September 16, 2024

You don’t need us to tell you that the data world – and everything it touches, which is, like, everything – is changing rapidly. These trends are driving the opportunities that will fuel your career adventure over these next few years. At the heart of these trends is a massive wave of data being generated and collected by organizations worldwide. With this data we can shift our focus as analysts from explaining the past to predicting the future. And in order to do this, we need to spend less time doing the same things over and over and more time doing brand new things. And accomplishing all these changes will require us to work together differently than we do now 1. Bigger, Larger and Faster Data You’ve probably already heard the fact that every two years we, as humans, are doubling the amount of data in the world. This literally exponential growth of data is impacting analysis in some big ways: 2. Predictive Analytics The vast majority of time spent by the vast majority of today’s analysts is on understanding data collected in the past, often in the form of reports and dashboards. Those days are coming to an end. The data and tools now available are allowing analysts to go beyond just convincing someone to do something and instead to often just do it themselves. For example: 3. Automation Of Tasks Once upon a time, analysts built a model in Excel, and once a month or so, they exported the model to PowerPoint and send it to (or even printed it out for) the managers who relied on regular reports. Soon, there were too many reports, so maybe they used macros in Excel to automate the creation of reports. Or maybe they were lucky enough to have a dashboard program that had some automation functionalities built in. The future promises even more than this:

As the internet has grown so has folks’ tendency to chat instead of talk. Whether it’s booking an appointment at your local paediatrician or asking your boyfriend if he wants to go out for some pasta, people like texting. This is what chatbots want to capitalize on – People’s aversion to talking on the phone. Companies all over the world have embraced chatbots with open arms. They bring along with them a sense of humanity that no IVR can match. One of the reasons is the complete lack of any voice based interaction. While those beautifully sounding ladies on the IVR systems might be speaking correct English (or any other language(s)), they speak it with such robotic emotions that the only thing the person on the other end of the phone wants to do , is strangle themselves (or the IVR lady). A lack of direct human interaction allow chatbots to be much more than a computer program. Employing the help of mankind’s greatest asset – Imagination, chatbots can (almost) substitute for a real live human person on the other end. How to build an Intelligent Chatbot using AI Computer Scientists from all over the world are doing great work in making sure that chatbots can really feel like talking to another live human. Conversational Artificial Intelligence (the field of study that chatbots fall under) has seen tremendous progress in the last few years. Tech giants like Facebook, Google and Apple are constantly coming up with both highly valuable research papers as well as publishing user apps that at the same time is both showing off the work they have done as well as training their chatbots to become even better. However, this huge praise that Chatbots have come to receive has also muddled up the market. There is a huge different between a generic chatbot that can only choose between a set of responses and intelligent chatbot which try to make sense of the user’s chats and respond accordingly. If you are a business, you need the latter but chances are the agency you hired to develop you a chatbot is trying to fool you by giving a very cleverly disguised version of a generic chatbot. Here are 8 things that your intelligent chatbot MUST have : 1. Chatbot Carry an Intelligent Conversation A conversation is much more than saying yes or no. Moreover, the longer a conversation the more complex it gets. To carry an intelligent dialogue, the bot must be able to maintain the context of the conversation at all times. It also has to understand that natural conversations don’t always progress linearly – the bot must be able to process an unexpected reply and adapt to changes in the course of the conversation. 2. Build Contextual Engagement A smart chatbot has to understand who it is chatting with. In order to provide a truly personal experience, the chatbot has to know about the user’s interests, attributes and personal information – then tailor the conversation to fit them. The bot needs to provide content, advice, and offers that exactly fit the user. If all the information is generic, it will be shallow, unengaging, and in many cases, not very useful. 3. Leveraging Real-Time Transaction Data.  Connected with the need for contextual engagement, an intelligent chatbot must be able to access real-time insights on transactions. Without real-time data access and analytics, the power of artificial intelligence (AI) and contextual advice (either human-based or with chatbots) is limited. 4. Chatbot Can Reuse Existing Content To have a meaningful impact, it is crucial for the chatbot to be able to access content created and maintained in digital repositories across all channels. From digital ‘brochureware’ to FAQs, rules and regulations, and rate information, bots must be able to access and leverage this insight in real-time. 5. Build Deep Knowledge To build engagement, a chatbot needs to be able to provide advice, not just balances. Personetics believes bots need to be purpose-built – with deep knowledge on issues important to the customer. With PayPal supporting payments through Facebook Messenger, the bar transactions through the bot channel has been set and is being raised. 6. Work Seamlessly Across Channels  Customers expect a consistent experience across the digital landscape – online, mobile app, Facebook Messenger, Amazon’s Alexa, etc. A bot can not be a silo, but should be able to traverse across and between multiple channels. This may be a challenge for organizations who still can not achieve this within internal channels (mobile, branch, online, call center). 7. Get Smarter Over Time  An intelligent chatbot must get to know customers better through over time as more conversations and transactions take place. It must improve based on how a customer reacts to information and advice provided by the chatbot over time. 8. Anticipate Customer Needs Almost half of all chatbots are only used once. This happens when a bot experience does not meet exceed expectations. To get customers in the habit of conversing with your chatbot, it needs to proactively reach out to customers with information, insight, and advice – presented at the right time and place based on predictive analysis of individual customer needs.

