Consumer & Retail

''Rooms to go'' uses Machine Learning to offer tailored add-on options to customers

Home furnishing retailer Rooms To Go has leveraged Google Analytics to gain a better understanding of their consumers. By identifying products that are often sold together the company was able to customise and personalise their customers'' experience which lead to increased sales and improved the overall shopping experience.

1-800 Flowers deploys chatbots to offer a personalised gift buying experience that will increase customer engagement and sales

1-800 Flowers launched its IBM Watson-powered concierge service - Gwyn (gifts when you need) in May, 2017 to help customers get more personalised results and engage them better.

30SecondsToFly struggles to scale the automation of SMB corporate travel management with the deployment of text chatbots

30SecondsToFly''s Claire, a travel assistant, uses natural language processing and machine-learning capabilities to assist travellers with their bookings. The technology can provide SMBs with a valuable customer service provider able to adhere to a company''s travel policies. The chatbot is available to process booking queries through SMS or the messaging platform Slack.

7-Eleven improved customer marketing and in-store capacity planning in Indonesia and Mexico using machine learning to predict demand variations

7-Eleven decided to use AI to optimise capacity planning and marketing. They established an information analysis environment to analyse patterns and gather valuable insights from point-of-sale data.

ASOS eliminates the risk of inaccurate data entering finance systems by implementing a machine learning solution for invoice handling

ASOS has implemented a machine learning solution in order to optimise its invoice handling processes. Using Celaton''s inSTREAM™ software as a service, the company is now able to automate its their ''Purchase to Pay'' process, improve efficiency and ensure that the data entering its financial system are accurate.

ASOS.com researchers demonstrate improved customer lifetime value predictions using neural networks and automatic feature selection but do not advise implementation due to increased cost

ASOS researchers demonstrate how the currently used customer lifetime value prediction system can be improved through the use of automatic feature selection. These predictions are used in business operations such as marketing for customising and targeting retention strategies. However, due to the increase cost associated with running the best performing system, it is not at this time considered a commercially viable solution.

Adidas improves online outfit recommendations with the use of machine learning

Through a partnership, Adidas has leveraged Findmine''s technology for its online customer experience. The retailer has implemented a machine learning system that pairs items to create outfits, a previously manual task that would take a merchant 20 minutes to complete. Now, Adidas has been able to increase the number of items featured by 960%, while merchants spend 95% less time on the task.

Adidas learns from its consumers'' design creation to better anticipate future demand trends using machine learning

With the use of machine learning, Adidas is able to reduce the typical 18 month timeframe of turning trends into commercially saleable shoes to just 24 hours. It does so by letting consumers design their own shoes in its prototype SpeedFactory, from which then the product ships immediately. As a result, by analysing the co-created designs with machine learning, the company gains insight into future trends and can efficiently anticipate future demand.

Adidas tests an interactive and virtual retail display wall for footwear optimising inventory displayed with machine learning that results in a smaller foot print store

Adidas debuted an interactive digital wall to display footwear according to demand predicted based on factors such as seasonality, demographic, events. The product display is based on demand prediction using machine learning.

Adore Me generates product insights by determining customer sentiment with a 92% accuracy based on natural language processing analysis of thousands of reviews

Adore Me, an ecommerce lingerie retailer, analyses 1000s of customer reviews and feedback through natural language processing with a 92% classification accuracy. This helps them understand popularity of products and opinions resulting in product insights and improvements.

AeroMexico''s customer-service can handle as many inquiries as two full-time employees per day through its chatbot

Aeromexico has introduced aerobot, a chatbot that handles customer service queries. Based on natural language processing, it has been trained to recognise the same questions asked differently. If it is not able to assist with a query, the chatbot redirects the client to a human attendant. The company claims that it is currently assisting 1,000 Spanish-speaking clients per day, which is roughly as many queries as two full time employees would handle, while saving costs.

AirBnB achieves gains in bookings using machine learning models to improve property search results

AirBnB has experimented with different machine learning models to improve the property search results for its users and currently uses a combination of neural networks with gradient boosted decision trees resulting in relative gains in bookings.

