How to Revise Marketing Mix Model under COVID-19 | Towards Data Science

COVID-19 has shocked the world and inevitably transformed how people work, shop and live. It not only sent economies spinning but also significantly impacted on how a wide range of advertisers are doing marketing. Influencer Marketing Hub recently unveiled COVID-19 Marketing Report saying 69% of brands indicated that they will decrease Ad Spend in 2020. And 74% of the firms they surveyed have slowed down their social media posting.
Beyond all doubt, slashing marketing spend will ensure short term survival and send grappled business a slim chance of recovery. However, the rigorous measurement of your marketing performance and having every one dollar drive more sales are way more critical and essential than ever. In today’s post, We are going to talk about how to better evaluate marketing efficiency under COVID-19 pandemic.
Methodologies to Measure Marketing Efficiency
To evaluate the marketing performance and optimize channel mix, the most important question we need to answer entails what Return on Investment (ROI) we are getting for the money we spend on each channel. Marketing Mix Models and Multi-Touch Attribution are popular techniques that tend to provide the depth of insights required to answer this question.
I am more inclined to go with the Marketing Mix Model to evaluate marketing ROI during COVID-19 pandemic for two reasons.
First and foremost, Multi-Touch Attribution is a bottom-up approach to evaluating marketing efficacy. It is focused on online marketing and sales and better at analyzing real-time user level data. Rather than evaluating every touchpoint user takes towards final conversion, Marketing Mix Modeling takes more of a top-down and macro-level view. For this reason, not only marketing related factors, but also factors such as macro economy, seasonality, weather, and the competitive impact can be examined and captured. From this standpoint, the unprecedented COVID-19 pandemic is not within everyone’s normal control and MMM can better capture the effect from it.
Moreover, MTA is mostly focused on the digital ecosystem while MMM can easily cover more offline channels. In light of Coronavirus, people are stuck at home. This consequently drove TV viewership up. According to the latest forecast from eMarketer, traditional TV is expected to add 8.3 million U.S. viewers this year, and this would be the first time that traditional TV viewing has increased since 2012👀. As a result, MMM may be doing a better job to measure the efficacy of TV performance compared to MTA.
A Brief Intro of Building Marketing Mix Models
In general, MMM employs the principle of Multivariate-Linear Regression, which is trying to form an equation between your Target KPI (Dependent Variable) and a set of drivers (Independent Variables) that will impact your KPI.
Your candidate drivers can be ultimately categorized into these two big groups:
Uncontrollable Factors: External factors that are out of our control, including Macro Economy, Seasonality, Holidays, and Weather etc.
Controllable Factors: Media and Marketing related metrics that we can play around with and optimize.
Usually, those uncontrollable factors are showing a linear relationship with your Target KPI, which means that every one unit increase in these factors will change your KPI by a certain amount (the change can be both negative and positive). However, for Media and Marketing related drivers, their impact is non-linear and three unique parameters have to be taken into consideration. They are Lag effect, Carryover Effect (Adstock) and Diminishing Return.
Lag Effect means how long it takes your ads to convert customers. If we are going to use sales revenue as the target KPI, the lag effect from lower funnel channels such as Social Media Engagement will be shorter than channels from upper funnels, e.g. TV and Radio which are about raising awareness and sparking interest.
Carry-over Effect (Adstock) is essentially described as that advertising will spread out over time and have a continuous impact on your KPI after it takes place. Let’s say if you see a Shopping Ads from Instagram, you will still remember those creative images for the next following days but with faded memories.
The formula for Adstock is: At = Xt + Adstock Rate * At-1
To check more details about Adstock, check GroupM Marketing Mix models across Consumer Electronics, Retail, Finance, Auto and CPG from here
Diminishing Return is originally an economics principle and here is a very classic example to elaborate it: When you are hungry, the utility you gain from eating the first burger will be higher than the utility the next burger brings to you.
Applying this law into the measurement of marketing performance, it indicates that for every one dollar spent on advertising, it will lift your KPI but at a decreasing rate.
