

A complete guide to
Marketing Mix Modeling
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Last modified: April 3, 2020
Marketing mix elements are broken down into three variables: incremental, base and other. These three categories are further subdivided into a range of factors that can influence the market performance of a product or service. Understanding each of these variables is crucial for marketers to make an accurate forecast of the effects of promotional activities and product distribution.
Examples of ATL marketing include television advertising, radio advertising, print advertisements (magazine and newspaper), and product placements (cinema and theatres).
Examples of BTL activities include sales promotions, discounts, social media marketing, direct mail marketing campaigns, in-store marketing, events and conferences.
Examples of TTL activities include 360° Marketing – campaigns developed with the vision of brand building as well as conversions and digital marketing (digital ads & videos).
CATEGORY | VARIABLES | METRICS |
---|---|---|
BASELINE | BASE PRICE | Undiscounted price of the product at which it is sold in the market |
AVERAGE SALES PRICE | Discounted price at which the product is sold in the market | |
ASSORTMENT (SKU) | Number of Stock Keeping Units of the product in a store/market to track the inventory of the product | |
VELOCITY | Rate at which product is moving when it is available in store (Units/Store) | |
DISTRIBUTION | Distribution of the product – No. of stores or No. of locations the product is available | |
PROMOTIONS | SALES PROMOTION | No of offers or No of days for which offers are running or the type of promotions like coupons, free shipping, price match guarantees, dollar-off etc. |
DISCOUNTS | AVERAGE PRICE DISCOUNT | Average Price Discounts on the product at a particular time period |
WEIGHTED DISCOUNT | Average Price Discounts on the products weighted based on their share to product sales | |
SEASONALITY & HOLIDAY | SEASONALITY & HOLIDAY | Dummy variables to capture the spike/dip in KPIs during holidays like Thanksgiving, Christmas, New Year, Back to School, Labour Day, President Day, Retailer Promotions days like Prime Day etc. |
MEDIA ACTIVITY | TV SPENDS | Marketing Spends for TV advertising |
REACH | Total No of consumers exposed to the ad | |
FREQUENCY | Total No of times the customers are exposed to the ad | |
TV GRP | Product of reach and frequency | |
DIGITAL SPENDS | Marketing spends for digital advertising | |
DIGITAL IMPRESSIONS | No of times the ads are exposed to customers | |
DIGITAL CLICKS | No of clicks on online ads | |
DIGITAL – OTHERS | Many other variables act as a measure for digital ads like click-through-rate, rich media, video view rate, cost-per-clicks, video likes, video comments etc. | |
SEARCH SPENDS | Spends for Search marketing | |
SEARCH IMPRESSIONS | Impression counted when Search page for product loads | |
PRINT SPENDS | Marketing spends for product in a medium like magazines, newspapers etc. | |
RADIO SPENDS | Marketing spends for radio advertising | |
COMPETITION | BASE | Base metrics for competition like pricing, distribution, seasonality, events, launches etc. |
MEDIA ACTIVITES | Competition media activities like spends, GRPs, impressions etc. | |
OFFERS | Count of competition offers on different platforms | |
DISCOUNTS | Discounts offered by competition on their products | |
OTHERS | SOCIAL MEDIA | Metrics to capture the activities of the brand or product on social media like page views, followers, sentiment score, reviews, likes, comment, retweets etc. |
EXTERNAL FACTORS | External variable affecting KPIs like macroeconomic factors | |
TREND | The trend of product category or product over time period | |
CYCLICITY | Metrics to capture product cycles like Sine or Cosine functions | |
EVENTS & LAUNCHES | Indicative variables for capturing significant product launches, special events, conferences etc. |
TV Ratings | Frequency |
---|---|
1-50 | 20 |
51-100 | 17 |
101-150 | 21 |
151-200 | 7 |
201-250 | 1 |
