12 Great Facts About Analytics for Retail Price Optimization

What is one of the most important factors a retailer should always monitor? It’s the price of products they sell. We all know that the selling price of a product is pre-fixed. But that doesn’t stop some retailers from offering discounts and giveaways.  In fact, customers today expect you to offer products at competitive prices. With so many stores mushrooming (online and offline), it has become necessary to have a flexible pricing policy. Guesswork won’t help you in the long run. It is a sure way to end up in losses.  Why not give a shot to analytics for retail price optimization? What is Retail Analytics?  In simple words, analyzing retail business & customer behavior data to help retailers make better decisions is known as retail analytics. Of course, it is hardly that easy in real life. Retail analytics is the process of using AI tools to collect and analyze historical and real-time data to derive in-depth insights. It allows you to make better decisions based on these insights.  This will be a win-win situation for you and your customers. Before we read more about analytics for retail price optimization, let us answer the following questions. These are some key factors that you must consider as a part of your retail analytics.  How willing are Customers to Pay for a Product? Also known as price sensitivity, this factor deals with the maximum amount a customer would pay for a product. Unless you know this, you cannot make adjustments to the prices of products you sell in your retail stores. What is the Average Revenue Generated per User?  How much does each customer contribute to your revenue each month? Knowing the answer to this question will help you understand who your most valued customers are and which products they are buying.  What Makes a Product Famous Among the Customers (Product Value)?  It can also be termed as feature value analysis, where you try to identify the most liked and least liked features of a product. Depending on the number of features/ aspects customers like or prefer in a product, they will fix a price for it in their minds. If you set your selling price over that amount, it will affect your sales as they may not wish to pay as much.  How do Customer Acquisition Costs and Customer Lifetime Value Affect Your Pricing Decisions?  You need to know how much you can invest in a customer. There is no point in running an extensive campaign if the customer doesn’t buy from your retail stores, right?  Retail intelligence and analytics for retail price optimization give an insight-based understanding. Here’s how retail analytics can optimize your pricing strategies, streamline your business operations and promotional plans, and help you become a leading retailer in the market.  1. You can Get Immediate Returns on Your Investment  Getting faster returns is the dream of every retailer. Who would want to wait for months and years as the interest accumulates and reduces the profit margin? Computer vision solutions for retail allow you to come up with a pricing policy that can be changed in real-time. Each time you adjust your short-term goals, you can tweak the prices accordingly. That too, without worrying whether the customer will pay as much or not.  A study by PwC found that 60% of the customers decide whether to buy a product or not solely based on its price. Fixing the price without knowing how things stand could lead to losses instead of increasing ROI. 2. You can Understand Your Customers’ Purchasing Behavior  It is eventually up to the customers to buy a product, isn’t it? Even the best discount offers don’t result in sales at times. This could be because-  Customers don’t prefer that product  The timing of the discount was wrong  The offer didn’t reach the target audience  How can you make sure such mistakes don’t happen? By using retail analytics to get insights about customers’ purchase behavior and interests. What drivers influence customers to buy a product? The answer to this question can help you optimize the price to increase sales and profits.   3. Automate Your Business Operations to Gain Competitive Edge  Who said automation is not meant for retailers? Why spend your precious time calculating and analyzing the market trends, measuring the price changes, and monitoring customer demands? Let technology do it on your behalf.  It would also reduce the risk of human error and give you more time to focus on implementing the pricing and promotional strategies. Machine learning algorithms can help automate pricing. It can be integrated with other retail applications you use to streamline your business operations. You can stay up to date with the latest changes in the market (check the next point).  4. Be Ready to React to the Changes in the Market  A successful retailer is the one who can make fast and accurate pricing changes in real-time. Effectively managing both offline and online sales is becoming more of a necessity in recent times. When you know that the demand for a product will increase or decrease in the coming days, you can plan your pricing strategies to attract more customers and increase your return on investment.  5. Make Use of the Feedback to Correct Your Pricing Strategies  The feedback here comes from the retail analytics software you use. The regular reports generated by the software will tell you whether the current pricing plan is effective or if changes have to be made. Real-time insights are derived from the latest information available.  This helps you make quick changes to the prices and adjust them immediately to suit the customers’ demands. Instead of taking feedback through surveys, you can get the required reports from the software. The constant feedback will keep you at the top of your game and ahead of your competition.  6. Support Your Decisions with Processed Data  A wrong decision could prove to be very costly for a retailer. While gut feeling cannot be ignored, relying entirely on it is a risky

Read More

How is Vision Analytics Retransforming Modern Industries?

