Marketing

''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.

20th Century Fox and IBM Research reduce movie marketing production time by using machine learning to select scenes for a movie trailer

20th Century Fox implemented IBM Research to select scenes for inclusion in a movie trailer for a feature-length horror film. This process can take a month manually, but was reduced to 24 hours using machine learning from start to finish.

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.

A UK registered charity predicts visitor flow to its building with Markov chain algorithms

A UK registered charity leveraged technology from ASI to predict visitor flow. The provider used wifi usage data to track crowd movement within the building and gather information on the amount of time people spent in each location. ASI created a model indicating the most likely routes people would take through the attraction, congestion points and locations prone to overcrowding using Markov chain algorithms. The algorithm simulated the movement of 500 different hypothetical visitors over a fifteen minute period to come up with the results.

Accenture redeploys after automating tasks with RPA system

Accenture has developed a system, SynOps, for analysing its various data input sources and automating things like contract review. While it has been using this system internally to automate processes in finance, marketing, accounting, and procurement, purportedly resulting in the redeployment of 40,000 staff, it is now selling it tp clients.

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.

Admiral automobile insurance planned to offer discounts to drivers based on data in their Facebook profiles but were prevented by Facebook

Admiral insurance had planned to analyse drivers'' Facebook data to offer discounts to those customers deemed less risky; Facebook intervened and prevented the project from going live as it was a violation of its terms of services.

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.

Aegon monitors spokespeople, competitors and industry topics using machine learning

Aegon has leveraged Signal AI''s machine learning technology to monitor the insurance industry''s news and measure press coverage effectiveness. By analysing headlines and identifying common themes, Aegon creates relevant and engaging content and optimise their PR campaigns.

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.

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 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.

Allen & Overy monitors legal press and media with machine learning

Allen & Overy monitors the legal press and relevant media for their company and their partners by leveraging Signal AI''s machine learning technology. The law firm has been able to make well informed decisions on content topics and outreach plans based on the insight.

Allstate is offering pay-as-you-drive plans based on measurement and analysis of customer real-time driving behaviour resulting in up to 30% reduction in premiums

Allstate insurance offers pay-as-you-drive plans in 30 US stages that reflect miles driven along with customer driving behaviour such as braking style, time of day and speed. Using sensors in the car along with advanced analytics drivers can lower premiums by up to 30%. Allstate better aligns premiums and risk with customers.

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.

American Express increased new online customer acquisition by 40% through machine learning targeted marketing savings significant direct mail costs

American Express has over 100 million credit card customers globally representing over $1 trillion in annual charge volume. Amex used machine learning modelling to better target and acquire customers online. This resulted in 40% increase in online acquisition and a decrease in direct mail campaign costs.

An anonymous global insurance company analysed customer contact recordings and claims data to drive sales yields using natural language processing

Insurance companies have a wealth of unstructured data, such as sales calls, that could provide valuable insights if understood, categories and actioned. A global insurance company worked with Re:Infer to analyse contact recordings, claims data and employee feedback to identify key issues and sentiment using natural language processing. Focusing on sales intelligence they were able to increase sales yields by identifying behaviours that lead to successful up-sell and cross selling. They also improved product intelligence, claims and employee insights.