Data is the most important asset of the retail industry. Data is useless if it does not help companies to make smarter decisions for business growth. As consumer behavior is evolving with time, it depends on how data are helping retail companies to get relevant business insights, improving customer experience leading to enhanced business outcomes. Predictive Analytics is the AI /ML digital tool that works on data to derive valuable insights and predictions, forecasting sales thereby helping retailers to plan ‘What Next’ moves. How Predictive Analytics can boost the retail industry? How is it helping retailers to generate sales and have a competitive edge in the market? We will discuss all these points in this blog today.

1. Product Demand Forecast

As a retailer, you want your business to expand and products to connect with your target audience. If you know what does your target audience demand and their preferences, your products will never be out of stock or understock, right? To keep your business running smoothly, predictive analytics helps to forecast the demand for exact products your target audience wants. The technology helps to ensure that you have enough items stocked to sell and can meet demands adequately and can predict your profitable months as well as sales deficit. It means predictive analytics helps retailers to assess demand and sales performances. Product performance can be gauged by indicators such as sales margins, the number of units sold besides other metrics. It helps to improve consumer engagement and satisfaction.

2. Pricing Forecast

Pricing forecasts is a major functionality of predictive analytics that uses real-time capabilities of machine learning, and data science technology, to bring out adequate answers to questions that as a retailer you’d like to know in advance. Few sample questions that predictive analytics can forecast regarding pricing elements are:

• For maximizing sales what’s the right price point?
• What should be the frequency for running price-based promotional activities?
• What should be the optimal attainable price of a customer?
• How the competitive pricing will impact sales?

Apart from the above factors, weather forecasting, real-time sales data also help to alter as well as induce the most perfect pricing part (best pricing ).

 3. Managing Inventory

As we have mentioned earlier, too, how predictive analytics helps in estimating product demand and what your target audience wants, it also helps towards managing your inventory properly and up to the mark. A poorly maintained inventory leads to a loss in sales. As a retailer, you will never like to hold on to those products that are not yielding sales. Moreover, you will always want to replenish stocks that your consumers want to buy. Predictive analytics helps to predict demand for products that help you to manage your inventory.

4.  Marketing Campaigns

As a retail business, you need to prioritize marketing campaigns from time to time.  Your business needs a powerful marketing plan that helps to boost your business potentials driving the best ROI. An inefficient marketing campaign management leads to a poor ROI because you cannot estimate the right budget and varied inaccurate calculations done on your part.AI-ML digital capabilities of predictive analytics help you to gain actionable marketing insights, and suggest you adopt individual campaigning for a specific target audience and be more budget efficient leading to maximum conversion of leads.

5.  In-Store Optimization

Predictive analytics helps in online monitoring of shopping activities, as well as in-store data analysis. Varied digital tools & technologies, IoT sensors, and surveillance cameras that are installed in retail stores, on product shelves, help to monitor the shopping activities of varied consumers. The technology helps you in the following ways:

• You can identify your shoppers’ favorite routes throughout the retail floor
• You can identify and distinguish popular, most searched products on varied shelves
• You can count the total number  of visitors  in your shopping zone at different times per day
• You can adequately calculate the average visit time of customers
• You can genuinely monitor queues

Predictive analytics helps you to gather these valuable insights. Using these insights, you can configure store layouts, plan the schedules of your staff, can distinguish the ideal opening hours of your retail stores, reduce customers’ waiting time in queues, and increase the high level of security.

6.  Shopping Cart

 If you own an e-commerce retail store, you can leverage the massive capabilities of AI-ML-powered predictive analytics to optimize your online store, product categories as per existing online shopping carts of consumers. For instance, your customers while browsing your online shopping stores may come across products they like so much. They save them, add them to their shopping carts. When they don’t buy them,  predictive analytics tracks them and lets you know why they didn’t buy and how you should respond to this kind of situation in the future.

