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Strategic Role of Data Science and Artificial Intelligence in Retail
The role of data science and artificial intelligence is increasing its footprint in a way that transforms business functions today. Retail and CPG (consumer packaged goods) are probably one of the industries most involved, in addition to other industries such as BFSI (banking, financial services and insurance) and others, in adopting these technological changes early in the game. Most industry analysts note that more than three-quarters of organizations plan to implement data science and artificial intelligence by 2021 in the retail sector.
From the retailer's point of view, the focus is clearly on a) improving the customer experience through personification and so-called hyper-personalization, b) increasing efficiency and optimization to reduce costs, and c) as much automation as possible to experiment the added value. Of course, these are driven by the "mindset", the "attitude" and the "ability" to go through the details in order to achieve the strategic direction.
What are some of the key challenges today? If we started looking at retailers and jotted down their statements of business problems or priorities, there could be many different capacities in which data science could play a role in driving success. For example, if we look at some questions that customers will require for a more intelligent answer, it is the following: how to improve the customer experience; how to better understand customer behavior; how to optimize costs, increase efficiency in most operations that work and that do not work well; how to deliver better value to the customer today compared to what they have received before; How do we approach and are aware of specific customer segment changes from the outset to better respond to them? how we innovate in products and services to increase acceptance, visibility and sales, etc.
According to analyst surveys, Artificial Intelligence Companies in the USA has implemented data science and artificial intelligence solutions in production, which means that there is a scope of around 70% to 80% where there may be room for improvisation of solutions. In the design phases, there may be room for new. To implement solutions, there may be scope to define and plan new strategies that guide a strong role for AI to realize the business value and impact it generates.
Use Cases of Data Science in Retail:
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Price optimization: A significant advantage of optimization mechanisms is having the right price for both the customer and the retailer. The price optimization tools include a number of online tricks and approaches to customers (which is done in secret). The data obtained from multichannel sources are analyzed. Helps define pricing flexibility, customer location, a customer's buying attitude, buying season, and competitor prices.
Using a real-time optimization model, retailers have the opportunity to attract customers, retain attention, and make personal pricing schemes. Essentially, it also helps retailers offer shoppers prices they consider fair on the products that matter most to them, which in turn increases consumer price perception and retailer profitability.
Customer lifetime value prediction: In retail, Customer Lifetime Value (CLV) is the total benefits that a customer can bring to the company throughout the entire customer-business relationship. More attention is paid to the revenue that is calculated from the customer's previous purchases, the intervals between purchases, and the number of repeat orders.
The CLV models collect, filter and clean data related to preferences, expenses, purchases and customer behavior with respect to the particular product and its prices to structure it in the input. After carefully processing the data, retailers have an idea of the potential value of existing and prospective customers. Statistical data science technologies and Machine Learning Development Companies designs help retailers understand their customers and the need to increase products or services.
Predicting trends through social media: Social media is a huge platform and people express everything through social media. As a retailer, there is very important information on social media that can help you identify trends.
Social networks consist mainly of unstructured data, which is a large number of texts, images and videos. Techniques such as natural language processing (NLP) are used to extract information through social networks. The data is then used to identify trends and estimate what customers would like to buy.
Customer sentiment analysis: It is a completely new data science tool that is popularly used in the retail industry.
So traditionally, retailers used focus groups and customer surveys to analyze the customer's experience with the product. This was a time-consuming process and also a bit expensive. The analysis of the customer's opinion is carried out with the help of data received from social networks and comments from online services. These fonts are readily available, fast, and free. Retailers perform analysis on the basis of natural language processing, text analysis to extract the definition of positive, neutral or negative sentiments. The final output received is in the form of ratings and reviews given by customers.
Recommended: Cost to Development of Artificial Intelligence
Data Science Applications in E-commerce:
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Fraud detection: When something is completely online, there are also high chances of fraud. This is true in the case of e-commerce websites when some users try to commit credit card fraud or perhaps they constantly buy products to return later. However, Data Science Development Companies helps these retail companies to detect fraud and suspicious customer behavior to minimize their losses.
Data analysis can detect anomalies that occur in credit card history and financial purchases due to credit card fraud and freeze the user's account. Clustering algorithms can also be used to detect clustering patterns of suspicious behavior, such as buying things and returning them multiple times, buying the same product in bulk, etc. In this way, data science can be used to manage increased fraud. more and more with the increasing number of customers on e-commerce websites.
Customer behavior and buying patterns: In addition to the business benefits of personalization, Big Data analytics can be beneficial in determining customer behavior and purchasing patterns. For example, what are the most sought-after retail brands among online shoppers? When do customers buy the most for the type of products you offer? When do online shoppers make high-value purchases?
Based on this knowledge, e-commerce retailers can predict market demand for products (or services) and design more appropriate marketing strategies to take advantage of this demand.
Online shopping patterns are also helpful in determining the correct inventory level for a product line. Online retailers can optimize their stock levels by predicting whether products in demand will be overstocked or understocked. Based on the information provided by Artificial Intelligence Development services and Big Data analytics, you can manage your e-commerce operations such as supply chain, inventory, marketing channels, and product pricing more efficiently.
Inventory management: Every company that sells some products needs to have an inventory of all the items it owns, the most popular items, etc. in order to satisfy customer demand. This is also true of an e-commerce website. An eCommerce business could never function if an item was listed as available on the website but was not actually available or the most popular items were in low stock, while there were large stocks of items that were never sold.
Hence, inventory management is extremely important, especially for large e-commerce companies like Amazon, Flipkart, etc. These companies sell thousands of items to millions of people every day, so they need efficient data analysis algorithms to keep their inventory current. These data analysis algorithms can understand the correlations between supply and demand and then create strategies to increase sales by always ensuring that items in demand are available.
If you are looking for the Best Data Science Service Provider to fast your business value with scalable data science technology, USM Systems is the right partner. Our data science solutions help identify fraudulent claims, increase churn, increase sales conversions, improve the customer experience, and make better predictions that are required to stay ahead of the competition in the future.
Developing Artificial Intelligence Solutions to optimize business processes needs to merge AI capabilities with cutting-edge and rapidly emerging technologies at the industry level. At USM Systems, we have a group of highly experienced technology professionals who have acquired best practices in incorporating various artificial intelligence technologies to obtain innovative and intelligent solutions.
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I'm a tech assistant. and content researcher at USM. I share my knowledge about information in modern technologies.
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