Talk of blockchain technology is everywhere, it seems — but what is it, and what does it do? 1. Don’t call it “the” blockchain The first thing to know about the blockchain is, there isn’t one: there are many. Blockchains are distributed, tamper-proof public ledgers of transactions. The most well-known is the record of bitcoin transactions, but in addition to tracking cryptocurrencies, blockchains are being used to record loans, stock transfers, contracts, healthcare data and even votes. 2. Security, transparency: the network’s run by us There’s no central authority in a blockchain system: Participating computers exchange transactions for inclusion in the ledger they share over a peer-to-peer network. Each node in the chain keeps a copy of the ledger, and can trust others’ copies of it because of the way they are signed. Periodically, they wrap up the latest transactions in a new block of data to be added to the chain. Alongside the transaction data, each block contains a computational “hash” of itself and of the previous block in the chain. Hashes, or digests, are short digital representations of larger chunks of data. Modifying or faking a transaction in an earlier block would change its hash, requiring that the hashes embedded in it and all subsequent blocks be recalculated to hide the change. That would be extremely difficult to do before all the honest actors added new, legitimate transactions — which reference the previous hashes — to the end of the chain. 3. Big business is taking an interest in blockchain technology Blockchain technology was originally something talked about by anti-establishment figures seeking independence from central control, but it’s fast becoming part of the establishment: Companies such as IBM and Microsoft are selling it, and major banks and stock exchanges are buying. 4. No third party in between Because the computers making up a blockchain system contribute to the content of the ledger and guarantee its integrity, there is no need for a middleman or trusted third-party agency to maintain the database. That’s one of the things attracting banks and trading exchanges to the technology — but it’s also proving a stumbling block for bitcoin as traffic scales. The total computing power devoted to processing bitcoin is said to exceed that of the world’s fastest 500 supercomputers combined, but last month, the volume of bitcoin transactions was so great that the network was taking up to 30 minutes to confirm that some of them had been included in the ledger. On the other hand, it typically only takes a few seconds to confirm credit card transactions, which do rely on a central authority between payer and payee. 5. Programmable money One of the more interesting uses for blockchains is for storing a record not of what happened in the past, but of what should happen in the future. Organizations including the Ethereum Foundation are using blockchain technology to store and process “smart contracts,” executed by the network of computers participating in the blockchain on a pay-as-you-go basis. They can respond to transactions by gathering, storing or transmitting information or transferring whatever digital currency the blockchain deals in. The immutability of the contracts is guaranteed by the blockchain in which they are stored.

Supply chain planning is a vast field involving many departments and stakeholders using different technologies resulting in data silos. Traditional supply chain models store information in Excel spreadsheets. They maintain multiple closed ERP systems for various parts of supply planning in these sheets. Various stakeholders involved in supply planning have been using these siloed systems with little  collaboration and visibility to others leading to problems like Moreover, in modern supply chains, globalization increases data integration difficulties. New business models with outsourced manufacturing, acquisitions, and partnerships or joint developments with partners have blurred the divisional boundaries.Data plays an integral part in such a system, and silos put the all-over business at a greater risk. For instance, it’s harder to share demand forecasts and demand changes with the suppliers. On the other hand, to minimize silos, having periodic on-site meetings among the teams and working on changes for hours is not cost-effective. It’s inefficient, ineffective, and costly for any last-minute changes. Moreover, the COVID-19 pandemic effect is continuing to sputter and break down the global supply chain. There are still many disruptions like out-of-place shipping materials and data mismanagement. IDC polled 532 companies across the pharmaceutical supply chain to assess the impact of the COVID pandemic’s supply disruptions. This article will highlight the core reasons behind the information silos and their effect on Supply chain planning. When does a Silo happen? Let’s say a global company makes an important decision like entering a new market or expanding into one, and the silo happens while making decisions by individual verticals such as :  Silos happen when different verticals don’t share relevant data horizontally. The number of verticals and divisions within a company increases along with the Company’s growth. Going by an old saying, “Too many cooks spoil the broth,” the system becomes inconsistent due to poor data sharing and management systems between teams that lead to silos. Why does Information Silo Occur? There are three significant reasons that can result in information silos :  Effect of Information Silos on Supply Chain:  Modern supply chains involve multiple departments and stakeholders. The demand for quick decision-making is based on a vast amount of data. Often, a lack of communication between the departments can impact each other. United States’ food wastage is a consequence of such data silos. According to the NRDF, of the total food wastage, approximately 40% of it is contributed by the US alone, which is somewhere around $218 billion per year!  Wrapping Up: Data silos are a dilemma companies face across industries, but they can negatively impact the logistics and transportation industries. A data-driven approach would be an optimal solution to prevent Information silos. Once an organization acknowledges the lack of data governance, the next step is to recognize the necessary process, tools, and models to create an effective data governance method. This is the most critical step in implementing a data-driven procedure, and the rest will follow gradually. Even to implement an AI/ML solution to eliminate the Silo problem, having a data governance maturity model is a must. Removing supply chain bottlenecks and silos ensures that your customers’ needs are met, and it helps to make the right decisions. Overall, eliminating data silos can increase efficiency and boost your Company’s bottom line.

