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From Insights to Action: The Role of Predictive Analytics in Business Transformation
AI and Retail: It is a Match!

Why use AI in Retail? (Plus Best Practices) Artificial Intelligence (AI) and retail are a good fit. The COVID-19 pandemic has accelerated digital transformation worldwide and is whipping up different business verticals to adopt various AI technologies. As per the UNCTAD survey, more than half of consumers of the emerging and developed economies are shopping online. The part of AI in the retail market in 2020 was valued at USD 1,80 billion and is expected to reach USD 10,90 billion at a CAGR of 35% by 2026. It seems like it is high time for going big or going home for retailers. The latest AI technologies are already helping early adopters with managing products and supply chains, improving operational efficiency, and meeting the expectations of the more demanding consumers. We are presenting a close-up on how to benefit from these changes. Read on to find out the best practices of using AI in retail and e-commerce in 2021. Consumers are going deeper in digital than ever before. The brightest example is Amazon, which has increased its profit by 200% since the beginning of the COVID-19 pandemic. It made Jeff Bezos 80% richer at the beginning of 2021 than before the pandemic. Apart from e-commerce, consumers tend to choose the internet for news, information related to health issues, and entertainment, leaving more data online. This fact implies two principal tasks: meeting the growing and ever-changing demand of consumers online and discovering valuable data-driven insights. Simultaneously, different generations of customers expect a personal touch from the markets in the time of uncertainty. For example, the younger generation recognizes the need to save more money than 55+ years old customers (43% vs. 20% correspondingly according to the Kantar research). It means that retailers should listen to their consumers and choose the appropriate pricing strategy. At the same time, the younger generation is far more open to reward brands providing excellent customer experience more than Boomers (44% vs. 31%, correspondingly). Thus, chatbots, recommender systems, and data-driven business intelligence enter the picture. When customers are spending more of their time online and are interacting with various brands each day, investing in qualitative customer experience is crucial. Far-flung supply chains are vulnerable, and it is a fact. While some groups of society are undergoing vaccination against coronavirus and the global economy starts recovering, retailers are still adapting their supply chains to the new normal. And this new normal implies mobility restrictions, closing the factories for quarantines, and slowing down the logistics flows. Though it is still hard to assess the impact of the corona crisis on the global economy, the one is clear — the recovery lasts long. For example, one of the global electronics manufacturers’ revenue decreased to 66% after the series of earthquakes in Japan in 2016. Recovery time for this case reached one year. Hence, reducing the level of dependency on far-flung supply chains and developing resilience with the help of AI comes in handy as never before. Per Bain & Company research, flexible and resilient supply chains are accelerating revenue growth, improving customer satisfaction, increasing savings and cash flow, and minimizing risks. See the infographics below for more details. Resilient supply chains competitive advantage So, how exactly the supply chain can develop resilience with AI under uncertainty? As per research, you should invest in these five capabilities to build a resilient supply chain, counting on the latest technologies:

Deep Learning Based Recommender Systems

Recommender systems are lifesavers in the infinite seething sea of e-commerce, improving customer experience. Recommender engines are eliminating the tyranny of choice, smoothing the way for decision-making, and boosting online sales. Moreover, ubiquitous AI technologies are sneaking into e-commerce, too, not only solving the problems of irrelevant recommendations but predicting the customer’s next steps. In light of the pandemic’s impact, online sales are projected to grow twice in the next three years. Under those circumstances, businesses should ensure a perfect customer experience with precise recommendations to stand out. Read on to find out how recommender systems work, the pros and cons of these recommenders, and which algorithms stand behind them. Not surprising that media, retail, job listings, education, real estate, and travel companies are already using deep learning. It brings a killer feature for predicting ratings, defining the following items in the basket, and providing a personalized customer experience. For instance, 80% of content watched on Netflix, and 60% of videos on YouTube came from recommendations. First, learn how traditional recommendation systems work before diving into the complex deep learning based ones. Traditional recommender systems (RSs) include content-based and collaborative filtering (CF) systems grounding their recommendations on historical interactions and user/item attributes. Content-based recommendations are mainly drawn on the user’s item and profile features, and CF seeks a similar audience’s preferences. Furthermore, the CF approach is divided into memory-based (using the nearest neighbor classification method) and model-based (includes machine learning and data mining techniques). Also, the hybrid approach reaps the harvest of both content-based and collaborative filtering methods. At the same time, these systems have their constraints. For example, the cold start problem is an issue of irrelevant recommendations for a new user who still has performed few system interactions. Also, one can face a data sparsity problem. Think of millions of items on Amazon and a small amount of actual item-user interactions per typical user. We have explained all the magic and constraints of traditional RSs before. Check out “Inside Recommendations: How a Recommender System Recommends“ for details. In general, deep learning (DL) is the subfield of ML learning from multiple levels of data representations and abstractions. Some tech companies are already using DL systems drawn on different neural networks (NNs) to make the customer experience better. For instance, YouTube, eBay, Yahoo, and Twitter choose deep neural networks (DNNs), while Spotify prefers convolutional neural networks (CNNs). Meanwhile, DNNs and CNNs are only a few types of networks applied, as we can continue this list of deep learning algorithms. Why do we need a range of them? The answer is related to the business domain, particular task, or recommender scenario. Owing to the use case, one might leverage different NNs or even a hybrid approach to DL-based recommendation systems. The principal feature that differs DL-based recommender systems from traditional ones is coping with complex interaction patterns and precisely reflecting the user’s preferences. Content-based and collaborative filtering models are relatively linear systems that cannot deliver such deep user insights. To explain how each particular RS operates and what kind of benefits it brings, we have developed an overview of the most popular techniques. Typical CNN architecture Convolutional Neural Networks (CNNs) are a good fit for unstructured multimedia data processing given effective feature extraction. They are processing the data like image, text, audio, and video. CNNs help to eliminate the cold start problem or empower traditional systems like collaborative filtering. This feature is critical for e-commerce, as most customers conclude their decisions by assessing goods’ visuals. CNNs is also an option for non-Euclidean data (non-ordinal or hierarchical data) like social networks, protein-interaction networks, and knowledge graph. For instance, this kind of system could be applied to Pinterest recommendations. Unfolded basic recurrent neural network Recurrent Neural Networks (RNNs) could become a killer feature for sequential data processing, defining temporal dynamics of interactions and sequential user behavior patterns. For example, YouTube recommends content for a particular time of the day or predicts the next piece of content drawn on the already watched one. The majority of websites today do not require a user to log in for navigation. In other words, it means no access to customers’ long-term interests or consumption habits. Meanwhile, a cookie mechanism (i.e., session mechanism) can solve this task. RNNs can help build session-based recommendations without user identification information or even predict what users can buy next based on their click history. Restricted Boltzmann Machine Restricted Boltzmann Machine (RBM) belongs to the most old-fashioned RSs rooted from 2007 but is still in play. Significantly, RBM, combined with collaborative filtering, won the Netflix Prize for better recommendations on the streaming platform in 2009. Moreover, RBM-based techniques are still scalable to large data sets and producing high-quality recommendations of items per particular user. Autoencoder basic neural network In essence, an autoencoder is a neural network that reconstructs its input data in the output layer. It has an internal hidden layer that describes a code used to represent the input. The autoencoder consists of two main parts. They are an encoder that maps the information into the code and a decoder that maps the code to reconstruct the input. The beauty of autoencoder is in its agility in data dimensionality reduction, data reconstruction, and feature extraction. Attention mechanism derives from computer vision and natural language processing domains. Simply put, it is a vector of importance weights that predicts the next item. The attention mechanism is based on correlation with other elements (e.g., a pixel in the image or the next word in a sentence). In essence, human visual attention stands as a source of inspiration for this technique. The system can “focus” on a particular element to make its following recommendation. Illustration of the self-attention module. The input is the embedding matrix of the latest interacted L items, and the output is the self-attentive representations. Applying an attention mechanism to the recommender system can help filter out uninformative content and choose the most representative items. It provides good interpretability at the same time. It is also possible to integrate neural attention models with DNNs or CNNs. When it goes about complexity or numerous training instances (an object that an ML model learns from), deep learning is justified for recommendations. While neural network models show higher results, it is also possible to tune up conventional RSs with neural architecture to be on par. However, it needs some extra manipulations. To define when you need to opt for DL based RS, keep in mind the following advantages and drawbacks: Benefits of DL based recommender systems Are there any drawbacks of applying a complex technique like DL for recommender systems? The answer is yes. Depending on the way of using, some potential limitations could enter the picture: Flaws of DL based recommender systems Deep learning-based recommender systems outperform traditional ones due to their capability to process non-linear data. Non-linear transformation, representation learning, sequence modeling, and flexibility are the principal benefits of applying DL for recommendations. Moreover, DL techniques could be tailored for specific tasks. For example, CNNs are a good fit for non-Euclidean data, and DNNs are for sequential data processing. Autoencoder helps to secure data dimensionality reduction, and neural attention-based systems are suitable for filtering needed data and choosing the most representative items.

