Charity Majors works as a systems engineer at Parse, a platform for mobile developers. Parse uses MongoDB for various purposes, including storing user data, DDoS protection and query profiling, and analytics for billing and logging. Charity provides advice on best practices for running MongoDB in production environments at scale, such as using replica sets, taking regular snapshots, automating setup and maintenance with Chef, and using provisioned IOPS volumes to improve performance.
How MongoDB has empowered the business to rapidly respond to market conditions.
By Michael Frost, Web Solution Architect at Flight Centre Ltd. Presented at MongoDB Sydney, 2012.
Leinster College is located in Dublin and offers courses in English, business, management, IT, and other fields. It has received accreditations from several respected organizations. The college has modern facilities including classrooms and provides student accommodation. It aims to prepare students to meet skills requirements in Ireland and globally.
Cartagena Data Festival | Telling Stories with Data 2015 04-21ulrichatz
This document provides an overview of data journalism and how to tell stories with data. It discusses exploring and understanding datasets to find stories, and provides examples of data journalism sources. The document encourages attendees to find a story in sample Titanic passenger data by exploring the data, coming up with story ideas, and creating a headline. It also warns about poor data visualization practices like using non-traditional charts, arbitrary double axes, including all data rather than selecting meaningful data, and finding meaningless correlations.
Big Data is on every CIO’s mind. It is presently synonymous with open source technologies like Hadoop, and the ‘NoSQL’ class of databases. Another technology that is shaking things up in Big Data is R (www.r-project.org, #rstats). R is an open source programming language and software environment designed for statistical computing and visualisation. The statistical software R is the fastest growing analytics platform in the world, and is established in both academia and companies for robustness, reliability and accuracy. For real big data analyses you have to access your data in your preferred database on the fly. In this talk I will give a short overview about R, the available connection to MongoDB and present some big data analyses using R and mongoDB.
Fondazione Bruno Kessler - Trento - Research seminar - 11 September 2013
The seminar presents the key features of VirtualSense, an open-HW platform for ultra-low-power wireless sensor networks, and discusses the state of the art, the open issues, and the ongoing reserach activities in the field of wireless sensor networks.
Also availabe at:
http://prezi.com/zx0hhbohspk8/virtualsense-and-the-state-of-the-art-in-ultra-low-power-wsns-short/
Charity Majors works as a systems engineer at Parse, a platform for mobile developers. Parse uses MongoDB for various purposes, including storing user data, DDoS protection and query profiling, and analytics for billing and logging. Charity provides advice on best practices for running MongoDB in production environments at scale, such as using replica sets, taking regular snapshots, automating setup and maintenance with Chef, and using provisioned IOPS volumes to improve performance.
How MongoDB has empowered the business to rapidly respond to market conditions.
By Michael Frost, Web Solution Architect at Flight Centre Ltd. Presented at MongoDB Sydney, 2012.
Leinster College is located in Dublin and offers courses in English, business, management, IT, and other fields. It has received accreditations from several respected organizations. The college has modern facilities including classrooms and provides student accommodation. It aims to prepare students to meet skills requirements in Ireland and globally.
Cartagena Data Festival | Telling Stories with Data 2015 04-21ulrichatz
This document provides an overview of data journalism and how to tell stories with data. It discusses exploring and understanding datasets to find stories, and provides examples of data journalism sources. The document encourages attendees to find a story in sample Titanic passenger data by exploring the data, coming up with story ideas, and creating a headline. It also warns about poor data visualization practices like using non-traditional charts, arbitrary double axes, including all data rather than selecting meaningful data, and finding meaningless correlations.
Big Data is on every CIO’s mind. It is presently synonymous with open source technologies like Hadoop, and the ‘NoSQL’ class of databases. Another technology that is shaking things up in Big Data is R (www.r-project.org, #rstats). R is an open source programming language and software environment designed for statistical computing and visualisation. The statistical software R is the fastest growing analytics platform in the world, and is established in both academia and companies for robustness, reliability and accuracy. For real big data analyses you have to access your data in your preferred database on the fly. In this talk I will give a short overview about R, the available connection to MongoDB and present some big data analyses using R and mongoDB.
