Медиапотребление в России – 2020 | Москва, октябрь 2020 by Deloitte
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Помимо обзора потребления контента по основным медиаканалам и мониторинга общих трендов, основной темой исследования этого года является влияние ограничительных мер на медиапотребление россиян во время и после режима самоизоляции.
Ограничительные меры оказали значительное влияние на медиапотребление россиян. С одной стороны, заметно увеличился средний индекс медиаактивности по сравнению с предыдущим годом, причем наиболее высокий рост характерен для показателей использования Интернета: увеличилась доля пользователей практически всех видов активности в Интернете. Наибольший рост показали прослушивание подкастов, музыки и радио, совершение покупок в онлайн-магазинах, а также выполнение рабочих обязанностей.
В то же время наблюдается сокращение аудитории многих медиаканалов (телевидения, печатных СМИ и т. д.) по сравнению с 2019 годом. При этом на фоне снижения доли аудитории активность и время, проведенные потребителями медиаконтента на любимых каналах, в 2020 году заметно увеличились.
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Archive: 2019, 2018, 2017
#analytics #economics #marketing #mr #report #sociology #trends
📻📲📺
Помимо обзора потребления контента по основным медиаканалам и мониторинга общих трендов, основной темой исследования этого года является влияние ограничительных мер на медиапотребление россиян во время и после режима самоизоляции.
Ограничительные меры оказали значительное влияние на медиапотребление россиян. С одной стороны, заметно увеличился средний индекс медиаактивности по сравнению с предыдущим годом, причем наиболее высокий рост характерен для показателей использования Интернета: увеличилась доля пользователей практически всех видов активности в Интернете. Наибольший рост показали прослушивание подкастов, музыки и радио, совершение покупок в онлайн-магазинах, а также выполнение рабочих обязанностей.
В то же время наблюдается сокращение аудитории многих медиаканалов (телевидения, печатных СМИ и т. д.) по сравнению с 2019 годом. При этом на фоне снижения доли аудитории активность и время, проведенные потребителями медиаконтента на любимых каналах, в 2020 году заметно увеличились.
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Archive: 2019, 2018, 2017
#analytics #economics #marketing #mr #report #sociology #trends
Adding is favoured over subtracting in problem solving
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A series of problem-solving experiments reveal that people are more likely to consider solutions that add features than solutions that remove them, even when removing features is more efficient.
Consider the Lego structure depicted in Figure 1, in which a figurine is placed under a roof supported by a single pillar at one corner. How would you change this structure so that you could put a masonry brick on top of it without crushing the figurine, bearing in mind that each block added costs 10 cents? If you are like most participants in a study reported by Adams et al.1 in Nature, you would add pillars to better support the roof. But a simpler (and cheaper) solution would be to remove the existing pillar, and let the roof simply rest on the base. Across a series of similar experiments, the authors observe that people consistently consider changes that add components over those that subtract them — a tendency that has broad implications for everyday decision-making.
What are the implications of Adams and colleagues’ findings? There are many real-world consequences of failing to consider that situations can often be improved by removing rather than adding. For instance, when people feel dissatisfied with the decor of their home, they might address the situation by going on a spending spree and acquiring more furniture — even if it would be equally effective to get rid of a cluttering coffee table. Such a tendency might be particularly pronounced for resource-deprived consumers, who tend to be particularly focused on acquiring material goods. This not only harms those consumers’ financial situations, but also increases the strain on our environment. On a grander scale, the favouring of additive solutions by individual decision-makers might contribute to problematic societal phenomena, such as the increasing expansion of formal organizations and the near-universal, but environmentally unsustainable, quest for economic growth.
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#case #economics #experiment #psychology #science #sociology
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A series of problem-solving experiments reveal that people are more likely to consider solutions that add features than solutions that remove them, even when removing features is more efficient.