Salest = exp(Intercept) * exp(β1*Pricingt) * exp(β2*Distributiont) * exp(β3*Mediat) * exp(β4*Discountst) * exp(β5*Seasonalityt) * exp(β6*Promotionst) *…
Salest = exp (Intercept + β1*Pricingt+ β2*Distributiont+ β3*Mediat+ β4*Discountst+ β5*Seasonalityt+ β6*Promotionst+ …)
Ln (Salest) = Intercept + β1*Pricingt+ β2*Distributiont+ β3*Mediat+ β4*Discountst+ β5*Seasonalityt+ β6*Promotionst+ …
Salest = exp(Intercept) * β1*Pricingt * β2*Distributiont * exp(β3*Mediat) * exp(β4*Discountst) * exp(β5*Seasonalityt) * exp(β6*Promotionst) *…
Ln (Salest) = Intercept + β1*Ln (Pricingt)+ β2*Ln (Distributiont)+ β3*Mediat+ β4*Discountst+ β5*Seasonalityt+ β6*Promotionst+ …
β = %ΔDependent_Variable / %ΔExplanatory_Variable
From our extensive experience in developing marketing mix models, these are the key features that need to be implemented:
Week | Sales | Pricing | Distribution | Competition Discounts | Competition Online Impressions | TV GRP | Online Impressions | Promotions | Discounts |
---|---|---|---|---|---|---|---|---|---|
07/01/2017 | 30,503 | $1,067 | 48 | 1.12% | 105.68 M | 0 | 22.82 M | 27 | 6.93% |
07/08/2017 | 27,037 | $1,068 | 47 | 4.33% | 0.00 M | 0 | 0.00 M | 5 | 8.55% |
07/15/2017 | 30,646 | $1,038 | 42 | 1.89% | 0.00 M | 0 | 0.00 M | 6 | 9.64% |
07/22/2017 | 40,887 | $954 | 35 | 1.10% | 0.00 M | 0 | 0.00 M | 6 | 13.75% |
07/29/2017 | 48,947 | $912 | 31 | 4.56% | 0.00 M | 0 | 0.00 M | 10 | 16.57% |
08/05/2017 | 37,910 | $1,010 | 38 | 3.64% | 66.62 M | 100 | 127.65 M | 12 | 11.15% |
08/12/2017 | 40,436 | $1,007 | 37 | 1.66% | 124.18 M | 93 | 125.34 M | 8 | 11.42% |
08/19/2017 | 49,343 | $994 | 33 | 2.50% | 96.87 M | 95 | 150.62 M | 10 | 12.90% |
08/26/2017 | 32,371 | $1,078 | 39 | 5.08% | 109.01 M | 90 | 206.28 M | 11 | 7.24% |
09/02/2017 | 28,665 | $1,060 | 40 | 1.10% | 115.16 M | 12 | 595.09 M | 2 | 7.20% |
09/09/2017 | 29,079 | $1,061 | 42 | 0.00% | 157.02 M | 17 | 284.73 M | 13 | 6.35% |
09/16/2017 | 22,794 | $1,098 | 41 | 0.15% | 145.53 M | 11 | 46.09 M | 7 | 5.74% |
09/23/2017 | 26,607 | $1,048 | 36 | 0.02% | 105.84 M | 0 | 13.62 M | 10 | 8.91% |
09/30/2017 | 21,153 | $1,100 | 39 | 0.00% | 118.05 M | 0 | 36.95 M | 11 | 5.99% |
10/07/2017 | 20,704 | $1,092 | 42 | 0.00% | 62.06 M | 0 | 16.33 M | 10 | 6.49% |
10/14/2017 | 19,364 | $1,082 | 44 | 2.10% | 73.75 M | 0 | 13.50 M | 11 | 4.94% |
10/21/2017 | 25,881 | $1,050 | 53 | 3.13% | 115.28 M | 0 | 4.19 M | 17 | 6.51% |
10/28/2017 | 25,903 | $1,018 | 46 | 2.30% | 78.39 M | 0 | 5.77 M | 11 | 8.96% |
11/04/2017 | 42,168 | $996 | 54 | 1.08% | 78.04 M | 0 | 8.84 M | 37 | 10.43% |
11/11/2017 | 36,524 | $1,002 | 57 | 7.11% | 90.22 M | 0 | 11.58 M | 18 | 10.29% |
11/18/2017 | 35,647 | $1,014 | 55 | 7.44% | 145.45 M | 0 | 20.57 M | 21 | 9.39% |
11/25/2017 | 98,776 | $948 | 41 | 16.57% | 180.83 M | 128 | 167.81 M | 10 | 13.89% |
12/02/2017 | 110,717 | $935 | 52 | 5.62% | 165.39 M | 115 | 215.72 M | 29 | 13.26% |
12/09/2017 | 43,575 | $1,039 | 56 | 0.00% | 155.02 M | 106 | 255.36 M | 17 | 8.71% |
12/16/2017 | 55,115 | $1,000 | 52 | 5.19% | 176.43 M | 94 | 373.02 M | 30 | 9.61% |
12/23/2017 | 82,843 | $961 | 40 | 4.87% | 164.09 M | 16 | 424.45 M | 40 | 11.40% |
12/30/2017 | 38,610 | $1,072 | 53 | 2.07% | 143.84 M | 0 | 173.51 M | 30 | 6.75% |
Optimization Inputs | Actual Values | Target Values |
---|
Variable | Start Date | End Date | Minimum | Maximum | Value | Minimum | Maximum | Value |
---|---|---|---|---|---|---|---|---|
TV GRP | 7/1/2016 | 12/30/2016 | 0 | 128 | 880 | 0 | 256 | 1052 |
Discounts | 7/1/2016 | 12/30/2016 | 4.94% | 16.57% | 9.37% | 0 | 20% | 10.31% |
Output | Metrics | Minimum | Maximum | Average | Sum |
---|---|---|---|---|---|
Actual | 19,364 | 110,717 | 40,822 | 1,102,204 | |
Sales (DV) | Optimized | 15,733 | 148,732 | 49,761 | 1,343,554 |
Lift % | -18.75% | 34.33% | 21.90% | 21.90% | |
Actual | 4.94% | 16.57% | 9.37% | 252.97% | |
Discounts | Optimized | 0.00% | 20.00% | 10.31% | 278.37% |
Lift % | -100% | 20.68% | 10% | 10% | |
Actual | 0 | 128 | 32 | 880 | |
TV GRP | Optimized | 0 | 210 | 38 | 1052 |
Lift % | 0% | 64.30% | 20% | 20% |
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