Fondazione Bruno Kessler - Trento - Research seminar - 11 September 2013
The seminar presents the key features of VirtualSense, an open-HW platform for ultra-low-power wireless sensor networks, and discusses the state of the art, the open issues, and the ongoing reserach activities in the field of wireless sensor networks.
Also availabe at:
http://prezi.com/zx0hhbohspk8/virtualsense-and-the-state-of-the-art-in-ultra-low-power-wsns-short/
We all know that feeling when we’re running out of our favorite product and don’t want to go through the hassle of running to the store or waiting days until it ships. Marketers can easily combat this pain by sending replenishment reminders; a helpful way to assure that a product is refilled before the consumer runs out.
Clients using Windsor Circle’s Replenishment Automator have seen an average Revenue Per Email (RPE) of $1.03, which is 10x the industry average, as well as a 7% lift in total revenue, and 10% lift in conversions.
In this webinar learn how you can use customer data to automate these reminders to get customers back to buy again and again.
Grow Customer Retention with Predictive Marketing and User-Generated ContentWhatConts
This document discusses how businesses can improve customer retention and unlock untapped revenue from existing customers through predictive lifecycle marketing and user-generated content. Specifically, it introduces predictive lifecycle triggers that use customer data to automate personalized communications, and how businesses can leverage user-generated content collected from customers. The document also shares case studies of companies that saw increased revenue and retention rates of 12-20% through these techniques. It concludes by recommending that established companies with sufficient resources consider implementing these strategies to gain a competitive advantage.
Secret Life of a Weather Datum end of project eventlifeofdata
This document outlines the methods and key themes of a research project studying the journey of weather data from its production to its reuse. The project uses case studies and interviews with participants across different parts of the data journey, including weather station operators, climate scientists, and financial data companies. It aims to understand the socio-cultural values and practices shaping how weather data is collected, shared, and interpreted. The methods discussed are interviews, observations, document analysis and digital ethnography. Key themes that emerged include the relationships between people and organizations across space and time along the data journey, the diversity of openness and transparency in different contexts, and the dependency of data infrastructure on cultural values like voluntarism and public service.
This document provides a summary of activities from a youth police academy held from April 29th to May 3rd. It describes the various training activities the youth participated in over the first two days, including inspections, color guard, tactics training, baton training, a radar simulator, and firearms simulator. On the third day, the youth took a field trip to the crime scene unit. Activities included a class photo with the aviation unit, a presentation on the special patrol unit, and a demonstration from the K-9 unit showing search dogs at work.
The document discusses the rise of NoSQL databases as an alternative to traditional relational databases. NoSQL databases are non-relational and are designed for massive scale and flexibility. They achieve horizontal scalability through lack of joins and lightweight transactions. Key features that vary across NoSQL products include data model (key-value, column-oriented, document-oriented), consistency model, and distribution/query methods. NoSQL is influenced by concepts like CAP theorem and eventual consistency.
O Diferencial de uma Estratégia Mobile...e Multiplataforma!Xpand IT
A experiência da Xpand IT em mobilidade é composta por um conjunto de projectos, que não são mais do que “histórias” sobre como implementar mobilidade nas empresas. Todo o saber acumulado ao longo dos anos permite-nos ter uma visão holística do que é um projecto de mobilidade e da importância de ter uma estratégia bem definida.
