Predictive analytics and machine learning are two related areas that are not mutually dependent. Predictive analytics is a set of old-school scientific methods for making predictions, but this can also be enhanced by modern approaches to get greater business value. One of these approaches is known as Machine Learning. These two domains differ hugely in their advantages for businesses, including online Retail. Let’s dive into it.

What do we mean when we say Predictive Analytics?

It is the branch of advanced analytics that is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining and statistics to analyze current data to make predictions about the future. Nowadays, when the trend for automated processes is on a surge, it also uses Machine Learning as a more advanced method of prediction.

The difference between Descriptive, Predictive and Prescriptive Analytics

Predictive analytics looks for correlations in past data, so it requires much historical data. The correlations that it discovers are then used by engineers to train the model and by business analytics as a source of the future trends or probable outcomes of something, such as real-time prediction of employee satisfaction in the business.

Predictive technologies, first of all, imply a set of means used to forecast numbers/patterns on the basis of previous data or records. This methodology is mostly used by mass media advertising and merchandising purposes, or to forecast the level of demand and supply. Tasks such as weather forecasting and stock exchange prediction were initially performed with predictive analytics tools. Amazon is a huge retailer that showcases ratings for merchandise and uses a potent recommendation engine to reveal users with similar tastes. This company was a pioneer in predictive technologies.

Most tools for this are not free of charge on the Web, but nevertheless there are tons of them. They can decrease the efforts needed to collect a big dataset from various sources, choose custom options within a domain of interest and clean the dataset. The next step would be to use the cleaned dataset for analysis with the developed algorithm fueled by Machine Learning. Deloitte showed a report on how useful predictive analytics could be. Step one in a powerful business strategy is diligent research of the market, and the aforementioned technique is helpful in achieving this. If a strategy is successful in more than half of the scenarios, it is more likely to be chosen by a business owner.

Machine Learning and Predictive Analytics

Machine Learning is powerful because of its ability to build the core of models to automate and optimize tasks across various fields. It is also used for making more consistent and accurate risk assessments, making recommendations for business intelligence purposes and performing other predictive tasks.

Machine Learning in Retail

Benefits of Predictive Analytics with Machine Learning

We already know that predictive analytics does not necessarily have to be fueled by Artificial Intelligence advantages to help analysts build marketing strategies, but this plan of work has drawbacks that significantly affect the quality of the final result.

Conventional MethodsMachine Learning-based methods
Predictive analytics in its initial form relies on classical statistical techniques as RegressionUses more advanced computational algorithms such as Decision Trees or Random Forest;
It works only on “cause” data and must be redone with “change” dataIt is self-learning and has automated improvement in response to pattern changes in the training data;
Cannot go without human aid in discovering associations between the reasons and the result.Machine Learning engineers work to create efficient AI software which enables them to get much more impressive results.

Predictive Analytics with Machine Learning has obvious benefits over classical methods. We can easily gain intuition about this from the text above. Predictions guided by smart (Machine Learning) approaches are our tomorrow. Progressive businesses should look into more proficient methods rather than simple predictive analytics tools and technologies used by statisticians.

Data for Predictive Analytics in Business

Benefits of Predictive Analytics

Although Machine Learning/AI-based approaches may be very potent in predicting business values, they require much raw data. So where do we get that data?

Data is literally everywhere, especially for developed businesses that have a rich history of sales and customer lists.

Common sources for Predictive Analytics in Business:

Websites. Websites, especially in retail, accumulate much value when it comes to user segments, preferences and possible issues. Out of the valuable data about customer interaction with the website, including his or her purchasing behavior, location, gender and other factors, one can build a perfect route for marketing activities.

Point-of-sales. Offline retail prosperity is also supported by collecting information from Point-of-sale systems. This information deals with purchases, i.e. the day of the week and the hour, as well as the number of products in the warehouse, and this will help in building strategies. Every business, whether a restaurant, retail, or grocery store, should have an appropriate type of point-of-sale that would refer to their business profile and would collect more information.

