Marketing Mix

MMM – Marketing Mix Modeling: measuring, optimizing and predicting marketing performance

Marketing Mix Modeling (MMM) is an advanced quantitative approach designed to measure the impact of various marketing activities on a company’s business outcomes—such as sales, market share, or profitability. It is a statistical model, often based on multiple linear regression, that takes into account both controllable variables (advertising, promotions, distribution, pricing) and uncontrollable ones (seasonality, weather, economic trends, competition) to isolate the specific effect of each lever.

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AI is revolutionizing marketing: opportunities and challenges in the digital era

The article “AI-powered Marketing: What, Where, and How?” by V. Kumar, Abdul R. Ashraf, and Waqar Nadeem, published in the Journal of Information Management, explores how artificial intelligence (AI) is transforming the marketing landscape. The authors highlight AI’s transformative capabilities across various marketing functions, while also addressing the ethical and operational challenges of its integration.

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Statistical tools and data interpretation : how decoding data like a pro !

Whether in marketing, finance, or health research, statistics are no longer the exclusive domain of math experts. Today, they are essential for analyzing the present, forecasting trends, and guiding strategic decisions with accuracy.

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Auto-Bid : when AI takes control of advertising bids

In today’s hyper-competitive digital advertising landscape, optimizing ad bids is a constant battle. Auto-bidding—automated bidding powered by artificial intelligence and machine learning—has emerged as a game-changing strategic lever.

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Auction: the invisible war redefining digital marketing

Digital marketing is no longer just about creativity and targeting. Behind every ad impression lies a silent yet critical battle: the advertising auction. Often perceived as a mere buying algorithm, this space is in fact a competitive battleground where brands and advertisers fight to capture consumer attention.

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The acceptable price : the subtle art of perception and value

In an era where price transparency is expected and fierce competition prevails across every market segment, the concept of an “acceptable price” has become a central strategic concern for brands. But how can a price be set that is both sustainable for the business and perceived as fair by the consumer ?

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ALBERT: The AI that drives your marketing campaigns like a digital strategist

Digital marketing has become a highly fragmented ecosystem. Between Google Ads, Facebook, TikTok, email marketing, SEO, and programmatic advertising, brands juggle dozens of platforms and thousands of data points. The question is no longer “How do we launch a campaign ?” but rather “How do we optimize it in real time, across all channels, without sacrificing performance ?”

That’s where ALBERT comes in — a cloud-native artificial intelligence designed to integrate, analyze, and manage your entire marketing stack.

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Agent-Based Modeling : the simulation that predicts the unpredictable

Why does a virus spread so quickly in some cities? How do crowds react in the face of a disaster? Why do financial bubbles suddenly burst?
These seemingly unpredictable phenomena can be simulated using a powerful approach : Agent-Based Modeling (ABM). Behind this name lies a revolutionary simulation method based on the interaction of thousands of autonomous agents to replicate complex dynamics.

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Intelligent Agents : the software that makes decisions for us

This digital age is no longer defined by simple programs executing predefined tasks. Today, we are entering a world where intelligent agents make decisions, adapt to their environment, and pursue objectives without direct human intervention.

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Adversarial Attacks : when AI gets hacked by its own data

AI has become a key ally for businesses, governments, and even content creators. But behind its technological prowess lies a critical weakness: its vulnerability to adversarial attacks. These manipulative techniques exploit weaknesses in machine learning models by feeding them deceptive inputs designed to corrupt their predictions.

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Algorithm