[{"_id":"project-settings","settings":{"translateMetaTags":true,"translateAriaLabels":true,"translateTitle":true,"showWidget":true,"customWidget":{"theme":"dark","font":"rgb(255, 255, 255)","header":"rgb(0, 0, 0)","background":"rgba(0, 0, 0,0.8)","position":"right","positionVertical":"bottom","border":"","borderRequired":false,"widgetCompact":true},"widgetLanguages":[{"code":"de","name":"Deutsch"},{"code":"es","name":"Español"},{"code":"ja","name":"日本語"},{"code":"ko","name":"한국어"},{"code":"ru","name":"Русский"},{"code":"zh","name":"中文(简体)"}],"activeLanguages":{"zh":"中文(简体)","ja":"日本語","de":"Deutsch","ko":"한국어","ru":"Русский","es":"Español","en":"English"},"enabledLanguages":["de","en","es","ja","ko","ru","zh"],"debugInfo":false,"displayBranding":false,"displayBrandingName":false,"localizeImages":false,"localizeImagesLimit":false,"localizeAudio":false,"localizeAudioLimit":false,"localizeDates":false,"disabledPages":[],"regexPhrases":[],"allowComplexCssSelectors":false,"blockedClasses":false,"blockedIds":false,"phraseDetection":true,"customDomainSettings":[],"seoSetting":[],"translateSource":false,"overage":false,"detectPhraseFromAllLanguage":false,"googleAnalytics":false,"mixpanel":false,"heap":false,"blockedComplexSelectors":[]},"version":475},{"_id":"en","source":"en","pluralFn":"return n != 1 ? 1 : 0;","pluralForm":2,"dictionary":{},"version":475},{"_id":"outdated","outdated":{"#Bayesian Inference: Elevating Predictive Accuracy":1,"#Hierarchical Bayesian models form the backbone of AIM, offering a statistical framework that adeptly handles the inherent complexity of marketing data. This approach is distinguished by its capacity to model data across multiple levels of hierarchy, acknowledging the nested structure that characterizes much of consumer data.":1,"#Hierarchical Bayesian Models: Statistical Underpinnings":1,"#In the rapidly advancing domain of digital marketing, precision and analytical depth are not just virtues but necessities. Our marketing mix modeling platform, AIM (Always-On Incremental Measurement), is engineered to meet these demands through its sophisticated fusion of <a ljsid-1=\"\">hierarchical Bayesian models</a> and dynamic nonlinear regression. This technical discourse provides a closer look at the statistical sophistication that powers AIM at a level that can resonate with both seasoned marketing analysts and data scientists.":1,"#Unpacking the technical fabric of AIM":1,"#Sep 10":1,"#Learn how MMM works, with advanced predictive analytics (hierarchical Bayesian models & nonlinear regression) giving precise insights in AIM.":1,"#How MMM Works: Next-Generation Predictive Analytics of AIM":1,"#Bayesian Analysis":1,"#Interested in exploring AIM in further detail? <a ljsid-1=\"\">Contact us</a> for a full consultation with our experts.":1,"#Incorporating the ability to adjust for external events and influences ensures that AIM’s predictions and recommendations remain relevant and precise, even in the face of unforeseen circumstances. This adaptability makes AIM an invaluable tool for marketers looking to navigate the complexities of the digital marketing landscape with confidence and precision.":1,"#In multichannel marketing campaigns, AIM’s advanced methodology facilitates the strategic allocation of marketing budgets, timing of campaign launches, and optimization of channel mix. By modeling the interactive effects of various marketing initiatives and their nonlinear impact on sales, AIM empowers marketers to craft strategies that are not only data-driven but also aligned with the dynamic nature of consumer markets.":1,"#Practical Insight: Strategizing with Precision":1,"#The integration of hierarchical Bayesian models with dynamic nonlinear regression within AIM does not merely add to its analytical arsenal; it transforms AIM into a sophisticated predictive engine that navigates the complexities of the marketing world with precision. This synthesis enables AIM to offer nuanced insights into marketing effectiveness, forecasting the impact of various strategies with a level of accuracy and depth unmatched by traditional models.":1,"#Synthesizing Advanced Methodologies: The Core of AIM":1,"#Consider the case where digital advertising spend exhibits a logarithmic relationship with sales, a common scenario in digital marketing. Initial increases in budget lead to significant sales uplifts; however, as spend continues to rise, incremental sales gains begin to diminish. AIM, through dynamic nonlinear regression, quantitatively models this relationship, identifying the point of diminishing returns to optimize advertising spend for maximum ROI.":1,"#Example: Nonlinear Dynamics in Digital Advertising":1,"#The essence of dynamic nonlinear regression lies in its ability to capture the complex, dynamic interactions between marketing variables. By employing advanced mathematical functions, this technique maps out the curvilinear relationships that often exist between marketing inputs and outputs such as spend and sales.":1,"#Modeling Nonlinear Interactions":1,"#Dynamic nonlinear regression extends the capabilities of AIM by modeling the intricate and often nonlinear relationships inherent in marketing data. This