It is common for us to look back into our past and then form corrective actions to adopt in the present. But that usually isn’t enough to satiate the human mind – concerns about the future usually leave us in a state of constant worry and pressure. And so, throughout history, people have relied on various methods for predicting the future, trying to be as prepared for the future as they can be.  Corporates are no different. Regardless of the business functions (Strategy, Marketing & Sales, Operations, or HR), predictability of the future is one of the most sought out capabilities. Such predictions of ‘probable outcomes/situations in future’ help businesses to be proactive and devise strategic plans to mitigate risks, prevent undesirable outcomes, realize greater sales, improved profits, and deliver greater customer delight. Of course, these predictions need to be highly objective and quantifiable, as they often have immediate implications in the way a business operates in the present.  

So, the obvious question now is ‘How do we make predictions that are objective, with a quantifiable trustworthiness attached to it?’. Predictive Analytics can help businesses with future-state predictions based on past, historic data related to their business use cases.

What is Predictive Analytics?

Predictive Analytics is the application of advanced analytics techniques to predict the outcomes of future events. Data scientists usually build these models by bringing together relevant data engineering practices, statistical models, artificial intelligence, and machine learning including deep learning techniques as required. This model is then run against all available historical data, which forms the foundation for the predictive model. The model then identifies hidden patterns and relationships between attributes in the data, learning to predict future outcomes based on the data. The variability of identified patterns in historic data would help the model to quantify the ‘prediction accuracy’ level. This accuracy level specified by the model could then be used as the benchmark for trustworthiness – but do remember that accuracy could also be an indication of data quality or data insufficiency. 

Predictive Analytics is finding an increased usage among businesses world-over, to utilize ‘big data’ irrespective of it being ‘structured’ or ‘unstructured’, numeric or text, and real-time or non-real-time.  The beauty of the approach is that predictive analytics models are also capable of performing ‘continuous learning’ and improving prediction accuracy by themselves, by making use of incoming, incremental data across time. 

Predictive Analytics – Opportunities to be Proactive

Predictive Analytics can be applied in various industries, across multiple functions of the business, and in numerous use cases. All these applications are opportunities for business teams to be proactive and act in advance to capitalize on opportunities & avoid any undesirable events. 

Here are some of the use cases for predictive analytics: –

1. Predictive Maintenance – One of the ways to improve profits, other than an increase in sales, is by controlling costs efficiently. For organizations that have a large number of machinery operated all-around the year, or are generally heavy on their usage of assets, it becomes highly important to track the efficiency of these assets and prevent equipment downtimes. Using Predictive analytics models, we can now predict the ‘need’ for maintenance, any deviant behavior, part replacement, etc. for every machine across the whole outlay. 

2. Customer Relationship Management – Customer Relationship Management is a critical function for any business, helping with customer retention and customer loyalty, thus making a direct impact on sales, revenue, and profits.  Predictive Analytics has always been finding an ever-increasing adoption in a variety of use cases in CRM, such as targeted marketing, churn prediction, lifetime value analysis, etc.

3. Quality Management  – Quality Management always has the risk of being viewed as a cost, but if you look closely it is a great enabler for better business outcomes. Effective quality management can decrease unnecessary expenses and also increase sales, revenue & profits. Both manufacturing & services sectors would benefit from getting insights on any potential quality issues in advance. Using Predictive Analytics, we can get insights into any threats to quality and thus be more efficient, prevent potential damage to brand value, etc.

4. Fraud Prevention & Risk Management – In Insurance & Banking, fraud in various forms is a concern that has been growing at an alarming pace. On top of this, they face a constant challenge of staying on top of risks related to customer behavior, competition, natural and man-made catastrophes, etc., which has only increased manifold especially in the wake of increased digital adoption, developing markets, and socio-environmental challenges. For all these various scenarios, Predictive Analytics helps to develop a better understanding of potentially hidden threats and opportunities ahead of time.

