Stat-Ease Blog


Breaking beyond A/B splits for better business experiments

posted by Mark Anderson on Oct. 12, 2020

Design of experiments (DOE), being such an effective combination of multifactor testing with statistical tools, hits the spot for engineers and scientists doing industrial R&D. However, as documented in my white paper on Achieving Breakthroughs in Non-Manufacturing Processes via Design of Experiments (DOE), this statistical methodology works equally well for business processes. Yet, non-manufacturing experimenters rarely make it beyond simple one-factor-at-a-time (OFAT) comparisons known as A/B splits—most recently embraced, to my great disappointment, by Harvard Business Review*. But to give HBR some credit, this 2017 feature on experimentation at least mentions “multivariate” (I prefer “multifactor”) testing as a better alternative.

To see an illuminating example of multifactor testing applied to marketing, see my April 21 StatsMadeEasy blog: Business community discovers that “Experimentation Works”.

Another great case for applying multifactor DOE came from Kontsevaia and Berger in a study published by the International Journal of Business, Economics and Managemental**. To maximize impressions per social-media posts, they applied a fractional two-level design on 6 factors in 16 runs varying:

A. Type of Day/Day of the week: Weekend (Sat, Sun) vs Workday (Thu, Fri)

B. Social Media Channel: LinkedIn vs Twitter

C. Image present: No vs Yes

D. Time of Day: Afternoon (3-6pm) vs Morning (7-10am)

E. Length of Message: Long (at least 70 characters) vs Short (under 70 characters)

F. Hashtag present: No vs Yes

The multifactor marketing test revealed the choice of channel for maximum impressions to be highly dependent on posts going out on weekends versus workdays. This valuable insight on a two-factor interaction (AB) would never have been revealed by a simple OFAT split.

Design-Expert® software makes multifactor business experiments like this very easy for non-statisticians to design, analyze and optimize for greatly increased returns. Aided by Stat-Ease you can put DOE to work for your enterprise and make a big hit career-wise.

*“Building a Culture of Experimentation”, Stefan Thomke, March-April, 2020.

**“Analyzing Factors Affecting the Success of Social Media Posts for B2B Networks: A Fractional-Factorial Design Approach”, August, 2020.