Modern stores: Indoor Analytics at retailers’ services

To make retail trade more effective, owners wished for centuries to understand what the clients want and what they see when they enter the shop. A lot was written and done on increasing the attractiveness of the premises and putting the goods in a particular fashion. Both owners and assistants were trying to develop their designer skills, good eye for clients, and intuition. It is at present, that the development of AI and Machine Learning made it possible for good analytics and statistical data to come to help when pure talent is not enough. Indoor analytics, as a part of AI, tells more about customers’ behavior: where they go at stores, what they see and what remains a blind spot, how much time they spend choosing the goods, and what emotions they experience. Indoor Analytics provides information on visitor numbers, the areas they visit, and their length of stay. An indoor map built on the data may depict customer flows and a heat map will provide insights into where they spend the most time. In this way, it is possible to identify spots within the premises that do not receive enough attention or that simply lie away from the conventional visiting route and optimize the layout accordingly. Apart from the evident benefit of placing the goods and promotions along the main route, this solution can help optimize human resources planning by analyzing customer numbers in different zones and at different times. Besides, visiting time tracking can give valuable insights into human behavior and the decision-making process and predict whether the pop-in visitor has the potential to become a loyal client. There are three main approaches to obtaining the necessary data. Wi-Fi networks Customers’ smartphones provide the signals needed to track where people go in closed spaces. All you need is to record this information is the right radio sensor technology like a WiFi infrastructure and a couple of WiFi access points. Of course, each customer has the option to refrain from Indoor Analytics by switching off the WiFi feature on their phone while they are in the store. However, in most cases, customers themselves bring the main tool for using this type of customer flow study with them. Beacons Bluetooth beacons are tiny radio transmitters that send out signals in a radius of 10–30 meters in interior spaces. They have a number of obvious benefits: they are cheap, easy to install, determine positions accurately up to 1 meter, and are supported by many operating systems and devices. Besides, beacons can be used for both client-based and server-based applications. For example, they enable indoor navigation for airline passengers using the app — cross-platform and with an accuracy of up to 1 meter. The server-based beacon tracking of persons or goods is only possible with third-party components (e.g. Infsoft Locator Node, Cisco, Aruba). Beacons (the most common types are the iBeacon and Eddystone) are the best choice for projects that demand high accuracy and want to include Apple devices. Security Cameras Existing security cameras have all the required information for further processing. All we need is to get the videos from cameras, visualize the processed images in a 2D top-down view, and calculate the statistics. The advantages of this method are quite obvious: it is cost-efficient because it does not require any additional hardware, and it is customers-independent so that you don’t have to rely on smartphones which potentially increases the accuracy of human flow metering covering the groups of customers who do not use — or are unwilling to use the store’s WiFi. On top of this, information from cameras can provide insights into the demographic characteristics of the customers as well as their emotions during shopping and their reactions to changes in the store layout. One of the cornerstones of Indoor Analytics is a special anonymization process that guarantees that the recorded information cannot be related to individuals in any way. All information is immediately anonymized and encrypted on a secure, on-site server in full compliance with personal data protection laws. Therefore, systems do not have access to personal information, so the customer’s right to privacy is not infringed. All solutions take care to anonymize and record customer flows in the store and then use big data applications to provide informative analyses that can be used in management decisions. Whichever approach you choose, it will soon transform your store into a better place for customers and give you the satisfaction of finally knowing what they want.