Of course, none of these are new approaches to data and analytics. Most big brands in the world have been doing this for many years – even for as long as a decade! What is important to realize is that with the advances in analytics and cloud computing, even the ‘kick-starters’ of the world can make use of the opportunities that predictive analytics presents.  

Analytics & Cloud Computing – A Perfect Partnership

Cloud Computing is already more than just a buzzword – most industries have already started reaping the benefits of cloud adoption and today, the technology world sees more and more products & services being built on the foundations of the cloud. Greater processing power and storage capability that can be scaled up and down on a need-basis in the cloud, in contrast with on-premise infrastructure is much more business-friendly. Cloud computing helps industries to work with larger volumes of data in real-time, thus setting the foundation for increased usage of data analytics, especially big data and predictive analytics. 

As per Gartner estimates in June 2020, by 2022 public cloud services will be essential for 90% of data and analytics innovation. Cloud computing can help to overcome constraints related to data volume, speed, etc., and hence can empower industries to reap huge benefits out of their predictive analytics & big data analytics models. Using these cloud computing platforms, data science teams not only develop & deploy custom analytics models in the cloud but also choose from a wide variety of “off-the-shelf” SaaS analytics offerings to suit their business needs.  

The Future

Digital adoption has surpassed the inflection point in a hockey stick growth curve and is growing at an ever-increasing pace. Well for one, if any businesses were hesitant to get their feet wet, COVID-19 has given them the extra boost to jump head-first! 

With all this digital transformation, remember – more data is being generated at every digital touchpoint in every second across the world. As per a Forbes report in 2018, more than 90% of the world’s data has been generated in the last two years. Information is considered to be the most valuable asset and resource by economists. Analytics helps businesses get ahead of their competition, contribute to the growth of the economy and society in general. In short, one can even say Predictive Analytics will empower the world to be better prepared & geared up for forthcoming days. 

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COVID-19 has brought about a sudden change in our behavior and attitudes as large-scale health crises tend to do. Spending on essential supplies skyrocketed while spending on luxury almost became zero. Even when the vaccine is found and this invisible enemy is defeated, the ghost of COVID-19 will loom large in our lives. Global consumption patterns of the retail industry are not likely to bounce back to the pre-covid days in the foreseeable future.

Apart from changing consumer behavior, consumer safety is also a big challenge for retailers as they open their stores once more. How does one keep the clients and staff safe? When is the best time for consumers of the at-risk demographic to shop? How to store merchandise so as to reduce congestion at different counters?

Let’s look at some of the ways technology can be leveraged to make the transition to a post-covid world easier for retailers worldwide. 

Footfall Analytics

Contact tracing has been an important tool in the fight against COVID-19. Depending on the location, people use govt-issued contact tracing apps or third party applications available online. But what about the people who visit the stores – how can customers be traced to mitigate risk without spending a fortune on a custom analytics solution that might impede their privacy? A footfall analytics solution could be the answer to this problem.

Footfall analytics have been used in retail to identify customers and their consumer-journeys at stores. This system can provide anonymous aggregate footfall analytics to help companies see where customers or employees spent the most (or least) amount of time, and in turn, helps improve merchandising decisions. This data can also be used to identify peak and non-peak hours accurately and promote social distancing to control the occupancy rate of each individual store. 

Demand forecasting

Traditional forecasting models of retail analytics solutions that use historical data such as buying behavior, seasonality, etc. have been particularly hit due to the coronavirus pandemic. Demand forecasting needs to replace its legacy algorithms with short-term forecasting models. These retail analytics solutions use models that take in real-time sales data from a Point of Sale (POS) system and analyze key demands, shortages and forecast necessary adjustments at the store level. 

Customer-facing applications

Many large retail chains have adopted customer-facing applications with varying degrees of success. But in 2020, these technologies have started getting customer acceptance like never before. Applications such as virtual queuing, contactless payment, self-checkout, and retail chatbots have been increasingly used by retailers and their customers. Retailers are leveraging technology such as AI (Artificial Intelligence) and IoT (Internet of Things) to offer hygiene centric shopping experiences. 