Vision analytics has always been considered a game-changer in the industry. It was expected to revolutionize the way security tasks were performed. Improving operational efficiency was another aim of vision analytics. Both the public and private entities are leaning towards computer vision analytics to revamp their business processes and gain the top position in the markets. Artificial intelligence, machine learning, deep learning, 3D imaging, etc., are some terms we often hear when people talk about vision analytics. We often read about vision analytics retransforming modern enterprises and SMEs. Before we see more about what these mean, let’s understand what computer vision analytics is. The process of analyzing digital image/video signals to understand the visual world using the latest technologies in place of the human eye is known as vision analytics. Identifying intruders & impostors, recognising & tracking objects, identifying behavioral patterns etc.. are some examples of vision analytics. The global computer vision market anticipates having a CAGR (compound annual growth rate) of 7.6% from 2020 to 2027. There has been a significant escalation in the demand for computer vision services during the last year due to the COVID-19 pandemic. Taking the increasing adoption of vision analytics into account, we can say that the following trends are going to rule the industry in the coming days. Latest Trends in the Vision Analytics Industry Artificial Intelligence AI has made it possible to analyze vast amounts of data in less time. Data can be in any form- text, images, or videos. Artificial intelligence in vision analytics is used to examine videos and detect patterns. It helps to identify and predict events based on existing data. The systems can communicate with each other and alert the user about a potential change in the pattern. For example, AI in the security department is used to analyze videos and identify suspicious activity such as trespassing, sneaking, breaking in, etc. Vision analytics can help detect the change before the actual event takes place and alert the concerned authorities. In the retail sector, AI in vision analytics is used to identify customer behavior patterns and purchasing trends. Deep Learning and Machine Vision Even though machine vision and deep learning are two independent elements, they complement each other and have abilities that overlap. Deep learning has given machine vision a new dimension. Neural networks are an example of deep learning that works well with machine vision. It helps identify the presence in an image/ video frame. It determines if the presence is good news or bad news. We can call them image-classifiers. Deep learning also helps in increasing the speed of a business process by improving operational efficiency. Many machine vision consulting services include artificial neural networks (ANNs) to provide a comprehensive system for automation in the manufacturing industry. Thermal Imaging Thermal imaging is the process that uses infrared and heat radiation to detect objects in the dark. The thermal cameras can distinguish the difference in temperatures so that we can detect the warmer objects/ beings. It becomes easy to identify the presence of a person or an animal against the cold and dark background. When terminal imaging is used with vision analytics, it sends alerts only for a fixed range of temperature levels. For example, the movements of trees, winds, vehicles, etc., are usually false positives when you want to find a human presence. This is especially useful for security purposes. The percentage of false security alerts can be reduced, thereby improving the efficiency of the security system. 3D Imaging Do you know that the 3D vision market is estimated to have a CAGR of 9.4% from 2020 to 2025? It is the next big thing in the market as the demand for quality inspection of the end products is touching the skies. With SMEs and large-scale enterprises wanting to automate their business, they are turning to 3D vision analytics for high-speed imaging, vision-guided robotic systems, and surface profiling. 3D imaging and vision analytics are also important as the industry is shifting from standard products to personalized products based on customer requirements. 3D smart cameras are said to rule the industries in the coming years. 3D imaging also helps in logistics for autonomous navigation via object detection, self-localization, etc. Use of Liquid Lenses for Vision Analytics Liquid lenses are single optical elements but with an optical liquid material that is capable of changing its shape as and when required. They are used in smart cameras and smart sensors though now we can find them being used in various fields such as biometric recognition and data capturing, reading barcodes, digital photography, and more. Heavy industries are investing more in liquid lenses to help with various manufacturing applications. The lenses have great focus and adjust to the changes in the voltage and current automatically. Apart from industries, public spaces are also going to be monitored using liquid lenses to track if people are following the safety norms or not. Embedded Vision In simple terms, embedded vision is the integration of a camera and a processing board. Instead of having more than one device to stay connected and deliver us the results, embedded vision systems directly work with algorithms. When an embedded system (a microprocessor-based unit) is combined with computer vision technology to digitally process the images/ videos and use machine learning algorithms to share the information with other cameras and systems in the network, it is known as embedded vision. The main reasons for embedded vision systems to become popular are low cost, lesser energy consumption, smaller in size, and lightweight. Embedded computer vision consulting services are used for robotics in the manufacturing industry (for factory automation), the healthcare sector (for medical diagnosis), gesture recognition (for transportation and logistics), the famous facial recognition systems and many more. Several multinational organizations and public sector industries have adopted vision analytics to retransform their operational processes. Vision Analytics and Retransformation of Modern Industries Below are some ways to see vision analytics retransforming modern industries in the global market. Public and Workplace Safety

Read More

How Data Analytics Helps Respond Covid Impact?

Regardless of the coronavirus disease (COVID-19) consequences in society and our workplaces, we are all working in extraordinary times. The sheer fluidity of transition has forced us to deal with this alone in March seems unreal. It is bewildering to think that a relatively isolated number of cases announced to the WHO on 31 December in Wuhan, China, meteorically increased to nearly 330k confirmed cases and 14.4k deaths in over 180 countries as of 22 March 2020. While society struggled with the public health and economic problems manifesting in the aftermath of COVID-19, corporations scrambling to realign themselves to this new paradigm are finding technologies to help. In particular, data analytics proves to be an ally for epidemiologists as they join forces with data scientists to address the severity of the crisis. The spread of COVID-19 and the public’s desire for information has sparked the creation of open-source data sets and visualizations, paving the way for a pandemic analytics discipline introduced. Analytics is aggregating and analyzing data from multiple sources to gain information. When used to research and global counter diseases, pandemic analytics is a new way of combating an issue as old as civilization itself: disease proliferation. To Craft The Correct Response – Data Analytics In COVID-19 In the early 1850s, London fought a widespread rise in the number of cholera cases, John Snow – the father of modern epidemiology – discovered cluster clusters of cholera cases around water pumping. For the first time, the discovery allowed scientists to exploit data to counter pandemics, drive their efforts to measure the danger, identify the enemy, and formulate a suitable response strategy. That first flash of genius has since advanced, and 170 years of cumulative intelligence have demonstrated that early interventions are disrupting disease spread. However, analysis, decision-making, and subsequent intervention can only be useful if it takes all the information into account first. Healthcare managers at Sheba Medical Center in Israel use data-driven forecasting to improve staff and resources distribution in anticipation of possible local outbreaks. These solutions are powered by machine learning algorithms that provide predictive insights based on all available disease spread data, such as reported cases, deaths, test results, contact tracing, population density, demographics, migration movement, medical resource availability, and pharmaceutical stockpiles. Viral propagation has a small silver lining: the exponential development of new data from which we can learn and act. With the right analytics tools, healthcare professionals can address questions such as when the next cluster will most likely appear, which population is most susceptible, and how the virus mutates over time. Ohn Snow, the founder of modern epidemiology, noticed cluster patterns of cholera cases around water pumps in the early 1850s, as London battled a rampant rise in the number of cholera cases. For the first time, this discovery enabled scientists to leverage data to combat pandemics, drive their efforts to quantify the risk, identify the enemy, and devise a suitable response strategy. That first flash of genius has since advanced, and 170 years of cumulative intelligence have demonstrated that early interventions are disrupting disease spread. However, analysis, decision-making, and subsequent intervention can only be useful if it is taken into account first. Accessibility of trusted sources of data has resulted in an unprecedented sharing of visualizations and messages to educate the general public. Take, for example, the dynamic world map created by the Center for Systems Science and Engineering at Johns Hopkins, and these brilliantly simple yet enlightening Washington Post animations. These visualizations quickly inform the public how viruses spread, and which human behavior can support or hinder the spread of viruses.  The democratization of data and analytics software, combined with the vast capacity to exchange information over the internet, has allowed us to see the incredible power of data being used for good. To See The Unseen (Data Analytics) Accessibility of reliable sources of data has resulted in an unparalleled exchange of visualizations and messages to inform the general public. For example, take the interactive world map created by the Center for Systems Science and Engineering at Johns Hopkins, and these beautifully simple but enlightening Washington Post animations. These visualizations quickly show the public how viruses spread, and which human behavior can support or hinder the spread of viruses. The democratization of data and analytics software, combined with the vast capacity to exchange information over the internet, has allowed us to see the incredible power of data being used for good. In recent months, companies have taken an in-house collection of pandemic data to develop their proprietary intelligence. Some of the more enterprising enterprises have even set up internal Track & Respond Command Centers to guide their employees, customers, and broader partner ecosystems through the current crisis. Early on in the outbreak, HCL realized that it would need its COVID-19 response control center. Coordinated by senior management, it gives HCL data scientists autonomy to develop innovative and strategic perspectives for more informed decision-making. For example, the creation of predictive analytics on potential impacts for HCL customers and the markets where HCL services are provided. We employed techniques such as statistics, control theory, simulation modeling, and Natural Language Processing ( NLP) to allow leadership to respond quickly during the development of the COVID-19 situation. For simplicity, we are going to categorize our approach under the umbrella of Track & Respond: TRACK the condition to grasp its significance, both quantitatively and qualitatively. Perform real-time topic modeling across thousands of international health agency publications and credible news outlets; automate the extraction of quantifiable trends (alerts) as well as actionable information relevant to the role & responsibility. Policymakers, public agencies, and other institutions worldwide have used AI systems, Big Data analytics, and data analysis software. All of these are used to forecast where the virus may go next, monitor the virus spreading in real-time, recognize drugs that could be helpful against COVID-19, and more.  People who work at the sites of the disease outbreak gather critical COVID-19 data such as transmissible, risk factors, incubation time,

