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As population soars through all rooves, so does food consumption, and most importantly, the wastage that comes with it. With sustainability efforts growing, a heightened emphasis on better methods to reduce food wastage is important, especially in restaurants.
UNEP’s 2024 Food Waste Index Report reveals the global food supply to comprise annual wastage worth over US$1 trillion as a result of market failure.
The environmental impact of this outcome is equally gruesome, with food waste contributing to 8-10% of global greenhouse gas emissions.
All these figures point towards smarter tech-enabled management as the only way to a more sustainable supply chain from farm to table.
So, how can businesses across sectors achieve this and benefit from the same?
Keep reading to know.
The first step to reducing food wastage is understanding the recurring loops in food waste. Factors like seasonal trends, popularity of menu items, and operational inefficiencies play a huge role in food wastage.
By diving into historical data, operators can forecast future waste scenarios and develop effective reduction strategies for restaurants.
For example, if a restaurant consistently sees increased waste during the holiday season, it can proactively adjust inventory orders and enhance staff training to better meet demand.
Similarly, a surge in waste for a particular dish might indicate that it’s not appealing to customers, prompting a potential recipe revision or menu update.
On similar lines, regular spoilage of certain ingredients may reveal weaknesses in supply chain management or storage practices.
Plus, establishing baseline waste levels and monitoring changes over time can help restaurants effectively measure the impact of their initiatives. Resultantly, they can further refine strategies for better sustainability and efficiency.
In all these attempts to achieve sustainability, emerging technologies stand as a primary gateway to the most effective methods to reduce food wastage.
Here’s how.
At the heart of lean manufacturing is a commitment to efficiency—eliminating waste and streamlining production.
Smart inventory management is a key enabler of this philosophy, tightly aligning stock with production needs to reduce surplus, cut storage costs, and minimize obsolescence.
By focusing on precise inventory control, manufacturers can boost productivity and ensure resources are fully optimized, supporting a more agile and cost-effective production process.
With data analytics, restaurants can monitor waste patterns as they occur. As a result, they can swiftly identify trends, such as high-waste menu items or inefficient preparation methods.
These insights contribute to the most effective ways to reduce food waste globally, enabling timely adjustments, whether it’s refining menu offerings or optimizing inventory levels.
This not only enhances sustainability efforts and drives cost savings but also aligns with the values of today’s eco-conscious consumers. Ultimately, real-time analytics empower restaurants to enhance their operations while showcasing their commitment to responsible practices.
Streamlining a menu is a strategic approach for businesses aiming to reduce food wastage and enhance operational efficiency.
By focusing on a concise selection of dishes, organizations can reduce the number of perishable items and optimize ingredient use across multiple offerings. This approach not only accelerates inventory turnover but also keeps ingredients fresh, thereby lowering the risk of spoilage.
Consider varying portion sizes, such as half-portions, to accommodate diverse appetites and reduce leftovers. Making side dishes optional can further mitigate waste by preventing unwanted servings.
Exploring creative uses for kitchen scraps can also yield new dishes that leverage leftover ingredients. Adopting a "nose-to-tail" or "root-to-stalk" philosophy encourages businesses to maximize every part of their ingredients, especially when dealing with surplus or imperfect produce.
Engaging staff in brainstorming sessions can generate innovative ideas and foster a culture of ownership in waste reduction initiatives.
IoT and AI are transforming the food and beverage industry by providing unprecedented control and predictive capabilities.
IoT contributes to managing food storage, handling, and waste disposal by enabling real-time monitoring with the following functions:
The biggest advantage of real-time monitoring is its ability to enable proactive decision-making. Manufacturers can gain immediate visibility and help them make data-driven adjustments to optimize the process at every stage.
AI enhances this system by analyzing massive data sets to forecast trends, set ideal inventory levels, and identify potential disruptions.
By integrating AI recommendations with IoT data, manufacturers gain precision in stock management, cut waste, and reduce costs. All this is made possible while building a more agile supply chain that adapts quickly to market demands.
Distributors can address issues like inaccurate transportation conditions immediately, while retailers can reorder stock based on actual demand, not just estimates. This creates a smarter, waste-conscious supply chain that minimizes waste and enhances operational efficiency.
Effective demand forecasting is a crucial aspect of how to reduce food waste in restaurants. By tapping into data analytics and historical sales insights, manufacturers can predict demand patterns with greater precision, enabling optimized stock levels and reducing waste.
