Marketing and advertising are some of the functional areas where AI is expected to drive the most ROI for enterprises. Unfortunately, the industry is moving so fast that it's challenging for both marketers and technologists to keep up with all the research advances, much less apply them to pressing business problems. If these accessible AI research analyses & summaries are useful for you, you can subscribe to receive our regular industry updates below. If you'd like to skip around, here are the papers we featured: This paper describes a practical system for Multi-Touch Attribution (MTA) for use by a publisher of digital ads. We developed this system for JD.com, an eCommerce company, which is also a publisher of digital ads in China. The approach has two steps. The first step ('response modeling') fits a user-level model for purchase of a product as a function of the user's exposure to ads. The second ('credit allocation') uses the fitted model to allocate the incremental part of the observed purchase due to advertising, to the ads the user is exposed to over the previous T days. To implement step one, we train a Recurrent Neural Network (RNN) on user-level conversion and exposure data. The RNN has the advantage of flexibly handling the sequential dependence in the data in a semi-parametric way.

In this piece, we cover how AI can help personalize the customer experience, leading to higher satisfaction rates and greater revenue growth. Customers are used to getting a personalized experience from each company they interact with. You can personalize the experience of your customers by building effective recommender systems. These are the systems that personalize product placement and search results for each consumer. When you recommend products or content that customers are more likely to purchase, this gives the customer a better sales experience while driving more revenue for businesses through cross-selling and up-selling.

Massachusetts-based supermarket chain Market Basket has announced plans for a second Rhode Island store. The 89,000-square-foot store in Warwick expected to open next year will be located at a site that was previously home to a Sam's Club and later an At Home store, according to a statement from Mayor Joseph Solomon and Market Basket President and CEO Arthur T. Demoulas. "Our city's central location in the state, combined with our growing business climate, continue to make Warwick a natural choice for multiple companies looking to expand their reach in the Ocean State," Solomon said in a statement. Privately-owned Market Basket currently has 81 stores in Massachusetts, New Hampshire and Maine. The company in March announced plans for a store in Johnston.

Predictive analytics systems are currently one of the most important areas of research and development within the Artificial Intelligence domain and particularly in Machine Learning. One of the "holy grails" of predictive analytics is the research and development of the "perfect" recommendation system. In our paper we propose an advanced pipeline model for the multi-task objective of determining product complementarity, similarity and sales prediction using deep neural models applied to big-data sequential transaction systems. Our highly parallelized hybrid pipeline consists of both unsupervised and supervised models, used for the objectives of generating semantic product embeddings and predicting sales, respectively. Our experimentation and benchmarking have been done using very large pharma-industry retailer Big Data stream.

Here's what retailers can get from using recommendation systems: Increased customer loyalty by sending offers based on specific customer needs. The idea is simple: we define a market basket for every customer and calculate the distance between the specific customer and others having similar items in the market basket. Then, we recommend customers buy the goods purchased earlier by those customers with similar market baskets. If a customer feature set coincides with an item feature set, then this customer gets a recommendation for this specific item.

This is an overview of what a recommendation system in retail is and how we implemented it at a grocery chain. These days, recommendation systems empower social networks, healthcare, finance, and e-commerce. At the end of 2016, Starbucks announced that they will be implementing an AI-based recommendation system in their cafes all over the world. This means that predictive analytics has finally found its way into retail. Like e-commerce entrepreneurs, retailers can now send customers personalized offers based on their behavior.

We have all had the experience. You are doing some on-line shopping. You drop a few items into your shopping cart and the website gets'helpful.' Across the bottom of the screen you see a collection of other items and you are told that they might be of interest to you. And the odd thing is, they mostly are kind of interesting.

We have all had the experience. You are doing some on-line shopping. You drop a few items into your shopping cart and the website gets'helpful.' Across the bottom of the screen you see a collection of other items and you are told that they might be of interest to you. And the odd thing is, they mostly are kind of interesting.