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Thoughts on Artificial Intelligence in Predictive Analytics for the Retail Industry

Machine Learning, a subdivision of Artificial Intelligence, has some very cool implementations across the Retail industry. My favorite, however, is using Machine Learning for Predictive Analytics. This is still a relatively new area, but I’m sure it will explode in 2020 and become much more popular.

Predictive Analytics and Machine Learning
While Machine Learning is used to 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 performing other predictive tasks that can be achieved with Predictive Analytics.

Predictive Analytics

Benefits of Predictive Analytics with Machine Learning
Predictive Analytics can handle assumptions regarding the future without the help of Machine Learning models; however, this fashion of operating has disadvantages:

Predictive Analytics in its initial form relies on classical statistical techniques as Regression;
It works only on “cause” data and must be re-done with “change” data;
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:

Using more advanced computational algorithms such as Decision Trees or Random Forest;
It is self-learning and has automated improvement in response to pattern changes in the training data;
Unlike conventional Predictive Analysts, Machine Learning Engineers usually write a complicated code in the Python programming language, which enables them to compute everything with the computer’s capacity and get much more impressive results instead of doing it manually using primitive programs such as Excel.
If one wisely considers 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 and technologies used by statisticians.

How is Predictive Analytics Used in Business?
Predictive Analytics is used in Business to prevent business losses, predict customer behavior in a long-term period, increase the share of a business segment, identify target markets based on real data and indicators, get insights on the best way to approach individual customers, and analyze everything from purchasing patterns to customer behavior and social media interactions.

Let’s take a closer look at what Predictive Analytics can do in each of these areas:

Customer Segmentation
Every company creates its own way of researching what market to dive into, taking into account what would bring the most value to their industry, products, and services. Real data and indicators help identify target markets with predictive Machine Learning approaches. The next step is to spot the most suitable market segments for the goods or services your business offers.

Churn Prevention
It’s much easier for a business to retain customers than to spend a lot of money on marketing campaigns to acquire new ones. Predictive Analytics can help prevent customer churn, avoiding the need to replace a loss of revenue. If you can quickly identify the traits of dissatisfaction among the existing clients in your database, you can not only avoid losing those customers but also identify the customer segments that are at risk of going elsewhere to conduct their transactions.

Predictive Maintenance
The budget for maintenance can be one and a half times larger if a company does not have any downtime prediction and prevention measures. Machine Learning tools can analyze unstructured data and metrics linked to technical equipment lifecycle management. Probable maintenance events and capital expenditure requirements can be predicted to avoid spending money on repairs rather than investing in infrastructure and equipment.

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 help determine the situations that can negatively influence business. Predictive Analytics can capture and quantify risk issues, examine them, and recommend actions to mitigate the factors causing it.

Quality Assurance
Insufficient quality control may critically affect customers’ satisfaction levels and their buying habits, ultimately impacting a company’s revenue and market share. So, a smartly applied Predictive Analytics approach 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 that some sections in a city have an abundance of stores and eateries – restaurants, fancy clothes stores, cafes, etc. There are also places where restaurants and shops close down, which is not going to change. This sparks the thought that a business owner should very carefully consider the place in which he wants to locate his business — regardless of the type of enterprise. Data Science and Machine Learning solve this question by learning data about the world’s most famous stores and their patterns, creating a time series analysis of different popular and not-so-popular places.

Predicting Product Needs and Prices for a Certain Customer
This may seem like the futuristic feature in Hollywood films, where a character comes into a room and his whole personality is being estimated in order to give him the fastest and most relevant service. Imagine if algorithms could predict a customer’s needs and preferences, based on the history of his previous in-store behavior. And this is not only applicable in online retail recommendation engines, but also it may become possible in brick-and-mortar stores that use Computer Vision to scan and analyze customers.

Up-selling and Cross-selling
It’s very important to maximize your company’s existing value as well as future revenue. Predictive Analytics can help here by suggesting which goods 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 amounts for an item, e.g., a type of shoes in a certain time period. Accurate pricing decisions are achieved by analyzing consumers, costs, and the competition. With logistical and storage data, it is possible to estimate future inventory requirements, maintain the availability of in-demand items, 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. Using Predictive Analytics here can help identify the best time to start decreasing or pushing prices in the other direction. AI can monitor features such as competitor prices and inventory levels, and then compare demands to calculate prices.

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