CommentSold Named "Live Video Commerce Platform Company of the Year" by Retail Tech Insights

A forecast can be numerically respectable and still leave stores with the wrong stock by Friday. In supply chain and omnichannel commerce, the buying problem is no longer only forecast accuracy. Product-location mix, channel behavior, promotional effects, vendor lead times and fulfillment shifts all move between planning cycles. Executives evaluating AI-powered demand forecasting should ask whether the system detects movement early enough to change replenishment and allocation before inventory becomes the problem. Planning cadence is a hidden constraint. Weekly or monthly forecast reviews may suit governance, but they do not match how demand shifts across stores, distribution centers, marketplaces and digital channels. A useful platform needs update frequencies that reflect the business rather than a static reporting calendar. Machine learning can help, but it should sit beside forecasting discipline. Demand cleansing, seasonal analysis, hierarchy logic and exception handling still decide whether the signal is clean enough for planners to trust. Hybrid intelligence is now the more useful standard. A stable item and a volatile item should not be forced through the same method. Statistical models can capture established patterns, while machine learning can find nonlinear relationships that surface across larger networks. External signals such as weather trends and social sentiment can sharpen a forecast when transaction history is no longer enough. The risk is input clutter. The system should identify which signals matter, not ask planners to defend every data point. “Manhattan Associates’ ActivePlanning, an AI-powered supply chain planning solution, combines hybrid AI demand forecasting with replenishment, allocation and Sightline’s forecast explanations.” Inventory connection determines whether forecasting becomes useful. A forecast that ends in another handoff leaves planners translating insight into orders, allocation moves, safety stock changes and service-level tradeoffs across separate tools. That delay is costly when service goals and inventory investment already pull in different directions. Replenishment should recalculate when conditions change, while allocation should reflect product lifecycle, margin opportunity, available stock and channel demand without breaking the link between prediction and placement. Trust also depends on explanation. AI recommendations can unsettle planning teams when the reason for a forecast change is buried behind a model score. Planners need to see whether vendor minimums, lead times, promotional effects and network movements drove the recommendation. The explanation should work at the level of a single order line and still expand to a region, distribution center, assortment or network view. Otherwise, users return to spreadsheets just to understand the tool they purchased. Planner productivity depends on how the system presents exceptions. A good tool should not turn every forecast movement into the same level of alert. It should let users move from an itemlocation view to a network view and compare internal and external demand positions without leaving suggested orders in a separate screen. That keeps judgment focused where it changes inventory decisions. Manhattan Associates (NASDAQ: MANH) fits buyers that need demand forecasting to shape inventory decisions without becoming an isolated analytics layer. Its ActivePlanning, an AIpowered supply chain planning solution, combines hybrid AI demand forecasting with replenishment, allocation and Sightline’s forecast explanations. Through Manhattan ActivePlatform, planning decisions can connect to warehouse, transportation and omnichannel execution systems. The product supports demand cleansing, seasonal analysis, self-tuning forecasts, productlocation hierarchies, external signals and configurable Lenses, while replenishment and allocation translate forecast movement into stock decisions. For retailers and wholesalers that need forecasts to adapt, explain themselves and alter inventory action quickly, Manhattan Associates merits close consideration. ...Read more

Retail analytics has become a valuable tool for retailers to understand their customers better, streamline operations, and drive business success. However, integrating and running retail analytics poses its own set of issues. This article will look at the top three frequent retail analytics difficulties that retail store owners may have while using retail analytics and provide viable solutions for each. Collecting Customer Data When collecting consumer data in the retail industry, targeting the proper data (rather than just information) and using effective collection methods is critical. Data quality is essential because erroneous data impedes rather than improves analysis. In the retail business, valuable data includes sales volumes, consumer footfall measures, profit margins, stock inventories, and the success of advertising initiatives. However, the many data sources add complexity and might pose obstacles to the collection and aggregation process, forcing merchants to experiment with different collection methods. For example, monitoring warehouse stock may necessitate the installation of IoT-connected sensors to automate the process and smoothly link it with other departments, such as purchasing. Furthermore, significant data compilation requires specialist software, which may entail further investment in training efforts to ensure efficient deployment. Retailers face hurdles in collecting customer data, such as data silos, inconsistent data formats, and integration issues. Merchants can build a solid data collection infrastructure to address these issues and use advanced analytics systems. Investing in solutions that automate data integration, cleaning, and validation processes guarantees that customer data is accurate and consistent. As a result, retail analytics can provide more precise insights. Supporting Sales and Marketing Demands & Forecasting To meet sales and marketing objectives and predictions, retailers must prevent supply and demand mismatches, which can lead to consumer waste and expired goods. This can be handled by rapidly sharing relevant data with suppliers to inform fulfillment and supply chain decisions. Modern analytics methods, including machine learning algorithms, can address this issue. These solutions allow merchants to predict demand correctly, optimize inventory management, and expedite supply chain operations.   