7. Social Media Marketing

Social media marketing, online advertising are important segments of retail marketing. As a retailer, you’d think social media marketing involves greater expenditure than offline advertising. But using predictive analytics, you can manage these upfront costs and invest in social channels where you gain the maximum outreach and visibility. Predictive analytics can help you maintain a strong social media presence. You don’t have to stress regarding unreasonable expenses because the digital tool helps you to invest smartly on those social channels where you get the maximum ROI and prospect of acquiring new customers.

Conclusion

Predictive Analytics can help to predict the future of your retail business. Whether you are an online retailer sharing an online marketplace with bigwigs or small start-ups in the offline retail space, predictive analytics helps you to extract valuable actionable insights for your business outcomes in the right direction. The aforementioned AI-ML-powered digital capabilities of predictive analytics are just a few significant use cases in the retail industry. There are many more benefits yet to unfold. Stay tuned to this space and we will have further elaboration on this topic with detailed analysis.

Are you a retailer in the e-commerce industry or a physical retail store and changing consumer behavior bothering you? Want to know how predictive analytics can help you solve this challenge and grow your retail business?? Reach us.

Big data is sometimes complex and difficult to understand. An increasing number of companies are opening up their doors to experience data based solutions. With increasing demand to understand and process data based solutions enterprises are eager to invest their time, money and resources on the opportunities to achieve business goals and improvise efficiency in a number of ways.

Deciding to utilize data science is not as simple as just collecting the information. It is important to understand the need of your business. It is necessary to figure out whether to bring machine learning in-house– introducing new departments to facilitate or to collaborate with external sources to manage the data, it totally depends on the size and need of the business.

Analysing data not only helps the business to simplify big data challenges but also helps management to make logical decisions to lead the company reach heights and achieve organizational goals like better profits and managerial efficiency. Here are five common ways in which data science can add value to your business:

Empowering Management

The management will have to learn to formulate tasks based on what they want to predict or recommend, rather than following the traditional basic procedure. Implementing a data strategy in an intelligent, structured way makes a big difference. In modern developed economies, businesses that will invest in the technology and talent will be in the best position to compete and succeed.

Identifying Target Audience & Trends

One of data analytics’ primary objectives is to identify emerging and prominent market trends. Once identified, these trends can help to provide more insight into consumers’ choice and expectations.

The more information that companies gather and analyse about their customers, the more they learn about their behaviors, needs, and preferences. This in-depth knowledge can help you tailor products and services to meet the consumer expectations and demands.

New Level of Business Productivity

Data science is still an emerging field and no matter how well you think your business is doing, you can improve your position in your industry by collecting and using data effectively. There is no longer any need to wait days, weeks or months for results. In some cases data analytics can happen in real time giving business leaders interesting insight into customer experience and business productivity.

Significant Decision-making solution

For years, executives have made million-dollar decisions based on gut instinct. With executives getting efficient at decision making, the right decisions will let the management provide impartial, data-led guidance for critical decisions which certainly will make significant organizational changes professionally on different level.

Facilitates sensible recruitment

Processing large number of CVs, applications, vast amount of data and data-driven documents gets easier with data science solutions at your HR departments rescue. Data science can help your recruitment team process much faster and recruit the right talent quickly and without compromises and confusions.

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It’s 2018, there’s no doubt about the fact that the world is aware of the wonders one can of do with Artificial Intelligence and Machine Learning.We are living in exciting and innovative times with futuristic technology literally at our fingertips.

How machine learning can help ones business is the question that is on every entrepreneur’s mind. We’ve got to the stage where we take intelligent machines for granted. We trust them to carry out complex tasks requiring extreme precision, speed, and accuracy – from pacemakers to auto-pilots. But while autonomous cars get all the headlines, it’s actually many other industries that may feel the greatest impact of the new tech. So,it’s very intriguing to learn howmachine learning improve businesses intelligence.