Advanced forecasting tools today incorporate machine learning to parse trends in market behavior, customer preferences, and seasonal shifts. These tools enable manufacturers to make proactive inventory adjustments, ensuring products are ready when needed.
For manufacturers, embracing sustainable practices as one of the methods to reduce food wastage is now a market expectation.
Tech-driven food waste management directly supports sustainability by reducing resource consumption, helping companies operate with a smaller carbon footprint, and creating a streamlined, eco-friendly supply chain.
This commitment to sustainability also strengthens customer loyalty. As consumers seek brands with clear environmental values, companies that prioritize sustainable manufacturing gain a competitive edge and build a reputation as responsible, forward-thinking leaders in their industry.
From a profitability point of view, food waste management isn’t just about reducing waste; it goes all the way from cutting costs to boosting operational efficiency.
The transition to tech-driven methods to reduce food wastage is not just a strategic necessity for food and beverage manufacturers; it’s also a transformative opportunity.
By approaching each phase of this journey thoughtfully and dispelling common misconceptions, restaurants can effectively tackle challenges that arise after peak sales events.
Further, embracing emerging technologies like AI helps food companies stand a chance to gain a competitive edge, positioning themselves for sustained success. All in all, the future of efficiency in food and beverage manufacturing is in the hands of those ready to innovate and harness the power of technology.
Thoughts and Trends Across Our Focus Areas
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Data Analytics in Insurance Sector: Use Cases and Implementation
The traditional insurance industry, once anchored in manual processes, is swiftly being outpaced by technological advancements, making way for data analytics in insurance sector. With more consumers flocking to digital platforms and data growing exponentially, the pressure to stay ahead is mounting. Insurers who fail to adopt data analytics risk falling behind, while those who embrace it gain a significant edge.Integrating data analytics allows insurers to gain clearer insights, enhance decision-making, and improve overall efficiency. While the transition may seem complex, adopting the right tools and strategies makes it both achievable and rewarding.By understanding its value and implementing the right strategies, insurers can utilize analytics to optimize operations and mitigate risks. This enables them to stay ahead in an increasingly competitive landscape.The Importance of Data Analytics in Insurance IndustryInsurance data analytics involves gathering and analyzing vast amounts of information on policies, claims, and risk profiles to uncover meaningful patterns, trends, and anomalies. By providing deeper insights, it is revolutionizing the insurance sector, enabling smarter decision-making and greater operational efficiency. Supporting this shift, Fortune Business Insights projects that the insurance analytics market will grow from USD 14.50 billion in 2024 to USD 44.77 billion by 2032. This surge, driven by increasing adoption, represents a CAGR of 15.1%.This data-driven approach enables insurers to assess risk with greater accuracy and optimize pricing models. It also helps detect potential fraud, resulting in better outcomes for insurers, agencies, and customers alike.As the insurance landscape becomes increasingly complex, leveraging data analytics allows companies to stay competitive and meet evolving consumer demands. The answer to “What is the role of data in insurance?” lies in understanding its practical applications. Exploring the uses of data analytics in the insurance industry can help businesses realize its resultant benefits.How Is Data Analytics Used in the Insurance Industry?Data analytics in insurance sector is redefining processes by enabling more precise decision-making, enhancing operational efficiency, and improving customer satisfaction. Below are some key use cases where insurers can significantly benefit from implementing data analytics:1. Fraud Detection and PreventionFraudulent claims are a major concern for insurers. Traditional fraud detection methods can be slow and ineffective. With data analytics, insurers can:Identify anomalies in claim amounts, frequency, and patterns through predictive analytics.Leverage text mining to detect deceptive language in claim narratives.Use external data sources, like credit scores and previous claim histories, to pinpoint potentially fraudulent claims earlier.By proactively flagging suspicious claims, insurers can minimize financial losses and protect the integrity of their operations.2. Automated and Intelligent Claims ProcessingManual claims processing is time-consuming and prone to errors. Data analytics in insurance industry can streamline the process by:Automating claims validation using sophisticated algorithms that assess legitimacy instantly.Cross-referencing claims with policy records to identify discrepancies or potential fraud.Expediting settlement amounts, especially for straightforward claims.Implementing automated alerts to notify staff when follow-ups are needed, ensuring timely communication with clients regarding their claims, and addressing any concerns.Identifying opportunities for cross-selling and upselling based on claim data, boosting overall customer engagement.Automating sales reporting to track producer activities and conversions, offering