Artificial Intelligence, and Generative AI, in particular, is being adopted by some of the leading companies worldwide for various purposes. Parfums Christian Dior, Gucci, Nike Fit, and many other brands are already implementing AI virtual try-ons for users. Moreover, Generative AI tools such as AI sales reps boost customer acquisition across companies. Likewise, Generative AI can also be modeled into AI mentors and AI travel assistants, assisting users every step of the way. Virtual Try-On Brands can drastically improve their sales numbers if consumers could get the real-time trial of how a product would look when actually used. Virtual try-on refers to allowing users to try on different products such as clothes, accessories, and any such items virtually. Virtual try-ons are easy, comfortable, quick, and most importantly fun for customers to try out. With the use of generative AI and augmented reality, these virtual try-ons have become quite popular within a limited time. Virtual try-ons can be implemented with a camera & screen on the brand’s outlet or it can also be done on a customer’s mobile camera. The key benefit of such a solution is to gain the confidence of users in real-time to improve the sales conversion ratio. The interactive experience through virtual try-ons is a cherry on the cake for companies as they stand out among its competitors. Here are some examples of top brands using AI virtual try on: L’Oréal’s Modiface: The leading cosmetic brand, L’Oréal, provides a wide range of makeup products. Their cosmetics are sold based on the confidence of consumers after applying them. Virtual try-on through Modiface has allowed the brand to enable users to foresee the possible results of their cosmetics. Prada’s virtual try-on: Here’s another brand that fosters the capabilities of Virtual Reality and Artificial Intelligence. Prada is a front-runner in luxury clothing and sets the right example among peers through the use of virtual try-on capability on their platform. Crateandbarrel’s virtual try-on for furniture: Not just cosmetics and clothes, here’s an example of Augmented Reality-based virtual try-on furniture. Through its mobile platform, this furniture brand allows users to visualize how their next couch might look in their living room before actually purchasing it. In addition to more sales for companies, it also helps consumers buy the right product for themselves. AI Sales Rep Another area where AI can help interact and engage user’s is through AI sales reps. With Generative AI, the AI sales rep can be an immediate assistance that any new online customer might need. Not only will they leverage Natural Language Processing to understand the user’s inputs, they will also answer them accordingly, in a human-like way. AI sales reps are scalable, available 24×7, and can even give any answer almost promptly. Brands can train their AI sales rep models to answer based on their actual policies and terms while making personalized recommendations to buyers. AI reps won’t necessarily replace human sales staff; rather, they will help them focus on high-priority and sophisticated activities instead of answering frequently asked questions. AI Mentor Not only the product industry but AI can also support the service industry to cater their clients and help facilitate better services to them. One such example is AI mentors or AI coaches that can help users learn and train on any particular skill or learning. These AI coaches and mentors can be a 24×7 replacement to a human coach and can provide up to date insights to users without any limitation of personal knowledge or experience, unlike a human coach. Alfa AI is an example of such a fitness AI coach that can help detect the movements of users and make the right recommendation during their workout routine. Such a tool can also come up with personalized training routines and diet plans, making it easier for users to customize the training to their comfort. AI Travel Assist Traveling is another area where we instinctively prefer to get guidance from someone to get answers and conclusions easily. AI travel assistants can help replace human travel consultants who can basically compare and recommend travel itineraries for your next vacation. With AI’s ability to comprehend and compare the vast amount of data available online, it can help recommend the best travel agenda for you. Based on your schedule, it can recommend your travel destinations, mode of transportation, the best flights to choose from, and even the most convenient hotels to stay in. Prompt engineering would allow users to further perfect the output from these virtual assistants to meet their budget and personal preferences. Not only that, but your travel consultant can also help provide some manual resolution on top of the AI assistance to help you get the precise outcome you desire. So if you are a consumer facing brand… Whether you are a consumer-facing business providing products or services, Generative AI can be transformative for your company, helping you achieve the greatest sales conversion and customer retention ratios ever. If you are unaware of how generative AI products can add value to your unique business or industry, you can reach out to us at www.innoboon.com, and we will have our AI experts discuss with you how AI can scale your business like never before.
