{"id":58971,"date":"2023-12-12T13:18:34","date_gmt":"2023-12-12T21:18:34","guid":{"rendered":"https:\/\/www.kochava.com\/?p=58971"},"modified":"2026-05-13T12:32:04","modified_gmt":"2026-05-13T19:32:04","slug":"https-www-aimplatform-io-blog-establishing-trust-in-marketing-mix-modeling-solutions","status":"publish","type":"post","link":"https:\/\/www.kochava.com\/ko\/blog\/establishing-trust-in-marketing-mix-modeling-solutions\/","title":{"rendered":"Establishing Trust in Marketing Mix Modeling Solutions"},"content":{"rendered":"[vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” row_position_desktop=”default” row_position_tablet=”inherit” row_position_phone=”inherit” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” flex_gap_desktop=”10px” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_row_inner column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” text_align=”left” row_position=”default” row_position_tablet=”inherit” row_position_phone=”inherit” overflow=”visible” pointer_events=”all”][vc_column_inner column_padding=”padding-3-percent” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color=”#FFFFFF” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”15px” column_link_target=”_self” overflow=”visible” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”1px” column_border_color=”#DDDDDD” column_border_style=”solid” column_padding_type=”default” content_layout=”default” gradient_type=”default”][vc_column_text css=”” text_direction=”default”]TL;DR Summary<\/strong>\r\n\r\nEstablishing trust in marketing mix modeling (MMM) solutions is critical for marketers making data-driven decisions, especially when encountering the methodology for the first time. AIM (Always-On Incremental Measurement) builds trust by providing short- to medium-term forecasts clients can validate against their raw geo-level data, typically achieving 96% two-week forecast accuracy. The platform uses an ensemble model approach that constructs thousands of individual models for each KPI throughout the day, evaluates each model\u2019s predictions against actual results, and selects only the best-performing models. This continuous real-time learning process ensures that the system doesn\u2019t just look backward at historical data but understands the present and forecasts the future with exceptional precision.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][vc_row_inner equal_height=”yes” content_placement=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” text_align=”left” row_position=”default” row_position_tablet=”inherit” row_position_phone=”inherit” overflow=”visible” pointer_events=”all”][vc_column_inner column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” flex_gap_desktop=”10px” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” overflow=”visible” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/2″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text css=”” text_direction=”default”]\r\n

An exploration of how AIM verifies its precision and efficacy<\/h2>\r\nIn today’s dynamic marketing landscape, making data-driven decisions is more crucial than ever. However, the introduction of new data modeling techniques can often be met with skepticism, especially when a client hasn’t used them before. One such technique that’s gathering a lot of traction is marketing mix modeling (MMM). How can marketers and key stakeholders gain confidence that MMM can accurately predict future outcomes and guide strategic marketing investments? Let’s explore.[\/vc_column_text][\/vc_column_inner][vc_column_inner column_padding=”padding-2-percent” column_padding_tablet=”no-extra-padding” column_padding_phone=”no-extra-padding” column_padding_position=”left” flex_gap_desktop=”10px” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” overflow=”visible” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/2″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid” column_padding_type=”default” gradient_type=”default”][image_with_animation image_url=”58973″ image_size=”full” max_width=”75%” max_width_mobile=”default” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”” border_radius=”none” box_shadow=”none” image_loading=”default”][\/vc_column_inner][\/vc_row_inner][vc_row_inner column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” text_align=”left” row_position=”default” row_position_tablet=”inherit” row_position_phone=”inherit” overflow=”visible” pointer_events=”all”][vc_column_inner column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” flex_gap_desktop=”10px” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” overflow=”visible” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text css=”” text_direction=”default”]\r\n

The Challenge of MMM Trust<\/h2>\r\nOne of the primary challenges our team encounters when introducing the MMM methodology to a client who\u2019s unfamiliar with it is overcoming trust barriers. The client needs to become confident in the methodology’s accuracy. It’s completely understandable; businesses are often wary of new technologies, especially when significant marketing investments and sales outcomes hinge on their results. Data-driven decisions are only good if the data behind them is accurate. Trust is not given; it’s earned.\r\n