Read More

Unraveling The Meaning From COVID-19 Dataset Using Python – A Tutorial for beginners

Introduction The Corona Virus – COVID-19 outbreak has brought the whole world to a stand still position, with complete lock-down in several countries. Salute! To every health and security professional. Today, we will attempt to perform a single data analysis with COVID-19 Dataset Using Python. Here’s the link for Data Set available on Kaggle. Following are the the Python Libraries we’ll be implementing today for this exercise. What Data Does It Hold The available dataset has details of number of cases for COVID-19, on daily basis. Let us begin with understanding the columns and what they represent. Column Description for the Dataset: These are the columns within the file, most of our work will working around three columns which are Confirmed, Deaths and Recovered. Let Us Begin: Firstly, we’ll import our first library, pandas and read the source file. import pandas as pddf = pd.read_csv(“covid_19_data.csv”) Now that we have read the data, let us print the head of the file, which will print top five rows with columns. df.head() As you can see in the above screenshot, we have printed the top five rows of the data file, with the columns explained earlier. Let us now get into some dept of the data, where we can understand the mean and standard deviation of the data, along with other factors. df.describe() Describe function in pandas is used to return the basic details of the data, statistically. We have our mean, which is “1972.956586” for confirmed cases and Standard Deviation is “10807.777684” for confirmed cases. Mean and Standard Deviation for Deaths and Recovered columns is listed, too. Let us now begin with plotting the data, which means to plot these data points on graph or histogram. We used pandas library until now, we’ll need to import the other two libraries and proceed. import seaborn as snsimport matplotlib.pyplot as plt We now have imported all three libraries. We will now attempt to plot our data on a graph and output will reflect figure with three data points on a graph and their movements towards the latest date. plt.figure(figsize = (12,8)) df.groupby(‘ObservationDate’).mean()[‘Confirmed’].plot() df.groupby(‘ObservationDate’).mean()[‘Recovered’].plot() df.groupby(‘ObservationDate’).mean()[‘Deaths’].plot() Code Explanation: plt.figure with initial the plot with mentioned width and height. figsize is used to define the size of the figure, it takes two float numbers as parameters, which are width and height in inches. If parameters not provided, default will be scParams, [6.4, 4.8]. Then we have grouped Observation Data column with three different columns, which are Confirmed, Recovered and Deaths. Observation goes horizontal along with the vertical count. Above code will plot the three columns one by one and the output after execution will be as shown in following image. This data reflects the impact of COVID-19 over the globe, distributed in three columns. Using the same data, we can implement prediction models but the data is quite uncertain and does not qualify for prediction purpose. Moving on we will focus on India as Country and analyze the data, Country Focus: India Let us specifically check the data for India. ind = df[df[‘Country/Region’] == ‘India’]ind.head() Above lines of code will filter out columns with India as Country/Region and place those columns in “ind” and upon checking for the head(), it will reflect the top five columns. Check the below attached screenshot. Let’s plot the data for India: plt.figure(figsize = (12,8))ind.groupby(‘ObservationDate’).mean()[‘Confirmed’].plot()ind.groupby(‘ObservationDate’).mean()[‘Recovered’].plot()ind.groupby(‘ObservationDate’).mean()[‘Deaths’].plot() Similar to earlier example, this code will return a figure with the columns plotted on the figure. Output for above code will be: This is how Data is represented graphically, making it easy to read and understand. Moving forward, we will implement a Satterplot using Seaborn library. Our next figure will place data points, with respect to sex of the patient. Code: Firstly we’ll make some minor changes in variables. df[‘sex’] = df[‘sex’].replace(to_replace = ‘male’, value = ‘Male’)df[‘sex’] = df[‘sex’].replace(to_replace = ‘female’, value = ‘Female’) Above code simply changes the variable names to standard format. Then we’ll fill the data points into the figure, plotting. plt.figure(figsize = (15,8))sns.scatterplot(x = ‘longitude’, y = ‘latitude’, data = df2, hue = ‘sex’, alpha = 0.2) Code Explanation: The “x and y” defines the longitude and latitude. data defines the data frame or the source, where columns and rows are variables and observations, respectively. The hue defines the variable names in the data and here these variables will be produced with different colors. alpha, which takes float value decides the opacity for the points. Refer the below attached screenshot for proper output. Future Scope: Now that we have understood how to read raw data and present it in readable figures, here the future scope could be implementing a Time Series Forecasting Module and getting a Prediction. Using RNN, we could achieve a possibly realistic number of future cases for COVID-19. But at present, it could be difficult to get realistic prediction as the data we posses now is too uncertain and too less. But considering the current situation and the fight we have been giving, we have decided not to implement Prediction Module to acquire any number which could lead to unnecessary unrest. Contact us for any business query