Consider the Lego structure depicted in Figure 1, in which a figurine is placed under a roof supported by a single pillar at one corner. How would you change this structure so that you could put a masonry brick on top of it without crushing the figurine, bearing in mind that each block added costs 10 cents? If you are like most participants in a study reported by Adams et al.1 in Nature, you would add pillars to better support the roof. But a simpler (and cheaper) solution would be to remove the existing pillar, and let the roof simply rest on the base. Across a series of similar experiments, the authors observe that people consistently consider changes that add components over those that subtract them — a tendency that has broad implications for everyday decision-making.
What are the implications of Adams and colleagues’ findings? There are many real-world consequences of failing to consider that situations can often be improved by removing rather than adding. For instance, when people feel dissatisfied with the decor of their home, they might address the situation by going on a spending spree and acquiring more furniture — even if it would be equally effective to get rid of a cluttering coffee table. Such a tendency might be particularly pronounced for resource-deprived consumers, who tend to be particularly focused on acquiring material goods. This not only harms those consumers’ financial situations, but also increases the strain on our environment. On a grander scale, the favouring of additive solutions by individual decision-makers might contribute to problematic societal phenomena, such as the increasing expansion of formal organizations and the near-universal, but environmentally unsustainable, quest for economic growth.
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Paper in PDF >>>
#case #economics #experiment #psychology #science #sociology
Stated “Versus” Derived Importance: A False Dichotomy. Taking a closer look at what these two methods really measure
by Keith Chrzan, Vice President, Marketing Sciences, Maritz Research and Juraj Kavecansky, PhD, Director, Marketing Sciences, Maritz Research
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A perennial question among applied marketing researchers is whether to measure stated or derived importance. The debate focuses on whether stated or derived importance is a better method, whether one is more valid or more actionable than the other and so on. Much of this attention is misguided, however, resulting from the mistaken conflating of two similar, but not identical, concepts. We illustrate an under-appreciated point made by Myers and Alpert over 30 years ago – that the choice between stated and derived importance is a false dichotomy: the two methods measure different constructs, they accomplish different objectives and they fulfill different information needs. Drawing upon brand studies with choice-based derived importance models and customer satisfaction studies with regression-based derived importance models, we show that, when done properly, both stated and derived methods have solid predictive validity, albeit with different strengths and weaknesses.
Motivation – Two Case Studies
Two disguised case studies illustrate what can happen if you measure importance badly.
✨1️⃣ Suckered by Stated Importance
A service company wanted to know which aspects of its service most satisfied its customers. They asked 400 of their customers to rate the importance of each of the aspects on a scale from 0=Not Important At All to 10=Critically Important. When the results came back, all of the aspects had average importances in the range of 7.4 to 7.8. The survey didn’t give the service company any valuable feedback about which aspects of the service customers valued more than others, so it was a waste of a few tens of thousands of dollars and some goodwill, because some customers expected the service provider to make changes based on the survey results.
✨2️⃣ Derived Importance Debacle
In the 1980s a medical supplies manufacturer had a 60% share of their market. A fancy consultant convinced the manufacturer that it should be using derived importance modeling to quantify the impact of attributes on customers’ choices. The consultant suggested a super-sophisticated method called multiple regression. He even put it in quotes, “multiple regression,” so that it would be clear to senior management how very cool and new and sophisticated it was to use regression instead of the stated importance methods the company had been using. So when the old stated importance methods said that ease of use was important to customers, the consultant and his regression analysis said that ease of use wasn’t important at all and that the key to incremental sales was size - the smaller the better. The manufacturer redirected new product development efforts away from easy to use products and toward small ones. The next year, a competitor launched an especially easy to use product and grew from a 15% share to 50%, almost overnight. The manufacturer, caught flat-footed, dropped from 60% to 30%, also almost overnight. Within a couple of years, the decision to emphasize size at the expense of ease of use had cost the manufacturer hundreds of millions of dollars. What happened?
These two case studies and many others like them illustrate some of the pitfalls associated with stated and derived importance measurement.
