Maximus Increase Media ROI Marketing Mix Modeling Platform Optimize Marketing Activities Marketing Mix Modeling and Advanced Analytics Platform marketing mix modeling

Salient Features

Maximus is a marketing mix modeling and advanced analytics and measurement platform. The tool is built with R using the latest and most advanced machine learning and statistical functions and methods.

  • A web application that can be accessed from any browser
  • Fast, interactive, and user friendly
  • Client data is not saved on the cloud
  • Written with advanced algorithms and math functions
  • Do not require a data scientist to apply data science
  • Beautiful and interactive graphs
  • Generates useful reports on the spot
  • Easy step-by-step flow
  • Run-through tutorial on every page
marketing mix modeling tool

01

Uncover sales drivers

02

Maximize marketing ROI

03

Optimize media budgets

04

Decipher customer journey

05

Predict results

Studio of Web Applications

Marketing Mix Modeling

Marketing Mix Modeling analyzes historical activities, calculate contribution to sales and ROI per activity and predict future results

  • Data exploration and summary statistics
  • Data correlation and analysis of trends and patterns
  • Data processing and transformation (dummy variables, halo effects, advertising ad-stock, lagged variables, moving average, log transformation…)
  • Auto modeling and manual modeling with the help of recommender based on best fit tests.
  • Compare between multiple models and easily add or remove variables
  • Group variables under categories such as media, promotion, seasonality
  • Tabulated report and charts of contribution to sales
  • Return versus spends and marketing ROI
  • Forecast single and multiple points
  • Response curves

View Marketing Mix Modeling case study

Multi-touch Attribution

Generate predictive attribution modeling based on the transition between channels and customer journey

  • Predictive attribution modeling based on Markov Chains, and comparison between Heuristic (first touch, last touch, linear) and predictive modeling
  • Channel exclusion such as ‘Direct’
  • Takes into consideration the customer path in the journey and splits between unique and multi-channel attribution
  • Attribution comparison charts
  • Transition matrices between channels with and without same channel conversion, and matrix including channels leading to no conversion
  • Customer journey analysis and time-lapse distribution between first contact and conversion, and last contact and conversion

Budget Optimization

Reach desired sales goals and maximize media ROI by reallocating budgets across campaigns

  • Media optimization based on S-curve estimation
  • Control S-curve and set minimum ROI required per campaign
  • Spend increment level by campaign
  • New spend estimation to reach desired sales goals
  • Recommended spends and sales return data by media and campaign
  • Comparison tables between current and recommended spends
  • Spend comparison chart

Market Simulation

Analyze the amount of switching between brands based on market share and set budgets and CPA

  • Expected switching in the market based on market share
  • Propensity probability distribution of brands. Percentage of customers who will cost more to be acquired
  • Spend and penetration plot
  • Spend and ROI plot
  • Propensity to purchase versus the cost of acquisition

AB Testing

Compare two versions of a campaign against each other to determine which one performs better

  • Campaign Planning:
    • Suggested campaign action size
    • Probability of possible outcomes
  • Campaign Evaluation:
    • Set actions/impressions, success/clicks, and success values
    • Set wish price (average market price and target value)
    • Estimated campaigns values
    • Probability that each campaign exceeds the target value-per-action
    • Scale factor and compare for larger campaigns

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