with reusable deployment strategies like blue-green deployment, canary, etc., reduces the cost and resource required to continuously improve business with iterative training and deployment of ML models.
Models without automated deployment are like a compass with no direction.
There are too many pilot programs to improve businesses with Machine Learning projects but most of them create no actual value with quick wins. It is not just a challenge facing small to mid-size businesses. Even big retailers have learned from their experience to overcome challenges posed with no unified monitoring system for Machine Learning projects.
Visibility into the performance of machine learning models and the computing resources needed to run the models are essential to accelerate AI adoption for retail success.
Based on a survey done by Boston consulting group and MIT in 2017, it was reported that only 40% were involved with ongoing strategies to adopt AI while 80% believed that applying AI will have a competitive advantage. Covid has changed the mindset of retailers to swiftly adopt AI. But where to start and how to start are the questions that linger in the minds of most retailers.
For starters pick a simple use case with a quick win. AI is not just for big retailers trying to attract customers. Small and mid-size retailers whether they have a physical store presence or online presence can grow their customer base by building a community-based shopping experience by understanding their customers.
Whether a business chooses to build or buy a Machine Learning Solution, they have to properly assess not just the cost of building the machine learning models but also deploying and monitoring those models to stay relevant.
There is significant progress made in the Machine Learning world wherein retailers can pick and choose readily available algorithms. Big Retailers like Walmart who have witnessed significant progress in their customer satisfaction especially during Covid are trying to externalize their already proven machine learning models. AI is becoming the norm for retail success as adoption is swiftly rising post-Covid.
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