Read More

20 Mistakes That Every Data Analyst Must Be Aware Of!

Computer Science is a research that explores the detection, representation, and extraction of useful data information. It is gathered by data analyst from different sources to be used for business purposes. With a vast amount of facts producing every minute, the necessity for businesses to extract valuable insights is a must. It helps them to stand out in the crowd. Many professionals are taking their founding steps in data science, with the enormous demands for data scientists. Despite a large number of people being inexperienced in data science, young data analysts are making a lot of simple mistakes. What Is Data Analytics? The concept of data analytics encompasses its broad field reach as the process of analyzing raw data to identify patterns and answer questions. It does, however, include many strategies with many different objectives. The process of data analytics has some primary components which are essential for any initiative. A useful data analysis project would have a straightforward picture of where you are, where you were, and where you will go by integrating these components. This cycle usually begins with descriptive analytics. That is the process of describing historical data trends. Descriptive analytics seeks to address the “what happened?” question. It also has assessments of conventional metrics like investment return (ROI). Of each industry, the metrics used would be different. Descriptive analytics does not allow forecasts or notify decisions directly. It focuses on the accurate and concise summing up of results. Advanced analytics is the next crucial part of data analytics. This section of data science takes advantage of sophisticated methods for data analysis, prediction creation, and trend discovery. This data provides new insight from the data. Advanced analytics answers, “what if? “You have concerns. The availability of machine learning techniques, large data sets, and cheap computing resources has encouraged many industries to use these techniques. Big data sets collection is instrumental in allowing such methods. Big data analytics helps companies to draw concrete conclusions from diverse and varied data sources that have made advances in parallel processing and cheap computing power possible. Types Of Data Analytics Data analytics is an extensive field. Four key data analytics types exist descriptive, analytical, predictive, and prescriptive analytics. Each type has a different objective and place in the process of analyzing the data. These are also the primary applications in business data analytics. Descriptive analytics helps to address concerns about what happened. These techniques sum up broad datasets to explain stakeholder outcomes. Such methods can help track successes or deficiencies by creating key performance indicators ( KPIs). In many industries, metrics like return on investment ( ROI) are used. Specific parameters for measuring output are built in different sectors. This process includes data collection, data processing, data analysis, and visualization of the data. This process provides valuable insight into past success. Diagnostic analytics help address questions as to why things went wrong. These techniques complement more fundamental descriptive analytics. They are taking the findings from descriptive analytics and digging deeper for the cause. The performance indicators will be further investigated to find out why they have gotten better or worse. That typically takes place in three steps: Predictive analytics aims to address concerns about what’s going to happen next. Using historical data, these techniques classify patterns and determine whether they are likely to recur. Predictive analytical tools provide valuable insight into what may happen in the future, and their methods include a variety of statistical and machine learning techniques, such as neural networks, decision trees, and regression. Prescriptive analytics assists in answering questions about what to do. Data-driven decisions can be taken by using insights from predictive analytics. In the face of uncertainty, this helps companies to make educated decisions. The techniques of prescriptive analytics rely on machine learning strategies, which can find patterns in large datasets. By evaluating past choices and events, one can estimate the probability of different outcomes. Such types of data analytics offer insight into the efficacy and efficiency of business decisions. They are used in combination to provide a comprehensive understanding of the needs and opportunities of a company. 20 Common Mistakes In Data Analysis It should come as no surprise that there is one significant skill the modern marketer needs to master the data. As growth marketers, a large part of our task is to collect data, report on the data we’ve received, and crunched the numbers to make a detailed analysis. The marketing age of gut-feeling has ended. The only way forward is by skillful analysis and application of the data. But to become a master of data, it’s necessary to know which common errors to avoid. We ‘re here to help; many advertisers make deadly data analysis mistakes-but you don’t have to! 1. Correlation Vs. Causation In statistics and data science, the underlying principle is that the correlation is not causation, meaning that just because two things appear to be related to each other does not mean that one causes the other. It is the most common mistake apparently in the Time Series. Fawcett gives an example of a stock market index, and the media listed the irrelevant time series Amount of times Jennifer Lawrence. Amusingly identical, the lines feel. A statement like “Correlation = 0.86” is usually given. Note that a coefficient of correlation is between +1 (perfect linear relationship) and -1 (perfectly inversely related), with zero meaning no linear relation. 0.86 is a high value, which shows that the two-time series statistical relationship is stable. 2. Not Looking Beyond Numbers Some data analysts and advertisers analyze only the numbers they get, without placing them into their context. If that is known, quantitative data is not valid. For these situations, whoever performs the data analysis will ask themselves “why” instead of “what.” Fallen under the spell of large numbers is a standard error committed by so many analysts. 3. Not Defining The Problem Well In data science, this can be seen as the tone of the most fundamental problem. Most of the

Read More

Predictive Analytics & Distribution | Know Its Impact!

