COVID-19 pandemic has impacted businesses as severely as it impacted individuals. Most companies had experienced huge declines in sales on the one hand and ‘panic buying’ on the other hand, which combines to add more confusion to demand estimation and production volume planning. Enterprises observe reduced resilience in key functions, infrastructure, and services. It leads to an increase in operating expenses, workforce disruption, and unstable revenue streams.
This article offers innovative solutions applicable to different divisions of your business, such as security of corporate communications, demand, sales and cash flow forecasting, supply chain optimization, and better customer service.
A Boston Consulting Group study revealed that 83% of businesses say AI is a strategic priority. The COVID-19 crisis turned priority into a necessity. It’s time to find out how to leverage the capacity of Big Data and AI for business to stay profitable and even thrive through the crisis. Here are some use cases that highlight the impact of big data on business.
Security Of Corporate Data And Communication Under Conditions Of Remote Work
A remote working regime makes corporate data even more sensitive. 94% of malware is delivered via email (Verizon), so the most vulnerable chain of the corporate security system is an employee who clicks obliviously on a malicious link in a letter.
Now AI-based solutions not only scan for external threats or viruses but also can analyze threats hidden in the letter content. It also makes it nearly impossible for workers to sell or share confidential data using their emails.
Early models of AI-powered solutions scanned databases to find suspicious links in databases with reported malicious sources. This means that somebody had clicked on it before, and damage had been done. Modern AI-based techniques apply comparison analysis of safe websites and web applications with those that seem to be phishing.
A successful use case of applying AI for email data security is a custom solution — an actionable intelligence system for cloud data management and security developed by Gloify for its customer.
This actionable intelligence system is based on anomalies analysis that is conducted on collected data from user sessions. It helps to predict cases of fraud and data theft and other issues. The custom-built Neural Network ingests the data, uses machine learning to categorize information, and determine which anomalies truly require immediate attention. This custom intelligence model integrates seamlessly with any organization email services. The implementation of this system helped the company to prevent data breaches and avoid costly downtime, as well as increase customer satisfaction and retention rates due to building marketing services based on behavioral analytics. In addition, due to the customizability of Gloify’s product, the organization reduced spending on costly legacy systems in the long run.
Enhanced Customer-centric Approach For Better Sales Performance And Demand Prediction
Customer behavior analytics
Using big data in a business environment, companies can enhance their customer acquisition and retention rates. The more data is analyzed, the more patterns are discovered in the user behavior, the more insights are derived to act on to raise and manage the customer base. These approaches are especially important during the crisis — a time of extreme turbulence in customer behavior, so businesses can deliver more value to customers.
Big data use in business for customer retention is well-represented by Coca Cola with its digital loyalty program. The interview for ADMA Coca Cola gives comments on the advantages of big data in their customer retention strategy. It helps to create marketing campaigns using relevant content for each specific audience, thus creating a more customer-centric approach in communication. The organization leverages Big Data capabilities to drive better consumer engagement & value for the business.
AI-based customer behavior analysis is able not only to process previous and ongoing data of customer interactions but also to predict their future behavior. By analyzing customer spending habits, companies can segment content and products in alignment with their specific needs.
Mining big data in the enterprise for better business intelligence, businesses can get deeper insights into customer behavior than ever before. The role of big data in business intelligence is defined by its ability to also process unstructured data in real-time. In the past, BI was able to process structured historical data only. Real-time big data analytics opened new horizons for better decision making in terms of customer relations.
The COVID-19 crisis made customers change their spending habits and capabilities. So now, it’s a big challenge for businesses to match demand and supply. It’s not only a question of success; on the accuracy of this match depends the business’s ability to stay afloat.
To achieve greater accuracy in demand forecasting, companies should rely not only on internal data but also on its external sources. Machine learning brings forecasting accuracy to new heights by using real-time data from both internal and external sources, such as weather forecasts, online news, and social media activities. This allows decision makers to see and consider a bigger picture with non-direct factors that influence buyer behavior and general demand.
According to Mckinsey Digital, AI-powered forecasting can reduce errors by 30 to 50% in supply chain networks. The improved accuracy can lead to a 65% reduction in lost sales due to inventory out-of-stock situations, and warehousing costs decrease between 10 to 40%.
The Pharma industry is one of the most sensitive to out-of-stock situations. So the Pharmaceutical network expressed demand for an out of stock system for drugstore inventories. Gloify delivered the solution with a predictive analytics module intended to predict the absence of drugs & remedies alongside BI-tool deployment for ad-hoc reports and predictive analytics. This out of stock system allowed suppliers to see the whole picture and consider sales & trends of their products at any point of sale.
Cash Flow forecasting
Cash flow forecasting is a big concern in times of crisis when the whole system slows down, and revenue streams dry up. However, AI solutions are well-prepared to support businesses with this issue.