The values of these three parameters are varied channel by channel. And they will not be the same for even the same channel when driving different KPIs or during different time periods. So, they are not predetermined or pre-assumed. Instead, it involves iterative model training and testing to get the most ideal combination of these three parameters for each channel impacting on specific KPIs during certain periods.
Multivariate Regression Models enable us to easily identify the significant drivers that impact your target KPI and quantify their impact with coefficients.
How to Capture COVID-19 Impact and Revise Your MMM?
Since COVID-19 is an unprecedented situation, similar impact was never captured and evaluated historically in your model and thus will make your MMM not function well. You may be already aware that if applying the MMM built prior to the Coronavirus outbreak to do a forecast, you will see an extremely high error rate while comparing to the actual. Without taking Coronavirus impact into account, your marketing efficiency would also be mistakenly evaluated.
How to revise our MMM to capture the COVID-19 impact? Here are some options you can give a shot.
COVID-19 Dummy Variable
This is a very straight forward and unsophisticated way to have Coronavirus impact fit into your model as an external factor. The biggest caveat of the Dummy variable is that it takes the value only 0 or 1 to indicate the absence or presence of certain factors. However, we have no idea when the pandemic will go to the end and there is a very real chance it might never go away. Also, the impact from COVID-19 is dynamic by flattening the curve rather than a fixed value. Therefore the Dummy variable with either 0 or 1 can’t fully and accurately pick up the decayed impact.
COVID-19 Related Indicators
To better capture the dynamic impact from COVID-19, we can directly approach pandemic related metrics such as number of Daily Incremental and Cumulative Confirmed Cases, Recoveries or Death etc. as the proxy. Google has provided free public datasets in Google Cloud Platform. You can go here and check more details.
Macroeconomic Indices
Coronavirus, a Black Swan incident, has resulted in a severe economic contraction. Therefore, besides COVID-19 related metrics, we can also indirectly leverage some macroeconomic indices to approximate the impact. The most intuitive one is the Unemployment Rate. Due to the Coronavirus, the U.S. unemployment rate soared to 14.7% in April, the highest rate since the Great Depression. Data is available and can be easily downloaded from U.S. Bureau of Labor Statistics.
One limitation of using Unemployment Rate is that data is usually refreshed and published on a monthly level. Therefore, it may not fully capture the daily or weekly fluctuation and variation.
Apart from this, considering that the stock market is always regarded as an economic barometer, indices in the stock market such as S&P 500 and Dow Jones are also potential candidates. Yahoo Finance is where I always go to download data of those indexes.
Counterfactual Analysis
Above proxies may help capture the general up and down of your business. Indeed, they may not be able to perfectly capture your trend. Another potential way to derive a metric of COVID-19 impact that is specific on your business is to utilize the MMM built prior to the Coronavirus outbreak to do a counterfactual estimation. To elaborate, Counterfactual Analysis measures what would have happened without COVID-19, so that we can quantify its impact by comparing the actual observed performance and counterfactual outcomes.
You may ask how to do counterfactual analysis if you never built MMM before? We can leverage the forecast package in R using historical data prior to the pandemic. Here are sample codes to do the prediction.
library(forecast)#Step 1: Convert sales data into time series. Here is using 2-year data to forecast next yearts <- ts(sales$Sales[sales.Period >= '2018-01-01' & sales.Period <='2019-12-31'])#Step 2: Fitting an auto.arima model in R using the Forecast packagesales<- auto.arima(ts)pred <- forecast(object = sales, h = 365)
So far, we have talked about four ways we can derive COVID-19 metrics and measure its impact as an external factor. Nevertheless, the COVID-19 is not only impacting our business externally, but also essentially affecting marketing efficiencies and effectiveness. On this account, what else do you need to do to revise your Marketing Mix Models to account different marketing efficacy during the pandemic?
You need to retrain your Marketing Mix Models with the latest data by splitting each of media and marketing metric into two parts. By doing so, we can get different coefficients prior and post COVID-19 and calculate corresponding ROI across marketing channels.
In the next article, we will talk about how to utilize R to build a mini optimizer based on your Marketing Mix Model results and improve your marketing efficacy.💖