From large companies to smaller companies, predictive analysis and analytics tools offer unparalleled benefits. Predictive tools will clarify what’s coming with unparalleled precision through the ingestion and application of different data points. They can also disperse massive information troves to reveal hidden insights, potential opportunities, and more. It’s no wonder that forecasts put the global market valuation at $10.95 billion by 2022, with predictive modeling being so useful. The impact does, of course, differ slightly from business to business. For example, how it works and what it might demonstrate in marketing is entirely different from what it could display in the delivery process. How Do Predictive Analytics Tools Affect Distributors? Following are some of the ways in predictive tools affect distributors: Enables Real-time Prediction For Predictive Analytics Techniques In most cases, real-time is a buzzword, but it applies here wholeheartedly. Intrinsically, real-time data comes from an up-to-date and endless stream of information. The streaming data is on the cutting edge, and it offers a clear image of what’s going on in the frontline. In delivery, real-time sources allow for the ability to communicate and make decisions that impact the future — in a split second. For example, development may be instantly scaled up or down to respond to changes in demand. It makes unparalleled production output that anticipates the demand, and not just returns to it. Data is the lifeblood of every productive company and provides continuous sources of real-time solutions. It is no small feat to incorporate the raw data into ongoing operations seamlessly. It is essential to develop foundationally not only the tools but additional services, like supporting teams that can take the ideas and bring them into practice. Swapping significant systems, for example, to IoT-powered tech, will not occur overnight. And the data that such an accomplishment will generate is almost infinite, so yes, the effort is worth it. The Competitive Advantage Under Predictive Analytics Techniques Organizations that use predictive analytics have a considerable advantage over competitors, particularly when it comes to market trends and preparation. Predictive analytics offer insight into what’s happening through data ingestion, which already happens in many cases. Most businesses gather an almost infinite supply of digital content. But analytics tools are learning it and making use of it — they make it more realistic. By tapping into not only customer data but also market and company performance insights, distributors can gain a leg up at any given time on what is happening. Organizations can detect real-time shortages, supply chain challenges, and demand changes. Helps In Identifying Fraud As Predictive Analytics Techniques Distributors process fraud and counterfeit goods regularly. Theft is another primary concern, particularly regarding global operations. Fortunately, predictive analysis can fight fraud by putting the abnormal behavior and events in the spotlight. Incoming data is analyzed to give a clear picture of behaviors and events in full. Spotting unwieldy patterns is much simpler, which shows that fraud or theft is going on in the course of a trip. For example, retailers may see exactly where an item is missing, and how much of a product or supply is affected. The outcome is an ideal source of insights helping organizations to reduce fraud, theft, and other erroneous issues. Through applying unusual results to real business insights, companies will discover not just who is responsible but also ways to avoid future occurrence of these events. Commercial Planning Of Predictive Analytics In Big Data It’s no secret that certain events happen in the distribution world that can directly affect a company’s performance and revenue. For example, mergers and acquisitions can set a significant dent in customer relationships. A former partner may not be viable anymore, and this is a transition that can happen almost without notice. That is, without the statistical tools in place for analytics. Predictive analytics can also predict how a partnership with prospective partners could be playing out, revealing when an acquisition might be problematic. The tools may illustrate risks associated with a business partnership, and even identify or suggest new opportunities for partners. Reveals Future Events The novel coronavirus is an excellent example of current events that have a significant impact on the supply chain and the broader market. One of the most instrumental advantages of predictive tools is that they not only help to understand but also to estimate what will happen over a given period. Before this particular case, almost no one could have expected that toilet paper would be such a product, unless, of course, they used trending data when it first began. The strength of predictive models is that they can prepare for and provide the details required to deal with these incidents well before they play out. In other words, predictive analytics may use current performance data, market trends, and human behavior to build a model or scenario. It can influence current events and help distributors prepare for what is to come, far outside the boundaries of what is considered natural. Predictive Analysis Is Essential. Undoubtedly, tools and solutions for predictive analytics are “mission-critical” and essential to achieving success in the ever-evolving world of today. Specifically, in the area of distribution and supply chain, they will have a great many perspectives to tackle industry and customer dynamics, potential issues, and much more. They also offer a robust and reliable method to handle fraud and theft. Predictive Analysis In Today’s World Important sectors where predictive analysis is useful in today’s world are: Banking and financial services With massive amounts of data and money at stake, the financial industry has long embraced predictive analytics to detect and minimize fraud, assess credit risk, optimize cross-sell / up-sell opportunities, and maintain valuable clients. Commonwealth Bank uses analytics to determine the probability of fraud in any transaction until it is approved-within 40 milliseconds of the start of the transaction. Retail Since the now infamous study that showed men who buy diapers frequently buy beer at the same time, retailers everywhere use predictive analytics for merchandise planning and price optimization,

Read More

Retail Analytics Helps You Grow Your Sales (Everything You Should Know)

