• COVID-19 and Internet

      СOVID-19 and Internet


      Recent events caused by coronavirus spread have highlighted quite a few problem areas in society, economics, technology… And it’s not only about the panic, which is inevitable and will come back with any following global issue. But it is really about the consequences: crowded hospitals, empty shelves in supermarkets, people having to stay at home and use up the Internet which turns out to not be enough for everyone who’s going through the hard days and nights of #stayathome.

      What already happened

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    • Habr — best articles, authors and statistics 2019

        2019 is coming to an end, and it's Christmas soon. It is also the time to grab all data and collect statistics and a rating of the most interesting Habr's articles for this period.



        In this post the best articles and best Habr authors 2019 will be presented, I also will show some statistical graphs that I find interesting or unusual.

        Let's get started.
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      • Machine Learning and Theory of Constraints

          Backlog prioritization requires simplification and weighting of tasks. Each one belongs to strategy like ads acquisition or CRO. We may consider turnover, operational costs, other metrics as input; profit margin, ROI — as output in case of retail. The perfect goal is to find 20/80 solution and focus resources on a single strategy at a time. Metrics tied to strategies gives the dimension of model. Sometimes unit economy relations are violated because of non-linearity. In practice it means low/insignificant correlation and poor regression. Example: it is impossible to separate acquisition and conversion — the quantity of acquisition affect its quality and vice versa. Decomposition of tasks/strategies assumes linear decomposition of nonlinear system. Besides nonlinear statistical evaluation of strategies is required when CJM can't be tracked or online/offline channels can't be separated.
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        • Marketing with ML decision making

          Backlog prioritization leads to the choice between strategies. Each one has its metrics. There is a requirement to choose the most important one. ML scoring is a solution when non linearity exists and economy is nonlinear. See introduction here. Two groups are considered. First (I) corresponds to web conversion {bounce rate, micro conversion, time, depth}. Second (II) corresponds to attraction of new visitors from organic channel {visits, viewers, views}. The target function is a number of commercial offers per day. The task is to reduce the dimension to get the optimal simple strategy. In this case online/offline B2B channels can't be separated: market is thin and new customers may have some information about 'the brand' from both channels. Therefore statistical evaluation is closer to reality than direct CJM tracking in this case.
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