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Introduction

 

Predictive analytics and machine learning are two related areas that are not mutually dependent, whereas Predictive Analytics can be introduced with or without Machine Learning. The differences between these two options are significant in what it can offer for the benefit of Business or Retail, in particular. We will talk about it in the text below.

 

What does Predictive Analysis mean?

 

Predictive analysis means the process of examining vast amounts of data to look for information and patterns, that are turned into numbers and insights valuable for humans to understand the future trends or probable outcomes of something, such as real-time prediction of employee satisfaction in Business.

 

What is Cognitive Analytics?

 

Cognitive analytics or Cognitive computing is a term very much related to tasks that lie within Artificial Intelligence, that is mimicking the way a human brain works through learning from existing historical data and drawing patterns from it. This kind of analytics implies that the algorithm makes conclusions from the existing knowledge bases and then inserts it again into the knowledge base in kind of a self-learning loop. In the first place it is achieved with Machine Learning algorithms, so Cognitive Analytics is the encompassing term for everything related to machines processing previously unstructured data, extracting meaning from it, so that what Machine Learning does. Ultimately, we can identify 3 ways to process huge amounts of data and draw insights from it, where Cognitive analytics appears to take the leading role due to its efficiency:

 

  • People. They can produce patterns, drawn from new, unstructured information, although for huge amounts of data this process will be extremely slow and tedious.
  • Big Data Analytics. It can rapidly draw insights from masses of structured information, although it cannot automatically produce it from unstructured data.
  • Cognitive Analytics. Finally, cognitive analytics combines 2 advantageous of the way a human brain works to process unstructured data successfully and deals with huge amounts of data quickly at the same time.

 

Predictive technologies and predictive analytics tools

 

Predictive technologies, first of all, imply a set of means used to forecast numbers/patterns on the basis of previous data or records. It has been used mostly for marketing and business perspectives to predict demand and supply patterns. Such tasks as weather forecasts, stock exchange, and socioeconomic activities were initially performed with predictive analytics tools. Amazon is one of the online retailers that first used predictive technologies. Its website displays listings of merchandise that customers are interested in and shows a list of products, bought by customers with similar interests.

 

TIA or Total Information Awareness is another predictive analytics tool the goal of which is to collect huge amounts of data on people (individual’s formation signature) from all reachable sources, process this data and detect possible terrorist activity.

 

Currently, there are lots of predictive analytics tools most of which are not free and are available to use online. They can decrease the time needed to collect a big dataset from various sources, clean this data is based on custom parameters and make an analysis with different technologies and algorithms. A report by Deloitte on the Analytics Advantage showcased that making analytics before starting any business or strategy influences a decision by making it more successful in 50% of cases.

 

What can predictive analytics do?

Armed with typical Machine Learning advanced techniques such as more complicated Regressions, Classifications, and Neural Networks, Machine Learning engineers can use computer’s power to predict strategically critical outcomes in Retail, E-Commerce and Marketing with Predictive Analytics. Enterprises need to be able to put AI to work in many departments and conducting multiple processes and making predictions to enhance their business agenda, especially Enterprise financial planning where Predictive Analytics can reveal the value hidden in the collected data. This allows organizations to work error-prone.

 

Predictive Analytics and Machine Learning

 

While Machine Learning is used to ground up and scale models for automatization and optimization tasks across various fields, it is also used for making more consistent and accurate risk assessments, making recommendations for business intelligence purposes and perform other predictive tasks that are achieved with Predictive analytics.

 

Predictive Analytics

 

Benefits of Predictive Analytics with Machine Learning

 

Predictive analytics can handle assumptions as to the future without the help of Machine Learning models, however, this plan of work has its disadvantages:

 

  1. Predictive analytics in its initial form relies on classical statistical techniques as Regression;
  2. It works only on “cause” data and must be re-done with “change” data;
  3. It still needs human analytics to investigate the associations between the cause and the outcome.

 

Meanwhile using Machine Learning for Predictive analytics has its strong points: 

 

  1. Using more advanced computational algorithms such as Decision Trees or Random Forest;
  2. It is self-learning and has automated improvement in response to pattern changes in the training data;
  3. Unlike conventional predictive analysts, Machine Learning Engineers write a complicated code in the Python programming language, most often, which enables them to compute everything with the computer’s capacity and get much more impressive results instead of doing it manually with the help of such primitive programs used before, like Excel.