Loyalty Card data. This card type is key to maintaining loyal relationships between a client and a business. These cards possess diverse personal information about customers (name, address, birth, etc.). Such information will help you build a persona and customize promotion strategies for each particular customer segment.

Mobile apps. Mobile apps can sometimes be more useful in collecting information and engaging customers as long as the mobile phone is always near them. Users are now more quick to surf the Internet on mobile while waiting or commuting. That’s when it’s easier for them to install a mobile application and buy things from your retail store online.

Supply chain systems. These kinds of systems have much data about the way useful items are transported and stored. You can optimize speed and efficiency for warehousing and commuting if you have historical data of the arrivals and departures of goods.

In-store sensors. Discover what your customers do and when every time they visit a shop. A true image of customer behavior will lead to higher sales and draw attention to the needed products.

CCTV Cameras. Videos of customers strolling through your shop is one of the best sources to derive insights for offline or in-store retail. You will surely spend some time defining the best technical stack, camera angles and camera position and building a map of the store. In the long run, it’s worth spending those efforts though. You would be able to streamline your business’s success by learning the micro-influences that affect how customers behave.

How is Predictive Analytics used in Business?

Every business deals with losses, seeks to improve customer-oriented long-term strategy, and targets an increase in shares of a business segment, i.e. identification target markets. This is related to predictive analytics. This class of data analysis demands real data and insights extracted from it. Individual approaches to customers can only be derived from shopping history and social media analysis.

Examples of Predictive Analytics in Business

Here are some business examples:

  • Customer segmentation. Divides similar customers into segments.
  • Churn prevention. Finds out why customers may feel dissatisfaction.
  • Predictive maintenance. Maintenance is provided before capital repairs (e.g. For downtime).
  • Risk modeling. Detect risk issues.
  • Quality assurance. Get insights into probable issues to stop their development.

Customer segmentation

Assumptions on which market to choose are built around the items and services a business is going to sell and their likely values. Target markets are identified by scraping data from internal and external sources, and then ML comes into the game by building the necessary models. The final aim is to spot those market segments that are appropriate for your business.

Churn prevention

It’s much easier for a business to retain customers rather than spend money on marketing campaigns to acquire new ones. Predictive analytics can help to prevent customer churn, avoiding the need to replace the loss in revenue. The key to customer retention is to identify the traits of dissatisfaction at the moment they damage your reputation.

Predictive maintenance

The lifecycle of technical equipment is managed by predicting defects and monitoring indices of a device at the right time. Machine Learning guides the right time for maintenance and reveals requirements for capital expenditure. In the long run, it helps to cut expenses for repair and examinations of infrastructure and equipment.

Risk modeling

Risk can have various forms and sources, and predictive analytics can capture and quantify risk issues and refer suitable actions to minimize the underlying factors.

Quality assurance

Insufficient quality control may critically affect the customers’ satisfaction level and spending and eventually impact the company revenue and market share. So, a smartly applied predictive analytics approach here can provide insights into probable issues and trends before they begin to affect the company.

5 ways to Implement Machine Learning for Predictive Analytics in Retail

Analytics as the foundation of retail profits

Predictive Analytics in Retail Examples:

  1. Locate your spot. Predict the best location for your business from the historical data on global stores.
  2. Retail customer analytics. Scan and analyze customers with Computer Vision to know about their choices in advance.
  3. In-store analytics. Get to know about the most popular spots at your shops and analyze customer behavior to improve sales.
  4. Up-selling and cross-selling. Predict which things go well together and can be effectively cross-sold.
  5. Make better pricing decisions. Find out the best scenario to push prices in the other direction.

Predicting the Best Retail Location

It’s hard to argue that “Location” is a critical factor in the success of a business. That is, you can often notice some sections in a city are more loaded with various shops, dining options, fancy clothing stores, cafes, etc. There are also places where restaurants or shops are being consistently replaced over the years, and that is not going to change.