Put simply, machine learning is about understanding data and statistics. It’s a technical process where computer algorithms find patterns in data, then perform functions – like improving the core functionality of existing software and analytic, uncovering previously inaccessible insights hidden in large data sets unstructured data formats, and taking over tasks like image recognition, text analysis, and repetitive knowledge work. The potential use cases are seemingly endless, from supply chain and risk detection to logistics and technical support to behavioural analysis and customer support.

Businesses can also use machine learning to up-sell the right product, to the right customer, at the right time. In 2018, marketers will continue to rely on machine learning to understand open rates when it comes to email– so you know exactly when to send your next campaign.

Retail companies are also tracking what ads or images you’re most likely to stop scrolling on, in order to target you with specific content. Machine learning is applied to reduce the risk of credit fraud in small businesses. Machines learn from historical datasets that contain fraudulent transactions and can identify patterns that represent a typical fraudulent transaction.

The value that machine learning can deliver will be dependent on the degree to which these systems can deal with structured and unstructured data and its quality. Similarly, Chatbots can also help achieve a faster customer service resolution, as well as provide quick histories of each customer for impeccable customer service.

Consequently, machines will not eliminate human intervention in decision making. Machines will simply change the nature and timing of our intervention. A machine self-learns so that the algorithms can predict accurately. However, final decisions have to be taken by living, breathing, empathetic entities.

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Advanced data analytic are rapidly becoming the “digital microscopes” that allow organization to reveal hidden insights and quickly act on them. We at Fusion Informatics offer data solutions that help in accelerating the digital transformation of an organization.

Digital Transformation is basically the transformation of the way an organization uses its current technology and makes greater use of it to optimize and improve the service to customers. If in today’s era companies do not digitally transform, they will not be able to keep up with their competitors and also will find it difficult to make use of the data they have.

During this digital generation there is so much of data exchange on a daily basis. Every organization now has an abundance of raw data but they need to analyse this data as well. Fusion Informatics provides data analytics services that help one in analysing raw data and using it to drive digital transformation. This would help the organization to make optimal use of the data on hand and customize their services in a way that would give the customers exactly what they are looking for. Companies today have learnt that simply storing data upon receiving it is not enough as they need to analyse this data in order to gain useful insights to make decisions prompting business growth.

Using the data analysis solutions that Fusion Informatics provide have proven very useful. These analytics are used in reducing overhead and production costs and have also been used to make qualitative improvements to the organizations. Data analytics has become the core driving force behind digital transformation and without it the companies that exist today will not be able to survive after another 50 years.

With the advent of network driven systems imbibed in our lifestyles, data has become the core of all services. Raw data available in massive multitudes serves as the basis of information which can be converted into knowledge that further provides value to all businesses. This chain is of utmost significance as it is shaping the way we are producing and consuming products and services day in and day out.

Data science is our era’s new fad and can be understood as a systematic process of obtaining insights into terabytes of information gathered over the last few years. It makes use of big data i.e. high volumes of structured and unstructured data for its analytical purposes. And just like any other scientific process, data science also involves identifying a problem, formulating hypotheses and conducting studies to determine the presence of a certain phenomenon.

Applications of this genius baby of computer science and maths are plenty. It has given rise to data intelligence, an analysis of various forms of data in such a way that it can be used by companies to expand their services or investments, or to analyze their own operations or workforce to make better decisions in the future. It can be used in the field of social science to determine what triggers reactions in people and how, in economics to understand how process fluctuate due to monetary reforms, in engineering to identify new methods of creating materials and products, in law to conduct trend analysis and develop frameworks, and so and so forth.

Data science thus plays a vital role in improving the quality and relevancy of daily processes ingrained in our lives. It runs on analytical creativity and looks out for future needs even before they’ve arrived. All big businesses like Netflix, Amazon, Procter & Gamble, etc have understood that data science is not something to be ignored but rather to be embraced and adopted. It’s time we all do too.