A recent conversation with Nerissa Laclé, Lead Consultant at Finext, revealed that AI integration into business strategies offers significant opportunities for efficiency, improved customer insights and increased margins. Using various AI techniques, this article discusses practical examples of how AI can help increase margins.
AI in brief
Artificial Intelligence (AI) is an emerging technology that is receiving increasing attention due to the many developments in the field. In simple terms, AI refers to the ability of computer systems to perform tasks that normally require human intelligence, such as learning patterns in data, making decisions, and performing tasks with little or no human intervention. AI is an umbrella term for a wide range of techniques and methods that aim to make computer systems exhibit intelligent behavior. The following are the 6 best-known techniques:
Machine Learning enables computers to be able to learn from processed data to make increasingly better decisions and predictions, such as recommending movies on streaming services based on viewing history.
Deep Learning is an advanced form of Machine Learning, in which computers learn to recognize patterns in information in a way the human brain would, such as understanding speech.
Computer Vision is the ability of computers to "see" and understand visual information, such as identifying road signs for self-driving cars.
Natural Language Processing (NLP) is a way for computers to understand and communicate with human language, such as finding Spam in your mailbox or ChatGPT.
Automation and Robotics is the use of machines and robots to perform tasks previously done by humans, such as automating customer service using chatbots.
Expert Systems are computer systems programmed with the knowledge and expertise of people in one particular field, such as the use of medical expert systems to make diagnoses based on symptoms.
AI to optimize your margins
The above techniques have been used to develop methods and applications that can support margin optimization. However, it is important to remember that qualitative data is crucial in the use of AI. Below we discuss some of the applications of AI.
Predictive Analytics is a method where Machine Learning can be used to analyze large amounts of historical data very quickly, recognizing patterns to predict the future. Organizations can use this to predict product demand based on historical sales data and external factors such as seasonality or market trends, allowing companies to optimize their inventory levels to meet demand and reduce excess inventory. Lower inventory costs naturally leads to higher margins.
Generative AI, such as ChatGPT, Bard or Co-Pilot, for example, is a branch of AI that, through Natural Language Processing and Machine Learning, among others, focuses on creating new data, such as images, texts or sounds, that did not exist before. It uses existing data, such as the Internet or public databases, to generate new and original content. For example, the marketing department can use Generative AI to more effectively produce content, such as images, texts or videos, for marketing campaigns. The same marketing budget can be used to target customers more effectively, which can lead to higher sales.
Chatbots are automated systems designed to conduct conversations with users via text or voice interfaces, using Natural Language Processing to understand questions and generate appropriate responses. Chatbots are often deployed on Web sites to provide customer service, provide information about products, services, opening hours, locations and other relevant topics or to schedule appointments. Using chatbots can save on customer service costs. This leads to lower costs and thus higher margin.
Quality checks can be performed with computer vision by analyzing images or videos of products to detect and classify defects, discrepancies or imperfections. AI can generally do this faster, more accurately and on a larger scale than humans, while also continuing to learn continuously. Naturally, higher quality leads to fewer returns or failures and thus effective use of resources.
Personalization and Customer Segmentation can be enhanced with Machine Learning because it can analyze large amounts of customer data as well as external data. Thus, it can create predictions and insights to better understand individual customer needs and create tailored offers and experiences. Applications include recommending products based on your click and or purchase behavior, dynamic pricing of airline tickets, personalized offers at the grocery store or personalizing email marketing campaigns by segmenting customers based on their behavior, characteristics and demographics. Being able to personalize and segment on a large scale can increase customer satisfaction and loyalty, which will ultimately lead to higher sales and margins.
Applying AI in practice can affect margins in several ways. For example, targeting the right audience more effectively, optimizing the production process and speaking to customers in a faster way.
Want to know what AI Based Margin Optimazation means for your organization? Then contact Barry Botman.