insights to improve lead assignment. This is based on factors like geographic location, expertise, and potential deal value.This reduces errors, shortens claims cycle times, and allows adjusters to focus on more complex cases.3. Risk Assessment and Personalized PricingRather than applying generic pricing, insurers can leverage data analytics to create granular risk profiles based on:Historical data on claims and customer behavior.Unconventional data sources like social media activity and purchase history.This enables insurers to tailor premiums more accurately, reducing risks and offering personalized policies that meet individual customer needs.4. Customer Segmentation and Targeted MarketingCustomer segmentation helps insurers move away from treating all customers as one group. Using data analytics, insurers can:Segment by Demographics: Categorize customers based on characteristics such as age, income, lifestyle, and risk appetite.Mine Lead Generation Data: Set up reporting from the system of record to extract valuable lead generation data, revealing the customer’s journey from first contact to sale.Design Targeted Campaigns: Leverage insights from customer data to create marketing campaigns that resonate with specific segments, for example, offering digital-first auto insurance products to young urban professionals.Refine Marketing Efforts: Use the detailed customer journey data to inform and improve future marketing strategies continuously.Boost Retention: Personalize service offerings to meet the diverse needs of each segment, ultimately enhancing client retention and satisfaction.By tailoring offerings to specific segments, insurers can improve customer engagement and drive conversion rates.5. Catastrophe Modeling for Better PreparednessInsurance companies face significant risks from large-scale events like natural disasters. Data analytics in insurance sector can help:Integrate real-time environmental data, such as oceanic temperatures or deforestation rates, for better catastrophe predictions.Predict potential loss areas by analyzing urban development patterns and identifying at-risk regions.Refine pricing strategies based on more accurate risk assessments, ensuring that insurers remain financially prepared.This data-driven approach helps insurers mitigate potential losses by enabling proactive risk management.6. Loss Reserving and Claims ForecastingProper loss reserving is critical to ensuring that insurers can meet future claim obligations. With insurance data analytics, businesses can gain:Accurate loss reserving by comparing current claims with similar past cases.Dynamic reassessments of reserve levels as claims progress and new data becomes available.Improved financial forecasting to ensure sufficient liquidity while preventing over-accumulation of funds.This helps insurers maintain financial stability and minimize over-reserving, freeing up capital for other investments.7. Litigation Risk PredictionDisputed claims lead to costly litigation. By using data analytics in insurance industry, insurers can:Calculate a litigation propensity score to determine which claims are most likely to result in legal disputes.Assign high-risk cases to senior adjusters with more experience in resolving complex legal issues.Reduce legal expenses by settling claims earlier in the process.This approach helps insurers lower litigation costs and settle disputes more efficiently.8. Telematics for Smarter Auto InsuranceA key question that often arises is, “What kind of data do insurance companies use?”.A prime example is telematics in the auto insurance sector. This technology offers insurers real-time, detailed insights into driving behavior, enabling them to:Offer personalized premiums based on actual driving patterns, such as speed, braking, and mileage.Promote safer driving behaviors by offering rewards for safe drivers, which can further reduce the likelihood of accidents.Improve claims accuracy by providing real-time accident data that assists in faster claims resolution.Enhance risk assessment and provide more accurate, fair premiums.9. Subrogation OptimizationIdentifying subrogation opportunities, where insurers recover costs from responsible third parties, can be challenging due to the volume of data involved. Here, text analytics can help by:Scanning unstructured data like that in medical records to identify potential subrogation cases.Automating the identification process, ensuring that opportunities are flagged early and accurately.This allows insurers to maximize recovery and reduce claim-related expenses.10. Enhancing Settlement EfficiencyFast and fair claim settlements are key to customer satisfaction. Data analytics in insurance sector can:Optimize settlement thresholds, ensuring that claims are processed quickly while preventing overpayment.Streamline settlement for natural disasters or other large-scale events by analyzing claims data for trends and insights.Ensure fairness in settlements by providing more accurate data on claim validity.11. Underwriting Enhancement through Predictive AnalyticsTraditionally, underwriting relied on static tables and limited historical data. However, with the advent of data analytics, insurers can gain a deeper, more nuanced understanding of risk. Using advanced algorithms, insurers now analyze vast amounts of data. Its applications include both conventional sources, like police reports, and modern ones, like telematics in auto insurance. This approach helps to:Predict future risks more accurately by identifying patterns in data.Tailor premiums based on factors such as lifestyle, exercise habits, or even genetic