Retail analytics focuses on providing insights into revenue, inventory, clients, and other critical factors that are essential to the decision-making process for merchants. The discipline covers many granular fields to build a full image of the health of a retail sector, and sales alongside overall areas for development and strengthening. Mainly, retail analytics are used to help make smart decisions, operate companies more effectively, and provide smart analytics of customer service. In addition to superficial data analysis, the field of retail analysis uses techniques such as data mining and data exploration to sanitize datasets to generate actionable BI insights that can be implemented in the short term. Also, businesses are using these tools to create accurate snapshots of their target demographics. Retailers may classify their ideal customers according to different categories such as age, tastes, purchasing habits, location, and more by using sales data analysis. The field focuses not just on interpreting data, but also on determining what information is required, how best to collect it and, most importantly, how it is to be used. Through prioritizing the fundamentals of retail analytics that concentrate on the process and not merely on the data itself, companies will uncover better insights and be in a more competitive position to excel in predicting market and customer needs. There are some excellent examples of retail analytics applicable to several businesses. One of the most significant benefits that the sector offers to companies is to maximize their production and procurement. Businesses may use historical data and pattern analysis to decide which items they will order, and in what amounts, rather than depending solely on past orders, thanks to statistical instruments. Also, they will improve inventory management to accentuate consumer demands for goods, reducing unused space, and related overhead costs. Aside from procurement operations, other retailers use analytics by integrating data from various areas to identify consumer patterns and adjust preferences. By combining sales data with several variables, companies can help recognize and predict emerging trends. It is closely related to marketing functions that benefit from analytics as well. Companies can use retail analytics to improve their marketing strategies by creating a deeper understanding of consumer tastes and gleaning more granular insights. Companies may build campaigns that focus on consumers and show higher success rates by combining demographic data with details such as shopping patterns, interests, and purchasing history. What drives the retail industry in a highly competitive market is in-store conversion, i.e., the number of shop customers vs. no people who left with a purchase. With customers becoming increasingly flexible in their purchasing habits and switching seamlessly between in-store and online, knowledge and observations are becoming crucial to understanding essential business factors such as inventory, supply chain, demand for goods, customer behavior, etc. More than 35 percent of the top 5000 retail firms struggle to do so, according to some reports. Retail analytics plays a vital role in this.  Benefits of Retail Analytics  Although there are multiple benefits that retail analytics can bring in, let’s look at how retail analytics tools help improve sales in-store. 1. Better Analyze Your Customers Customers are the backbone of your retail business; they are the ones that come into your shop, visit your online store, and determine what to buy. They perform conversions. And, how do you get to learn their purchasing habits, why they buy a product and why they don’t. It is where market analytics allows you to better understand your customers based on customer segments and consumer loyalty, which will enable you to improve sales. 2. Optimize Your Spend On Marketing Budgets A retail company needs to target a customer accurately. Marketing plays a leading role in advertising and targeting the right consumers, and again retail analytics tools that support maximizing marketing spending can help you plan consumer awareness, evaluate advertising effectiveness, and calculate marketing returns. 3. Target Customers Using Hyper Location Customers are attached to a familiar place where they often work, live, and transfer their closest buying centers. With the percolation of social media and its convergence with the web, targeted web-based advertising is becoming relevant and easy to reach local consumers at specific times on specific social media. However, analyzing this big data about your retail company is difficult, and retail analytics solutions now have a feature to get to the hyper location path. 4. Improve Your Product Offerings Sortiments or product lines are crucial to sales because the size at which the goods are available, the scope and depth of the offerings are critical for consumers to assess a product, try it and decide to purchase it. For a multitude of businesses and their items going through the market, it makes it difficult to know which items consumers want more and which ones need to be put in the store’s prime locations. The optimization of variety comes into play here. Retail analytics tools bring with them significant advantages in knowing product attributes for performance, carrying out replenishment analysis and maximizing package size, etc. 5. Price Analytics Sortiments or product lines are crucial to sales because the size at which the goods are available, the scope and depth of the offerings are critical for consumers to assess a product, try it and decide to purchase it. For a multitude of businesses and their items going through the market, it makes it difficult to know which items consumers want more and which ones need to be put in the store’s prime locations. The optimization of variety comes into play here. Retail analytics tools bring with them significant advantages in knowing product attributes for performance, carrying out replenishment analysis and maximizing package size, etc. 6. Inventory Analytics To retailers, getting the right product in the right location at the right time may sound like a major cliche. But this is the critical slogan for every retail company to succeed. According to IHL Group, a multinational consulting company, retailers are always trying to enhance the inventory management process, assign the correct inventory to customers, and

Read More

Data Analytics Helping Accountant Excel! Role Of Data Science In Accounting.

If the C-suite were to shape a rock band focused on the standard positions, the guitarist would be the ambitious CEO, and the resourceful COO would play lead guitar. The level-headed CFO will possibly be positioned as the guitarist, a significant band member, but put in the background and tasked primarily withholding the band on track to help the other members shine. This perception of the CFO as a back-office number cruncher who controls schedules monitors costs and maintains the lights on might have been accurate in the past, but the new CFO is squarely at the heart of the corporate strategy. Data’s core position in today’s business climate is the impetus for this transition. Today the CFO is the company’s co-pilot, finding the most successful clients, evaluating risk through scenario preparation, measuring customer loyalty through data collection, and designing new KPIs. Corporate boards are continually considering a future CFO in terms of whether he or she will take over as CEO eventually.   CFOs should have global and diverse experience, be up-to-date on technology, be able to recognize and recruit the right talent and, most importantly, know how to lead, as per one of the KPMG Global CEO Survey. The study also found that 85 percent of CEOs agree that the most significant strategic advantage a CFO can bring to a company is to use financial data to achieve sustainable growth. CFOs need new enterprise performance management (EPM) tools to serve this strategic role— and many see the cloud’s ability to unleash the power of their data and turn their business into an analytics powerhouse. CFOs and the finance department will need a live view into all business areas, with resources that allow them to provide real-time analyzes of changing situations, suggest actions, and offer effective strategic planning and forecasting. In a recent CFOs survey of Oracle as well as other business leaders, 90 percent of the executives said that the ability to create data-based insights is very crucial to the success of their organization. Still, more than half questioned the strength of their organization to handle large data inflows. So the more data an organization uses, the more reliable the research will be. So,  almost half of the financial decision-makers in Europe and the Middle East, for example, expanded the number of data sources they evaluate to better understand the effect of this surprising change after the Brexit vote. How To End The Tyranny Of The Spreadsheet (Data Science In Accounting) In business, where you always stand depends on where you are seated, and the finance department is well placed to offer a holistic view of the company. The CFO’s ability to link key areas around the enterprise— marketing, supply chain, manufacturing, services, and human capital management — to build a holistic, real-time business image is vital to risk management and value creation. That calls for the right resources. The ubiquitous spreadsheet is one adversary of such real-time analytics. Consider how an annual budget is produced by the finance department of the business or any department within the organization. The budget process is mostly done through a series of spreadsheets that are sent to various stakeholders, with the usual concerns: Is this the latest version? Who made the most recent alterations? Was the data correct, or have the consolidation process made mistakes? Usually, the finance department spends most of its time tracking down and checking the data— and not enough time evaluating it. Due to the many data systems and reporting tools acquired over the years, organizations rely heavily on spreadsheets and Data Analytics in Finance to organize the information. Because data is siloed in their respective units, to build budgets and strategies, LOB members must first dig into the data. Finance then spends massive amounts of time testing and rolling this unconnected data into more detailed predictions and plans. Finance teams with Data Analytics in Finance need to build better models for financial and organizational improvements if businesses are to stay ahead of the market. Today’s digital finance team is moving from simple, traditional transaction analysis to more sophisticated predictive analysis, such as statistical-based modeling, dynamic market management, and risk-adjusted business simulations. To do so, they need access to a centralized data system that drills both intensely across transactional data, and broadly through core functional divisions of the organization. Finance companies need to use analytics that interacts with cross-functional drivers such as customer loyalty, process management, and business decision-making. And, unlike in the past, these observations are obtained in real-time, not just at daily reporting times— providing a continuous view of the company’s birds. Agile CFOs Measure Non-Financial Data, Too In addition to having a profound impact on existing business models, digitization and globalization have also changed the way we evaluate business performance. Today, intangible assets like brands, customer relations, intellectual property, and expertise have become the primary drivers of the overall success of a business. Measuring the success of a company in all of these fields involves data from around the organization. It is a challenge for finance to track these non-financial key performance indicators (KPIs) with the same degree of methodological rigor it gives to financial metrics— like productivity and return on investment. A new report by the American Institute of CPAs and the Chartered Institute of Management Accountants on financial leaders found that the most forward-thinking CFOs are more likely to monitor non-financial KPIs such as talent pool, customer experience, business process performance, brand credibility, and competitive intelligence; Therefore, sustainability and social responsibility are also increasingly relevant for consumers, workers, and the result, and are steps that CFOs will recognize What’s unique in monitoring this information is not just that the data is non-financial; it’s unstructured too. Many of the data regarding brand credibility and consumer loyalty may come from social media, for example. CFOs need to rapidly track, analyze, and evaluate unstructured data and collaborate with organization-wide subject matter experts to develop new performance metrics that incorporate this data. As a result, KPI