A premium automobile manufacturer in the UK spent over $3 billion on product recalls due to malfunctioning braking systems, rearview cameras, airbags, and other minor issues. Another European luxury automobile maker’s previous three annual reports indicated it spent about nearly $7 billion recalling products for problems ranging from door locks to batteries, fuse boxes, fuel tanks, and wheel speed sensors. When automakers are focusing on developing self-driving and semi-autonomous vehicles, errors like these can hardly be overlooked. The entire network follows stringent quality control processes. Despite this, as the cases above demonstrate, lapses continue to occur. Anomaly detection can play a critical role across the industrial landscape in this context. Understanding Anomaly Detection Anomaly detection, described by Forbes as ‘one of the most underrated BI tools of 2020’, is an area of artificial intelligence that analyzes an organization’s data for deviations from normal behavior. Some inconsistent data points–known as outliers– may emerge, and detecting them will be critical to proactively averting scenarios like those described above. In some applications, the anomalies themselves are of interest, and the observations stemming from them could be the most significant in the entire dataset. Anomaly detection has applications in cyber-security, fraud detection, fault detection, system health monitoring, detection of ecosystem disturbances with computer vision, medical diagnosis, law enforcement, manufacturing industries, and more. Eliminating anomalous data can increase the accuracy of statistics such as the mean and standard deviation, improve data visualization, and enhance machine learning algorithms. Typically, various tools and techniques are used for anomaly detection. Anomaly Detection In Industry 4.0 Big data, machine learning, AI, and IoT devices are paving the foundation for a data-driven culture. IoT allows machines to exchange real-time data over the Internet. As part of the Internet of Things revolution, digital sensors and networking technologies are added to the analog devices we use every day, ushering in an era that we call Industry 4.0. According to some estimates, by 2025, there will be 64 billion IoT devices connected to the internet. This means that businesses will have to deal with a huge deluge of data. According to the Boston Consulting Group, IIoT is one of the nine principal technologies that make up Industry 4.0. By combining these technologies, a “smart factory” will be created where machines, systems, and humans can work in harmony, coordinating and monitoring progress along the assembly line. An important goal of Industry 4.0 will be predictive maintenance, which will be driven heavily by anomaly detection. As in traditional business applications and IT infrastructure, IoT devices can be monitored for errors. In industrial and manufacturing settings where IoT devices are used to facilitate modernization and automation, anomalies might indicate the need for maintenance on a machine. Identifying a potential problem early can help reduce unplanned downtime. A McKinsey insight report found that advanced analytics can predict machine failure before it occurs, reducing downtime by 30% to 50%. Additionally, it increases equipment life by approximately 40%, improving productivity in all areas. An inadequate maintenance program can reduce an equipment’s productivity capacity by up to 20% while unplanned or last-minute machine downtime costs industrial manufacturing organizations $50 billion a year. By leveraging IIoT data, manufacturers can gain meaningful insights into their businesses and schedule pre-emptive equipment structural health checks. Benefits Of Anomaly Detection In IIoT: Cost and Time Savings: By detecting anomalies early on, you can ward off potential losses and liabilities. Often, noise and outliers can produce false positives, and get in the way of early anomaly identification. By using configurable time frames and historical pattern analysis, you can enhance detection latency and accuracy. Deeper Insights: Detecting anomalies is only one step of a complex process that includes issue triage, root cause analysis, troubleshooting, and feedback-based system tuning. By engineering anomaly detection models from the ground up to provide advanced insights, you can investigate scores of issues like anomaly timeframes, severity scores, and correlated metrics. Advanced dashboards help visualize these insights. Proactive Intervention: After the early detection of anomalies, you can generate insightful summaries to help with the investigation and fine-tune alert thresholds and severity levels according to operational feedback. How Our IIoT Software Solution Helps With Anomaly Detection and Much More.. Valiance provides a proprietary IoT-based platform for remote monitoring and diagnosis of a power plant’s assets. Various sensors and scalable cloud software help operational teams look at asset health in real-time and give business stakeholders insight into plant efficiency and output. For instance, one of our clients, who was struggling with manual intervention, wanted to enable centralized data collection through sensors and reduce the manual work involved.  They also wanted to monitor their assets like turbines, and sensors such as thermometers and energy meters through certain dashboards. Valiance delivered an IIoT-enabled platform to the client, enabling staff to view real-time data and monitor asset performance. Certain KPIs and threshold levels were defined at the backend, based on which the anomaly could be detected (notified to the client) and necessary steps taken in a timely manner. In another instance, by enabling centralized data collection on near real-time basis, our platform allowed the customer to perform plant-wise and region-wise analysis on power generation targets, monitor the actual output, identify gaps, and coordinate timely intervention.  Earlier this had not been possible due to delays in data collection, data consolidation, and sharing. However, after installing the platform, the time lag reduced from two to three weeks to less than a day, resulting in annual manpower savings of nearly four months. Leverage Anomaly Detection To Improve Your Industrial Asset’s Performance Anomaly detection is poised to become one of the most exciting developments in the business world, adding even more ‘intelligence’ to the AI revolution. But how can you get started? Schedule a call today to find out.