predispositions, moving beyond basic demographic information.Enhance the overall accuracy of underwriting decisions, allowing for more personalized policies.As artificial intelligence (AI) and machine learning continue to evolve, their influence on data analytics in insurance underwriting is set to expand, further driving efficiency and precision across the process. The growing role of AI in automating insurance tasks like underwriting and claims management is reshaping the way insurers approach risk assessment, a trend that will only accelerate in the coming years.How to Successfully Implement Data Analytics in Insurance Sector?Implementing data analytics in insurance industry requires a structured, strategic approach to ensure optimal results. From defining key business challenges to leveraging advanced analytics for decision-making, insurers can follow a step-by-step framework to realize the full potential of data.1. Define the Business ProblemThe foundation of a successful data analytics implementation begins with problem framing. Insurers must clearly identify the key challenges they aim to address, such as optimizing premium pricing, detecting fraud, predicting policyholder churn, or improving claims processing. A well-defined problem statement aligns analytics efforts with business objectives, ensuring that the insights generated are actionable and relevant.2. Data Collection and IntegrationData serves as the backbone of analytics-driven decision-making in the insurance industry. Insurers must aggregate data from various sources, including: First-party data: customer profiles, historical claims, policy detailsSecond-party or external data: market trends, demographic insightsIntegrating data from legacy systems, IoT devices, and third-party sources enables insurers to develop a comprehensive understanding of several key factors. These include risks, customer behaviors, and operational efficiencies, a better understanding of which will enhance decision-making.3. Data Preparation and Quality AssuranceRaw data is often unstructured, incomplete, or inconsistent. To derive meaningful insights, insurers must process and refine their datasets. This stage involves:Data Extraction: Retrieving data from legacy systems and multiple repositories.Data Cleaning: Removing inconsistencies, duplicates, and errors to ensure accuracy.Data Structuring: Standardizing formats and handling missing values for seamless analysis.Quality data is essential for reliable outcomes in data analytics within the insurance sector. It helps prevent biases and inaccuracies in risk assessments, claims predictions, and fraud detection models.4. Advanced Analytics and Model DevelopmentOnce the data is structured, insurers can apply various analytical techniques to generate actionable insights:Descriptive Analytics: Identifies past trends in claims, policies, and customer behaviors.Diagnostic Analytics: Examines underlying reasons behind key business trends, such as rising claim costs or customer attrition.Predictive Analytics: Forecasts future trends, such as policyholder risk levels, fraud likelihood, and expected claims volume. Prescriptive Analytics: Recommends actions to mitigate risks, improve customer engagement, and optimize underwriting models.Machine learning and AI-powered models play a crucial role in automating predictions, enhancing accuracy, and enabling real-time decision-making in risk assessments and pricing strategies.5. Insights Visualization and Reporting Effective data visualization and reporting help insurers present complex analytics results in a simplified, intuitive format. Interactive dashboards and real-time reports enable underwriters, claims professionals, and other stakeholders to identify trends quickly, assess risks, and make informed decisions.Advanced analytics platforms play a key role in this process. Tableau, widely used in the insurance sector, simplifies data visualization, helping insurers quickly assess risk, detect patterns, and refine policy pricing. Its real-time dashboards also enhance customer engagement by providing insights into policyholder behavior. Another platform, Power BI, offers a comprehensive view of operations through interactive reports and performance tracking. This, resultantly, enables insurers to monitor claims processing efficiency and optimize customer service.Choosing the right BI tool can help insurers ensure that data-driven insights are accessible, actionable, and effectively utilized across departments. 6. Continuous Evaluation and OptimizationThe effectiveness of data analytics initiatives should be regularly assessed to ensure ongoing improvements. Key considerations include:Measuring the impact of analytics-driven decisions on profitability, efficiency, and customer satisfaction.Identifying gaps or unexpected trends that require further analysis.Refining models and strategies to adapt to evolving market conditions and regulatory requirements.By establishing a continuous feedback loop, insurers can enhance their analytics frameworks, improve predictive accuracy, and maintain a competitive edge in the evolving insurance landscape.Final ThoughtsBy weaving AI and data analytics in insurance sector, businesses can cut costs, speed up claims, and make smarter decisions that set them apart. With competition getting fiercer and customers expecting more, relying on data-driven insights isn’t just about keeping up; it’s about staying ahead. And while adopting new technology might seem daunting, the right tech partners can make the transition seamless. With their support, insurers can unlock real value and build a future-ready business.