Airbnb improves search results ranking with the use of deep learning

Airbnb increased similar property listing recommendations click-through rates by 21% with machine learning embeddings

Airbnb uses machine learning to personalise consumer search results. Results are personalised based on over hundreds signals that go well beyond explicit price and room requests to the type of property listings that you click on that suggest preferences for architectural style, decor and feel. AirBnB used a novel embedding-based solution to represent 4.5 million active listings using 800 million consumer property search click stream sessions. Click-through-rates on recommended similar property listings increased by 21% and 4.9% more guests discovered their listings as a result.

Airbnb increases booking conversions by 5% using machine learning to match property hosts'' guest preferences with consumers

AirBnB wanted to better better understand what accommodation requests are accepted or rejected by property hosts. Using logistic regression techniques they modelled whether hosts preferred stays that resulted in limited gap nights, lots or fewer stays, and also how far in advance the bookings were made. AirBnB changed their consumer search results to emphasise host properties that would more likely be accepted. This led to a 5% increases in booking conversions. This demonstrated a two-sided marketplace with search results that were a function of not only consumer but host preferences.

Albert Heijn, a Dutch grocer, reduced time to hire by 67% and improved candidate satisfaction by using machine learning to better match applicants to roles

The Dutch retailing corporation, Albert Heijn, used machine learning to improve candidate expectation management, increase satisfaction and establish a better fit between applicants and the traineeship they applied for.

Alibaba Group''s LuBan platform automatically designs 400M online advertising banners using image processing and reinforcement learning

The Alibaba Group''s Luban platform uses machine learning for image processing to design online advertising banners for products sold on the Alibaba Group commerce sites. Its ability to generate banners at the rate of 8,000 a second allowed it to create the different ones needed to promote products for the Group''s biggest online shopping day.

Alibaba has reduced customer service staff needs for its marketplace vendors by introducing a chatbot

Alibaba has introduced a text-based chatbot, Dian Xiaomi, which vendors operating on its marketplace platforms vendors can implement. Alibaba claims that AI algorithms are helping to drive internal and customer service operations including smart product and search recommendations: Alibaba’s software tracks customer browsing and interactions with the website to offer product recommendations.

Alibaba improves online ad performance by 45% using reinforcement learning to optimise real time bidding

Alibaba uses reinforcement learning for optimising its real-time ad auctioning

Alibaba''s marketing arm is producing 20,000 lines of copywriting per second with machine learning and natural language processing

Alibaba’s digital marketing arm Alimama leverages machine learning and natural language processing to automatically generate copywriting material. The engine is trained on human made quality content on Alibaba’s e-commerce sites. The technology is now capable of generating 20,000 lines of copy in a second for products.

Alliance Boots achieves inventory savings and improved service levels with machine learning optimisation algorithm

Alliance Boots has deployed Manhattan Solutions'' replenishment and demand forecasting technology, which leverages machine learning to analyse data and produce accurate forecasts. Through the solution, it has been able to reduce inventory and stock levels while also improve customer service and productivity across Europe.

Amazon is testing its Prime Air drone delivery service promising to fly parcels to customers within 30 minutes of ordering.

Amazon has been testing drone delivery to enhance its Amazon Prime offerings through Prime Air, which will be able to autonomously fly packages to customers in less than 30 minutes, given they fulfil requirements, such as weight, size and distance of route.

Amazon makes personalised product recommendations to customers with machine learning

Amazon has leveraged machine learning to provide its customers a personalised customer experience and recommendations of products. Using a website called Scout, which collects data on their habits, demographics, and preferences, Amazon shows customers image feeds and asks shoppers to like or dislike a product. According to their feedback it then adjusts the results and thus it is even able to assist customers that do not know what they are looking for. The site is currently available for seven product categories.

Amazon provides customers with product suggestions according to its recommendation algorithm which improves upon machine learning techniques to scale with its large product catalogue

Amazon''s recommendation algorithm was developed as an improvement over existing techniques to address their shortcomings and scale to Amazon''s large datasets. The product recommendations are used as a marketing tool to encourage repeat customers and increase customer order size.