These solutions harness the power of data from multiple sources for given periods of time to get a representation of current results and future predictions. Well-segmented and visualized analytics enable understanding of established patterns and generate accurate cash flow projections for the future.
Advanced features of cash flow forecasting solutions also offer tools for planning, including outcome calculations according to different scenarios with reductions in revenue or hidden expenses.
Recently, huge account companies Raedan and Xero, benefited from AI-powered cash flow forecasting solutions. A Raedan representative shares their experience: “This solution helps us to monitor cash flow where we didn’t have instant access before. Now we can monitor cash flow on a weekly basis for our clients and see if there are any potential problems.”
Supply Chain And Delivery Services Optimization And Management
During this pandemic, the global supply chain has been disrupted due to manufacturing slow down, factory and port closures, unplanned changes in the shipping routes – all of these factors lead to major delays for all “links’’ across the supply chain.
Due to quarantine, an increase in demand for food and items delivery has been observed. Those who kept up with the cloud kitchen trend before the crisis were well-prepared for this situation. However, more traditional brick-and-mortar businesses needed to learn quickly how to optimize the delivery process.
The understanding of the role of big data and business analytics in solving problems is a key to better decision making.
Here’re what Big Data and Artificial Intelligence have to offer for the supply chain management optimization:
Real-time visibility to create transparency of partners availability, state of inventory and operational issues
AI can be useful for handling end-to-end visibility issues, calculate the probability of unforeseen events considering certain circumstances, and bring more accuracy to the forecasting. AI-powered solutions can gather and analyze big amounts of data from different sources, joining forces with IoT infrastructure. Real-time visibility allows supply chain executives to find operations bottlenecks and to develop contingency plans that help to minimize losses caused by unexpected situations.
Pre-estimate realistic demand
AI-powered solutions provide organizations with customer behavior analysis and demand forecasting, described above in this article, that does a great job for your accurate estimation of demand and adequate offerings.
Identify and evaluate shipping capacities
Machine learning helps to predict possible problems that can occur with vehicles or items during the shipping process. Gloify has developed Machine Learning solutions for our customers to deal with such situations — exception prediction tool for supply chain automation. We worked deeply on the Machine Learning model for logistics incidents prediction and supplier-related incidents. It focuses on forecasting a group of exceptions. The tool predicts if the consignor provides a higher or lower volume than advised or the consignor fails to ship at all. Applying this tool, the client received higher precision for the forecasts (80%).
Digital Twin to be prepared for future challenges
Digital Twin model projections help enterprises to create more efficient plans for shipping routes, speed up network operations, and foresee possible problems before they cause damage.
The companies that adopted digitization before the crisis will continue to thrive through access to real-time information on all processes for better decision making and having tools in place to operate without physical contact. The others are feeling the need to do it.
AI-powered Chatbots To Save Costs On Customer Support
Businesses can reduce up to 30% of costs for customer support services using Chatbot.
That’s how AI-powered chatbots help a business to optimize the sales process and free more productive time for your indispensable workers:
Natural Language Processing enables chatbots to interpret human language input, identify the intended message, and generate appropriate responses. To do so, NLP applies syntax and semantics analysis to uncover intent through grammar form and understand the information within a context. Then, Natural Language Generation technique scans databases to derive context intention and to structure a human-like answer.
Due to its ability to learn from past conversations, chatbots can keep a consistent dialogue, which is helpful for customer service;
Chatbot can track the customer journey and be proactive, initiating conversations around products and features user viewed on the landing pages or those that have been described in promotion emails;
Natural Language Processing allows a chatbot to recognize a user’s irritation in the dialog. If that occurs, chatbot sends your worker an invitation to intervene;
Chatbot proved to be effective as a personal assistant that gathers useful information before sending it to a human consultant or specialist in a field. AI makes it smart enough to assign tickets to the right agents. It’s very helpful with client onboarding.
Chatbot is a priority for service companies that decided to implement AI solutions. It’s extremely cost-effective in financial and law advisory and other high-profile fields where time, hands, and minds of experts are too expensive to waste it on generic dialogue.
A crisis makes businesses change priorities: implementation of artificial intelligence in business once was a part of planned development, but now this is an urgent measure that helps to reduce costs of human resources and enables realistic demand, sales, and cash flow forecasting. Using artificial intelligence in business intelligence, decision-makers are getting not only analytical insights but also a plan of action with consideration of all factors that influence an industry. It produces an increase in operational efficiency and cost decrease.
Gloify AI & Big Data experts have developed many custom AI-driven solutions for supply chain, retail, fintech, healthcare, and user mobility.
To solve your specific problems, we can make a custom solution for your use. Just get in touch to discuss the details.