In the future, people will remember the times we live in as “The Pandemic Reality”. A time when every industry had to innovate rapidly to keep itself afloat. The retail industry is no different – the industry has to focus on how to safely open its doors to customers while also maintaining its supply chain. 

Black Rock IT Solutions has worked with retail giants around the world, helping them create footfall analytics solutions and sales forecasting solutions that have helped them tide through these unprecedented times. If you need a reliable and experienced IT partner to help your business meet future market conditions, drop a mail to sales@blackrockdxb.com

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It is obvious that the better the customer feels about the product, the better the sales.  A delighted customer usually becomes an evangelist for the product and thus, an extended member of the sales team. To ensure a delightful customer experience, the product team should know the customer – his motivations, his pain points, his aspirations. 

Product Analytics can help them figure this out.

What is Product Analytics?

Data about customers are everywhere, and collecting this data is the first step in the process of understanding it. Once data is collected, the necessary steps to obtain meaningful insights from these data points need to be carried out – these become valuable pointers on the table during product strategy discussion. The whole process of ‘understanding the customer’, starting from data collection to generation & presentation of actionable insights for decision making is called Product Analytics.

Product Analytics is especially crucial for technology products, as customer footprints are placed digitally here and can be accurately tracked to better the performance of the product and make it better suited to the customer’s expectations.   It also enables automated classification/segmentation of users across their life cycle based on multiple parameters and factors. The data generated would also be beneficial to understand customer churn and probable reasons for it. Such analysis would also pave the way to predictive insights which can point out customers who are probable to churn in the future.  Analytical insights on customer journeys across various ‘funnels’ would also help to identify pitfalls and avenues for improvements in user interface and user experience offered by product websites/apps. 

Product Analytics must be adopted as a vision by the product team and should be driven as a holistic operation that encompasses continuous data collection from multiple sources (about customers), continuous integration of such data into a consistent single source of truth as a historical data warehouse, algorithms to discover meaningful insights from the data and presentation of the insights in self-intuitive visual formats via dashboards & reports, back to the product team. Strategies developed based on customer understanding can help to meet customer needs (even latent needs), improve customer experience, and thus create a delighted customer.

Why Product Analytics?

We live in an era that is becoming increasingly digital every day. The digital transformation wave has reduced switching costs for customers in almost all sectors & segments of the business.  Customers are empowered with capabilities to gain more product information, make faster buying decisions, and make their opinions on product websites/apps known to the public domain within no time. 

Thus, marketing strategy and advertisements for the product must be crisp and targeted segment-wise. It helps to increase the quality of incoming leads and conversion rates, and decrease marketing expenses as well. It is also crucial for organizations to be proactive & have systems in place to ensure customer retention and increase sales by upselling/cross-selling.

With the volume, variety, and velocity of incoming data increasing day by day, any organization would require an automated, tightly coupled, robust Product Analytics system in place that accepts data from multiple sources, processes it, and provides actionable insights, all in real-time. It enables the product teams to understand what the users do in real-time rather than assuming these actions from gut intuitions. According to a Mckinsey report,

“Companies that make extensive use of customer analytics are more likely to report outperforming their competitors on key performance metrics, whether profit, sales, sales growth, or return on investment. For example, companies that use customer analytics comprehensively report outstripping their competition in terms of profit almost twice as often as companies that do not.”

Who should adopt Product Analytics & when?

Product Analytics is a vision that needs to be adopted as a culture at any product company, irrespective of their size and volume of business.  Anytime is the right time to start with product analytics and it has to be considered a continuous exercise rather than a single-time activity. Digital transformation is bound to bring on more innovative changes to the business world, as it does today, across sectors like banking, retail, healthcare, education, travel & logistics, agriculture, etc. Data will always be available in plenty and competitive advantages will continue to be built around customer data.

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A hill-state in the north of the Indian subcontinent – famed for its untouched natural beauty, snow-capped mountains, and pristine lakes – has been especially vulnerable to fatalities caused by road accidents. 