Demand forecasting is an essential approach for reducing wasteful expenditures, and merchants require data from a variety of sources in order to estimate accurately. Analytics data proves to be an excellent source of knowledge in this regard. Retailers may better match product demands to specific locations by leveraging analytics data, understanding which stores may expect more consumers and when, identifying popular products and offers, and anticipating peak times and high seasons that involve additional labor. Keeping Up with Other Retail Competitors In the highly competitive retail market, collecting inadequate data can put companies in danger of slipping behind their competitors. As the retail scene becomes more competitive, companies wish to avoid this dilemma.   Merchants must invest in big data technology and skills to remain competitive in both physical and online retail. Continuous innovation and development are critical for remaining ahead of the competition in the agile retail business. They must stay current on industry trends, compare themselves to competitors, and invest in analytics technologies that offer unique insights. By doing so, retailers may distinguish themselves and maintain a robust competitive position in the market. ...Read more

Live video commerce platforms are transforming the retail technology landscape by bringing together real-time engagement and digital purchasing capabilities. Through interactive shopping experiences, streamlined commerce solutions, and intelligent technology integration, these platforms help retailers strengthen customer relationships and improve business performance. As consumer expectations continue evolving, live commerce will remain an important tool for delivering engaging, efficient, and customer-focused retail experiences. Enhancing Customer Engagement through Interactive Live Shopping Experiences In real-time shopping hours, the hosts can showcase the product, explain features, showcase benefits, and answer customer questions on the spot. This is an interactive format that allows consumers to learn more about a product than they learn from a picture or written description. They can view how products work and make comparisons, as well as get instant answers on specifications, usage or price. There are also interactive elements like live chat, polls, reactions, and answers to questions that further encourage engagement. These tools allow viewers to actively interact with hosts and other viewers and build a sense of community that is not often found in online shopping experiences. The experiences are known to generate deeper emotional engagement with audiences and stand out in crowded markets. Another key influencer of engagement in live commerce is influencer participation. Influencers often have audiences that are very active and listen to their advice and suggestions. Working with influencers can help retailers reach larger audiences, draw in a specific customer base, and boost engagement at live events. Driving Retail Sales with Real-Time Commerce Solutions The live video commerce platforms are turning out to be very effective, not just at engagement but also at boosting sales performance. When product discovery, education, interaction, and purchasing are all integrated into one experience, it simplifies the customer journey and helps drive quicker buying decisions. Real-time product demos are vitally important in conversions. There is often a lack of confidence in the quality, functionality or suitability of a product in online purchasing. Live demonstrations alleviate these concerns by demonstrating products and explaining features and benefits in detail. This transparency minimizes buying obstacles and boosts purchaser confidence. Exclusive offers, limited-time pricing, bundles and exclusive promotions during live events are some of the strategies used in many live commerce events. These tactics will pressure and encourage customers to make immediate purchases. The catchy content, coupled with the timely offers, can lead to better conversion rates than typical digital marketing strategies. The fewer pages you have to navigate, the less friction and the smoother the purchasing experience. Also, live commerce can be used by retailers to introduce new products and gather instant feedback from customers. Businesses can measure the reactions of the audience, respond to queries on the spot, and determine the interests of the product in real-time. This instant feedback allows organizations to fine-tune their marketing better and enhance their next product releases. One of the biggest advantages of live commerce solutions is the abundance of customer data they have to offer. Retailers can gain insights into audience behavior, engagement, product interest, purchases, and conversion to better understand what people want. The insights obtained by businesses allow for better product decisions, improved merchandising choices, and more effective marketing campaigns that resonate with customers and contribute to their future growth. Advancing Retail Innovation through Intelligent Commerce Technologies Retailers can use advanced analytics technologies to assess the performance of live commerce strategies more accurately. Audience engagement, retention rates, product click-through, sales conversions, and customer acquisition costs are all performance metrics that offer insights into the effectiveness of the campaign. These analytics help companies optimize content strategies on an ongoing basis and maximize ROI. Mobile commerce is still a key part of the live commerce equation. Smartphones have become the most common device used by most consumers for shopping purposes, so a mobile-friendly experience is imperative. Live commerce platforms are now equipped with enhanced streaming capabilities, user-friendly interfaces, robust payment security features, and mobile-friendly designs for a smooth shopping experience across devices. Live commerce platforms are also getting augmented reality technologies that have been introduced. The experiences help to alleviate uncertainty and increase the confidence of buyers, especially for items like furniture, fashion and home decor. As companies strive to adopt a single digital commerce approach, integration with wider retail tech ecosystems is growing in significance. These live streaming platforms can seamlessly integrate with inventory management systems, CRM platforms, marketing automation tools, social media channels, and e-commerce sites. This connectivity optimizes the efficiency of the process and boosts the customer experience when using a variety of touchpoints. Another benefit of intelligent commerce technologies is that they are scalable across the globe. Live video commerce allows retailers to connect with audiences in various regions without the need for physical retail locations. Digital broadcasts can help businesses reach a wider audience, attract international customers, and facilitate global business expansion plans. ...Read more