Proving the Accuracy of AIM, a Next-Generation MMM Tool<\/h2>\r\nAt AIM, we prioritize proving the model’s efficacy from the get-go. Our introductory onboarding step involves presenting clients with short- to medium-term forecasts derived from the model. These forecasts serve as a quantifiable proof of concept. By allowing clients to validate the accuracy of these forecasts against their raw data, we enable them to see the model’s precision firsthand. Once the client has had time to see if the forecasts proved true, we can move forward with the next steps.\r\n\r\nTo illustrate, the AIM system is designed to provide a forecast of the client’s total key performance indicators (KPIs). Total KPIs include paid media and organic earned KPIs for a specific geo-location over a two-week period. KPIs might include metrics such as the number of installs, registrations, and first-time purchases in the UK. Doing this at a geo-level serves as the source of truth, as the numbers are not manipulated by any other measurement system. The client can verify the accuracy of the forecast by comparing it to their raw geo-level data.\r\n

How AIM Achieves High Accuracy: The Ensemble Model Approach<\/h2>\r\nOur modeling approach is not just about building a single model; it’s about creating an ecosystem of models that work in tandem. For each of the client’s KPIs, or marketing goals, we construct individual models. These models, when combined, form what we term an “ensemble model.” This ensemble ensures that the different models inform each other, creating a holistic view of the market forces and marketing activities.\r\n\r\nLet’s take an app-based client as an example. For such a client, the KPIs might include app installations, registrations, first-time purchases, and the number of purchase events. We would design a model for each of these KPIs. These individual models then update daily and collectively contribute to the ensemble model.\r\n

Ensuring Realistic Outcomes<\/h2>\r\nOne of the strengths of our ensemble approach is its built-in checks and balances. This continuous refinement happens in the background, ensuring the final output is both robust and precise.\r\n

Here’s How It Works<\/h2>\r\n1. Continuous Learning in Real-Time<\/strong>\r\nUnlike traditional models that rely heavily on historical data, our system is attuned to the present. It learns in real-time from marketing activities as they unfold. This means that while we start by building a robust model based on past data, the real magic happens when our system begins its continuous learning journey from activities happening right now.\r\n\r\n2. The Power of Ensemble in Real-Time<\/strong>\r\nThe AIM system doesn’t rest on its laurels. Throughout the day, it constructs thousands of models for each KPI. These models make predictions based on data from the past two weeks, but here’s the catch: they make these predictions without being exposed to the actual results from these two weeks. In essence, they’re “blind” to the real outcomes. This approach ensures that each model’s forecast is unbiased and purely based on its understanding of the data.\r\n\r\n3. Self-Evaluation and Iteration<\/strong>\r\nAfter making its predictions, each model is then exposed to the real results from the past two weeks. This allows the model to give itself an error rating based on how accurate its predictions were. This self-evaluation is crucial, as it sets the stage for iterative improvement. The system continuously builds and evaluates model after model, learning and refining with each iteration.\r\n\r\n4. Selection of the Best<\/strong>\r\nOut of the thousands of models built throughout the day, only the best make the cut. The system back-checks each model against the actual results, marking its error rate. The model with the least error\u2014the one that’s most attuned to the actual outcomes\u2014gets selected. It is then refreshed in the system, ready for clients to view and use.\r\n

The Result: A High Degree of Accuracy<\/h2>\r\nThe proof lies in the numbers. Regularly, our ensemble model approach showcases its prowess by achieving an impressive two-week forecast accuracy of 96%. This high degree of accuracy is not just a one-off occurrence but is consistent, reinforcing the reliability of our system. Such precision allows businesses to strategize with confidence, knowing that the data they’re basing their decisions on is both robust and trustworthy.\r\n