A evolução da mobilidade ao longo dos últimos anos levantou um conjunto de desafios para as organizações. Entre qual a tecnologia a utilizar até aos dispositivos a suportar, são várias as questões que se colocam – muitas vezes desvalorizando aspectos importantes que podem fazer toda a diferença na forma como uma iniciativa mobile será encarada. Qual é a sua estratégia mobile? Quem são as áreas chave? Quais são os mecanismos existentes na organização para garantir que todos falam a mesma linguagem e que estão alinhados com uma visão de mobilidade comum a toda a companhia? E como se conjuga essa estratégia mobile com os modelos de Governance existentes? Estas são apenas algumas das perguntas com as quais as conversas sobre mobilidade empresarial deveria começar.
A escolha das tecnologias e dos moldes nos quais implementar os projectos continua a ser importante, claro, mas numa segunda fase. E quando se atinge este estágio, está então na altura de perceber o que é melhor para a organização. Desenvolvimento nativo, uma abordagem híbrida ou o velho sonho de desenvolver uma vez para disponibilizar em qualquer plataforma? Quais as vantagens, desvantagens, cenários de aplicabilidade, riscos, investimento associado, entre outros?
A escolha das tecnologias e dos moldes nos quais implementar os projectos continua a ser importante, claro, mas numa segunda fase. E quando se atinge este estágio, está então na altura de perceber o que é melhor para a organização. Desenvolvimento nativo, uma abordagem híbrida ou o velho sonho de desenvolver uma vez para disponibilizar em qualquer plataforma? Quais as vantagens, desvantagens, cenários de aplicabilidade, riscos, investimento associado, entre outros?
Sérgio Viana - Associate Partner & Microsoft Solutions Lead da Xpand IT
Any Data, Any Analytics, Simplified. Pentaho Business Analytics 5.0, purpose-built for the future of analytics, provides an open, unified platform to access, integrate and blend any data, in any environment, across a full spectrum of analytics. This presentation corresponds to a live demo of the Pentaho Business Analytics with special enpahsis on what is new on Pentaho 5.0.
Ricardo Pires - BI Division Manager & Pentaho Official Trainer, @Xpand IT
Xpand IT presentation during the Pentaho & Big Data Ecosystem - Live Seminar 2013
Strongly Typed Languages and Flexible SchemasNorberto Leite
We like to use strongly type languages and used them along side with flexible schema databases. What challenges and strategies do we have to deal with data coherence and format validations using different strategies and tools like ODMs versioning, migrations et al. We also review the tradeoffs of such strategies.
Challenges in opening up qualitative research datalifeofdata
Reflections on the challenges encountered in enabling open research data for the Secret Life of a Weather Datum project. A pecha kucha presentation given at the iFutures 2015 PGR conference, Information School, University of Sheffield, July 2015.
The document discusses Jonathan Marmor's upcoming presentation on exfm's use of MongoDB. It will cover what exfm does, which includes having a browser extension that turns websites into playlists. It will then discuss how exfm uses MongoDB, including an overview of its environment, data models, server architecture, management tools, and future plans. It will also cover Jonathan's upcoming Monthly Music Hackathon in NYC.
This document describes advertising opportunities on the Sletat.ru travel website. Sletat.ru attracts over 10 million monthly views and 36 million annual unique visitors. It provides opportunities for non-standard advertising campaigns through interactive modules, landing pages, and branding of the homepage. These customized ads aim to maximize audience engagement and increase brand awareness more than traditional ads.
The document discusses PowerPivot for SharePoint. It provides details on how PowerPivot works, including how the PowerPivot workbook is rendered. It describes components like PowerPivot Services and Analysis Services. It also covers installing and configuring PowerPivot for SharePoint farms.
The document provides a summary of a mystery shopper research report conducted by Business Insight in 17 popular stores across 5 sectors in Baku, Azerbaijan. The research evaluated service levels across 8 criteria on a 5-point scale. On average, stores scored 3.41 out of 5 for the 6 criteria related to staff service levels. Perfumery stores had the highest average score of 3.95 out of 5. The report provides detailed ratings for each sector and makes comparisons across outside parameters, domestic parameters, staff appearance, staff behavior, and attitude toward customers.