AI is becoming a game-changer across sectors in the fast-changing world of technology. One such sector is recruiting, which is changing. Hiring used to be laborious, time-consuming, and biased. AI is revolutionizing recruiting. This article will discuss how AI is changing talent acquisition, from sourcing to onboarding, and its pros and cons. AI-Powered Candidate Sourcing Advanced Data Mining Techniques AI-driven technologies mine unstructured data for insights. NLP algorithms extract keywords, talents, and experience from resumes and online profiles. These algorithms link applicant credentials to job needs by recognizing context and semantics. Semantic Matching and Contextual Understanding Due to keyword matching constraints, traditional keyword searches sometimes provide irrelevant results. AI-powered candidate sourcing uses semantic matching algorithms to comprehend candidate context and relevance beyond keywords. AI systems may find people with appropriate talents and experience who may not have utilized precise keywords by examining synonyms, related phrases, and contextual indicators. Continuous Learning and Optimization AI systems learn from user input and improve their accuracy and relevance. These algorithms refine search results and suggestions based on historical results using machine learning to adapt to changing recruiting demands. Recruiters may provide input on recommended applicants to help the system learn from successful hiring and improve its suggestions. Unbiased Candidate Search AI-driven candidate sourcing ensures a more thorough and impartial search, promoting diversity and inclusion. AI algorithms reduce human biases in recruiting by concentrating purely on credentials and merit. Removing name, gender, and race from applicant profiles prevents unconscious prejudice and assures fair assessment. Enhancing Candidate Screening and Assessment Objective Evaluation Criteria AI-driven screening technologies objectively evaluate applicant credentials using established criteria. These techniques quantify applicant appropriateness by examining skills, credentials, experience, and education. The technology may automatically filter out prospects who don’t fit recruiters’ job needs by setting thresholds and criteria. Natural Language Processing (NLP) for Resume Analysis Resume text is analyzed by NLP algorithms to extract important information and insights. From unstructured language, these algorithms can detect important talents, credentials, job titles, and work experience, helping recruiters swiftly analyze applicant profiles. Standardize and organize resume data into organized forms using NLP-based resume parsing tools to compare and analyze applicant credentials. Cognitive and Personality Assessments AI-driven evaluation tools evaluate applicants’ eligibility for the post by assessing cognitive and personality traits as well as hard skills and certifications. Psychometric tests analyze cognitive, problem-solving, and personality attributes to better understand applicants’ behavior and job fit. Using verified evaluation methods, recruiters may make better recruiting choices and find applicants that fit company values. Continuous Performance Monitoring AI-powered evaluation tools let recruiters track applicants’ success. Tracking applicants’ evaluations helps recruiters discover strengths and weaknesses and provide targeted comments and assistance. Continuous performance monitoring allows data-driven decision-making and provides fair and transparent candidate evaluations. Personalized Candidate Engagement Real-time Interaction and Support AI-powered chatbots and virtual assistants help applicants throughout the recruiting process in real time. These intelligent systems can answer questions, give job opportunity information, and assist applicants. Chatbots improve candidate engagement and satisfaction by providing tailored replies and suggestions using natural language comprehension and sentiment analysis. Automated Communication and Follow-up AI-powered communication solutions may automate interview scheduling, follow-up emails, and applicant status updates for recruiters. Automation reduces recruiters’ human work and administrative expense by engaging applicants quickly and consistently. By communicating with candidates often, recruiters may update them on their application and improve candidate experience. Personalized Content and Recommendations AI systems customize material and suggestions based on candidate choices, behavior, and interactions. Recruiters may adapt job suggestions, career guidance, and learning materials to applicants’ talents, interests, and career goals. Personalized content improves applicants’ recruitment engagement and employer brand connection. Recruiters may better attract and retain top talent by providing customized material. Feedback and Performance Analysis AI-powered engagement tools let recruiters collect applicant input and evaluate performance throughout the recruiting process. Recruitment professionals learn how to improve by asking candidates about their interview, application, and satisfaction experiences. Performance analysis tools assist recruiters enhance communication and