Every year, supply chain disruptions cost businesses an average of $200 million. The present world situation, be it the COVID-19 pandemic or the Suez Canal choking in 2021 to the ongoing political war, has exponentially increased the risks of these disruptions. Market volatility, supplier inconsistency due to political and geographical barriers around the world, COVID and the war-hit workforce, and working in a new standard setup have hindered the regular flow of supply chains. This situation demands the supply chain to be more agile and well connected as fast as possible. The rising demand squeezes the scope of any errors and needs more accurate demand forecasting to lower the loss rate. In recent years, most leading companies have started implementing AI/ML to leverage speedy decision making, accurate demand forecasting, better inventory management, speed in operations, dynamic logistic system, and delivery control. In a survey, Gartner said that the usability of AI and ML would double in the next five years in the supply chain. Again, as per another study, Global spending on IIoT Platforms is expected to rise from $1.67 billion in 2018 to $12.44 billion in 2024, representing a 40% compound annual growth rate (CAGR) over the next seven years. Considering the vastness and increasing complexity of the supply chain network, it’s the need of the hour to implement an automated system to manage the entire network better. This article will briefly discuss the change in the supply chain landscape after the pandemic, the implementation of AI/ML in the supply chain and their benefits and challenges, and the last use case of AI implemented in the Supply Chain. Aftermath Of Supply Chain Landscape Post COVID Pandemic COVID-19 pandemic has exacerbated the pre-existing challenges of the logistic and supply-chain industry and added a few more to that list. It changed the fundamental consumer behavior and demanded the adoption of agile ways of working.  In a survey by EY USA conducted in 2020, 97% of industrial companies revealed that the pandemic had a severe negative effect. Few sectors of the industries, e.g., the life sciences sector, did report a positive impact on their businesses during the pandemic. For instance, 11% said that their customer demand increased by 71%, and the rate of launching new products to the market increased by 57%. But the challenge is that these companies had to increase their essential product creation, e.g., COVID 19 vaccine, twice as much as before. Source: EY USA So overall, the pandemic demanded more resource power, intense inventory management, and, more importantly, accurate predictive analysis of the market. Next Step for Supply Chains:  From the EY conducted survey, 60% of the executives conveyed that the pandemic catalyzed their strategic needs for the supply chain. The future supply chain demands agile, flexible, efficient, resilient, and digitally networked. The pandemic has pushed many sectors to the digital platform and workforce to work remotely. On the other hand, it made onsite resources mandatory to adopt the COVID-19 norms and work in the new normal. The survey showed an increase in automation and AI and machine learning investments, with 37% already deploying these technologies and 36% planning to do so soon. Moreover, digital and autonomous technologies will assist in making people’s jobs more accessible and the supply chain more efficient and optimized. How Can AI / ML Help ? Implementing AI/ML in the supply chain has numerous benefits. Some of the significant benefits are: Predictive Analysis: Demand Forecasting uses the power of automation to analyze all the data that the organization can collect, from demographics to price changes to consumer sentiment, and make sense of it against the sales history. Companies can use machine learning models to enjoy the perks of Predictive Analysis for demand forecasting. These patterns analyze the historical data to identify the patterns. So, in the supply chain, the models can be used to find the issues before any disruption is caused. A solid supply chain forecasting system means that the company has the resources and intelligence to respond to emerging issues and threats. Furthermore, the effectiveness of the response grows in direct proportion to how quickly the business can respond to problems. Optimized Inventory Management: An appropriate AI/ML model helps any company manage the over and understocking problem, thus improving inventory management. It can analyze the customer and market demand from the survey data and enable continuous improvement in a company’s efforts to meet the desired level of customer service at the lowest cost. You can also use AI and ML to analyze large data sets much faster and avoid human errors in a typical scenario. Avoid Forecast Errors: The AI/ML algorithm helps organizations deal with large data sets. The data processing is done with tremendous variety and variability. IoT devices, Intelligent Transportation Systems, and other powerful technologies enable the supply chain to gather massive data. The subsequent model helps companies have better insights and achieve more accurate forecasting, preventing enormous disruption or loss. According to a survey by McKinsey, AI and ML-based supply chains can reduce forecasting error by 50%. Improve Supply Chain Responsiveness: To minimize the cost of improved customer experience, most B2C companies are implementing AI/ML models. The AI/ML model induces automatic responses; AI chatbots help serve the customers promptly and thus handle demand-to-supply imbalances. The data analysis power of the ML model from the historical data helps the supply chain managers to understand the customer demands and also helps in better planning of vehicle routes and goods delivery. Thus it reduces the driving time and cost and enhances productivity. Challenges To Implementing AI/ML In Supply Chain: COVID-19 pandemic has exacerbated the pre-existing challenges of the logistic and supply-chain industry and added a few more to that list. It changed the fundamental consumer behavior and demanded the adoption of agile ways of working.  In a survey by EY USA conducted in 2020, 97% of industrial companies revealed that the pandemic had a severe negative effect. Few sectors of the industries, e.g., the life sciences sector, did report a positive impact on their