The document summarizes the results of a research report on the lifestyle of residents in Baku, Azerbaijan. Over 600 residents of Baku were surveyed across 11 districts using random sampling. Key findings include:
- 28 Mall was identified as the most popular shopping center, while traffic issues were seen as the main problem facing Baku.
- Zara was the most commonly cited clothing brand. Most respondents said they do their clothing shopping at Sadarak/Bina Shopping Center.
- Respondents were asked what first comes to mind when they think of Baku - most associated it with terms like "capital", "homeland", and "Caspian Sea".
The research report summarizes the results of a survey of 619 residents of Baku, Azerbaijan conducted in January 2016. Some key findings include:
- The Capital Bank was identified as the most reliable bank by 33.1% of respondents.
- Nearly half of respondents expect the dollar exchange rate to rise over the next month.
- 60.5% of respondents reported decreasing their monthly expenses, primarily related to food and personal belongings.
- Nearly half of respondents think their financial situation will improve over the next year, while 30% think it will get worse.
We all know that feeling when we’re running out of our favorite product and don’t want to go through the hassle of running to the store or waiting days until it ships. Marketers can easily combat this pain by sending replenishment reminders; a helpful way to assure that a product is refilled before the consumer runs out.
Clients using Windsor Circle’s Replenishment Automator have seen an average Revenue Per Email (RPE) of $1.03, which is 10x the industry average, as well as a 7% lift in total revenue, and 10% lift in conversions.
In this webinar learn how you can use customer data to automate these reminders to get customers back to buy again and again.
Grow Customer Retention with Predictive Marketing and User-Generated ContentWhatConts
This document discusses how businesses can improve customer retention and unlock untapped revenue from existing customers through predictive lifecycle marketing and user-generated content. Specifically, it introduces predictive lifecycle triggers that use customer data to automate personalized communications, and how businesses can leverage user-generated content collected from customers. The document also shares case studies of companies that saw increased revenue and retention rates of 12-20% through these techniques. It concludes by recommending that established companies with sufficient resources consider implementing these strategies to gain a competitive advantage.
Secret Life of a Weather Datum end of project eventlifeofdata
This document outlines the methods and key themes of a research project studying the journey of weather data from its production to its reuse. The project uses case studies and interviews with participants across different parts of the data journey, including weather station operators, climate scientists, and financial data companies. It aims to understand the socio-cultural values and practices shaping how weather data is collected, shared, and interpreted. The methods discussed are interviews, observations, document analysis and digital ethnography. Key themes that emerged include the relationships between people and organizations across space and time along the data journey, the diversity of openness and transparency in different contexts, and the dependency of data infrastructure on cultural values like voluntarism and public service.
This document provides a summary of activities from a youth police academy held from April 29th to May 3rd. It describes the various training activities the youth participated in over the first two days, including inspections, color guard, tactics training, baton training, a radar simulator, and firearms simulator. On the third day, the youth took a field trip to the crime scene unit. Activities included a class photo with the aviation unit, a presentation on the special patrol unit, and a demonstration from the K-9 unit showing search dogs at work.
The document discusses the rise of NoSQL databases as an alternative to traditional relational databases. NoSQL databases are non-relational and are designed for massive scale and flexibility. They achieve horizontal scalability through lack of joins and lightweight transactions. Key features that vary across NoSQL products include data model (key-value, column-oriented, document-oriented), consistency model, and distribution/query methods. NoSQL is influenced by concepts like CAP theorem and eventual consistency.
O Diferencial de uma Estratégia Mobile...e Multiplataforma!Xpand IT
A experiência da Xpand IT em mobilidade é composta por um conjunto de projectos, que não são mais do que “histórias” sobre como implementar mobilidade nas empresas. Todo o saber acumulado ao longo dos anos permite-nos ter uma visão holística do que é um projecto de mobilidade e da importância de ter uma estratégia bem definida.