candidate experience by tracking candidates’ engagement with recruiting materials. Predictive Analytics for Talent Forecasting Data-driven Insights and Forecasting AI algorithms find talent demand and supply patterns, trends, and correlations in massive data sets. Predictive analytics technologies estimate talent requirements using past recruiting data, market indicators, and macroeconomic variables. These data help companies predict worker demand changes and plan recruiting. Skills Gap Analysis and Training Needs Assessment Predictive analytics technologies detect skills gaps in the company. Organizations may identify the skills gap and create focused training and development programs by comparing existing worker capabilities to projected job needs. Predictive analytics also uncover skill trends and market demands, helping companies match personnel development with business goals. Scenario Planning and Risk Management AI-powered prediction models allow scenario planning and business performance assessment of multiple workforce situations. Simulations of recruiting methods, workforce compositions, and talent acquisition scenarios help firms assess the risks and benefits of alternative approaches. Predictive analytics solutions assist companies manage personnel shortages, skills gaps, and market changes. Strategic Workforce Planning and Talent Acquisition Talent acquisition and strategic personnel planning are guided by predictive analytics. By predicting talent demand by job position, function, or area, firms may create customized recruiting and talent acquisition strategies. Predictive models help companies prioritize recruiting, manage resources, and establish talent pipelines for future labor demands. Strategic personnel planning helps businesses have the proper people to grow and innovate. Streamlining Interview Processes Automated Scheduling and Coordination AI-powered interview systems ease scheduling and coordination for recruiters and applicants. Automatic scheduling technologies use interviewer and applicant availability to recommend interview times based on preferences and restrictions. Recruiters may decrease schedule disputes and delays by eliminating manual coordination, making the interview process easier for all parties. Video Interviewing and Assessment Recruiters can quickly perform remote interviews and assessments using AI-powered video interview platforms. These tools examine applicants’ non-verbal and communication abilities using face recognition, sentiment analysis, and behavioral evaluation. Video interviewing helps recruiters make better recruiting choices by revealing applicants’ personalities, communication styles, and cultural fit. Structured Interviewing and Evaluation To guarantee interview uniformity
Back Story A consumer has a Direct-To-Home (DTH) television subscription from a telecom service provider. The provider offers an internet connection and offers free over-the-top (OTT) channels to attract subscriptions. The consumer takes on a subscription for an internet connection along with the offer. They contact the customer care center to activate the offer. The mobile app of the service provider has automatically linked the new internet connection to the DTH connection based on the common registered mobile number. The customer still receives multiple calls and messages requesting to renew the DTH subscription. Annoying, to say the least. Disturbing, is more like it. Why? Because the customer data is in silos. The immediate impact is increased expenditure to man customer care centers. What if? What if all the data across the enterprise was extracted, cleaned and transformed, and loaded into a data lake. The impact: Queries can be run at blazing speeds Reports can be run at any time Dashboards can be populated and displayed Sales data can be analyzed to forecast demand, leading to resource capacity planning Resource utilization data can be analyzed to plan scheduled maintenance activities Customer usage data can be analyzed to forecast up-selling and cross-selling activities These planned activities will be more impactful since it is based on predictive analytics. And The organization can craft an imaginative, innovative customer experience. How? InnoBoon Technologies has the skill sets and experience to do all of this with Data Engineering: Planning and executing the Data Migration from legacy system and silos Designing and building Data Pipelines to swiftly move data to the Data Lake or Data Warehouse Implement ETL or ELT frameworks Build queries, reports and dashboards for action and decision-making at various levels of the organization. Call us. #data #dataengineering #datamigration #datapipeline #datawarehouse #datalake #etlframework #fastquery #instantreports #dashboards #datavalue #impact #telecom