Data is the lifeblood of any successful business. It is fundamental to the way we run our personal and professional lives. Virtually every encounter generates data, be it software applications, social media links, mobile communications, or the growing numbers of digital services. Each of these encounters then generate even more data. It is estimated that the world’s data will grow to 175 zettabytes by 2025. Nearly 2.5 quintillion bytes of data was generated on a daily basis in 2020. With data being at the forefront, it is essential to remember that eventually data is as data does. Meaning, the true power of data will be realized only by what it achieves. Only by leveraging the scope, magnitude and exponential rise of data can we generate insights and tell apart the leaders from the laggards. As it stands, most companies recognize the potential of data but they still struggle with mobilizing it for meaningful impact. It is in this context that automated data pipelines become such an integral element of the conversation. Towards An Automated Data Pipeline? In the simplest terms, a data pipeline is the process by which raw data is moved from a source to a destination after simple or complex transformations are performed on it. When it comes to cutting-edge technologies, fully automated data pipelines may not seem like a priority. However, if you want to unlock the full potential of your data universe by extracting business intelligence and real-time insights, you need better control and visibility into your data source and destination. Developing a true data-driven ecosystem comes when you can extract data from its source, transform it, integrate it and analyze it for business applications. There’s eventually more to it than making the data people happy– disparate data can hold the entire company back. This is corroborated by the fact that 55% of B2B companies say their biggest challenge lies in leveraging data from disparate sources in a timely manner. Further, up to 80% of analytics projects still require manual data preparation and ingestion. According to 76% of data scientists, preparing their raw data for analysis is the least enjoyable part of their job. However, while the challenges of manually extracting data from various applications, transforming formats with custom code, and loading them into siloed systems are real, they can hardly be set aside. As businesses move from managing data to operationalizing AI, Gartner estimates a 500% increase in streaming data & analytics infrastructure. Furthermore, the current wave of supply chain disruption is forcing several companies to automate their data pipelines for better insights and visibility. Why Invest in Data Pipeline Automation? There can be different types of data pipelines like batch, real-time, cloud-native and open source. More broadly speaking, they can also be manual or automated. Automated platforms facilitate the implementation of even the most intricate data management approaches. You no longer have to worry about internal deployments; instead, you have access to a seamless, end-to-end environment for data collection, cleansing, and processing. You can diversify your data sources without worrying about data silos. An automated pipeline would simplify the process of maintaining and monitoring custom scripts to automate big data processes, cut operating costs, and connect all the technologies in your stack seamlessly. It is also less error-prone and provides a unified and centralized view of the entire process. Some of the major benefits include: Improved efficiency: Automating a company’s big data pipeline allows you to redirect up to 20% of engineering staff to more value-added activities. It also enables you to accelerate the implementation of big data projects, replace manual scripting with automated workflow management and data integration, reduce development time, eliminate coding errors, and provide faster business processing. Consolidated view: Automation also provides a consolidated view into workflows and real-time data visualization. It maximizes the performance of service-level agreements and enables IT to identify and correct potential issues, monitor and quickly identify the root cause of errors and failures, streamline various processes and consolidate steps. Superior BI/ Analytics: A fully automated data pipeline design enables your organization to extract data at its source, transform it into a usable format, and combine it with data from other sources, thereby increasing data management, business intelligence, data processing, and real-time insights. Dark data profitability: Gartner defines black data as “information assets (that) organizations collect, process and store during regular business activities, but fail to use for other purposes.” 7 Utilizing business intelligence and customer insights empowers businesses to generate revenue from dark data by strategizing and optimizing internal processes. Increased data mobility: Data can be moved quickly across applications and systems in real-time with a fully automated data pipeline. Data pipelines deliver key performance indicators and other metrics for marketing, sales, production, operations, and administration. Sharper customer insights: Full automation of the data pipeline eliminates the need to code or format data manually, allowing transformations to all take place on-platform, enabling real-time analytics and granular insights. Integrating data from different sources produces better business results. Compatibility with cloud-based architecture: 90% of advanced analytics and innovation will be carried out in the cloud by 2022. Cloud-native technologies give businesses the flexibility to grow and adapt to changing conditions quickly. Data pipelines will become even more critical as new technologies emerge on the edge. The Future of Data Pipelines As businesses place greater demands on their pipelines, their construction and deployment will become easier. While designing a data pipeline architecture today requires assembling separate tools for data integration, transformation, quality and governance, the industry is rapidly moving to a scenario where everything can be bundled into an integrated pipeline platform for corrective action without the need for intervention. At Valiance, our best practices have helped scores of clients leverage the benefits of data pipelines and  realize tangible benefits. Get in touch with us today to find out how we can use our AI and Analytics expertise to design the ideal pipeline architecture for your business environment.