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Article in PDF >>>
#analytics #case #marketing #methodology #mr #psychology #science #sociology
by Keith Chrzan, Vice President, Marketing Sciences, Maritz Research and Juraj Kavecansky, PhD, Director, Marketing Sciences, Maritz Research
🤔⚖️🤩
A perennial question among applied marketing researchers is whether to measure stated or derived importance. The debate focuses on whether stated or derived importance is a better method, whether one is more valid or more actionable than the other and so on. Much of this attention is misguided, however, resulting from the mistaken conflating of two similar, but not identical, concepts. We illustrate an under-appreciated point made by Myers and Alpert over 30 years ago – that the choice between stated and derived importance is a false dichotomy: the two methods measure different constructs, they accomplish different objectives and they fulfill different information needs. Drawing upon brand studies with choice-based derived importance models and customer satisfaction studies with regression-based derived importance models, we show that, when done properly, both stated and derived methods have solid predictive validity, albeit with different strengths and weaknesses.
Motivation – Two Case Studies
Two disguised case studies illustrate what can happen if you measure importance badly.
✨1️⃣ Suckered by Stated Importance
A service company wanted to know which aspects of its service most satisfied its customers. They asked 400 of their customers to rate the importance of each of the aspects on a scale from 0=Not Important At All to 10=Critically Important. When the results came back, all of the aspects had average importances in the range of 7.4 to 7.8. The survey didn’t give the service company any valuable feedback about which aspects of the service customers valued more than others, so it was a waste of a few tens of thousands of dollars and some goodwill, because some customers expected the service provider to make changes based on the survey results.
✨2️⃣ Derived Importance Debacle
In the 1980s a medical supplies manufacturer had a 60% share of their market. A fancy consultant convinced the manufacturer that it should be using derived importance modeling to quantify the impact of attributes on customers’ choices. The consultant suggested a super-sophisticated method called multiple regression. He even put it in quotes, “multiple regression,” so that it would be clear to senior management how very cool and new and sophisticated it was to use regression instead of the stated importance methods the company had been using. So when the old stated importance methods said that ease of use was important to customers, the consultant and his regression analysis said that ease of use wasn’t important at all and that the key to incremental sales was size - the smaller the better. The manufacturer redirected new product development efforts away from easy to use products and toward small ones. The next year, a competitor launched an especially easy to use product and grew from a 15% share to 50%, almost overnight. The manufacturer, caught flat-footed, dropped from 60% to 30%, also almost overnight. Within a couple of years, the decision to emphasize size at the expense of ease of use had cost the manufacturer hundreds of millions of dollars. What happened?
These two case studies and many others like them illustrate some of the pitfalls associated with stated and derived importance measurement.
🍀
Article in PDF >>>
#analytics #case #marketing #methodology #mr #psychology #science #sociology
A review of statistical methods for determination of relative importance of correlated predictors and identification of drivers of consumer liking
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This article attempts to deliver the following message to the researchers and practitioners in the sensory field:
✨1️⃣ Theoretically, drivers of consumer liking is based on relative importance of explanatory variables in a linear model. The problem is complicated when the variables involve linear dependence, which is the common situation in sensory and consumer data.
✨2️⃣The commonly used methodologies, e.g., conjoint analysis, preference mapping and Kano’s model, have serious limitations for determination of relative importance of correlated attributes and identification of drivers of consumer liking.
✨3️⃣The conventional statistics, e.g., correlation coefficient, standard regression coefficient and P values of tests for regression parameters, etc., are inadequate and invalid measures of relative importance of correlated attributes.
✨4️⃣There are three state-of-the-artmethods for determination of relative importance of correlated attributes. They are the Lindeman, Merenda and Gold’s method, Breiman’s Random Forest and Johnson’s relative weight.
This article also provides statistical background and almost exhaustive main references on the topic of relative importance of variables scattered in various academic journals in different fields. The information will help the sensometricians and researchers with more statistical knowledge to embrace the mainstream of the research on the topic and to pursue advanced methods for drivers of consumer liking.