 

If to wisely consider the factors above, the benefits of Predictive Analytics with Machine Learning are obvious. Predictions with Machine Learning models are our tomorrow and progressive businesses should rely on them, rather than simple predictive analytics tools predictive and technologies used by statisticians.

 

How is predictive analytics used in Business?

 

Being productively applicable in many industries, predictive analytics is used in Business to prevent business losses, predict the customer behaviour in a long-term period, increase share of a business segment, identify target markets based on real data and indicators, get insights on the best way to approach individual customers, analyzing everything from purchasing patterns to customer behaviour and social media interactions.

 

If to take a closer look at what Predictive Analytics can do in each of them, we single out these separate tasks:

 

Customer segmentation

Every company makes its own way of researching what is the market to dive into, taking into account the values that would bring the most value for their industry, products, and services. Real data and indicators help to identify target markets with predictive machine learning approaches, where the next step is to spot those market segments that are the most suitable for the goods your business offers.

Churn prevention

It’s much easier for a business to retain customers rather than spend much money on marketing campaigns to acquire new ones. Predictive Analytics can help in preventing customer churn, avoiding the need to replace the loss in revenue. If you timely identify the traits of dissatisfaction among the existing clients in your database,  you will stay away from losing those customers and even customer segments that at risk to drop out.

Predictive maintenance

Budget dispensed for maintenance can be one and a half times bigger if a company does not have any downtime prediction and prevention measures. Machine Learning tools can analyze unstructured data and metrics linked to the technical equipment lifecycle management. Probable maintenance events and capital expenditure requirements can be predicted to avoid spending much of the costs the company is investing in infrastructure and equipment on repairing works and examinations.

Risk modeling

A massive amount of historical data collected throughout the company’s existence is a source of valuable information to derive risk areas and trends to ultimately conclude which situations can negatively influence business. As far as risk can have various forms and sources, predictive analytics can capture and quantify risk issues, examine them and refer suitable actions to minimize the factors, causing it.

Quality assurance

Insufficient quality control may critically affect the customers’ satisfaction level, their buying amounts, and in result 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 in Retail

 

Below are some cases of Predictive analytics used for AI in Retail:

 

Predicting the Best Retail Location

 

It’s hard to argue that “Location” may appear to be a critical factor in the success of a business. That is, you can often notice some sections in a city are much more loaded with various shops or dining buildings – restaurants, fancy clothes stores, cafes and etc. There are also places where restaurants of shops are being replaced over the years and that is not going to change. It hints a thought that a business owner should very carefully consider the place he wants to locate his business of whatever kind. Data Science and Machine Learning solve this question, learning on the data about the world’s most famous stores, their patterns or time series analysis of different popular and not so popular places.

 

Predicting Product Needs and prices for a certain customer

 

This may seem a kind of futuristic feature from Hollywood films where a character comes into a room and his whole personality is being estimated to give him the most relevant and quick service. Imagine if the algorithms could predict a customer’s needs and preferences, basing on the history of his precious in-store behavior. And this is not only online retail recommendation engines feature, this as well may become possible in real stores with Computer Vision on board, that scans customers and analyzes them.

 

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, Machine Learning engineers can use regression and historical methods such as time-series to predict the expected sales amount of an item, e. x. shoe type for a certain time period. Accurate pricing decisions are achieved by analyzing 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, however many other factors that influence price have appeared in E-Commerce so 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 such features as competitor prices, inventory levels and compare demands to find out what prices should be like.

 

Most popular ways to implement Predictive Analytics in Marketing

 

There are some tasks performed with Predictive Analytics that can be specified as those related to AI in Marketing and improving its strategy.

 

Customer Lifetime Value

Those customers and that have the biggest lifetime value, represented in how much money they spent, how consistent was their paying history and how much time they already buy something from you. Identify such customers – is the hardest thing to do for marketing, on the other hand these kind of insights help companies to optimize the marketing strategy, subsequently increasing the share of this business segment and gain information about the most valuable customers.

 

Product Propensity

A combination of data on purchasing activities and online behavior metrics from such sources 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 contains 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 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 combination to create your own analytics tool which can be used to monitor how your company’s reputation changes and what people think on factors, influencing it.

 

Conclusion

Currently, most modern businesses begin to rely on the Electricity of our time – AI solutions, which give the older Predictive Analytics methods a new life and turn it into highly efficient instrument to get business insights and predictions. Predictive analytics have beneficial applications in several industries such as Retail, E-commerce, and Marketing in the first place. It becomes the fuel to drive the company’s business decision and foresee their success in the future.