approach is crucial for understanding how various inputs, such as marketing spend across different channels, translate into business outcomes. A key advantage of this dynamic feature is its ability to allow changes in creatives and network efficiency to propagate through to the parameter distributions faster than models that place all historical data into a single bucket. This rapid adaptation enhances the model’s responsiveness and accuracy, enabling more timely and effective marketing decisions.":1,"#Dynamic Nonlinear Regression: The Complexities of Marketing Data":1,"#AIM’s capacity to adjust for external influences—such as weather, national holidays, sporting events, and even pop concerts—on sales and conversions is a significant addition to this approach. For instance, a sudden heatwave can significantly boost sales of cooling products or beverages; AIM can predict these spikes by analyzing historical weather-related sales data. Similarly, national holidays or major sporting events can lead to changes in consumer buying patterns, which AIM anticipates by adjusting its models based on these events’ historical impacts. By accounting for these external factors, AIM provides a more nuanced and accurate understanding of the marketing dynamics at play.":1,"#The practical implementation of hierarchical Bayesian models in AIM leverages both the observed data and prior distributions to formulate a comprehensive view of the marketing landscape. This approach not only incorporates the variability observed within the data but also integrates expert knowledge and data from existing attribution platforms, even when that data is incomplete. Additionally, it includes insights gathered from questionnaires and historical incremental studies. The result is a single hybrid attribution algorithm uniquely tailored to your business, providing a nuanced and accurate understanding of marketing effectiveness.":1,"#Operationalizing Hierarchical Models":1,"#Central to the hierarchical Bayesian approach is Bayesian inference, a process that iteratively updates the probabilities of hypotheses as new data emerges. This methodology employs Bayes’ theorem as a mathematical tool for updating the likelihood of our models’ parameters, based on the posterior distribution. This iterative process is particularly adept at refining model predictions in real-time, enhancing the predictive validity of marketing outcomes as the campaign unfolds.":1,"#5. Potente planificación de escenarios:":1,"#4. Pronósticos precisos:":1,"#Jul 28":1,"#AIM is a cutting-edge MMM platform designed specifically to address the challenges faced by UA marketers. AIM’s unique approach leverages advanced machine learning that continuously adapts to new market information. The result? Precise insights that your UA buying team can take action on.":1,"#This focuses on comparing the sales generated to the cost involved in the effort. It’s about maximizing sales while minimizing the cost. In simpler terms, it’s ensuring that for every dollar spent on marketing, you realize the greatest impact on sales.":1,"#With your permission we and our partners may use precise geolocation data and identification through device scanning. You may click to consent to our and our 185 partners’ processing as described above. Alternatively you may click to refuse to consent or access more detailed information and change your preferences before consenting.":1,"#MMM gaming user":1,"#flow diagram with icons showing how media mix modeling works":1,"#Creating a marketing mix model involves training a model using historical data from sales, conversions, and ad spend derived from marketing efforts. This process is not just a science but also an art, striking a balance between automated modeling tools processing large data sets, and the detailed work of experienced data scientists. Multiple iterations are created to develop the most accurate model.":1,"#What is Marketing Mix Modeling?":1,"#here":1,"#MMM outputs can then be used to analyze the impact of marketing efforts on sales and conversions. For example, it allows marketing managers to see which elements contribute most to total sales, and the incremental gain in sales that can be obtained by increasing the use of a particular marketing channel. Importantly, it can also help in optimizing the marketing budget by identifying the most and least efficient marketing activities.":1,"#This is the big-picture metric. It considers both the sales made and the costs incurred to give you an overall understanding of the value gained from your marketing efforts.":1,"#Efficiency:":1,"#Marketing mix modeling (MMM), also called media mix modeling, is a statistical method that analyzes time series data from sales & marketing.":1,"#Want to know more, get in touch":1,"#How to get started?":1,"#The success of each marketing channel can be understood by evaluating how much it contributes to incremental sales (e.g. sales that wouldn’t have happened if not for your marketing efforts). MMM breaks this down into three main components:":1,"#Effectiveness:":1,"#Marketing mix modeling (MMM), also called media mix modeling, is a statistical method that analyzes time series data (chronological data) from