Predictive Analytics and Machine Learning are two related areas of AI solutions development 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 they can offer to benefit companies in the Business or Retail industry, in particular. We will talk about it in the text below.
What does Predictive Analysis mean?
Predictive analysis is the process of examining vast amounts of data to look for information and patterns, which are turned into numbers and insights valuable for humans to understand the future trends or probable outcomes of something — such as real-time predictions of employee satisfaction in the Business industry.
What is Cognitive Analytics?
Cognitive Analytics or Cognitive Computing is a term very much related to tasks that lie within Artificial Intelligence. AI mimics 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 existing knowledge bases and then inserts them into the knowledge base again in kind of a self-learning loop. This is achieved with Machine Learning algorithms, so Cognitive Analytics is the encompassing term for everything related to machines processing previously unstructured data and extracting meaning from it. So, that’s what Machine Learning does. Cognitive Analytics appears to take the leading role due to its efficiency. Ultimately, we can identify 3 ways to process huge amounts of data and draw insights from it:
- 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 advantages of the way a human brain works to successfully process unstructured data and at the same time quickly deal with huge amounts of data.
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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 supply and demand patterns. Tasks such as weather forecasts, stock exchange, and socioeconomic activities were initially performed with Predictive Analytics tools. Amazon is one of the first online retailers to use 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 (i.e., an individual’s formation signature) from all available sources, process this data, and detect possible terrorist activity.
Currently, there are lots of Predictive Analytics tools — most of which are available to use online but are not free of charge. These tools can decrease the time needed to collect a big dataset from various sources, clean this data based on custom parameters, and make an analysis with different technologies and algorithms. A report by Deloitte on the Analytics Advantage revealed that using analytics before starting any business or strategy made any related decision more successful in 50% of the cases studied.
What Can Predictive Analytics Do?
Armed with the typical advanced Machine Learning techniques such as more complicated Regressions, Classifications, and Neural Networks, Machine Learning engineers can use a computer’s power (i.e., Predictive Analytics) to predict strategically critical outcomes in Retail, E-commerce, and Marketing. Enterprises need to be able to put AI to work in many departments to execute multiple processes and make predictions to enhance their business agenda — especially in financial planning, where Predictive Analytics can reveal the value hidden in the collected data. This allows organizations to operate with fewer costly mistakes.
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.
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.
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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:
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.
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.
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.
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.
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 for Retail in 2023:
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.
The way you market, price, and sell your products can be changed significantly with demand forecasting in Retail solutions. 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.
Read also: Developing Your Own Marketplace Platform
Most popular ways to implement Predictive Analytics in Marketing
There are some tasks performed with Predictive Analytics that can be classified as related to AI in Marketing and thus improving an enterprise’s marketing strategy.
Customer Lifetime Value
Some customers have a high lifetime value, which is determined by how much money they spend, how consistent their paying history is, and how many times they have already bought something from you. Identifying such customers is the hardest thing to do in marketing. On the other hand, these kinds of insights help companies optimize their marketing strategies, subsequently increasing the share of this business segment and gaining information about the most valuable customers.
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.
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.
Currently, most modern businesses have begun to rely on the electricity of our time – AI solutions, which give the older Predictive Analytics methods new life and turn it into a highly efficient instrument to get business insights and predictions. Predictive Analytics has beneficial applications in several industries such as Retail, E-commerce, and Marketing. It becomes the fuel to drive a company’s business decisions and predict its success in the future.
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