Case Study – 14-year-old apparel company expands rapidly A lifestyle apparel brand established in 2008, known for their premium clothing and accessories has expanded across the US and 30 other countries. From 2016, the then 8-year-old company has grown a phenomenal 400% in 6 years. This is what the CEO has to say. “We can look at our reports during the day and identify our weaknesses, and respond in a way to turn those back to the growth path. We need those dashboards! Data migration enabled us to scale our business.” Their disparate data was spread across different systems and silos, in various formats. The data processing could not provide timely, accurate reports. The DWH Advantage: ⦁ Discovery: Assess, identify all the data, and the current and future requirements⦁ Extract: Extract all the data. Design and build a new data warehouse, suited to the data and to the requirements.⦁ Clean and load all the data into a Single Source of Truth (SSOT). Provide access to all stakeholders across the organization. Build queries, reports, dashboards for fast and easy use. Data Pipeline and Data Platform Benefits ⦁ Both structured and unstructured data can be accessed and processed⦁ Aggregate data from multiple, disparate sources⦁ Data streaming and shorter processing time provides superfast results⦁ Scale your data volume up; Enable managing humongous amounts of data⦁ Significant savings – data is processed a lot faster, and the storage and processing services are cost effective Do you want to find out how Data Engineering can bring immense value to your business? Call us. Where’s the magic? #dataengineering #datamigration #etlframework #datawarehouse #ssot #dataanalytics #fastquery #dashboards #costsaving #fashionretail #apparel
Reasons why most banks are lacking in providing a good customer experience: ⦁ Today’s customers are looking for better, more personal experiences when they deal with their banks. However, banks rarely tend to talk to their customers. Gone are the days when customers knew the staff, tellers and managers in their banks.⦁ Most banks are business entities with a large number of customers; they do not recognize their customers’ individual preferences.⦁ Banks do not segment their customers based on their varied interactions. Most banks believe all humans interact and respond in very similar ways and tend to offer standard services across their customer base regardless of their personality or financial situation.⦁ Finance is an important aspect in everybody’s lives. But the main institution that handles a person’s finances does not provide useful information at the right time, which can help customers make better financial decisions and feel comfortable.⦁ Like most large institutions, banks too tend to move slowly, given the volume of data they handle. Most often banks deal with their high-net worth customers with special attention, but tend to be barely helpful when dealing with others. Is there a way out of this quagmire? If banks need to attract customers and keep them, they need to offer experiences that are better than what is on offer now. However, looking at this situation from a bank’s perspective could explain why this is so. Handling vast volumes of data, including details of all transactions, customers’ demographic data, general and specific economic data, and rules and regulations, can be overwhelming. A way out is to segregate a bank’s functions into small modules and work with smaller-sized problems in a deliberate manner, yet connect each function’s data intelligently. Current advances in data engineering and analytics, high-efficiency infrastructure, and relatively easy access to technology can enable banks to up their service standards. What can be improved? Easing the onboarding process – account opening and introduction to other products and services Proactively providing personalized information about: ⦁ products and services⦁ terms and conditions both internal and compliance-related⦁ status of accounts held by the customer⦁ privacy, security, and anti-fraud information⦁ quick and easy access to support staff⦁ intuitive and friendly automated support system⦁ aggregate customer feedback and proactively address concerns and complaints Where’s the magic? The magic lies in digital transformation. We at InnoBoon, have extensive capabilities in extracting data from disparate sources, transforming the data in a homogenous set, loading, and applying analytics to generate meaningful information about customers to all the touchpoints of a customer’s journey through their interaction with the bank. We have experts in predictive analytics to forecast customer demand. Call us to have a conversation about how technology can enhance customer experience and provide avenues to bring down costs. #fintech #fintechproducts #fintechproductdevelopment #fintectsoftwaresolutions #customerpainpoints #banking #datatransformation #dataanalytics #predictive analytics