Practical applications
This article reviews some new methods for determination of relative importance of correlated explanatory variables to response variable in a regression model. The methods can be used for identification of drivers of consumer liking. The article also provides the sources of the corresponding computer packages and codes implementing the new methods. The packages and codes are freely available and easy to use. The R packages “relaimpo” for the LMG method, “randomForest” and “party” for the original and modified Breiman’s Random Forest method are available at https://cran.r-project.org. The R or S-Plus code “johnson” for Johnson’s relative weight is available from the online supplementary Appendix S1 of this article.
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Article in PDF >>>
Source >>>
#analytics #methodology #mr #science #sociology #statistics #tools
🔬🤓⚗️
This article attempts to deliver the following message to the researchers and practitioners in the sensory field:
✨1️⃣ Theoretically, drivers of consumer liking is based on relative importance of explanatory variables in a linear model. The problem is complicated when the variables involve linear dependence, which is the common situation in sensory and consumer data.
✨2️⃣The commonly used methodologies, e.g., conjoint analysis, preference mapping and Kano’s model, have serious limitations for determination of relative importance of correlated attributes and identification of drivers of consumer liking.
✨3️⃣The conventional statistics, e.g., correlation coefficient, standard regression coefficient and P values of tests for regression parameters, etc., are inadequate and invalid measures of relative importance of correlated attributes.
✨4️⃣There are three state-of-the-artmethods for determination of relative importance of correlated attributes. They are the Lindeman, Merenda and Gold’s method, Breiman’s Random Forest and Johnson’s relative weight.
This article also provides statistical background and almost exhaustive main references on the topic of relative importance of variables scattered in various academic journals in different fields. The information will help the sensometricians and researchers with more statistical knowledge to embrace the mainstream of the research on the topic and to pursue advanced methods for drivers of consumer liking.
Practical applications
This article reviews some new methods for determination of relative importance of correlated explanatory variables to response variable in a regression model. The methods can be used for identification of drivers of consumer liking. The article also provides the sources of the corresponding computer packages and codes implementing the new methods. The packages and codes are freely available and easy to use. The R packages “relaimpo” for the LMG method, “randomForest” and “party” for the original and modified Breiman’s Random Forest method are available at https://cran.r-project.org. The R or S-Plus code “johnson” for Johnson’s relative weight is available from the online supplementary Appendix S1 of this article.
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Article in PDF >>>
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#analytics #methodology #mr #science #sociology #statistics #tools
Модель «Создание команды» Алана Дрекслера и Дэвида Зиббета
👩🏻💻🤝👨🏼💻
Модель Дрекслера-Зиббета описывает 7 фаз, которые проходит любая группа при совместной работе. По мере прохождения по этим фазам (этапам) происходит трансформация рабочей группы в команду.
Каждый шаг в модели соответствует главному вопросу, который человек задает себе в данной фазе.
Первая фаза «Почему я здесь?», вторая — «Кто вы?», третья — «Что мы делаем?», четвертая — «Как дальше нам это делать?», пятая — «Кто, как, что, когда и где это делает?», шестая — «Ура!», седьмая — «Нужно ли нам продолжать?».
В описание фазы также включены основные «проблемы», которые наиболее остро встают перед командами на соответствующих этапах развития.
С первой по четвертую фазы выделяют период «Создания/Развития», с четвертой по седьмую — период «Исполнения/Сохранения».
В модели также описывается логика «возвратов» на более ранние фазы, которые происходят, если команда не справляется с основной «проблемой» на очередном шаге.
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#development #efficiency #methodology #psychology #sociology #tools
👩🏻💻🤝👨🏼💻
Модель Дрекслера-Зиббета описывает 7 фаз, которые проходит любая группа при совместной работе. По мере прохождения по этим фазам (этапам) происходит трансформация рабочей группы в команду.
Каждый шаг в модели соответствует главному вопросу, который человек задает себе в данной фазе.
