{"id":31115,"date":"2022-11-21T18:49:30","date_gmt":"2022-11-21T17:49:30","guid":{"rendered":"https:\/\/www.makingscience.com\/?p=31115"},"modified":"2022-11-21T18:49:30","modified_gmt":"2022-11-21T17:49:30","slug":"maximise-your-sales-with-marketing-mix-modeling","status":"publish","type":"post","link":"https:\/\/www.makingscience.com\/en\/blog\/maximise-your-sales-with-marketing-mix-modeling\/","title":{"rendered":"Maximise your sales with Marketing Mix Modeling"},"content":{"rendered":"<h3><b>What is Marketing Mix Modeling?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">According to the paper <\/span><b>&#8220;Challenges and Opportunities in Media Mix Modeling&#8221; by Chan, D., &amp; Perry, M. (2017)<\/b><span style=\"font-weight: 400;\">, Marketing Mix Modeling (MMM) are the statistical model used by advertisers to measure the effectiveness of their advertising spending and exists in various forms since the 1960s. MMMs, as we will call them from now on, use aggregated historical time-series data to model sales performance as a function of advertising variables, other marketing variables and control variables such as weather, seasonality and market competition.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Metrics such as return on advertising investment (ROAS) and optimised advertising budget allocations are derived from these models because they provide valid causal results.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In other words, MMMs attempt to answer causal questions for the advertiser. For example:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What was my ROAS on TV last year?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What would my sales be if more or less money were spent next year?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">How should my media budgets be allocated to maximise sales?<\/span><\/li>\n<\/ul>\n<h3><b>What does Marketing Mix Modeling bring to our business?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">With MMM, we will achieve two different things:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Attribution<\/b><span style=\"font-weight: 400;\">: we will analyse all the variables that influence the client&#8217;s business results and know which are the most relevant to enhance them. The advantage compared to user-level models is that this model does not only talk about digital data, but by using aggregated data, we will be able to incorporate offline investment data, types of offers, and seasonality, among others.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Predictions<\/b><span style=\"font-weight: 400;\">: we will discover which variables have been the most relevant in the period analysed; an MMM will indicate how to boost each variable and predict how the results will change in each scenario presented to it.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">We will only be able to make high-value activations, but only as decision making and for this, we will need an analyst to interpret the data. However, you do not have localised users by using aggregated data, so you cannot use an MMM to activate audiences or optimise campaign algorithms.<\/span><\/p>\n<h3><b>Choosing the best model for your business<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">In an MMM project, we will work and create different types of models, and the initial objective will be to choose the one that best suits our business:<\/span><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.makingscience.es\/wp-content\/uploads\/2022\/09\/Captura-de-pantalla-2022-09-07-a-las-12.34.46-1024x408.png\" \/><\/p>\n<p><span style=\"font-weight: 400;\">In the following graph, we can see the actual sales history of the customer (pink line) and how this model predicted that time period (blue line).<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Ultimately, we will choose the model that best fits the trend and the customer&#8217;s sales peaks.<\/span><\/p>\n<h3><b>What insights can we extract from our MMM model?<\/b><\/h3>\n<p><img decoding=\"async\" src=\"https:\/\/www.makingscience.es\/wp-content\/uploads\/2022\/09\/Captura-de-pantalla-2022-09-07-a-las-12.38.13-1024x502.png\" \/><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Prediction of potential sales increase:<\/b><span style=\"font-weight: 400;\"> this graph shows all campaigns or actions included in the model. The dots are the actual investment and sales of that campaign, and thanks to the trend lines, we can see the sales potential of each campaign as we increase the investment.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone wp-image-32008\" src=\"https:\/\/www.makingscience.co.uk\/wp-content\/uploads\/2022\/11\/Screenshot-2022-12-14-at-17.41.32-300x238.png\" alt=\"\" width=\"673\" height=\"534\" \/><\/p>\n<p>&nbsp;<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b><b><b><b>ROI of the campaigns and variables:<\/b><span style=\"font-weight: 400;\"> in the graph, we can see in dark blue the weight in the percentage of total sales that the campaign or action has had (calculated by the model). In light blue, we can see this action&#8217;s significance on the total investment (these are data that we give to the model). And finally, the orange dots show us the ROI of each action, identifying which are the most profitable for our business.<\/span><\/b><\/b><\/b><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Do you want to know more about what Marketing Mix Modeling can bring to your business? Don&#8217;t hesitate to <a href=\"https:\/\/www.makingscience.co.uk\/contact\/\" target=\"_blank\" rel=\"noopener\">contact<\/a> our team of experts. We are waiting for you! \ud83d\ude80<\/span><\/p>\n<blockquote><p>Discover how UK companies activate their data. In a recent study, Making Science asked 600 marketing professionals how they measure and activate their data. <strong><span style=\"color: #ff00ff;\"><a style=\"color: #ff00ff;\" href=\"https:\/\/www.makingscience.co.uk\/uk-companies-and-data-white-paper\/?utm_source=gi&amp;utm_medium=referral&amp;utm_campaign=UK_WHITEPAPER_DATA_2\/11_BLOG\" target=\"_blank\" rel=\"noopener\">Dowload the white paper<\/a><\/span><\/strong> and find out how to increase ROI.<\/p><\/blockquote>\n<p><a href=\"https:\/\/www.makingscience.co.uk\/uk-companies-and-data-white-paper\/?utm_source=gi&amp;utm_medium=referral&amp;utm_campaign=UK_WHITEPAPER_DATA_2\/11_BLOG\" target=\"_blank\" rel=\"noopener\"><img decoding=\"async\" class=\"aligncenter wp-image-31156 size-full\" src=\"https:\/\/www.makingscience.co.uk\/wp-content\/uploads\/2022\/11\/UK-white-paper-blog-banner.png\" alt=\"\" width=\"1200\" height=\"470\" \/><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>What is Marketing Mix Modeling? According to the paper &#8220;Challenges and Opportunities in Media Mix Modeling&#8221; by Chan, D., &amp; Perry, M. (2017), Marketing Mix Modeling (MMM) are the statistical model used by advertisers to measure the effectiveness of their advertising spending and exists in various forms since the 1960s. MMMs, as we will call [&hellip;]<\/p>\n","protected":false},"author":23,"featured_media":31109,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[870],"tags":[451,450,460,452],"class_list":["post-31115","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-martech-adtech-en","tag-attribution-en","tag-forecasting","tag-marketing-mix-modeling-en","tag-mmm-en"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/posts\/31115","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/users\/23"}],"replies":[{"embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/comments?post=31115"}],"version-history":[{"count":0,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/posts\/31115\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/media?parent=31115"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/categories?post=31115"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.makingscience.com\/en\/wp-json\/wp\/v2\/tags?post=31115"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}