Although not wearing seatbelts and drunken driving are among the leading causes for these accidents, a surprisingly high number of them were also caused by bad road conditions like potholes, steep gradients, a lack of crash barriers and roads under construction. Adding to this are the accidents caused by extreme weather conditions – the state has extremely different weather conditions than the rest of the country, with snowstorms and hail being a regular occurrence. 

The government realized that in this era of digitization, a comprehensive digital solution to manage road accidents and safety is one of the primary tools they should consider using to minimize the losses these accidents cause, and began to search for a solution that could effectively capture the complex data that needs to be recorded in the difficult terrain of their state.

Why go digital for road safety?

In most of the countries, stakeholders in the transportation sector currently depend on legacy software platforms and solutions to perform different operations for generating accident and safety reports, analyzing issues, and recommending countermeasure actions. The tools and techniques employed are also holding back their capability to process data from different sources. There are also challenges in terms of rigidity while accepting inputs, user-friendliness, and so on. To overcome these inabilities of the outdated legacy systems, and to approach road safety management scientifically, to arrive at targeted solutions that improve road safety, a digital solution is the need of the hour. 

Enter Experion

Experion, in a project funded by the World Bank, worked with a renowned the British transportation firm to develop an ingenious digital solution that brings all types of data sources, analytics, and dimensions into a single solution framework and makes it easy to perform all analysis and recommendations on a single platform.

The solution is a step towards scientifically addressing road safety management – it helps identify accident-prone spots, reasons for accidents, and measures for taking remedial action in this direction. This solution also helps vital stakeholders such as the Public Works Department, Police Department, Transport Department, and Department of Health and Family Welfare to collaborate effectively and efficiently to ensure road safety.  

The State  Govt zeroed in on this solution to help them battle the number of road accidents they had been facing, and to help minimize the number of lives lost to these avoidable mistakes. They have implemented the solution and have been successfully working with it for over five years now. 

What features does a road safety management system need?

The solution should provide near real-time, complete, and relevant information on road accidents occurring in any part of the country or state to concerned authorities.  

The solution should identify accident-prone locations and analyze trends in road accidents to monitor the roads’ safety. This will enable stakeholders to plan more efficient strategies in line with international safety standards to reduce the number and severity of road accidents and their impact on human lives. 

Advanced geospatial algorithms must be used to identify road safety problems of each specific region and recommend counter-measures to reduce casualties from road accidents. 

How solutions like this one can help 

The solution is equipped with a real-time alert mechanism that enables the concerned stakeholder to reach the accident site and capture various critical parameters of the accident like the GPS coordinates of the accident location, type of vehicle, details of those involved in the crash and pictures. This data can be analyzed and generate meaningful insights such as accident data status, accident severity analysis, reasons, and impacts of the accident, etc. 

As a platform that would be used by different members of the local government bodies, it was built to ensure a superior user experience to all stakeholders, making it easier for them to report even the minor accidents that go unreported due to the complex manual procedures involved previously.

Leveraging the meaningful insights our system provides, the concerned authorities can develop and implement safety performance indicators – measurements that are statistically related to accidents or injuries and are used in addition to accident or injury statistics to indicate safety performance or to understand the process that leads to accidents.  

The digital solution monitors and assesses the road safety situation in each stage of road development and enables stakeholders to make interventions early if they show undesirable results. The solution also allows users to measure and compare safety measures implemented in different countries and compare the before and after scenarios and data after specific actions were taken. 

The solution tracks, stores, and analyses the most salient road safety issues and sorts them according to their contribution to fatalities and injuries. With this, the road authorities can set road safety targets and relevance standards. These targets and standards can be used to educate the road users, the drivers, the vehicle, and the infrastructure manufacturers to improve the road safety situation. 

The solution enables authorities to formulate and develop superior road safety strategies and action plans based on inputs, targets, and safety performance indicators customized for each geographical region. The system also allocates each action plan to the most suitable department for implementation and monitoring. It collects feedback and analyzes its effectiveness and suggests alternatives or tweaks if the accident rates do not reduce.