sales and marketing. The goal of this analysis is to calculate the impact of various marketing efforts on sales and then predict the results of future strategies.":1,"#Think of it as a way of understanding the effectiveness of different marketing activities and how they each influence your sales. By analyzing your historical data, such as sales, the model quantifies the impact of your marketing channels. This is done mathematically by identifying patterns and trends in the relationship between marketing efforts and sales outcomes. To do this accurately, the model takes into account other market dynamics that affect sales, such as seasonality and channel saturation.":1,"#Why use MMM?":1,"#This is about measuring sales brought in by each effort in a marketing channel. For example, if you post a new ad or increase spending, how much does that increase sales?":1,"#Introducing Always-on Incremental Measurement (AIM):":1,"#Finally, the model can be used to simulate various marketing scenarios in a ‘what-if’ analysis, which can help marketing managers make informed decisions about future strategies and investments.":1,"#M&CSaatchi Performance & AIM":1,"#Education":1,"#Explainer: A Two-Minute Guide To Marketing Mix Modeling (MMM)":1,"#<b ljsid-0=\"\">Large Distributed Training Clusters: </b>AIM leverages large distributed compute clusters in the training phase, allowing us to develop and train a multitude of complex models.":1,"#<b ljsid-0=\"\">Real-time Results: </b>The third element is real-time results. AIM keeps pace with your campaign performance as it evolves, adjusting your model to reflect these results in real-time.":1,"#<span ljsid-0=\"\">1. </span>Dynamic Model Building":1,"#Model building and training at AIM is a three-phase process:":1,"#AIM is structured around three core elements:":1,"#<b ljsid-0=\"\">Historical Data Analysis: </b>Initially, AIM uses historical data to understand the unique characteristics of your brand. By examining past performance, identifying patterns and trends, and understanding the influencing factors on your outcomes, AIM sets a solid foundation for your model.":1,"#<span ljsid-0=\"\">2. </span>Core Elements":1,"#AIM leverages advanced machine learning and the distinct dynamics of your brand to create an evolving model that continually learns from your marketing efforts. Here we delve into the technical specifics that make AIM the industry-leading platform it is today.":1,"#<b ljsid-0=\"\">Ad Spend: </b>The first element is your expenditure across various media channels. AIM analyzes where your marketing budget is being effectively employed, providing valuable insights into your spending strategy.":1,"#<b ljsid-0=\"\">Nonlinear Time Series Models: </b>These clusters are used to train our advanced nonlinear structural time series models, which capture complex patterns and trends in your data.":1,"#AIM’s sophisticated methodology lies at the heart of its effectiveness. It employs cutting-edge, nonlinear structural time series models to interpret and forecast your business dynamics. This advanced methodology provides AIM with a unique capacity to understand and respond to complex trends.":1,"#At its core, AIM is a next-generation marketing mix modeling (MMM) tool that transcends traditional analytics. It constructs a dynamic, comprehensive profile for your brand, grounded in robust data and refined through machine learning. This facilitates a better understanding of your marketing performance and offers actionable insights that can drive growth and efficiency.":1,"#<b ljsid-0=\"\">Real-time Updates: </b>After the initial phase, AIM enters a continuous learning phase. It updates your models every day with real-time campaign results, ensuring the data guiding your decisions is always up to date. This dynamic aspect of AIM allows it to adapt to changes in the marketing landscape or your business, ensuring your strategies remain relevant and effective.":1,"#<b ljsid-0=\"\">Stochastic Optimization: </b>The model outputs are then optimized using stochastic methods, leveraging evolutionary algorithms to perform optimization that yields the most robust results.":1,"#AIM employs dynamic model building to always monitor and update factors that influence your business. This model encapsulates all the factors affecting your sales funnel – from seasonality and network capacities to incremental drivers and beyond.":1,"#Final Thoughts":1,"#<span ljsid-0=\"\">5. </span> Data Inputs":1,"#<span ljsid-0=\"\">3. </span>Advanced Methodology":1,"#<span ljsid-0=\"\">4. </span>Model Building and Training":1,"#<b ljsid-0=\"\">Sales Funnel: </b>The second element is your sales funnel. AIM models the entire customer journey, from awareness to conversion. It considers all stages of the funnel, giving a comprehensive view of your customer conversion process.":1,"#<span ljsid-0=\"\">How AIM Works:</span> Dynamic, Data-Driven, and Decisive":1,"#by Gary Danks":1,"#Marketing":1,"#© 2024 AIM BY KOCHAVA":1,"#Marketing Mix Modeling (MMM) | A Two-Minute Guide":1,"#Home":1,"#Let’s Talk":1,"#Connect with an AIM expert and learn how we can help you maximize your ad spend efficiency.":1,"#How it Works - AIMPlatform":1},"version":475}]