Introduction In the contemporary business world, data represents an untapped source of value. In almost every industry, data is produced from a variety of internal and external sources. Data engineering is rapidly evolving from a business option to a necessity in your company. Business operations generate a growing amount of data, which may be utilized to open new revenue sources and reveal fresh information about your consumers and their behavior. To implement the techniques and procedures that are part of a data engineering project and add value to your customers and your business, our data engineering team will need to understand how you view your data from a business viewpoint. Data discovery and interpreting Data discovery is the process of exploring your existing data to see what insights it contains. By creating a data catalog, data engineering allows you to gain a better understanding of your data. A data catalog is an organized inventory of your data that allows you to understand what data exists and how it is formatted within your company. A data catalog enables your team to quickly find a specific dataset or file and use the information to optimize operations, create new products or services. Data engineering also aids in data comprehension by generating data dictionaries that explain the structure of datasets. The entire process of aggregating data from disparate sources and updating it into a data warehouse employs an “ETL” framework, which stands for Extract-Transform-Load. Data warehouse to aggregate data As your company begins to investigate the data it has gathered, it will recognize the potential for developing new products and services. As this occurs, the data engineering team will need to be able to access and use the data in a timely and efficient manner. Data engineers can assist in the development of a data architecture that will allow your company to quickly access the data it requires. A data warehouse or a data lake enables easy and seamless access to data. A data warehouse is a centralized storage location for raw data. It is a system that gives you quick and easy access to your raw data. This data warehouse could be built with a mix of technologies such as data virtualization, data integration, and data migration. By understanding your organization’s needs and goals, data engineers will help you decide which of these tools is best for your business. Data engineers can also assist you in determining which data warehouse software is best suited to your needs. Data Virtualization for Live Access to Data Data virtualization is a method of storing raw data while also providing a customized user interface for accessing and using that data. Data virtualization enables you to create a database that only stores the information required by your business. Depending on your organization’s needs, this database contains all your raw data as well as filtered data. Data engineering will assist you in determining how to construct a data warehouse that houses all your raw data, while remaining user-friendly enough for your employees to access the information quickly and easily using ad hoc query methods. Data Platform to manage Data Life Cycle A data platform is a centralized tool for managing the life cycle of your data. This data platform will help you automate common data tasks like data ingestion and cleansing, as well as store your data in a centralized location. Dashboards, reports, and data feeds to other applications are created, to improve the efficacy of the stored data. A data platform is also used to train AI systems, build machine learning models, and perform predictive analytics. By understanding your company’s needs and goals, data engineers will help you decide which tools would be best for this part of your data platform. Standards and best Practices to manage data accuracy and consistency Increasing data accuracy and consistency, reducing data latency, and improving data quality are all examples of organizational efficiencies. Data engineers will assist you in increasing the accuracy and consistency of your data by implementing standards and best practices. The amount of time it takes to transfer data from one location to another is referred to as data latency. Data latency is frequently caused by your data being stored in multiple systems. Data engineers will assist you in reducing data latency by implementing centralized data storage and data ingestion methods that keep all your data in one place. The accuracy and consistency of your data are referred to as data quality. Data engineers will assist you in improving data quality by developing data quality checks for new data and implementing quality control measures to ensure that your data is accurate and consistent. Summary of the engagement ⦁ Discover and interpret⦁ Build an ETL Framework – collect all data into a data warehouse of a data lake⦁ Cleanse or scrub the data of inaccurate and unnecessary data; find relationship across the entire data set⦁ Analyze how your business uses data⦁ Build dashboards, reports and data pipelines to other applications and business entities⦁ Further analyze data to find deeper insights in the stored data. Use artificial intelligence and machine learning technologies, predictive analytics. Conclusion Data engineering is a critical role in any company that relies on data to run its operations. Data engineers must understand how your business views data to create a centralized data architecture that allows your company to quickly access and use data most efficiently. How? InnoBoon has a track record of implementing data engineering projects across multiple domains, and geographies. We use tools like Redshift, Snowflake, Airflow, Spark, Kafka, and others. Call us. Let’s have a conversation on how best your data can be engineered to extract tangible value for your organization.