Real Estate Data Analytics to Shape the Future
The real estate industry is experiencing a seismic shift, fueled by groundbreaking technological advancements. At the heart of this evolution lies the transformative power of real estate data analytics, uplifting every aspect of the sector. However, many are still left wondering what to do with real estate data.Forward-thinking industry leaders are leveraging it to reimagine property transactions, streamline management processes, and unlock new efficiencies. In today’s tech-driven world, industries everywhere face a crucial choice: adapt or risk being left behind. Real estate is no exception, and those who utilize data effectively are better positioned to thrive in an increasingly competitive market.The Impact of Data & Analytics in Real EstateStrategic use of real estate data analytics is a game-changer, driving business growth by enabling smarter pricing, deeper market insights, and more targeted marketing efforts. Brokers who embrace data-driven strategies achieve faster sales, higher customer satisfaction, and increased revenue, gaining a clear edge over competitors who don’t. If you want to find out how you can utilize real estate data analysis, here are some pointers to give you a heads-up.Transforming Building Construction Data and analytics enable smarter project planning, execution, and management, propelling the construction industry toward a more efficient, data-driven, and connected future by:Identifying Opportunities: Firms leverage third-party insights to spot development opportunities, monitor competition, and assess market trends.Risk Management: Historical data helps identify potential risks and ensure proactive measures are taken.Optimized Operations: Predictive analytics refine project timelines, control budgets, and enhance resource allocation.Real-Time Tracking: IoT-driven analytics provide live monitoring of materials, labor, and equipment, improving efficiency.Growth Strategies: Contractors and suppliers use data insights to secure new projects and drive business expansion.Proactive Property Maintenance Data and analytics enable property managers to stay ahead of maintenance challenges, addressing issues before they escalate. Predictive systems leverage sensor data to monitor equipment like HVAC units and plumbing, identifying signs of wear or irregularities early.For example, fluctuations in temperature or energy consumption can signal potential failures, prompting timely repairs that avoid costly breakdowns. This futuristic approach not only reduces maintenance expenses but also maximizes system lifespans and minimizes disruptions. This, in turn, ensures resources are used efficiently, delivering smarter and more reliable property management.Smarter Buying and Selling Real estate data analytics has streamlined the buying and selling of residential and commercial properties. Key advancements include:Predictive Property Valuation: Machine learning predicts property values by analyzing key factors like location, property features, and market dynamics, offering buyers and sellers better insights.Enhanced Property Search Experience: This uses advanced tools like - 3D modeling for immersive property visualizationAerial imagery for a comprehensive view of the surroundings.Virtual tours that provide remote property explorationThese innovations simplify decision-making and offer a more engaging experience for potential buyers.AI-Driven Property Recommendations: Smart algorithms analyze buyer preferences and match them with the most suitable listings. This results in faster, more efficient transactions and improved client satisfaction.Meeting Evolving Client Requirements The best brokers are experts in understanding the ever-changing factors that impact property values and investment opportunities. In today’s fast-paced, competitive market, what was relevant just a few months ago may no longer apply. Successful brokers keep their finger on the pulse, using expert market understanding based on current data, including interest rates and market shifts.Using this, they guide clients on the optimal times and locations to buy or sell, helping them build lasting trust with clients and, in turn, maintain their competitive edge.Smarter Revenue Management The integration of real estate data analysis has changed how rental pricing is determined, making it more precise and adaptive with:Dynamic Pricing: Algorithms analyze vast datasets on market trends, competitor rates, and supply-demand balances to optimize rental rates in real time.Historical Insights: Property managers leverage past data to anticipate market changes and fine-tune strategies for maximum occupancy and profitability.Predictive Analytics: Advanced models utilize automation for pricing adjustments, responding to market fluctuations with speed and accuracy.Financial Optimization: A data-driven approach ensures smarter pricing decisions, leading to improved revenue and better financial outcomes for property owners.Gaining a Competitive Edge Through Market InsightsIn real estate, success hinges on a broker’s ability to attract and serve both buyers and sellers. Just like in any competitive industry, keeping an eye on rivals is crucial. Here's how to analyze real estate market data effectively:Tracking Performance: Use data analytics to monitor which companies and agents are closing the most deals and how they rank in customer satisfaction.Competitor Analysis: Gain insights into rivals’ marketing strategies to understand their strengths and weaknesses.Strategic Improvement: Learn from competitors' playbooks, refine strategies, and implement better approaches to outperform them.Simplifying Decision-Making with Data VisualizationReal estate data analytics has the power to boost performance and profitability, but its true value lies in how effectively it is utilized. Complexity often becomes a major roadblock, discouraging even the most driven brokers. To override this challenge, top-performing brokers rely on real estate