Businesses can use an AI-driven demand forecasting solution to predict demand based on historical sales data, customer behavior, market trends, inventory movements, seasonal trends, and other relevant business data. Historical trends and manually developed assumptions are typically used in traditional forecasting. The AI-driven systems can process more comprehensive and diverse data sets, uncover patterns that might not be noticeable on their own, and update predictions as new data arrives. Industries utilizing the technology include retail, manufacturing, consumer goods, healthcare, logistics, food and beverage, automotive, and ecommerce. Unleashing Tomorrow's Insights with AI Demand Forecasting Increasingly, businesses have more than one supplier, sales channel, distribution center and product category. Forecasting technology can be used to help coordinate these interdependent activities. Too much inventory could bind up capital, make storage or markdown costs more expensive, and too little inventory can result in a shortage and lost sales. Improved visibility across demand will help businesses find a better balance. Consumer behavior online is ever evolving, and it can create headaches for businesses that only use traditional forecasting methods. For businesses with many products, it can be challenging to make manual predictions for each product. AI can be used to study demand trends on a large product portfolio. Demand fluctuates based on holidays, weather, events, promotions and other periodicities. Multiple variables can be used in an AI system to create forecasts. The interest in forecasting technology has been growing with supply chain disruptions. Companies need tools to adapt plans when demand suddenly shifts or there is a lack of products due to supply conditions. Retailers and consumer brands can use forecasting systems to predict the impact of promotions, pricing and marketing initiatives. Forecasts can be utilized for production planning and procurement by manufacturing companies. With better estimates, companies can match their raw-material purchases and production with customers' needs. Forecasts can be useful for businesses that have demand-sensitive staffing needs to help predict when there will be increased or reduced demand for their operations. AI Innovations Revolutionizing the Future of Forecasting Models can be developed to identify relationships between demand and seasonality, pricing, promotions, location, product characteristics, and customer behavior, among other things. AI systems do not just make predictions based on fixed forecasting periods, but can also adjust their forecasts based on new information such as sales, inventory, or market data. Today, businesses collect data via sales channels, ecommerce platforms, customer databases, supply chains, and smart devices, adding to inputs for predictive models. Companies can simulate various price, promotional, supply and market demand changes and assess potential impacts on inventories and operations. If there are unusual shifts in purchasing patterns, it could be that a new trend, data problem, supply issue, or other event is going on that needs to be investigated further. Forecasting platforms can integrate with enterprise resource planning, inventory management, supply chain, ecommerce and procurement systems. This enables forecasts to be used to inform downstream decisions and not be standalone products of analysis. Business users must know the reason a forecasting system has adjusted its forecast, especially if the forecast affects their purchase, production or financial decisions. AI predictions are not definite; they are tools for decision-making. When markets have unusual events or when key information is lacking in available data, an experienced planner can add context. Forecasting capabilities can be increasingly deployed without having to construct large internal infrastructures or have complex forecasting models. AI's Dynamic Future in Executive Coaching Real-time data, autonomous forecasting, machine learning, scenario modeling, supply chain integration and more personalized business forecasts are making a difference in the future of AI-powered demand forecasting. AI models will be able to handle various types of information concurrently. The ability to analyze sales and inventory data, customer activity, market signals, operational data, and conditions together is increasing. Predictive platforms could be linked more closely than ever with sales and inventory decisions, production planning, and restocking. With the rise of businesses trying to get ready for uncertainty instead of a single anticipated result, scenario analysis will become more complex. Businesses can run a few different demand scenarios and make contingency plans based on those scenarios. There are variations in demand and operational constraints in retail, manufacturing, healthcare, logistics, and other areas. While forecast accuracy will still be a key consideration, businesses will look more broadly at the value of forecasting systems for their operations. A helpful platform should enable organizations to adapt and respond to evolving scenarios, not just make a forecast. Effective forecasting strategies rely on the proper management of information, its proper structure and its proper monitoring, where information is reliable and appropriately structured. Data governance, data integration and data monitoring are important components of these strategies. AI-based demand forecasting systems are thus transitioning from being analytical tools to more comprehensive decision-supporting systems. AI-driven forecasts, coupled with robust data management and human expertise, can create more adaptive strategies for inventory, manufacturing, procurement, and supply chain planning within an organization. ...Read more