Customer centricity is key for success in retail and retailers pursue multiple strategies to win customer loyalty to drive traffic, basket size and eventually profitability. Only driving customers to store is not enough in today’s competitive environment, rather retailers need to ensure customer satisfaction with merchandise, prices, promotions, aesthetics, and in-store experience to reduce customer churn. Retailers today are not only investing in ATL marketing but also are coming up with innovative solutions to drive customer engagement. Video analytics is one such tool in the retailers’ arsenal to study purchase patterns and customer behaviour for devising strategies for day-to-day retail operations and marketing. Retailers are drawn to this emerging technology due to its high ROI and easy implementation; Good news is they can leverage their existing passive infrastructure such as CCTV cameras to embed video analytics and make them Smart! Video analytics can provide crucial insights into customers’ mindsets and assist retailers in multiple ways. Here are some examples: 1. Customers’ Journey mapping for optimal layout planning: Brick and Mortar (B&M) retailers, unfortunately, don’t have the same visibility over their customer’s activities as do E-commerce retailers. Websites track multiple KPIs such as time spent on a product display page (PDP), bounce rates, online conversion rates etc to devise customer-centric strategies. With video analytics, B&M retailers can generate extremely useful insights such as: a) Automatically monitor in-store videos to assess high/ low impact areas for optimal product placement. For example, slow-moving products can be placed in high impact areas to prevent markdowns. Cross-selling can be improved by placing complementary products together on the same shelf. Sell-through rates of multiple SKUs can be adjusted by changing shelf placements. Layouts can be designed better to facilitate easy navigation of products b) Identify sub-optimal layouts that confuse customers. Design store elements such as service kiosks, collection centres and checkout tills placements to engage customers effectively c) Diagnose issues in lighting, temperature, and signage that might result in poor conversion 2. Improved Planogram Compliance: Retailers find it challenging to adhere to frequently changing planogram designs. Head-offices don’t have enough information to dynamically change store-specific planograms to drive higher basket size and average order volumes. Using insights from video analytics, planograms can be changed more frequently, and head offices can efficiently monitor run-time, stores’ adherence to the latest plans. 3. Measurement of Promotion Effectiveness: Retailers rely on sales data to monitor promotion effectiveness, but it provides a very objective view. Retailers are unable to capture the level of interest promotions generate and identify crucial gaps between interest level and actual conversion rates. Video analytics can easily solve this problem by monitoring customer behaviour and traffic at promotion zones in the store. Advanced algorithms can facilitate studying certain customer behaviour such as picking a product from a promotional shelf as the interest shown in the promotion and calculating a conversion rate. There are multiple opportunities to combine this data with promotion location data in-store to optimize store/brand promotions. It could also be shared with CPG brands to negotiate better rates for product placement. 4. Reduction in operating costs through leakage prevention: Globally, retailers lose around 2-3% of revenue due to shrinkage and trends are worrisome despite the adoption of RFID technology. Shrinkage increased around 21% in 2019 as industry security executives reported increases in the number of shopliftings, organized retail crime and employee theft incidents. With video analytics in place, any suspicious customer behaviour can be flagged as alerts to the store managers’ phones to prevent theft and leakages. 5. Improvement in partnerships with leading brands: Brands spend a significant amount of money to invest in a workforce that monitors retail stores’ compliance around product placement in stores. Video analytics can be used to send alerts to area managers if such contracts are violated. For example, if retailer A is not stocking Brand B’s products at an optimal location, alerts could be sent to both brands’ area managers and the retailers’ store managers for corrective measures. 