Read More

Converting Big Data To Smart Data | The Step-By-Step Guide!

Over the last few years, Big Data has become one of the biggest buzzwords for businesses worldwide. With data of all sorts being generated in record amounts each year, capturing and analyzing this knowledge would give businesses greater visibility into their clients and their markets than ever before, and maybe even encourage them to foresee what may happen in the future. Here are just one of many amazing big data stats: They submit 204 million emails per minute, upload 2.5 million pieces of content on Twitter, send 277,000 tweets, and publish 216,000 photos on Instagram. There is a massive amount of data out there, just enough to learn. But it can be time-consuming as well as challenging to make sense of millions (maybe billions) of data points without powerful technology, particularly when this data becomes unstructured. That is often the case for digital online data in the form of news stories, social media messages, feedback from blogs, and much, much more. Besides, such is the difficulty of this cycle that a reaction towards big data has been somewhat current. Now there are concerns about the value of big data being overstated because it is too “huge” and unruly. There are two primary forms of Smart Information, which are often addressed by industry experts. Another type is information collected by a sensor, then sent to a neighboring collection point, and acted on before being sent to a database for Analytics. Such data comes from Intelligent Sensors, in particular within the Industrial Things Internet (IIoT) networks. The other kind of Smart Data is the Big Data stored and waiting to be translated into actionable information. Data heading to and from a Smart Sensor is “sensor data” for this report. The word, Smart Data, would apply to Big Data which was tested for useful information. Consumer Journey Analytics weaves hundreds of communications through multiple channels from the company internet. It incorporates thousands of activities to create a journey for the customers of a company. It is a data-driven methodology that is used to identify, interpret, and impact the experience of consumers. However, if the input is “false,” it is both annoying and offensive. Further, it may result in the loss of a client. The Customer Experience Assessment (or Customer Analytics Voice) utilizes tools and techniques to collect the perceptions, thoughts, and feelings of the customer. Consumer Analytics speech stresses the customers’ mental state. Machine Learning Smart Data Machine learning is often a method of preparation with Artificial Intelligence applications but can also be used as a system of understanding and decision making. While Smart Data’s use and prominence has grown, it has also been used with Machine Learning algorithms designed to find Business Intelligence and insights. Machine Learning allows companies to process data lakes and data centers, thus generating smart results. Traditionally, companies pursuing Big Data Business Intelligence have used Data Scientists who spend time searching for trends and correlations within the databases of an organization. Artificial Intelligence and Smart Data Decisions are made during the scanning and filtering process of creating Smart Data as to which data should be filtered and which should be released. During this method, Machine Learning and Artificial Intelligence (AI) employ specific criteria. AI is a continuous attempt to create wisdom inside computers, allowing them to function and act like human beings. Artificial Intelligence has provided autonomy and can address specific goals. Financial services companies, for example, can use AI-driven Smart Data for consumer identification, fraud detection, market analysis, and enforcement. Collecting Data Organizations with less knowledge of Big Data often gather everything and then archive it in a Data Warehouse, Data Lake, or often what a Data Swamp is. We obtain Big Data intending to use it “until we decide to use it.” While these companies may believe they are gathering quantitative data for years, the data may lose quality or quantity or may even be in the wrong format. Their money would be best used to collect data appropriate for their company. An enterprise can be knowledgeable about the data it collects and retains in a Data Lake. Data takes time and money to collect, compile, and manage. Collecting Intelligent Information can be an effective strategy for small and medium-sized organizations, rather than “pure” Information. The emphasis on Smart Data collection helps a company to use cost-effective solutions to handle it. Collecting only the essential data will minimize the use of Self-Service BI systems, preventing workers from getting lost in the mass of irrelevant data. Smart data collection is not just about removing the excess data. Smart data can come from various outlets, and an agile enterprise can combine these resources to develop a highly focused business intelligence model. The point of view is right away. Big data is unusable, lacking order. It is just a collection of random knowledge that would take years to absorb, and may not provide any results even then. But when the form can be easily overlaid and evaluated, big data tends to become smart data. At Talkwalker, we have a way to explain just how this is going to happen, and how it can be a little like searching for a partner in life. From a machine, all social data is just words on a page through different sources, including Twitter posts, Facebook posts, news articles, websites, and discussion sites. The first step, as you would on Google, is to search for a particular subject in that data. Let’s claim we’re typing “Talkwalker” into our framework for social data analytics. At this point, we would have a very long list of URLs or post titles in no particular order, without any other criteria or filters. With such a narrow filter, the knowledge that we can obtain from such details is, as you can guess, also quite restricted. All we’d ever say is how many times a specific word has been listed online. The detail is by no way meaningless. It may, in turn, be important Information for businesses looking

Read More

Integrating Data Analytics At Every Level Of Your Organization! professionals’ Guide.