Retail customer analytics

This may seem like a kind of futuristic feature from a Hollywood film where a character comes into a room, and his whole personality is being judged to give him the most relevant and quick service. Imagine if you had a tool that could predict a customer’s cravings in the blink of an eye, but you would definitely need his history though. This is not only a feature for online retail recommendation engines, but it may also become possible in real stores with Computer Vision onboard scanning and analyzing customers.

In-store analytics

In-store analytics helps to better realize a customer’s needs and preferences while investigations into different customer behaviors help to improve customer experience and boost sales. The main highlights here are product replacement, a store design change to attract customers to needed products, and an increase in street capture of passersby.

Up-selling and cross-selling

It’s very important to maximize your company’s existing value as well as future revenue, and predictive analytics can help here with suggestions on which goods may be combined and relate to which market segment.

Demand forecasting

The way you market, price and sell your products can be changed significantly with demand forecasting. For example, AI experts use mathematically grounded tools to predict the expected sales of an item, e.g. shoe type for a certain time period. Accurate pricing decisions are achieved by analyzing the consumer, cost and competition. With logistical and storage data, it is possible to estimate future capacity requirements, maintain availability and make accurate decisions on pricing.

Make better pricing decisions

Sometimes retailers face challenges when it comes to making a decision on price changes. For most of them, seasonal trends and tendencies are given priority in making those decisions. Anyhow, other factors that influence price have appeared in e-commerce thus far. Using predictive analytics here can help to identify the best time to start decreasing or pushing prices in the other direction. AI can monitor features such as a pricing map of the market and compare demands to find out what the prices should be like.

Predictive Analytics for Sales and Marketing

There are some tasks performed with Predictive Analytics that can be specified as AI in Marketing used to improve business strategy.

Predictive Analytics for Sales and Marketing examples:

  1. Customer Lifetime Value. Get insights into the most valuable customers.
  2. Product propensity. Define the most effective marketing campaigns and social media channels.
  3. Sentiment analysis. Estimate the information on your brand and products to find what people really think.

Customer Lifetime Value

There are customers that have the biggest lifetime value, represented in how much money they spent, how consistent their payment history was and how much time they have been buying from you. Identifying such customers is the hardest thing to do for marketing. On the other hand, these kinds of insights help companies to optimize the marketing strategy, increasing their share of a business segment by gaining information about the most valuable customers.

Product Propensity

A combination of data on purchasing activities and online behavior metrics from sources such as social media and e-commerce enables the predictive analytics approach to find correlations and provide insights to define which marketing campaigns and social media channels are effective in relation to your company’s products and services.

Sentiment analysis

The internet has endless sources that may contain relevant information about your company’s image and reputation. However, it is irrational to try to find and extract all of the information manually. Automatic web search together with crawling tools to find customer feedback and posts referencing your organization’s name will make a good analytics tool, which can be used to monitor how your company’s reputation changes over time.

Big data for Retail

It can rapidly draw insights from masses of structured information, although it cannot automatically produce it from unstructured data.

How to capitalize on Big Data

Benefits of Big data for Retail

Get the full and comprehensive image of each customer – get to know each customer’s habits, and it will be paid off by their loyalty.
Make use of coming and fading trends – optimize your price according to the current market situation, knowledge and when you can derive the maximum value from your products.
Customer service on a new level – recorded calls and social media content might contain useful information for a better customer service system.

1. Get the full and comprehensive image of each customer. Retail big data analysis can help you create a comprehensive image of each customer based on factors such as customer likes/dislikes, their propensity for using coupons, customer gender, location, social media life and others.

If even a few of the named factors become known, they can be used in an efficient marketing campaign. Investigating affordable and efficient micro-influencers on customers may have a more powerful effect than hiring an expensive superstar to advertise your production.

2. Make use of coming and fading trends. Product price optimization is essential for every business to keep up with the rising or decreasing market demand. A permanent monitoring of relevant keywords in the search engine will help you predict upcoming trends. This makes it possible for retailers to launch an effective pricing strategy and offer new products.

