How MMX Framework Helped a Gaming Company With 15% Higher ROI

Problem Statement

Business Context

The client is an American subsidiary of a leading player globally in the Gaming Entertainment industry. Their products are in three core segments – 

1. Gaming Consoles
2. Games – Digital and Physical Games
3. Peripherals – Controller, VR etc.

The client does annual ad spend of ~ USD 200M across all the product segments. However, the decisions on budget allocation have always been driven by business heuristics backed by basic data analyses.

Situation 

Owing to the scale and nature of operations, the client has always had multiple marketing teams such as Social Media, TV, Digital Marketing, Channel Marketing etc. taking care of their own initiatives and coming up with budget requirements to fit their needs. The system lacked a scientific and centralized approach for marketing budget planning.

This led to frequent occurrences of sub optimal ROI from products or channels, failure of initiatives run by different teams. Above all, budget allocation was a big challenge for the leadership.

Objective

The two core problems that were identified to be addressed were to understand – 

1. Effectiveness of various channels to understand which channel should be allocated how much budget (ad-spend)
2. Product dynamics to identify optimal spend for every product the client is selling or going to launch

Team had to design and develop a data backed scientific solution to cater to above needs. 

Approach

The study was split into 3 phases –

Data Analysis

Team gathered data from all marketing teams and external media collaborators and conducted an extensive 4 week analysis sprint to build hypotheses and validate the same. Since, the objective was to achieve maximum ROI, the impact of features was always studied on Sales (volume). This included performing various uni, bi and multivariate analysis to understand impact of features such as –

1. Impact of Television GRP on Sales
2. Impact of Clicks, CTR on Sales
3. Impact of partner spend (channel marketing) on Sales
4. Impact of Pricing on Sales
5. Collinearity or Multi-collinearity in features, especially marketing features

and many more

A few key insights observed were – 

1. Most of the marketing features show extremely high correlation. Therefore for better performance and right attribution it is important to leverage techniques such as PCA.
2. Games have a very fast diminishing curve for sales. 80% of sales happen in the first 60 days. Therefore it is important to plan marketing before or around launch period
3. Adstock effect in Game marketing doesn’t persist for long
4. Marketing of other products or in association with other products creates a Halo effect leading to positive impact on game sales. For e.g. marketing of consoles or game and console combo will boost game sales
5. Marketing behaves very differently for Consoles, Small & Big Physical Games, Digital Games etc.. Therefore, one blanket ML model may not bring in a lot of value.

Model Development

An important decision one needs to make before beginning development work is Model selection. A few key driving factors in this study were – 

1. Do features have a linear or no linear relationship with the target variable? In our space, marketing features have diminishing returns curve which is similar to either an S-curve or in most cases log curve
2. Is model explainability a requirement? When we talk about attribution then explainability becomes an important area. Several ML/DL models perform well but can’t add much value when it comes to explainability of impact by features

The data was not vast which allowed us more liberty to pick even basic models. And, as most will understand, marketing spend doesn’t have a linear relationship with sales. Spending on marketing gives diminishing returns which means after an extent, every incremental dollar we spend will start giving lower return than earlier and eventually negative ROI.

Therefore, the team leveraged a Linear Regression model which was converted to a log-log model to fit the need of marketing features. 

Another important piece to be figured out was the granularity of prediction and how many models are needed to be developed. As described earlier, since the behavior of marketing is very different when it comes to Consoles vs Games or Digital vs Physical Games, the team built 3 different models – 

1. Gaming Consoles
2. Physical Games
3. Digital Games

To tackle multicollinearity PCA was leveraged prior to model development.

The output from this step were the equations (and coefficients) for the three ML models. These equations were to be used in the next step.

Optimization Framework

Once the models for Sales prediction were developed, the next step was to leverage those to assess optimal spend that should be allocated to –

Product → Marketing Channel → Week

in order to achieve maximum ROI

Decision Variables – Variables for which optimal values were needed to be identified were – 

Decision variable -> PiMjWk

Where,

P – Product

M – Marketing channel

W – Week

Constraints –

In media mix model optimization constraints on marketing spend for every channel play a very important role to achieve maximum ROI. Since, the diminishing curve for every product is different, the team created a framework to analyze diminishing returns from every marketing channel spend as well as overall spend for every product separately. This step helped in identifying the spend thresholds beyond which 

• ROI starts diminishing 
• Sales saturates

In addition to these, there were various other constraints from business stakeholders on how much should be the minimum and maximum spend for any particular product.

All these constraints were specified in the optimization framework which eventually brought a lot of value.

Objective function –

The objective of the study was to maximize sales within the limits of spend constraints, therefore the fitness function was also designed on same lines. Sales predictions model equations were already present from previous step, so that only thing required was to aggregate sales across all weeks for all products – 

log(Sales Product1Week1) = 0 + log(1Price11) + m x log(2TV GRP11) + n x log(3Clicks11) + ….

log(Sales Product1Week2) = 0 + log(1Price12) + m x log(2TV GRP12) + n x log(3Clicks12) + ….

.

.

log(Sales Product2Week1) = 0 + log(1Price21) + m x log(2TV GRP21) + n x log(3Clicks21) + ….

.

.

.

log(Sales ProductnWeekn) = 0 + log(1Pricenn) + m x log(2TV GRPnn) + n x log(3Clicksnn) + ….

All the above equations when summed up represented total sales for the year. Therefore, the aggregated sum of all the sales prediction equations was fed to the non-linear optimization model that generated the optimal values for all the decision variables that could help in maximizing sales and ROI.

Spend Planning Tool

Final step of the solution was to build a tool that can help all the business stakeholders come up with budget allocation for any product. Key features of the tool were – 

Input – 

1. Users needed to feed product details such as Price, Launch date, Max budget etc. (all features except marketing spend)

Output – 

1. Tool had an inbuilt optimization engine that recommended optimal spend across all “products x marketing channel x week” 
2. Effectiveness of all the marketing channels

Impact

The study when it went to the implementation phase helped the client realize a 25% drop in marketing spend with a lift of 20% in revenue for the Fiscal Year. 15% in ROI lift was attributed to better MMX Framework.