Your marketing mix shall revolve around the motto of let’s give the people what they want for an enriched customer-oriented approach. Trying this recipe would definitely land you some qualifying leads as AI is swiftly changing the landscape of customer experience. It is taking it to the next level of personalization also known as customization. It helps companies to target existing customers and retain them by tailoring products and services to cater to specific groups.
B2C companies gauge buyer expectations that are changing rapidly over time. They are investing heavily in providing a top-notch consumer experience. Research shows that 84% of marketers are effectively utilizing AI to bolster customer trust, while 88 % of marketing teams of top-ranking firms are dedicated to redefining the user experience to stay one step ahead and compete with giant leagues in the marketplace.
Unconsciously, we all are sailing in the same boat of personalization experiments. Do you ever wonder why every time you sign in to Netflix to binge upon that Money heist episode, you finally find the free time to watch; you get a handful of shows and movie recommendations that fit perfectly to your taste. But how? How does it possibly possess know-how about my favorite genre?
And now where do you rank their customized recommendation model? This article will unravel how B2C is using AI techniques to approach content optimization from a profitable perspective.
Does Netflix Use AI or Machine Learning?
The credit goes to the Recommendation Engines, a data filtering tool to filter a catalog with the help of collected data and machine learning algorithms, that predicts relevant content for the user. Content recommendation methodology revolves around similar pattern identification, thus recommending and suggesting similar shows/movies in which the viewer has demonstrated interest.
ML has considerably impacted personalization in the world’s most famous subscription-based online streaming company and statistics reveal that 75% of users pick movies based on the company’s suggestions and they are headed on the mission to make that digit significantly higher.
Magic of Deep learning in Amazon Product Recommendation
AI is doing wonders for the E-commerce giant and Amazon personally brags their 35% sales through personalized product recommendations and 56% of buyers are already retained by the user-friendly shopping experience. Whenever a user opens Amazon, the platform always has new product recommendations that specifically click user interest as if the suggestions were perfectly tailored for him.
Deep learning helps us understand on-site human behavior, delivering results that give insights about buyer persona. Robust techniques are used to analyze and consider certain data points like the product pages that a customer views, purchased products, given reviews and ratings.
The system then further recommends based on mentioned metrics, it gets into the mind of the purchaser and suggests what he/she is likely to buy next. What keeps the customer hooked to Amazon and brings buyers back to make more purchases? Well, it’s none other than the strategy to make each viewer feel that the homepage is designed for their lens. The secret recipe of the booming eCommerce platform is the analysis of real-time customer data and greeting them with a relevant catalog that feels personal and relatable to shopaholics.
Amazon product recommendation analogy is the prowess to space rocket sales. Nonetheless, fostering conversion rate optimization in a customer-driven market is brilliantly mastered by these two companies.
Walmart Grocery Store has smart substitute for out of stock products
The popular grocery store has felt the pain point of online shoppers and came up with a wise and reasonable solution of substituting out of stock products, they know that customers are a valuable asset and they cannot disappoint them in their buying journey, so when online buyers encounter an out of stock scenario while purchasing an item, they are presented with a wide variety of items that can serve as a replacement for it.
Deep learning algorithms take into consideration a wide variety of variables; price, brand, cumulative buyer data, size and type of data, customized preference, and existing inventory, they calculate them in real-time and show the next best available product as a substitute. Customer approval in this regard is highly crucial and data is gathered and fed back into trained ML models to enhance the precision of future suggestions. Customer preference and satisfaction is well attended to to ensure a rise in eCommerce sales.
Srini Venkatesan, executive vice president at Walmart Global Tech has reported that online grocery substitutions are accepted by 95% of customers.
Research conducted by Austin, Texas-based Jungle Scout shows 51% of consumers preferred shopping at Walmart for food and groceries compared with 23% for Amazon.
Starbucks, Coffee giant fueling its growth with Deep Brew
Favorite coffee chain, a mandatory pitstop, between work and home, observed its customers’ migration from densely populated cities to far and peaceful suburbs, a shift from choosing to dine in at cafes to grabbing coffee at drive-thru, from grabbing coffee in the early morning to coffee runs in mid-morning or at weekends.
Starbucks has used AI and the machine learning to invent Deep Brew, which allows the sought after coffee brand to treat their customers with a personalized drive-thru experience. Personalized menu boards powered by AI to give customized suggestions about beverages and snacks based on certain metrics like weather, time of the day, inventory status, best-selling or popularity of the item, customer own preferences and last but not the least; customer purchase history.
They have smartly put IOT at work in connecting espresso machines and prevented constant maintenance which has further automated inventory management. Brewers are happy to use AI-driven solutions to understand customer choices and proactively suggesting customers what they could be interested in.
AI in Retail & E-Commerce
AI is spurring transformation in business worldwide, Dubizzle, an eCommerce platform, popular among UAE customers uses image recognition, spam detection and personalized recommendations by training models with robust algorithms. Spam detection is used to prevent irrelevant ads on the website and AI-enabled technology that is efficient in processing thousands of ads. Forecasting techniques are used to determine business KPIs for well-informed decisions.
As per industry trends, used cars make up the largest segment of buyer transactions on OLX. Humongous web traffic produces large data sets for OLX. The company prioritizes customer-centricity and makes a handsome fusion of ML and predictive analytics.
Understanding customer demand is at the forefront of buying and selling businesses. In order to make changes on deals and offerings entailing customer satisfaction, different data points are carefully analyzed like, trending car models, vehicle specifications, pricing details, time is taken by the buyer to look for the perfect automobile, an average number of messages exchanged between buyer and seller, tradeoffs and financial liquidity.
Smart AI-enabled systems streamline business processes, improve products and services to reach potential customers and get ahead in a fiercely competitive marketplace. Orchestrate a personalized experience and increase customer satisfaction with smart and intelligent AI solutions.
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