Retailers can use beta tests to examine different customer segments and get to know their purchasing habits and the best price fit.

3. Customer service on a new level. Usually, customer support records calls with users for “quality control”. Retail data analysis will use these calls to derive evidence of where the service could be improved.

It is already known that retail stores use motion sensors and cameras to investigate where their customers spend the most time, and they purposely change the layout of products on shelves to sell needed items first. This makes customers leave the shop with more items than intended.

Social media reviews and comments can also be analyzed to find out the customer’s opinion about an item.

Retail data analysis

We now know how essential retail data analysis is in order to stay competitive in the retail environment. Let’s look at some real-world examples.

Use cases of Big data for Retail

1. Offline and online data integration

Office Depot, a retail company that sells office supplies, has two brands operating in 13 companies. As a retail leader in the supply, the company manages to keep its position ahead of its peers by integrating online and offline business.

The company makes use of big data to integrate data from their offline assortment, website, customer call center and ERPs. Targeting needs within particular customer groups help Office Depot to stand out among its competitors.

2. Analyze a terabyte of data daily

Processing almost one terabyte of data daily enables Groupon, an e-commerce company, to keep subscribers updated about discounts on activities, goods, travel and other services.

This immense amount of data requires a big data platform to store and examine it. Otherwise, it would be too expensive. The company uses a major data framework to integrate, import, transform and examine data in real-time. Leading stakeholders are thus able to generate summaries and usefully visualize information about millions of users in bite-sized formats.

3. Survive retail boom days, Black Friday and Cyber Monday

Black Friday and Cyber Monday are undoubtedly the most exciting days for customers and the most intense period for retailers. Aldo, a shoe and accessory retail company with headquarters in Canada, knows how to deal with mad sales using big data advantages. The company is supported by a service-oriented big data architecture to integrate several data channels in billing, payment and fraud detection.

4. Keep flexible and agile

Rakuten is a French retailer based on a third party pricing model. They collect huge datasets of client and seller activities to ensure the success of all transactions between parties. They implemented a big data platform to unite buyer and seller datasets with an Oracle database storing information on over one hundred million Rakuten products.

5. Big data to augment Salesforce

The hopping network myWorld Solutions AG engages customers in collecting cashback and scores for purchases within the network, which has more than 70,000 sellers in forty-seven countries. The corporation uses a big data platform with a connector to Salesforce to collect, transform and clean data about merchants and customers before launching it into Salesforce and Marketing clouds.


What is the benefit of Predictive Analytics for Business?

The main benefit is that AI-based predictive analytics can use historical data of a business to make predictions about future prices and trends on the target market and can analyze customer behavior to streamline your current business strategy.

Is it suitable for both online and in-store?

Predictive analytics has techniques that are good both for offline and online retail. There will be a difference in how these techniques operate online and in-store. For example, an online strategy will use only digital data on transactions or users, while offline analytics will involve data from CCTV cameras, point-of-sale, and in-store sensors.

What is the difference between ML and conventional methods for Predictive Analytics?

Conventional methods still need human efforts to analyze correlations between the cause and the outcome, while ML-driven methods are trained to give outcomes with minimum human resources involved, quicker and smarter.

How AI Predictive Analytics will influence my current business processes?

An essential part of implementing an AI predictive analytics model into an existing solution is to set the data pipeline correctly to store and structure data that eventually will be analyzed. As a result, the application’s architecture will have to include a proper data collection pipeline.


With the wider advent of Machine Learning solutions into all business types, Predictive Analytics solutions based on AI will enhance the performance of both online and in-store retail businesses. The use of data for prediction of product propensity, customer segmentation, store redesign, price optimization and other decisions will give companies a chance to outperform competitors who have less effective conventional techniques in their arsenal. Predictive analytics solutions are also fit to power Sales and Marketing activities with a large potential to derive insights from social media data.

Further Reading

  1. Big Data in Retail: Common Benefits and 7 Real-Life Examples –
  2. Five Big Data Use Cases for Retail –


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