What is data analytics and how it is used by large organizations to support strategic and organizational decisions? Senior leaders offer insight into the problems and opportunities involved. Most data and analytics (D&A) conversations begin by focusing on technology. It is critically important to have the right resources, but executives too often ignore or underestimate the value of the people and organizational components needed to create a productive D&A process. When that happens, D&A initiatives that fail — not deliver the insights required to move the organization forward, or inspire trust in the actions necessary to do so. The stakes are high with International Data Corporation predicting global D&A market spending to surpass $200 billion a year by 2020. A stable, efficient D&A feature encompasses more than just a stack of technology, or a couple of people isolated on one level. D&A will be the organization’s heartbeat, integrated into all leading sales, marketing, supply chain, customer service, and other core functions decisions. Why can I develop successful D&A capabilities? Start by creating an enterprise-wide plan that provides a clear picture of what you’re trying to achieve and how progress will be evaluated. One of America’s prominent sports leagues is a perfect example of an organization making the most of its D&A feature, applying it to cost management plans, for example, reducing the need for teams to fly back-to-back nights from city to city for games. Throughout the 2016–2017 season, thousands of travel-related restrictions, player exhaustion, ticket sales, arena capacity, and three major TV networks had to be taken into account. With 30 teams and 1,230 regular season games stretching from October through May, there were trillions of scheduling choices available. Companies should follow the lead of the league by understanding first that good D&A starts at the top. Make sure the leadership teams are entirely engaged in the company in identifying and setting goals. Avoid allowing the setting of objectives and decision-making to take place in organizational silos that can generate shadow technologies, conflicting versions of the reality, and paralysis of data analysis. Ask: Is the aim to help boost the company output before launching some new data analysis initiative? System jump start and cost efficiency? Drive policy, and speed up change? Growing market share? More successful innovation? Any of that? Leadership teams must understand that it takes bravery to be successful because, as they embark on the journey, data analytics observations will always point to the need for decisions that may entail a course correction.  The leaders need to be frank about their ability to integrate the findings into their decision-making. Further, they should keep themselves and their teams accountable for that. Consider a large global life sciences company that spent a huge amount of money to develop an advanced analytics platform without knowing what it was supposed to do. Executives allowed their development team to buy a lot of items, but none understood what the developed tools were meant to do or how to use them. Luckily, before it was too late, executives identified the issue, undertaking a company-wide needs assessment and restoring the platform in a manner that inspired trust in its ability to drive productivity and promote business transformation. In another instance, a global financial services company, focused on stakeholder expectations, has developed a robust development infrastructure. But executives soon discovered after creating it, that they lacked the organizational structure and resources to use the platform effectively. When these requirements were met, the organization was able to use a great platform to generate substantial operating cost savings. Data analytics is the most in-demand technology skill for the second year running, according to KPMG’s 2016 CIO Survey. Still, almost 40 percent of IT leaders claim they suffer from skill shortages in this critical sector. Formal, organized structures, procedures, and people committed to D&A can be a competitive advantage, but this significant opportunity is lacking in many organizations. Companies who develop a D&A infrastructure to meet their business needs have in our experience teams of data and software developers who are experienced in using big data and data scientists who are entirely focused on a D&A initiative. Although processes vary, the team should be integrated seamlessly with existing D&A suppliers and customers in the sector, working in collaboration with non-D&A colleagues — people who understand both the market problems and how the business analytics functions — to set and work towards practical and specific strategic objectives. The teams will need full executive leadership support, and their priorities should be aligned entirely with the company plan. In an era in which data is generated on a scale well beyond the capacity of the human mind to process it, business analytics leaders need D&A that they can trust to inform their most important decisions— not just to cut costs but also to achieve growth. And the best would use D&A to predict what they want or need from their customers before they even know they want or need it. Volatility, sophistication, and confusion can better characterize today’s business analytics decisions governing the macroclimate. In this uncertain climate, forward-thinking companies are identifying and exploiting data as a strategic tool to improve their competitive edge. Data Analytics facilitates proactive decision-making by offering data-driven insights into products, consumers, competitors, and any aspect of the market climate. Today analytics are applied on a need-based basis in most organizations. Although most companies are still considering making investments in data analytics and business intelligence, they need to realize that the process of incorporating advanced analytics into the corporate structure requires far more than investing in the right people and resources. A data-driven culture is the core of this framework and is a crucial factor for the effective introduction of analytics into the organizational system. The integration cycle begins with a data-driven resolve. Big data analysis and advanced analytics must be accepted at the corporate level as an operational feature to be powered by the data. Projects and assignments undertaken must be analyzed from an analytical

Read More
Services
Data Engineering
Azure
AWS
GCP
Big Data
Data Warehousing
AI & Machine Learning
Artificial Intelligence
Natural Language Processing
Vision Analytics
Large Language Model (LLM)
Business Intelligence
PowerBI
Tableau

DataToBiz is a Data Science, AI, and BI Consulting Firm that helps Startups, SMBs and Enterprises achieve their future vision of sustainable growth.

Hire Developers
Get In Touch
Use Cases
Digital Transformation
AI Insurance Automation
Boutique Analytics
Ecommerce Analytics
Manufacturing Analytics
Digital Lending Automation
Sales Analytics
OEE Analytics
HR Analytics
Procurement Analytics
IT JIRA tracking
Products
PrepAI
Tally BI
Marketing Cockpit
Virtual Try On Platform
HirelakeAI
SensiblyAI
DataToBiz CV Platform
Quick Links
Careers
About
Partner Program
Knowledge Base
Case Studies
Industries

DataToBiz is a Data Science, AI, and BI Consulting Firm that helps Startups, SMBs and Enterprises achieve their future vision of sustainable growth.

Hire Developers
Get In Touch
DMCA.com Protection Status
Hire Developers
Request a Quote

Get a Free Data Analysis Done!

Need experts help with your data? Drop Your Query And Get a 30 Minutes Consultation at $0.

They have the experience and agility to understand what’s possible and deliver to our expectations.

GetMee

Founder & CEO

Drop Your Concern!