Первая фаза «Почему я здесь?», вторая — «Кто вы?», третья — «Что мы делаем?», четвертая — «Как дальше нам это делать?», пятая — «Кто, как, что, когда и где это делает?», шестая — «Ура!», седьмая — «Нужно ли нам продолжать?».
В описание фазы также включены основные «проблемы», которые наиболее остро встают перед командами на соответствующих этапах развития.
С первой по четвертую фазы выделяют период «Создания/Развития», с четвертой по седьмую — период «Исполнения/Сохранения».
В модели также описывается логика «возвратов» на более ранние фазы, которые происходят, если команда не справляется с основной «проблемой» на очередном шаге.
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#development #efficiency #methodology #psychology #sociology #tools
Цифровая конкуренция брендов: о том, как ИТ-бизнес формирует свою репутацию
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На рынке пока не сложились бренды, ассоциированные с цифровой трансформацией. При этом уже сложился кризис доверия: запутанная терминология и позиционирование компаний, декларации без результатов, низкоинформативные публикации, субъективные рейтинги.
Репутация — функция личных рекомендаций и продуктовых референсов. Среди особенностей рынка заказчика: конец «цифрового романтизма», усталость от информационного шума, рационализация запроса к рынку, накопление внутренней экспертизы, запуск собственных фильтров для отбора команд, активное использование сети рекомендаций, ориентация на команды с опытом в профильной сфере.
Таковы основные выводы исследования ЦСП «Платформа», проведенного в партнерстве с поставщиком решений для анализа данных в России Ctrl2GO.
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Slides in PDF >>>
#marketing #mr #presentation #product #report #sociology #strategy #trends
💪🤖👎
На рынке пока не сложились бренды, ассоциированные с цифровой трансформацией. При этом уже сложился кризис доверия: запутанная терминология и позиционирование компаний, декларации без результатов, низкоинформативные публикации, субъективные рейтинги.
Репутация — функция личных рекомендаций и продуктовых референсов. Среди особенностей рынка заказчика: конец «цифрового романтизма», усталость от информационного шума, рационализация запроса к рынку, накопление внутренней экспертизы, запуск собственных фильтров для отбора команд, активное использование сети рекомендаций, ориентация на команды с опытом в профильной сфере.
Таковы основные выводы исследования ЦСП «Платформа», проведенного в партнерстве с поставщиком решений для анализа данных в России Ctrl2GO.
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Slides in PDF >>>
#marketing #mr #presentation #product #report #sociology #strategy #trends
Состояние рынка социологических и маркетинговых исследований в 2020 году by РИН
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Исследование проводилось в рамках опроса ESOMAR GMR
Метод сбора данных:
онлайн опрос
Выборка:
компании, занимающиеся маркетинговыми и социально-политическими исследованиями на российском рынке, а также компании, занимающиеся разработкой ПО для рынка исследований.
Список компаний для опроса взят с портала Sociologos + списки членов ОИРОМ и Группы 7/89 + активные участники ResearchEXPO 2016, 2017, 2018, 2019 и 2021. Всего компаний в списке: 600.
Приняли участие в опросе 91 компания (15% участников рынка, которые дают 72 % всего оборота).
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#mr #presentation #report #sociology
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Исследование проводилось в рамках опроса ESOMAR GMR
Метод сбора данных:
онлайн опрос
Выборка:
компании, занимающиеся маркетинговыми и социально-политическими исследованиями на российском рынке, а также компании, занимающиеся разработкой ПО для рынка исследований.
Список компаний для опроса взят с портала Sociologos + списки членов ОИРОМ и Группы 7/89 + активные участники ResearchEXPO 2016, 2017, 2018, 2019 и 2021. Всего компаний в списке: 600.
Приняли участие в опросе 91 компания (15% участников рынка, которые дают 72 % всего оборота).
🍀
Report in PDF >>>
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#mr #presentation #report #sociology
