Gleb Mashchenko, RoasUp

Ad234dd95cba9583d22026dcbb44ce9a?s=47 wnconf
November 26, 2018

Gleb Mashchenko, RoasUp

Mathematical Approach to Advertising on Facebook

(White Nights Conference Moscow 2018)
The official conference website —



November 26, 2018


  1. Mathematical Approach to Advertising on Facebook Gleb Mashchenko Business Development

    Director, RoasUp, Inc.
  2. ROASUP is a performance marketing agency for mobile games focused

    on user acquisition from Facebook 2 Our Clients
  3. Facebook is a Powerful UA Platform for Mobile Games

    3 * The AppsFlyer Perfomance Index - Edition VII, H1 2018 Power Ranking*
  4. Facebook Ad Account Structure 4 Facebook Account Ad Campaign

    Ad Set Ad Set Ad Set Ad Ad Ad Ad Campaign
  5. Ad Set Parameters 5 to show WHAT to show

    WHERE to optimize HOW Creative Headline Text CTA Audience Placement Device Optimization Conv. window CBO BUDGET Now you have to set 25+ parameters for an ad set!
  6. Conversion Funnel and ROAS 6 Impressions Clicks Installs Payers

    10000 100 50 3 $$$ Spend $$$ Revenue ROAS = Revenue Spend
  7. ROASUP Data-driven Mathematical Approach 7 • Collect data from

    Facebook • Automated data analysis
  8. How Do Ad Set Parameters Influence ROAS? 8 #

    Parameter H* 1 Creative 847 2 Optimization 199 3 Optimization conversion window 54 4 Location 40 5 Gender 28 * Kruskal W. H., Wallis W. A. Use of ranks in one-criterion variance analysis. // Journal of the American Statistical Association. — 1952, 47 № 260. — pp. 583–621. Asymptotic significance (p << 0.001)
  9. Creative Performance 9 Most of creatives do not receive

    enough spent for valid A/B tests. Facebook automatically optimizes creatives in an ad set
  10. Creative Performance 10 Performance of creatives dynamically changes

  11. Creative Performance 11 ROAS is not distributed normally among

    launches of any creative Standard methods of classic statistics are not applicable! K-S d=0.27405, p<0.01, Shapiro-Wilk W=0.59, p=0.0000
  12. Creative Performance 12 • ROAS distribution estimation • Bayesian

    Statistics • Automated analysis of creatives performance dynamics ROASUP Approach
  13. Optimization and Conv. window Performance 13 LTV of users

    from different optimizations varies LTV Days
  14. Optimization and Conv. window Performance 14 LTV is high,

    but CPI is high too! Cohort ROAS Days
  15. Optimization and Conv. window Performance 15 CPI ROAS CPI

    and ROAS depend on optimization and targeting High CPI is not an issue if ROAS is high!
  16. Optimization and Conv. window Performance 16 • Analysis of

    cohort ROAS for each optimization and targeting • Maximization of ROAS through selection of optimization and conversion window for each targeting • Performance dynamics tracking ROASUP Approach
  17. Ad Set Control Problem 17 Ad set dynamics is

    described by non-stationary stochastic process
  18. Ad Set Control Problem 18 Lifetime of the most

    of ad sets is less than 7 days
  19. Ad Set Control Problem 19 N of Ad Sets

    N of Ad Sets Opt. day Opt. day Data imbalance: +90% of ad sets do not meet KPI The first day is optimal for turning off for the most of ad sets
  20. Ad Set Control Problem 20 Simple methods of machine

    learning lead to the issues: • Unstable classification of ad sets (classes imbalance, sparse data) • Complex dynamic of an ad set depends on the fast changing market and changing performance of creatives • Low amount of data
  21. Ad Set Control Problem 21 • Deep Reinforcement Learning

    • Generative models (GAN, VAE) • Autoencoders ROASUP Approach
  22. Conclusion 22 Efficient user-acquisition on Facebook requires automated mathematical

  23. 23 Thank you! Gleb Mashchenko Business Development Director, RoasUp,

    Inc. Email: Facebook: gleb.mashchenko Skype: gleb.mashenko Telegram: @mgleb