{"id":15887,"date":"2026-09-15T16:20:17","date_gmt":"2026-09-15T15:20:17","guid":{"rendered":"https:\/\/coreanalytics.stageweb.builders\/core-analytics-methodology\/"},"modified":"2026-10-07T10:34:47","modified_gmt":"2026-10-07T09:34:47","slug":"core-analytics-methodology","status":"publish","type":"page","link":"https:\/\/coreanalytics.it\/en\/core-analytics-methodology\/","title":{"rendered":"Core Analytics Methodology"},"content":{"rendered":"\t\t
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Home<\/a> \/ Core Analytics Methodology<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\tMeasure Marketing, Explain Results.\t\t\t<\/h1>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Marketing Mix Models built to separate baseline, incremental contribution, and external factors.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\n Discover Solutions<\/span>\n <\/g><\/g><\/svg><\/span>\n<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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\n\tFrom Business Question to Model<\/span>\n\t\n \n <\/g><\/svg> <\/span>\n \t\t<\/span>\n\t<\/span>\n<\/h4>\n
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Every project starts with a concrete decision: allocate budget, estimate incrementality, evaluate saturation, simulate scenarios, or distinguish creative effect. The model is designed around these questions, not the other way around. <\/p>\t<\/div>\n<\/div>\n

\n\tMarketing Mix Modeling, Not Just Media Mix Modeling<\/span>\n\t\n \n <\/g><\/svg> <\/span>\n \t\t<\/span>\n\t<\/span>\n<\/h4>\n
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We analyze the contribution of media within the entire business system: promotions, price, distribution, seasonality, market trends, competition, sales channels, and brand dynamics.<\/p>\t<\/div>\n<\/div>\n

\n\tA Flexible Modeling Approach<\/span>\n\t\n \n <\/g><\/svg> <\/span>\n \t\t<\/span>\n\t<\/span>\n<\/h4>\n
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We use classical regressions, inferential models, Bayesian approaches, and hierarchical frameworks based on available data, granularity, and problem complexity.<\/p>\t<\/div>\n<\/div>\n

\n\tShort-Term, Long-Term, and Brand Building<\/span>\n\t\n \n <\/g><\/svg> <\/span>\n \t\t<\/span>\n\t<\/span>\n<\/h4>\n
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We estimate the immediate effect of campaigns and the contribution that accumulates over time, connecting media, awareness, consideration, brand equity, and future baseline.<\/p>\t<\/div>\n<\/div>\n

\n\tCreativity, Media, and Context<\/span>\n\t\n \n <\/g><\/svg> <\/span>\n \t\t<\/span>\n\t<\/span>\n<\/h4>\n
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When data allows, we compare different periods, copy, and planning to distinguish creative effect, media pressure, and contextual factors.<\/p>\t<\/div>\n<\/div>\n

\n\tFrom Analysis to Decision System<\/span>\n\t\n \n <\/g><\/svg> <\/span>\n \t\t<\/span>\n\t<\/span>\n<\/h4>\n
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Results feed into CoreMMMix and CoreForecast, making the model an operational base for visualizing contributions, comparing scenarios, and supporting planning.<\/p>\t<\/div>\n<\/div>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\tRecommended Techniques<\/span> \t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\tModel Design\t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We design the causal structure of the model starting from baseline, media, promotions, price, distribution, seasonality, and external factors.<\/p>

Why it matters:<\/strong> Avoids superficial attributions and clarifies the role of each lever.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\tRegression & Bayesian Modeling \t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We use classical regressions and Bayesian approaches based on available data, granularity, and problem complexity.<\/p>

Why it matters:<\/strong> Allows managing imperfect data or correlated variables without losing model readability.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\tAdstock, Decay & Half-Life \t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We estimate how long media effects last after exposure, distinguishing immediate response, carry-over, and accumulation over time.<\/p>

Why it matters: <\/strong>Separates the tactical impact of the campaign from its contribution to the future baseline.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\tSaturation & Response Curves \t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We derive response curves to understand when a channel continues to scale and when it enters a saturation area.<\/p>

Why it matters:<\/strong> Helps reallocate budget where marginal value is still positive.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\tCreativity vs Media Effect \t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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When data allows, we compare different copy, formats, and periods to distinguish creative effect, media pressure, and context.<\/p>

Why it matters:<\/strong> Makes an often qualitative evaluation more objective. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\tValidation & Robustness \t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We verify economic consistency, estimate stability, collinearity, alternative specifications, and predictive capability.<\/p>

Why it matters:<\/strong> Makes the model more defensible in internal discussions and subsequent updates. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\tMost Requested\t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\tBaseline vs Incremental\t\t\t<\/h3>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Show observed sales broken down into baseline, incremental media contribution, promotions, and external factors. Goal: clarify what belongs to structural demand and what is generated by marketing levers. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\tSaturation Curve\t\t\t<\/h3>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Represent a growing curve with diminishing returns, highlighting the efficiency area and the saturation area. Goal: show where an extra euro still produces value and where it starts to dissipate. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\tShort-Term vs Long-Term\t\t\t<\/h3>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Pair two adstock curves: a faster one for tactical effects and a more persistent one for brand contribution and future baseline.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\tCreativity vs Media\t\t\t<\/h3>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Compare two periods with different copy, planning, and context, highlighting how the method helps separate media pressure and creative effect.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\tWant to understand what your data truly reveals?\t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We start with business questions, evaluate the quality, granularity, and depth of available information, and define the most suitable methodological path to isolate marketing’s contribution.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\nBook Your Call<\/span>\n<\/path><\/path><\/path><\/g><\/path><\/path><\/path><\/g><\/svg>\n<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Home \/ Core Analytics Methodology Measure Marketing, Explain Results. Marketing Mix Models built to separate baseline, incremental contribution, and external factors. Discover Solutions From Business Question to Model Every project starts with a concrete decision: allocate budget, estimate incrementality, evaluate saturation, simulate scenarios, or distinguish creative effect. The model is designed around these questions, not […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-full-width.php","meta":{"footnotes":""},"class_list":["post-15887","page","type-page","status-publish","hentry"],"yoast_head":"\nCore Analytics Methodology - Core Analytics<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/coreanalytics.it\/en\/core-analytics-methodology\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Core Analytics Methodology - Core Analytics\" \/>\n<meta property=\"og:description\" content=\"Home \/ Core Analytics Methodology Measure Marketing, Explain Results. 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