Demand forecasting is the practice of anticipating future customer demnd over a given period using historical data and other information. Or, to put it more simply, we can forecast future demand for a specific product by analyzing various market factors such as price changes, product designs, competition, advertising campaigns, consumer purchasing power, employment opportunities, population, and so on. Demand forecasting helps organizations make sound business judgments in a competitive market by providing essential information for capital investment and growth decisions. Forecasting is also helpful in accounting, production planning, process selection, capacity planning, facility layout planning, inventory management, etc. It also aids the S&OP process by making pricing and promotion plans easier to design. Demand planning has been routine for many company planners during the past decade, with many businesses incorporating S&OP methods, complex ERP systems, and new AI technologies into their operations. However, the COVID-19 outbreak generated massive demand shocks and intensified industry-wide instability. As the virus began to spread over the world, many businesses were impacted by previously unimaginable occurrences. Lockdowns compelled various manufacturing industries to cease operations, and it became critical to rethink the older demand planning models. Some items like groceries, tissue paper, and medical essentials like sanitizers were seeing massive demand due to panic buying, while other commodities like cars and automobiles faced a sudden slump. Crises are always unplanned. But wise organizations can plan for emergencies, however unexpected. Being at the forefront of AI and ML, our analysts at Valiance have put together several learnings that can help you not merely survive but thrive in a crisis. Learning #1 Greater visibility into channel data The rapid and anomalous shift in demand patterns during a crisis calls for greater visibility into channel data. By gaining insights into the stocks of all their channel partners, companies stand a better chance of restocking and enhancing fulfillment based on actual stock and sell-through information. Supported by powerful insights, they can comprehend client desires in near real-time and shift the needle from demand speculation to demand sensing. For instance, internet traffic is likely to pick up if in-store sales have degenerated due to lockdown. Providing attractive online offers would help convert this traffic into sales. This decision can only be made if the company has good channel visibility and can analyze data from different channels to determine where to focus its sales and promotional efforts. Channel visibility also empowers business leaders to integrate sales, inventory, and manufacturing to leverage previously underutilized channel intelligence. Organizations can make effective business decisions covering the entire gamut of the demand management process, including demand planning, collaborative forecasting and replenishment, revenue recognition, promotions and incentives, sales commissioning, and product life cycle management. Learning #2 Scenario-based thinking and control tower solutions While forecasts are based on the premise that the future will be similar to the present, scenario planning focuses on the future. It entails developing various narratives for distinct courses that will lead to diverse futures. Thus, scenarios present different, longer-term possibilities depending on unknown risks and uncertainties. It is also structured on a dynamic series of interacting events, causal processes, and critical decision points. In place of purely quantitative forecasting, it provides more versatility and preparedness to cope with risk and uncertainty. It involves understanding alternative futures, determining what may occur, and planning the exit strategy out of any crisis so that the possible solutions and outcomes have already been thought out when the crisis hits. This control tower-driven approach aims to consider intricacies and dependencies across a range of parameters. By comprehending the spectrum of potential outcomes, you can stress-test your portfolio of planned strategic movements against extremes and verify that your strategy is successful in various scenarios. Learning #3 Short-term approach with forecast modifiers Businesses must employ forecast modifiers for the short term to mitigate disruptions like COVID-19, taking time, size, channels, and product mix into account. By applying data from previous occurrences, we can improve our estimates for the future. Learning #4 Incorporating machine-learning algorithms When demand is consistent, seasonal, and expanding, a typical forecasting method incorporating sales data from preceding periods is effective. However, if demand increases or decreases abruptly, as, during a crisis, the models would require time to adjust to the new circumstances; hence, the projections will be too low or too high. Therefore, it is crucial to deactivate these conventional algorithms if demand fluctuates rapidly, particularly if they are linked to automated replenishment systems. Machine learning algorithms adapt rapidly and can select more accurate forecasting algorithms independently. Many AI systems of today create forecasts using hundreds of distinct algorithms. As the system detects a deterioration in the performance of a generally effective algorithm, it can instantly switch to more effective models and extract structural insights from the various SKU-store pairings. If this is not allowed, manual overrides must be used; in many cases, just adjusting the average sales of the previous days or weeks would suffice. When further data becomes available, and volatility subsides, model selection can be reconsidered. A daily adjustment of pricing, promotion, and markdown ensures maximum profit. The forecasting process also improves shelf space, product delivery, and deployment of personnel. A precise estimate of demand—by the hour, day, location, and price- can help make crucial decisions concerning inventories, staffing requirements, contact centers, and fleet personnel. Other recommendations The AI-ML Approach to Demand Forecasting Let us know if you’d like to future-proof your demand planning or build out scenarios that will help your business stay resilient during a crisis. Our AI/ ML solutions have enabled scores of companies to optimize their supply chain planning. Speak with our experts. Let’s craft your AI and data analytics journey together.