data analytics software, which offers:Modern Visualization Tools: These tools transform overwhelming datasets into clear, actionable insights, making it easier to interpret complex information.Real-Time Dashboards & Reports: Brokers can access real-time data that simplifies the tracking of key metrics such as:AppointmentsOffersContractsSalesInformed Decision-Making: With a clearer view of their performance, brokers can make data-driven decisions that fuel business growth and improve overall efficiency. Generative AI: A Game Changer in Real EstateGenerative AI is at the spotlight of the stage that is real estate data analysis, giving companies a powerful competitive advantage. By leveraging large, proprietary datasets alongside third-party data, firms can create AI models that offer unique insights into property trends, tenant and owner preferences, and emerging market opportunities.AI also streamlines complex tasks, such as optimizing property designs and layouts, improving efficiency across operations. Combining data-driven insights with AI’s predictive power enables real estate companies to develop customized solutions and maintain a leading edge in the market.Harnessing Data for a Sustainable Future in Real EstateData and analytics are at the forefront of advancing sustainability in the real estate sector. By monitoring energy consumption, water usage, and waste management in real-time, companies can identify inefficiencies and take action to reduce their environmental footprint. Predictive real estate data analytics also help design energy-efficient buildings from the start, cutting carbon emissions and lowering operational costs.Moreover, sustainability metrics provide transparency, allowing companies to highlight their environmental efforts to investors and tenants who prioritize green practices. With global benchmarks and ratings, real estate operators can make informed decisions, enhancing both their sustainability performance and appeal to eco-conscious stakeholders.Final WordBeyond innovation, the impact of data and analytics on real estate constitutes a fundamental transformation. By optimizing property valuations, deepening customer insights, and enhancing operational efficiency, real estate data analytics are setting new industry standards. With 2025 sales projections forecasting a 9% rise in existing home sales and an 11% jump in new home sales, the role of data-driven decision-making has never been more critical. As technology evolves, these tools will continue to reshape the industry, paving the way for smarter decisions, seamless operations, and safer, more profitable investments.
5 reasons to consider before developing a mobile application for your company
These days the scenario in which a business operates has become highly competitive which is why the industry leaders are continuously vying for ways to thrive in their sphere. Eyeing to make steady gains over time, they work diligently harnessing the power of technology along with prominent visuals. Research shows that the enterprise mobile app sector is expected to grow over $60 billion by 2020 and it also have a 3x higher readership level than the company intranet.We all have heard about the impact of having mobile apps and how that is beneficial for businesses in making digital innovation. However, not everything is either said specifically as to why modern businesses should invest in building mobile apps. So here are the six best reasons to help you validate why mobile apps are imperative for the success of your business-High user engagementOne of the main challenges business faces nowadays are to engage their customers. Due to shrinking attention level, it is nearly difficult to grab the attention of the target customers. Thus business needs to find innovative ways to engage audience introducing user-friendly application development and target the right market to generate high ROI along with customer loyalty. Increase the chances of social sharingThe influences of social media mostly on Instagram, Facebook and Twitter have increased the chance of having access to these sites through mobile phones. Though mobile apps it’s easier to share or recommend products or services in social circles. Also, your brand would appear in the news feed of others when someone ‘likes’ your product. Research showed that about 66% customer’s purchase behavior is greatly influenced by social media so once you compel them to buy your products, direct them on your mobile app for a smooth payment gateway. Mobile apps provide a quick solutionMost big players in the tech/IT industry have realized the benefits of having a mobile app for their business so they focus on providing a seamless experience to its users. Mobile apps allow the users to have all sorts of information on products, prices, news feeds just at the touch on mobile screens. A smooth interface of an application saves a lot of time of the user and if the speed is synchronized then you will surely win all the glory and retain visitors in your website. It is known to all of us that mobile app’s page load faster than that of a website opened from a browser on your mobile device.Experience hassle-free purchase processDue to the rise of mobile wallets and UPI payment apps, mobile payments are surging worldwide at a fast pace. Therefore to ease the process of purchase if you integrate these products into your mobile app it would certainly lead to a much higher conversion rate. Get customer feedbackHaving a mobile app to record customer’s feedback will benefit you to gain deep insight into customers’ behavioral pattern which will help you to plan your campaigns effectively. This eliminates the need for conducting the manual surveys or getting reviews for your brand.There are manifold ways businesses can leverage mobile apps to their advantage. You need to be present where your customers are for generating revenue and to mark your footprint in the evolving digital world.
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