Conclusion<\/h2>\r\nOur real-time MMM system is a testament to the power of continuous learning and iterative improvement. By consistently building, evaluating, and refining models in real-time, we ensure that our clients always have access to the most accurate and up-to-date marketing insights. It’s not just about looking back; it’s about understanding the present and forecasting the future with unparalleled precision.\r\n\r\nAt AIM, our commitment to transparency, combined with our ensemble model approach, ensures that clients can have confidence in the marketing mix model’s recommendations before they choose to invest. As we continue to refine and improve our methodologies, we remain focused on providing our clients with accurate, actionable insights to drive their strategic marketing decisions.\r\n\r\nLooking for an MMM solution or struggling to trust your current solution? Book a meeting<\/a> with our experts.[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” row_position_desktop=”default” row_position_tablet=”inherit” row_position_phone=”inherit” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” flex_gap_desktop=”10px” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][toggles style=”minimal”][toggle color=”Default” heading_tag=”default” heading_tag_functionality=”default” title=”How does AIM prove the accuracy of its marketing mix modeling to build trust?”][vc_column_text css=”” text_direction=”default”]AIM proves accuracy by providing clients with short- to medium-term forecasts during the introductory onboarding process. These forecasts enable clients to validate the model\u2019s precision against their raw geo-level data, which serves as the source of truth because it\u2019s not manipulated by other measurement systems. Once clients have time to verify that forecasts proved true, they can move forward with confidence in the system\u2019s recommendations.[\/vc_column_text][\/toggle][toggle color=”Default” heading_tag=”default” heading_tag_functionality=”default” title=”What is the ensemble model approach and how does it improve accuracy?”][vc_column_text css=”” text_direction=”default”]The ensemble model approach involves constructing individual models for each of the client\u2019s KPIs (such as app installations, registrations, first-time purchases, and purchase events) that work together and inform each other, creating a holistic view of market forces and marketing activities. These individual models update daily and collectively contribute to the ensemble model, with built-in checks and balances that ensure realistic outcomes. This approach provides more robust and precise final outputs than single-model systems.<\/span>[\/vc_column_text][\/toggle][toggle color=”Default” heading_tag=”default” heading_tag_functionality=”default” title=”How does AIM\u2019s real-time learning process work to ensure accuracy?”][vc_column_text css=”” text_direction=”default”]AIM\u2019s system constructs thousands of models for each KPI throughout the day, making predictions based on the past two weeks of data without being exposed to actual results\u2014keeping predictions unbiased. After making predictions, each model is exposed to real results, giving itself an error rating based on accuracy. The system continuously builds and evaluates models, learning and refining with each iteration, then selects only the model with the least error to refresh in the system for client use.[\/vc_column_text][\/toggle][toggle color=”Default” heading_tag=”default” heading_tag_functionality=”default” title=”What level of forecast accuracy does AIM typically achieve?”][vc_column_text css=”” text_direction=”default”]AIM regularly achieves an impressive two-week forecast accuracy of 96%. This high degree of accuracy is consistent rather than a one-off occurrence, reinforcing the reliability of the system. Such precision allows businesses to strategize with confidence, knowing that the data they\u2019re basing their decisions on is both robust and trustworthy.[\/vc_column_text][\/toggle][toggle color=”Default” heading_tag=”default” heading_tag_functionality=”default” title=”Why is trust a primary challenge when introducing MMM to new clients?”][vc_column_text css=”” text_direction=”default”]Businesses are often wary of new technologies, especially when significant marketing investments and sales outcomes depend on their results. Clients need to become confident in MMM\u2019s accuracy because data-driven decisions are valuable only if the underlying data is accurate. Trust is not given but earned through proven results, which is why AIM prioritizes demonstrating model efficacy through verifiable forecasts from the<\/span> outset.[\/vc_column_text][\/toggle][\/toggles][\/vc_column][\/vc_row]","protected":false},"excerpt":{"rendered":"

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