Why Predictive Merchandising is The Future Of Retail

Why Predictive Merchandising is The Future Of Retail
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The growing consumer awareness has considerably changed the dynamics of the retail sector. The demographics have shifted towards convenience in shopping. Consumers look forward to getting some specific products with the convenience of access at a faster pace. The flourishing e-commerce culture has also pushed the retail sector under a lot of pressure. With margins being stressed from every end, the stakeholders are finding ways to enhance their efficiency. The retailers are bearing high costs in maintaining an eCommerce supply chain and obtaining raw materials. The steep rise in the competition is also raising difficulties for the retail sector.
The retail sector needs to adapt to the changing consumer preferences and behavior to survive in the growing competition. The demand for personalized service has compelled retailers to pursue technology and automation to enhance their efficiency. Research suggests that retailers can automate half of the activities in the retail sector. Here automation is not about killing jobs but innovating use cases in the retail sector and generating new jobs. The stakeholders have already started investing in technological innovations in their supply chain and processes. More than 40 percent of the executives in the retail and consumer sector have confirmed that their companies have started with intelligent automation. Here Artificial Intelligence (AI) is infused into the program, making them capable of making estimations and recommendations. They can further take autonomous decisions and keep updating the algorithm. This forms the basis of predictive merchandising. Retail brands are connecting with offshore software development companies to develop intelligent technologies.

What is Predictive Merchandising

Predictive merchandising is using advanced technologies and data science techniques to accurately forecast outcomes and solve different challenges to the retail business. The prediction uses varied data and analytics to gain deeper insights into consumer behavior. The accurate predictions help the retailer in maintaining the right balance between demand and supply.
AI-Powered Predictive merchandising is expected to grow in the retail sector in the coming years as the most promising segment. It is expected to amount to 19.9 billion by the year 2027.

What to Expect From Intelligent Automation?

Using AI in machines enhances their performance and makes them capable of reasoning, learning, analyzing, and decision-making. The aim is to reduce human errors and make predictive decisions to boost the performance of any retail entity. Stocking the right product will have a direct impact on sales and profits. The expectations can be primarily divided into four categories.
  • User engagement: External touch points can be created to enhance user engagement.
  • Learning: Intelligent machines should continuously conduct analytics using varied data sources, learn the new developments and give effective suggestions.
  • Logical and data-based reasoning: The intelligent machines should be capable of taking autonomous decisions
  • Task automation: Most of the manual tasks should be automated and performed intelligently
AI has evolved as an integral part of any machinery system in different aspects such as assessing needs and processing data.
Predictive models are created and applied across the supply chain to make difference across three different important aspects of any retail business.
1. Inventory and warehouse management
AI improves demand forecasting by gathering insights from different types of data, consumer feedback, marketplace, and analytical tools. The gathered insights can be used to forecast the demand and procure a significant quantity of the right commodity to avoid shortage or wastage of any commodity.
2. Personalized shopping experience
Advanced CRMs and machines analyze customer behavior, draw patterns, and help in creating a personalized retail experience. The consumer data can be utilized in outbound marketing. AI predictions can be used to enhance the digital experience of the user. Retail brands can surface the products in alignment with consumers’ tastes to improve the user experience.
3. Demand forecasting
Mining insights from different sources including the marketplace and competition analysis, AI predictions can be utilized to make proactive changes in procurement strategy, business strategy, marketing, and merchandising. Demand forecasting also helps to improve the planning and sourcing strategies.

Benefits of Using Predictive AI-Powered Predictive Merchandising

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1. Attract customers
The retailers can employ AI to predict the demand and devise cost-effective procurement strategies, putting them in a better position to offer better deals on purchases. The personalized shopping experience also plays a vital role in attracting consumers. The data-driven outbound marketing strategies are set to fuel the growth of any company and capture a larger customer segment.
2. Enhance customer engagement
The predictive analysis in combination with the market insights can help retailers create innovative customer engagement strategies. Merely reacting to the change will help them only adapt to the change. An innovative and proactive approach will help them stay ahead in the market with a compelling user experience.
3. Data-driven insights
Retail operations involve an array of activities in supply chain management, marketing, and distribution. Every aspect can be associated with plentiful data generated every day. Using Artificial Intelligence we can collate the data generated from these disparate sources and analyze it to develop consumer-first strategies.
4. Brand recognition
Consumer-centric strategies, personalized experience, and innovative marketing can help a company create its unique brand identity. In this digital age where every stakeholder is capable of employing disruptive technologies such as Artificial Intelligence (AI) and Machine Learning (ML), exploiting its potential logically and profitably makes a difference.
Moving ahead in 2023, we will witness AI-powered visual intelligence heightening the offerings in predictive merchandising. The visual presentation of products along with Call-to-Action buttons such as ‘Buy Now’ help the retailers attract consumers and collect data regarding customer preferences. Many companies are using image-based data analysis to develop analytics reports helpful in creating brand recognition. AI-powered brand merchandising supplements retailers with authentic user data, helping them stay proactive in surfacing products in demand and make profitable business strategies.


The retailers and brands should harness the logical and analytical power of AI to optimize different supply chain processes and demand forecasting. Intelligent automation can help retailers address the demand hikes and dips without compromising on process and profit. The future in retail is about adopting technology and thinking differently. Technology and design thinking experts are going to become an integral part of the retail sector in future. AI-powered predictive merchandising is making the way to better business performance, higher operational effectiveness, and extensive insights enablement. Intelligent technologies like predictive merchandising that create a next-generation customer experience and business model are soon to become imperative for every retail outlet to thrive in the digitally active economy.