A PwC poll revealed that over half of the respondents said their buying habits had become “more digital” as a result of the pandemic. In 2020, online shopping accounted for 19.6% of retail sales, up from 15.8% in 2019. As customers realized how quickly and easily they could buy what they wanted from their homes, many became lifelong converts.  However, even as e-commerce continues to grow, it’s not all smooth sailing. Retail brands and marketplaces with thousands of products in their inventory face two common problems: One: An increasing number of product views that do not lead to conversions Two: Replicating the personal touch of in-store shopping AI can help address both these challenges effectively through laser-sharp personalization. Personalization in the E-Commerce Era A sad reality for many e-commerce retailers is that product views do not translate into better revenues. Customers may flip through scores of options, but are they really making purchases? One study revealed that 70% of consumers do not look past the first three pages, making the subsequent pages irrelevant inventory.  Many businesses have attempted to bridge the gap by offering some form of personalization. However, to most online retailers, this is little more than a revolving carousel of recommendations that works on segmentation. It simply lumps customers into large demographic groups based on historical data and stereotyped categories such as gender and age. Though this form of segmentation can suggest a few relevant items, it does not really count as personalization as every client in the specified category sees the same product. Many other sites use machine learning technologies that examine product correlations, identifying which goods are often bought together. This insight is limited in scope; it would be far more useful to examine the interaction between the specific customer and the type of goods he/she engages with. But where neither segmentation nor machine learning have delivered the customized experiences customers desire, AI can make a real difference because of its innate personalization capabilities. Ecommerce personalization refers to a set of practices in which an ecommerce marketplace showcases dynamic content based on consumer data, such as demographic factors, purchase intent, interests, browsing habits, purchasing patterns, and device type. It’s an important consideration as studies show that personalized product recommendations drive revenue. Here are a few recent statistics that underscore the importance of personalization: Clearly, when brands can provide convenience, customer understanding, and emotional engagement, customers are likely to be more loyal, resulting in higher profitability. How AI Can Help Personalize With AI, e-commerce businesses can provide the personalization shoppers crave, transforming every visit into a highly personal experience. New, AI-powered solutions take a one-product-to-one-shopper approach, building unique behavioral profiles based on over 300 data points per customer and predicting the next steps with a fairly high degree of certainty. This kind of e-commerce shopping experience is highly adaptable, reacting and changing in real-time based on the customer’s unique demands and needs. If done right, no two customers will ever have the same experience. Here’s how AI-powered personalization can help online retailers. Meet Customers at Their Point of Need There is no better way to create enhanced consumer experiences than by anticipating desires and providing customers with their favorite items at the right time. AI employs sophisticated machine learning algorithms to monitor browser history, page clicks, social activities (likes, shares), prior purchases, dwell times, geography, and so on, to determine a customer’s interests and preferences. By analyzing patterns of frequently purchased items and adapting web pages and features to meet the demands of individual customers, AI can meet customers at their point of need. As a result, businesses can better manage their inventory while customers benefit from an enjoyable shopping experience.  Enhance the In-Store/Offline Experience AI is also capable of enhancing the in-store experience. For example, AI-enabled kiosks and robots can assist customers in locating products within the store through voice commands or touch screen interfaces. AI can also assist in developing product suggestions based on a customer’s profile, shopping history, search queries, etc. AI-driven virtual assistants can respond to customer questions and make data-driven recommendations whenever customers require them. Augmented Reality interfaces, powered by artificial intelligence, will help customers test things without physically trying them on, thus aiding the purchase decision and enhancing the customer experience. Improve Customer Support In customer service, artificial intelligence significantly saves service time and helps minimize operational expenses. Using AI-powered chatbots and messaging agents to address customer issues in real-time will make customers feel cherished and enhance their overall experience. Programs powered by artificial intelligence can also deliver automated communications to customers of a scheduled service, a replacement part, or a recurring order. It can also facilitate creating automated service requests and provide support for order status, basic order changes, returns, and refunds. Promote Omnichannel Buying Eventually, the online and offline experiences should work together to boost sales. For instance, one retail giant has come up with the concept of offering various 4-star rated products at a  physical store by using its product recommendation engine to identify trending products and customers’ favorites. This ensures that the offline experience is profitable for both the company and the customer; the company saves retail real estate by stocking products with a high likelihood of conversions, while customers can shop with confidence, knowing they are buying products that are peer-approved. Others use AI-gathered data for personalized targeting, allowing customers to try best selling products at the physical stores, thus simplifying the shopping experience and saving the company the hassle of having to deal with product returns or investing in full-fledged retail stores. Reach Customers With More Targeted Campaigns AI can help retailers reach prospects with on-point offers. A smart use of AI-powered customization collects a shopper’s preferences at the time of purchase and pairs them with relevant product data to give suggestions across numerous touchpoints. AI-driven emails can recommend products based on shoppers’ style profiles, preferences, and browsing history. It can encourage repeat purchases. If a consumer recently bought jeans, offer them matching shirts, shoes, and