6. Reduction in loss of sale by enabling in-time replenishment: As per leading research, almost 30% of customers switch stores if they find their products out of stock for the 1st time, this figure increases to 70% by 3rd instance. Replenishment on the shop floor is guided mostly by in-store staff and a sudden uptick in-store traffic and sales volume can lead to empty shelves and missed sales opportunities. Video analytics platforms could be used to send alerts & reports about possible stockouts and cut down on lost sales figures. References: https://www.repsly.com/blog/consumer-goods/how-stockouts-can-hurt-your-business: Comprehensive study conducted by professors at the University of Colorado and IE Business School Madrid on retail out of the stock reduction in the FMCG industry. https://hbr.org/2004/05/stock-outs-cause-walkouts https://cdn.nrf.com/sites/default/files/2020-07/RS-105905_2020_NationalRetailSecuritySurvey.pdf
CCTV cameras have seen exponential growth in the last decades, with more than 1 billion devices globally in 2021, driven by highest camera density in China and India for monitoring premises, vehicles, public places, etc., with Chennai leading at 657 Cameras per square KM. While these surveillance systems are indeed assisting crime investigations with data points, data indicates they have not helped reduce crime rate. Per the report from National Crime Records Bureau in October 2020, India recorded 1.6% increase in crime from 2018 to 2019, with crimes against women increasing by 7.3%. Moreover, there is no definite co-relation between the CCTV cameras density and the crime rate, as similar crime rates are noticed across cities with varying CCTV penetration. India Today report says, “Every three minutes, a theft or burglary happens in India”. Despite being one of the top-most countries in the world in terms of surveillance density, why are criminal activities on the rise? With such vast coverage in premises across domains – industries, education institutes, construction sites, residential and commercial buildings, hospitals, etc., do these cameras give meaningful insights to sustain or grow businesses, or maintain and secure premises – unless actively monitored? At the least, is it convenient to take advantage of recorded video feeds to identify missing objects, process deviations, employee negligence, etc.? Basis a survey among local brick & mortar retailers, most feel recorded videos are rarely viewed for it is extremely inconvenient to go through long stretch of videos. Despite cameras installed in the premises, there is no reliable way to automatically ensure or monitor staff diligence be it as simple as monitoring opening time, closing time, etc. Video Analytics for Rescue! Video analytics has been exponentially growing in the last few years, and the global market size is set to grow from USD 4 bn in 2019 to USD 22 bn in 2027, driven by rapid growth in Asia Pacific – primarily India and China. Growth will be fueled by the need for proactive and preventive surveillance system through smart video analytics system than a reactive solution from current passive cameras. With the rapid evolution of analytics at edge and cloud, the capabilities of surveillance system are increasing multifold to offer automatic monitoring and live-alerts for suspicious activities, unauthorized entries, missing objects, crossing boundaries, etc. across domains such as banks, vehicles, factories, retails, inventory rooms, education institutes, smart cities etc. It is a lot more than just security! While security evolves to be preventive and 24X7 fool proof, the underlying AI/ ML technologies offer unbelievable insights that are humanly impossible to obtain. They make premises/ cities smart by seamlessly enabling superior understanding of the footfall trends, walk-in trends, traffic patterns, staff diligence and efficiency, visitors’ demographics, crowd control, time-lapse videos, parking management, etc. With changes in behavior driven by COVID, these technologies offer social distancing, mask compliance, temperature detection, contact-less attendance, and services etc. to enable seamless operation in the post-COVID world Gone are the days with passive cameras, it is certainly the decade that will see places becoming smart, leveraging AI/ ML technologies to grow businesses, enhance security and crowd control. New business models are emerging with no upfront investments but only affordable subscriptions Want to be a front-runner? Contact BotsEye for next-gen video analytics solutions! References: The Hindu Journal Report, 2021 Mint report on CCTV Surveillance Allied Market Research – Video Analytics Statistics National Crime Records Bureau