The development of open benchmarking platforms could greatly accelerate the adoption of AI agents in retail. This paper presents comprehensive simulations of customer shopping behaviors for the purpose of benchmarking reinforcement learning (RL) agents that optimize coupon targeting. The difficulty of this learning problem is largely driven by the sparsity of customer purchase events. We trained agents using offline batch data comprising summarized customer purchase histories to help mitigate this effect. Our experiments revealed that contextual bandit and deep RL methods that are less prone to over-fitting the sparse reward distributions significantly outperform static policies. This study offers a practical framework for simulating AI agents that optimize the entire retail customer journey. It aims to inspire the further development of simulation tools for retail AI systems.

Customer experience plays a critical role for a profitable organisation or company. A satisfied customer for a company corresponds to higher rates of customer retention, and better representation in the market. One way to improve customer experience is to optimize the functionality of its call center. In this work, we have collaborated with the largest provider of telecommunications and Internet access in the country, and we formulate the customer-agent pairing problem as a machine learning problem. The proposed learning-based method causes a significant improvement in performance of about $215\%$ compared to a rule-based method.

Automation has become widely recognized for saving employees time and effort by carrying out high volume, repetitive and typically error-prone tasks. By utilizing robotic process automation (RPA), enterprises can more easily manage their manual, time-intensive tasks while also boosting accuracy, timeliness, and compliance. RPA also has been leveraged for cost avoidance. During the COVID-19 pandemic, companies across a variety of industries used automation to ensure business continuity and optimize costs. For example, airline companies developed automated processes to refund customers and reschedule flights.

Thorsten Stremlau, co-chair of TCG's Marketing Work Group, discusses how security of data systems for AI can be kept strong Attacks on artificial intelligence (AI) differ from the typical cyber security threats seen on a daily basis, but this does not mean they are at all infrequent. Hacking continues to become increasingly sophisticated, and has evolved from simply hiding bugs in code. Unless properly secured, hackers are able to tamper with these systems and alter its behaviour in order to'weaponise' AI. This provides a perfect way for hackers to obtain sensitive data or corrupt systems designed to authenticate and validate users, with no easy fix should an attack be successful. Where security is considered, it is not only important to look at the aspects of a rogue AI, but also how the data sets of a system can be secured.

As the dust settles on the disruption that was 2020, retailers are dedicating their efforts to regaining a sense of normalcy in the new year. With a shift to an increase--and perhaps outsize--role that e-commerce has taken in the new pandemic economy, brands are adjusting to the new normal, and catering their strategies to the avenues they see as a risk-averse way forward. While less aggressive in terms of growth, expect to see a commitment to loyalty and retention on the part of retailers in 2021. In fact, a recent survey of CMOs found that 73% are going to be depending on current customers as opposed to growing new markets in the coming year. Luckily for retailers, we sit at a confluence of technological solutions--namely artificial intelligence and machine learning--that are perfectly suited for the moment, offering brands the opportunity to make the most of their data to foster these ongoing customer relationships.

AI-based marketing enables brands to personalize the customer experience while providing real-time decisioning based on the actionable insights that are obtained through the analysis of massive amounts of historical and current customer data. In fact, according to a report from IBM, 50% of brands that were surveyed are already using AI to quickly access insights, automate campaigns and processes, and they are eager to embed it directly into customer touchpoints. Additionally, the report revealed that executives indicated that they are very interested in AI-enhanced CX, with 70% believing their industry is ready to adopt AI/CX, and 75% predicting that AI will play an important role in the future of their brands. Not surprisingly, 57% stated that responding to customer expectations for more personalized experiences is their number one reason for adopting AI. AI-based marketing uses artificial intelligence to make automated decisions that are based on data collection, data analysis, along with observations of economic trends that may impact marketing campaigns.

As one of Canada's Big Five banks, the Bank of Nova Scotia is taking an approach to data, analytics, and AI intended to better understand and serve customers, said Grace Lee, its chief data and analytics officer. Her charter is to advance business growth, customer experience, and operational efficiency through the use of AI, machine learning, and data-driven insights at the bank better known as Scotiabank. The stakes in customer retention are high: Scotiabank has more than 10 million retail, small business, and commercial customers in Canada, as well as 10 million retail and commercial customers in Latin America, the Caribbean, and Central America. The bank has about 90,000 employees and assets of about $1.2 trillion. Over the past couple of years, Scotiabank has engaged in an AI strategy that is very focused on last-mile execution, Lee said.

Machine learning (ML) in telecom can help network operators enhance their services, increase their profits, and reduce customer churn. As the number of smartphones and other smart device users is increasing, the chances for the telecommunications industry to increase sales is always on the rise. As the market seems to move ahead every day, telecom providers look to improve services to ensure customer retention. Mapping key trends and focusing on how their strategies work are some of the challenges that a telecommunication provider currently faces. Apart from merely mapping a company's strategies and fixing towers, mapping competitor's strategies and social media help businesses to achieve a broader base to reach out to their customers.

Data analytics technology is becoming a more important aspect of business models in all industries. SaaS companies are no exception. They need to leverage analytics strategically to maximize their revenue. The importance of customer loyalty and customer service has become increasingly well-known and companies have needed to adapt their business models accordingly to gain a competitive edge. One survey found 83% of customers attributed their brand loyalty to the ability to trust a company.

Customer service is absolutely imperative to a business' success. Luckily, opportunities to generate amazing customer service experiences have skyrocketed in recent years. We're now able to do more with less. And much of that is thanks to AI. The biggest problem with AI, though, is that people get really excited and think, "Oh I need AI", but they don't really know what AI is or how to use it.