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Accelerating Innovation through Trustworthy Experimentation

ExP Experimentation Platform

Accelerating software innovation through trustworthy experimentation

The Experimentation Platform was a project headed by Ronny Kohavi from 2006. It went live in 2007 and the team was merged into Bing in 10/2010.Starting March 2014, the mission is back as part of the Artificial Intelligence and Research group:Ronny Kohavi is the General Managerfor the Analysis Experimentation team.

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The Surprising Power of Online Experiments: Getting the most out of A/B and other controlled tests Ron Kohavi and Stefan Thomke

In HBR Sept-Oct 2017

Today, Microsoft and several other leading companies—including Amazon, Booking.com, Facebook, and Google—each conduct more than 10,000 online controlled experiments annually, with many tests engaging millions of users...These organizations have discovered that an “experiment with everything” approach has surprisingly large payoffs. It has helped Bing, for instance, identify dozens of revenue-related changes to make each month—improvements that have collectively increased revenue per search by 10% to 25% each year. These enhancements, along with hundreds of other changes per month that increase user satisfaction, are the major reason that Bing is profitable and that its share of U.S. searches conducted on personal computers has risen to 23%, up from 8% in 2009, the year it was launched. At a time when the web is vital to almost all businesses, rigorous online experiments should be standard operating procedure. If a company develops the software infrastructure and organizational skills to conduct them, it will be able to assess not only ideas for websites but also potential business models, strategies, products, services, and marketing campaigns—all relatively inexpensively. Controlled experiments can transform decision making into a scientific, evidence-driven process—rather than an intuitive reaction. Without them, many breakthroughs might never happen, and many bad ideas would be implemented, only to fail, wasting resources.

The Benefits of Controlled Experimentation at Scale ( slides )Aleksander Fabijan, Pavel Dmitriev, HelenaHolmström Olsson, Jan Bosch.SEAA 2017

Online controlled experiments (for example A/B tests) are increasingly being performed to guide product development and accelerate innovation in online software product companies. The benefits of controlled experiments have been shown in many cases with incremental product improvement as the objective. In this paper, we demonstrate that the value of controlled experimentation at scale extends beyond this recognized scenario. Based on an exhaustive and collaborative case study in a large software-intensive company with highly developed experimentation culture, we inductively derive the benefits of controlled experimentation. The contribution of our paper is twofold. First, we present a comprehensive list of benefits and illustrate our findings with five case examples of controlled experiments conducted at Microsoft. Second, we provide guidance on how to achieve each of the benefits. With our work, we aim to provide practitioners in the online domain with knowledge on how to use controlled experimentation to maximize the benefits on the portfolio, product and team level.

\left \{ \begin{array}{@{}l} {\phi(u)-\phi(u_{n})+\frac{1}{r_{n}}\langle u-u_{n},u_{n}-x_{n}\rangle \geq0, \quad\forall u\in C};\\ \varphi(v)-\varphi(v_{n})+\frac{1}{r_{n}}\langle v-v_{n},v_{n}-y_{n}\rangle \geq0,\quad \forall v\in Q;\\ {x_{n+1}= \alpha_{n} u_{n}+ (1- \alpha_{n})T(u_{n}- \rho_{n} A^{*}(Au_{n}-Bv_{n}))};\\ y_{n+1}= \alpha_{n}v_{n}+ ( 1- \alpha_{n})S(v_{n}+ \rho_{n} B^{*}(Au_{n}-Bv_{n})),\quad \forall n\geq1; \end{array} \right .

0<\alpha\leq\alpha_{n}\leq\beta<1 ( \alpha, \beta\in (0,1) );

\liminf_{n\rightarrow\infty}r_{n}>0 \lim_{n\rightarrow\infty }|r_{n+1}-r_{n}|=0 .

\{(x_{n}, y_{n})\} ( 1.13 ).

, , -, \{ (x_{n},y_{n})\} ( 1.13 ).

In Theorem 4.3 taking B=I and H_{2}=H_{3} , from Theorem 4.3 we can obtain the following convergence theorem for split convex minimization problem ( 1.14 ) SCMP(\phi,\varphi) .

\left \{ \begin{array}{@{}l} \phi(u)-\phi(u_{n})+\frac{1}{r_{n}}\langle u-u_{n},u_{n}-x_{n}\rangle \geq0,\quad \forall u\in C;\\ \varphi(v)-\varphi(v_{n})+\frac{1}{r_{n}}\langle v-v_{n},v_{n}-y_{n}\rangle \geq0,\quad \forall v\in Q;\\ {x_{n+1}= \alpha_{n} u_{n}+ (1- \alpha_{n})T(u_{n}- \rho_{n} A^{*}(Au_{n}-v_{n}))};\\ y_{n+1}= \alpha_{n}v_{n}+ ( 1- \alpha_{n})S(v_{n}+ \rho_{n} (Au_{n}-v_{n})),\quad \forall n\geq1; \end{array} \right .

0<\alpha\leq\alpha_{n}\leq\beta<1 ( \alpha, \beta\in (0,1) );

\liminf_{n\rightarrow\infty}r_{n}>0 \lim_{n\rightarrow\infty }|r_{n+1}-r_{n}|=0 .

\{(x_{n}, y_{n})\} ( 1.14 ).

, , -, \{ (x_{n},y_{n})\} ( 1.14 ).

Acknowledgements

The authors would like to express their thanks to the reviewers and editors for their helpful suggestions and advice. This work was supported by the National Natural Science Foundation of China (Grant No. 11361070).

Open Access This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

The authors declare that they have no competing interests.

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