How Machine Learning Solutions are transforming the World of Financial Services?

How Machine Learning Solutions are transforming the World of Financial Services?
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The Fintech sector has progressed beyond imagination. Just a few years ago, it took several weeks to get loans approved, but today, everything is processed online and it takes barely a day. Likewise, financial frauds used to occur very often and the financial safety of the user was a big concern worldwide. However in recent times, such fraudulent transactions have reduced considerably, though, online transactions have increased immensely.
How did this happen? What changed?
The mobile revolution and the emergence of trending technologies like machine learning and artificial intelligence have brought a paradigm shift in the fintech industry. Machine learning technology has undoubtedly transformed financial services in a big way.
The algorithm used by Machine Learning solutions is used to identify correlations and work patterns within the large amount of data used in sequences, operations, and events. Hence it is used successfully in process automation, customer support optimization, financial monitoring, portfolio management, algorithmic trading, etc., and much more!
So, in this blog, we will outline how machine learning services has impacted the fintech sector! Rather, we will have a glimpse at the machine learning applications in this sector. Let’s commence.

Significant Role of Machine Learning Solutions in the Fintech Sector!

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Risk Management
In the Fintech industry, risk management refers to the process of identifying, analyzing, and migrating or accepting uncertainties i.e. potential risks while making any investment. It also includes taking precautionary steps to reduce such financial risk. Machine learning solutions along with AI and deep learning can help in making informed decisions about financial reports or loan applications. The predictive analysis used in Machine learning can predict potential risks from this unstructured data, and curb financial fraud to some extent.
Detecting and Preventing Banking Frauds
Today, banks and other financial organizations are paying heavily to prevent fraudulent practices. A plethora of large financial institutes are still using outdated security measures and have no systems to deal with new kinds of threats. However, machine learning algorithms are proving beneficial in this scenario. These can be used to find patterns involved in thefts and identify the hack-prone areas or loopholes in the banking systems. Besides, machine learning models are used in image processing, data mining, language processing, etc. Machine learning provides a predictive model depending on historical data for making vital business decisions using result-oriented predictions.
Complying with Ever-changing Rules and Regulations
Adherence to regulatory compliance like HIPAA is vital for financial firms and they spend billions to comply with them. Yet, new rules keep popping up, and following all these rules every time becomes almost impossible, despite spending a lot of time, money, and effort. But, AI and ML platforms can help automate this process as they can read through the pile of regulatory documents and guidelines. They can track and monitor any regulatory updates faster while minimizing risks related to non-compliance.
Robo-advisors are digital advisors or platforms that provide algorithm-driven, automated financial planning services with very little or no human intervention. The Robo-advisor collects data like age, financial status, current income, future goals, etc. from clients and uses this data to provide vital advice to them about the kind of investment they need to do based on their future goals. These Robo-advisors do a predictive analysis based on current market trends and the clients’ goals and find the best-suited investment plan for them. Likewise, they offer easy account setup, account services, robust goal planning, portfolio management, comprehensive education, attentive customer service, and security features, etc. Some experts also predict that these digital advisors could completely wipe out the human advisors in near future and save huge costs for Fintech app development services.
Chatbots are being used by several financial institutes and they work as full-scale robotic assistants. These chatbots respond immediately to the customers, hence the customers feel satisfied. Most of the time, the customers are not even aware whether they are interacting with a non-human assistant.
Sentiment Analysis
Sentiment analysis is yet another application of machine learning in financial services; also called news analysis. Machine learning applications are majorly dependent on data. But these applications cannot depend just on the data from stock and trade prices. So in recent times, these are evolving to gather additional data from social media trends, news trends, etc. to do sentiment analysis. The algorithms can do predictive analysis using the available data, also taking into consideration the thoughts and opinions of the users, etc.
Safety and Security of User Data
Needless to say, users’ data is the biggest concern for every financial firm. If this sensitive data leaks or gets hacked by any chance, it can not only lead to financial losses but also result in defamation in the market.
Traditionally, the users are being provided with a username and password for secure access. In case of account recovery or lost password, mobile number validation is required or some security questions are asked. But in the future, with the use of machine learning and artificial intelligence, anomaly detection apps can be developed that make use of biometric data like voice recognition, facial recognition, or retina scan. Several machine learning development companies are shifting their focus towards this area. Such measures will surely improve the data security of the users to a much better level.
Influencing Stock Markets
Stock markets are impacting the Fintech sector too. Every day, huge trading operations are being carried out and they gather tons of historical data. Machine learning algorithms take advantage of this data and identify patterns of stock market dynamics to make predictions. The traders make use of such vital data and accordingly decide the strategies. Based on these patterns, they can make trading forecasts and get benefitted in the stock market world.

Final Words:

Machine learning development services have influenced the world of financial services in myriad ways. It is playing a significant role in improving several processes like loan approval, risk management, portfolio management, financial analysis, etc. It prevents fraudulent practices and helps in stock forecasts too. Besides these benefits, machine learning developers and experts all over the world are experimenting to invent several other applications that can empower the fintech industry to serve their customers better, outsmart the competition, enhance productivity, and save costs effectively.
So this read ends here! I hope it was insightful.
Have you leveraged Machine learning services in your business endeavours? We would like to hear about your experiences.
In case of any technical assistance concerning the development of machine learning solutions reach out to Biz4Solutions, a highly experienced outsourcing software company in India. We are a leading Machine learning Company in India and USA and excel in delivering high-end services to the global clientele.