How AI can help you grow your business
10/10/2023
Why and how to use AI and machine learning to support your growth?
Artificial intelligence (AI) and machine learning have attracted real interest in recent years. These techniques open up multiple possibilities for companies to promote their growth. What are the possible use cases of AI and machine learning in your business? How can you turn them into development levers for your business? Let's see that.
How to define artificial intelligence and machine learning?
Artificial intelligence is a technology based on the analysis of large amounts of data and the use of mathematical algorithms to accomplish complex tasks and identify trends (for example, predicting outcomes). As for machine learning, it is a branch of AI. Its role is to teach machines to process and exploit the data provided to learn and improve. Companies have every interest in integrating these technologies to improve their efficiency and productivity: large-scale data analysis, automation of repetitive and time-consuming tasks, personalization of the customer journey, etc. The opportunities are immense and concern many areas.
The use of recommendation systems
Item recommendation to increase e-commerce sales
Recommendation systems are powered by machine learning and use customer data to personalize the shopping experience. Recommendation engines are particularly interesting for e-commerce since they generally have a significant impact on sales. In the context of an e-commerce site, these recommendation models can in particular: • Offer customers articles viewed by other customers whose behavior, preferences and interests are similar (user-based recommendation system or “Nearest Neighbors” algorithm) Example in e-commerce: “Customers who viewed this item also purchased…” Other examples: YouTube video recommendations, blog post recommendations, etc. • Suggest items based on similar purchases made by other customers (product-based recommendation system) Example in e-commerce: “Customers who purchased [PRODUCT NAME] also purchased…”
Text analysis to offer solutions adapted to users
The use of recommendation systems goes beyond the simple framework of e-commerce to extend to numerous sectors. Text analysis makes it possible, for example, to identify events in users' lives in order to offer solutions adapted to the situations specific to each client. Examples: offering childcare equipment to a user who has just had a child, offering a bank loan to a user who is about to get married, etc. These suggestions may be based on users' social media posts or other data sources.
Data collection for risk calculation
There are many uses of AI in the world of banking and insurance, for example to model risks. Concretely, this involves analyzing historical data and associated events or results to estimate the probability of certain events (risk calculation). This is how banks and insurance companies calculate the risk profile of their clients. The collection and analysis of historical data can be useful for any sector or subject (predicting customer satisfaction, for example).
Text Mining to identify trends and customer preferences
Definition of Text Mining
Text Mining consists of using artificial intelligence to extract relevant data from large quantities of data. Insights can come from customer reviews, social media conversations, etc. One of the most used methods for Text Mining is “RegEx” (Regular Expressions).
Possible uses of Text Mining
Text Mining allows companies to better understand what customers think and say about their products and services: how their customers feel about an experience, product or service. service, what are the needs and preferences of customers, etc. Thanks to Text Mining, companies can, for example, analyze satisfaction questionnaires or customer reviews more closely. For example, Google uses Text Mining to determine the most relevant pages based on Internet users' searches. This is what allows it to constantly improve the search engine results and offer the most useful results to users. One of the practices used in Text Mining also consists of writing a script to collect information from unstructured data (html pages, for example), which is different from “structured data” such as XML or JSON. . This type of practice proves useful in the case where one wishes to extract information from html pages published on the Internet and whose data is public (on CMS), for example data relating to companies published in html format, economic data, data relating to the stock market, the weather, etc.
Machine learning to improve marketing practices
Machine learning offers businesses immense marketing possibilities. In particular, it allows them to better understand their customers and their expectations. Here are some examples of possible uses of machine learning in marketing: • Anticipate customer needs and preferences (thanks to predictive models), • Improve advertising targeting, • Analyze the results of marketing campaigns and improve subsequent ones, • Determine the most effective channels to communicate with customers, • Etc. Companies can thus improve the effectiveness of their marketing campaigns, which become better targeted and more personalized.
AI to automate processes
This is a use of artificial intelligence that concerns absolutely all companies. This is process automation. This may involve the automation of repetitive tasks (examples: inventory management, invoice processing, customer reminders, etc.). This type of use makes work easier, reduces margins of error, saves time and generates savings. Beyond these various gains, process automation has advantages from a business point of view. It effectively allows companies to improve their decision-making, identify development opportunities, optimize the management of their resources or even anticipate trends or problems.
AI and predictive analytics
Predictive analytics uses data to predict trends, needs, risks or events, drawing on past and current data to model predictions. The more numerous and varied the data, the more precise and reliable the predictions will be. In the field of health, AI can, for example, predict diseases based on the analysis of symptoms. Fraud detection is another possible use of predictive AI, in the fight against financial crimes. From a business point of view, predictive AI can help companies better anticipate the future and improve decision-making (evaluate whether it is relevant to commit funds to develop a new branch of activity, For example). Can we imagine that one day there will be a model to predict the future on demand, based on information relating to millions of people and analyzing their lives? An algorithm that would suggest what to do, what to choose by answering a few questions after being fed with information.
AI to improve customer service
The use of AI in customer service proves beneficial both for customers (who can receive immediate assistance at any time) and for businesses (who can serve more customers and offer them a better experience). Here are some possible uses for using AI in customer service. • Use algorithms to categorize the mass of emails sent to customer service, transmit each email to the right person and detect the type of action to take. • Use generative AI to synthesize the content of emails exchanged with the customer concerned or even provide the customer advisor with contextualized and personalized response proposals (time saving). • The use of chatbots is another concrete example of using AI to improve customer service. Thanks to them, customers can receive support 24/7.
Where is your business with AI and machine learning?
Whatever your sector of activity, your company produces large quantities of data on a daily basis. Have you thought about how you could collect and use them? Using big data in business can help you make better decisions, assess whether it is relevant to develop new products or services, and therefore help your business grow. The data collected today can be useful to you tomorrow to meet new needs. Think now about how to collect your data correctly to avoid losing it. Setting up a data lake bringing together all of your historical data is a good option. You will then add the most relevant data analysis models to meet your future needs. Do you want to take stock of your data or think about how to integrate AI into your company to grow your business? Contact us to analyze your needs.