What is driving the marketplace today? According to Gartner, CX drives over two-thirds of customer loyalty, which represents more than brand and price combined. 86% of consumers say they will leave a brand after as few as two poor experiences, while 65% of consumers find a positive experience with a brand to be more influential than great advertising. Customer experience monitoring is no longer a luxury; it’s vital to your business’s very survival. While providing superlative service is the ultimate goal of every business, it is important to strike a balance between operational efficiency and profitability as well. Speech analytics is an integral component that helps you truly understand your customer so that you can remain highly profitable and in sync with their needs.  Speech analytics is a software technology that transcribes and extracts profound insights, trends, and metrics from every voice call with AI services that encompass transcription, speech technologies, and natural language processing. By comprehending, analyzing, and extracting insights from voice conversations, you can assess agent performance, the customer’s experience, and enterprise-wide strengths and weaknesses. Why Speech Analytics? According to McKinsey, in 2019, only 37% of contact centers believe they are creating value with advanced analytics. With contact centers receiving and making thousands of phone calls each day, ranging from sales and billing inquiries to reviews and complaints from customers, manually listening to and scoring every call received can be arduous. As a result, on an average, only about 5% of calls are scored, causing a lot of information to slip through the cracks. Here is where speech analytics comes into play. When a phone conversation concludes, a speech recognition platform converts the conversation into text for near-immediate analysis. Speech analytics reveals precisely how sentences are uttered and thus reveals their underlying meanings, enabling you to evaluate the types of calls you receive, how your agents handle them, and how the customer felt throughout each conversation. Often, the data is not synthesized into meaningful information for various reasons, including unclean or incomplete data, spread of data across multiple platforms, and a lack of understanding of which metrics are the most important. Combining advanced analytics such as speech and text will provide a clearer picture of the decisions that can improve your contact center’s performance. Maximizing Conversations with Speech Analytics Speech and text analytics allow businesses to comprehend the entire customer journey by discovering information they may not yet possess. It can identify trends and patterns that lead to vital business insights by analyzing millions of transactions across all channels, including traditional phone call recordings, call transcriptions, and even emails and text messages. Here are a few tangible benefits of incorporating speech analytics into your contact center. Surface actionable data Speech and sentiment analysis give you a deeper understanding of your customers’ pain points and help you identify business or product areas that require further research or improvement. You can quickly identify changing customer trends and adapt proactively to meet customers’ desires and needs. Boost agent retention Feedback is a crucial component for an agent to succeed. However, contact center managers cannot monitor and analyze every call. Speech analytics empowers you to extract the sentiment of both the customer and the agent under broad themes. For instance, an agent who is scored as demonstrating a “lack of knowledge” or “lack of compliance” will need to be mentored. Agents who demonstrate concepts of courtesy and ownership effectively can be recognized and compensated for their exemplary performance, leading to a boost in morale.   Enhance the client experience By combining speech and text analysis, it is possible to derive meaning from interactions across all channels and transform data into structured, usable, and even graphical information that is clearly understood by the entire organization. Understanding the hiccups within your multi-channel ecosystem, how customers perceive your organization, and where representatives miss the mark are all useful tools for making better, more data-driven decisions. Real-World Benefits and Use Cases McKinsey reported in 2019 that contact centers that utilized advanced analytics such as speech and text were able to decrease average response time by up to 40%, increase self-service containment rates by 5 to 20%, reduce employee costs by up to $5 million, and increase service-to-sales call conversion rates by nearly 50%— all while meeting customer expectations and engaging employees.  While deployment also includes streamlining your data and internal processes, when implemented right, you can use this information to drive improved results without the need for highly specialised data professionals. Speech to text analytics can: How Valiance Uses Speech to Text Analytics Valiance is a global AI & data analytics consulting firm helping clients of different sizes create decisioning software products using AI & Cloud Computing technologies. We use Amazon Transcribe, an Automatic Speech Recognition (ASR) tool, to generate transcripts out of audio/video files. We help customers monitor the conversation between the customer care executive and the customer through a speech to text algorithm, which checks if the right keywords are being used and flags inappropriate words. When this algorithm is run, the agents are scored and conversation insights such as call sentiment are extracted. These insights form the basis for training the agents further. It can also lead to product revisions and policy changes. Without this information, the real feedback from the customer may never reach management.  We help customers: Wrapping Up It’s time to gain a competitive advantage by leveraging the wealth of information at your fingertips and transforming it into actionable insights. Speech and text analytics enable you to get the most out of every conversation by delving beneath the surface of what customers are saying and getting to the core of their questions in real time. Moreover, AI-driven analytics provides predictive insights thanks to the continually learning, automated technology, so you can proactively address any gaps in your customers’ needs or your agents’ actions.