Conclusion

Our transportation practice is determined to contribute towards safer roads, utilizing advanced digital technologies like artificial intelligence, machine learning, IoT, and advanced analytics. If you have a requirement we can help with, please contact us at sales@blackrockdxb.com.

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mHealth apps have been able to provide an even more granular level of data with the introduction and adoption of wearables, and both patients and doctors alike are benefiting from this democratized data. In a press release from Stanford University School of Medicine, Dean Lloyd Minor, MD, said: “We found that current and future physicians are not only open to new technologies but are actively seeking training in subjects such as data science to enhance care for their patients”.

This huge inflow of data from wearables has also changed the way data is collected, stored, managed, visualized, and utilized. It has even caused many notable changes in the way the healthcare sector operates by opening up new avenues for the accuracy of diagnosis, the prevention of disease, tracking the effectiveness of treatment, and providing safe medications. It has proven that a patient-centric individual approach to medicine is what elicits the most effective results, rather than a blanket cure for everyone.

What is healthcare analytics?

Healthcare Analytics is a broad term that means different things to different stakeholders in the healthcare sector. 

To a healthcare practitioner, healthcare analytics data can help in consulting remotely, accurately diagnosing ailments, bettering the effectiveness of the treatment they suggest, monitor their patient’s health status, and use predictive analysis to help them make decisions.

For governments, healthcare analytics services are essential to monitor and track their citizens, a feature that’s especially vital during the virus-ridden times we live in. They are able to analyze trends in the disease at regional and community levels and appropriately intervene, aiding them not only in curbing the spread of viruses but also in formulating the right kinds of strategies to mitigate large scale spread. They are also able to track fraudulent claims, mismanaged insurance companies, and illegal drug distribution. 

Pharmaceutical companies use healthcare analytics platforms for research and development, sales and marketing, drug discoveries, and customer engagement.

And finally, patients can use health data they generate to stay more in control of their own choices. Having their health-related data analytics readily available means they can take the necessary steps needed to better their health and also gives them the option to work with their healthcare providers, rather than working with the old notion of blind trust. 

How can data analytics change healthcare?  

The vast scope of data analytics in healthcare is difficult to condense into a few short pointers. But if the most pertinent impacts it will bring about had to be picked out, these would be the top contenders for how this revolutionary technology is changing the landscape of healthcare around the world. 

Comprehensive Utilization of Electronic Health Records

There is a lot of data collected in hospitals every single day. One big step forward for the health sector was for all this data to be converted into electronic records instead of physical copies – what is now called Electronic Health Records or EHR. Now imagine this being taken a few hundred steps further – what if all this data could be analyzed to leverage the best possible use of all this information – using healthcare analytics for quality and performance improvement? 

Reducing Preventable Harm

“Preventable harm” is defined as the presence of an identifiable, modifiable cause of harm – medication adverse effects, central line infections, and venous thromboembolism for instance.  The huge amounts of data that are generated and stored in hospitals can be analyzed to provide crucial answers to why these happen. This enables doctors to make better decisions, resulting in much better patient care. This hospital found that they reduced preventable harm by 40% after they decided to operate in a more data-centric fashion. 

Eliminating Diseases

Yes, you read that right. Data analytics can help the world completely eliminate life-threatening diseases or pandemics like COVID-19. For instance, the project Visualize No Malaria, initiated by a renowned NGO partnering with the government of Zambia, aims to completely eradicate malaria from the country. The project is focused on using data to meet its goals and has trained healthcare workers around the country to use data to track, report, and treat malaria before it spreads. They also use data collected to figure out where crucial supplies need to be provided first, avoiding wastage of these resources by distributing them where they are not needed. The same strategy can be implemented to control the spread of  COVID-19 – by tracking, monitoring, and isolating potential positive patients, and ensuring PPE are delivered where it is most crucially needed. 

Improve Computational Medical Treatment

Computational Medicine is an emerging discipline that leverages sophisticated software,  computer models, and analytics to figure out the root causes of disease and find the most accurate diagnoses. With sufficient data and analytical tools, you can build models that answer complex questions in the medical world like how certain diseases develop, how environments shape a patient’s wellbeing and how biological systems change from healthy to unhealthy states. To read more about how computational medicine utilizes data analytics to enhance the way doctors detect and treat diseases, you can read this paper from John Hopkins.

Empowering The Patient 

With data being democratized, patients will have the information they seek at their fingertips – they will be more likely to stick to a doctor-prescribed health plan when they are aware of the actual numbers in the game. Data analytics, if shared, can lead to collaborative physician-patient decisions which allow the patient to feel more in control, achieving far better results from the treatment planned. 

In conclusion

Considering the pace at which individuals, as well as enterprises across the globe, are adopting digitization, data analytics is set to revolutionize the healthcare industry in the immediate future. With a more patient-centric approach, medicine will finally be seen as an approachable and democratic domain, and more and more individuals will become eager to make healthy choices, once they see data-driven results making a different world over, that is also accessible to them at their fingertips.

At Black Rock IT Solutions, we have ample experience in developing world-class healthcare solutions that adhere to healthcare regulations around the world. If you have a remarkable idea for a data-driven solution in the healthcare sector and are looking for an IT partner, drop a mail to sales@blackrockdxb.com

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Imposing personal safety adherence measures such as redesigning the floor layouts, marking safe social distancing, etc. are immediate solutions, but they may end up creating discomfort in the long term. In this scenario, adopting technology can help retailers tackle these challenges while reducing costs and improving efficiency.

In this article, we look at how retail analytics can play a significant role in helping businesses to overcome these challenges.

In-store analytics

In-store analytics empowers retailers with interactive, dynamic, and real-time dashboards with insights into the customer’s in-store behavior. For instance, customer interaction in a specific zone or an area in the store. In-store analytics’ unprecedented potential to track and analyze customer store behavior patterns enables retailers to adopt better in-store product placement strategies ensuring customer engagement and, in turn, a superior customer experience. These insights coupled with geographical data can also help stores to evaluate the COVID-19 exposure in the geographies where their stores exist. 

Footfall analytics

Measuring the number of visitors will enable the retail stores to analyze and compare store performance over a period of time with respect to customer visits. People analytics can be leveraged to assess employee – visitor ratio, employee performance, schedule employee shifts, predict visitor trends, and forecast optimum recruitment intervals. Various departments such as facilities management, operations, inventory management, and supply chain management, can utilize the insights generated to make faster and smarter decisions. 

Customer sentiment analysis

Capturing customer sentiment on the retail premise helps retailers gauge the efficiency of the services they offer. Customers can be asked to share their feedback using their mobile phones at the entrance or exit doors about various parameters such as safety, comfort, and confidence. They can also run sentiment analysis on data from facial analysis, social media networks, and different online forums. These can help them to improve their offerings and enhance the customer experience.

Cross-platform analytics

Customers today are more empowered and connected than ever before. Retailers must track customer intent much before they visit the store by analyzing their behavior from across touchpoints such as a desktop webpage, native & third-party applications, and mobile sites in one place. This cross-platform analytics will help retailers understand how the customer moves through various channels before they visit the store for purchase. They expect companies to provide seamless experiences across channels that reflect their history, preferences, and interests. Understanding the whole customer journey and discovering their behavioral insights allows retailers to create a consistent and unified experience through all the different channels.

In Conclusion

Retailers who want to thrive in the future must leverage the vast potential that retail analytics offers. They must make smarter and faster decisions based on meaningful insights from retail analytics to ensure increased revenue, higher profits, enhanced customer engagement, and superior customer experience.   

Black Rock IT Solutions has developed retail analytics solutions to help global retail enterprises operate more efficiently. Experion’s expertise and experience in assisting organizations to realize their strategic and operational goals has enabled us to gain an edge in the workings of the retail industry. To know more about how we help businesses ensure business continuity and enhance customer satisfaction amidst the COVID-19 crisis, drop a mail to sales@blackrockdxb.com.  

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