A evolução da mobilidade ao longo dos últimos anos levantou um conjunto de desafios para as organizações. Entre qual a tecnologia a utilizar até aos dispositivos a suportar, são várias as questões que se colocam – muitas vezes desvalorizando aspectos importantes que podem fazer toda a diferença na forma como uma iniciativa mobile será encarada. Qual é a sua estratégia mobile? Quem são as áreas chave? Quais são os mecanismos existentes na organização para garantir que todos falam a mesma linguagem e que estão alinhados com uma visão de mobilidade comum a toda a companhia? E como se conjuga essa estratégia mobile com os modelos de Governance existentes? Estas são apenas algumas das perguntas com as quais as conversas sobre mobilidade empresarial deveria começar.
A escolha das tecnologias e dos moldes nos quais implementar os projectos continua a ser importante, claro, mas numa segunda fase. E quando se atinge este estágio, está então na altura de perceber o que é melhor para a organização. Desenvolvimento nativo, uma abordagem híbrida ou o velho sonho de desenvolver uma vez para disponibilizar em qualquer plataforma? Quais as vantagens, desvantagens, cenários de aplicabilidade, riscos, investimento associado, entre outros?
A escolha das tecnologias e dos moldes nos quais implementar os projectos continua a ser importante, claro, mas numa segunda fase. E quando se atinge este estágio, está então na altura de perceber o que é melhor para a organização. Desenvolvimento nativo, uma abordagem híbrida ou o velho sonho de desenvolver uma vez para disponibilizar em qualquer plataforma? Quais as vantagens, desvantagens, cenários de aplicabilidade, riscos, investimento associado, entre outros?
Sérgio Viana - Associate Partner & Microsoft Solutions Lead da Xpand IT
Any Data, Any Analytics, Simplified. Pentaho Business Analytics 5.0, purpose-built for the future of analytics, provides an open, unified platform to access, integrate and blend any data, in any environment, across a full spectrum of analytics. This presentation corresponds to a live demo of the Pentaho Business Analytics with special enpahsis on what is new on Pentaho 5.0.
Ricardo Pires - BI Division Manager & Pentaho Official Trainer, @Xpand IT
Xpand IT presentation during the Pentaho & Big Data Ecosystem - Live Seminar 2013
Strongly Typed Languages and Flexible SchemasNorberto Leite
We like to use strongly type languages and used them along side with flexible schema databases. What challenges and strategies do we have to deal with data coherence and format validations using different strategies and tools like ODMs versioning, migrations et al. We also review the tradeoffs of such strategies.
Challenges in opening up qualitative research datalifeofdata
Reflections on the challenges encountered in enabling open research data for the Secret Life of a Weather Datum project. A pecha kucha presentation given at the iFutures 2015 PGR conference, Information School, University of Sheffield, July 2015.
The document discusses Jonathan Marmor's upcoming presentation on exfm's use of MongoDB. It will cover what exfm does, which includes having a browser extension that turns websites into playlists. It will then discuss how exfm uses MongoDB, including an overview of its environment, data models, server architecture, management tools, and future plans. It will also cover Jonathan's upcoming Monthly Music Hackathon in NYC.
This document describes advertising opportunities on the Sletat.ru travel website. Sletat.ru attracts over 10 million monthly views and 36 million annual unique visitors. It provides opportunities for non-standard advertising campaigns through interactive modules, landing pages, and branding of the homepage. These customized ads aim to maximize audience engagement and increase brand awareness more than traditional ads.
The document discusses PowerPivot for SharePoint. It provides details on how PowerPivot works, including how the PowerPivot workbook is rendered. It describes components like PowerPivot Services and Analysis Services. It also covers installing and configuring PowerPivot for SharePoint farms.
The document provides a summary of a mystery shopper research report conducted by Business Insight in 17 popular stores across 5 sectors in Baku, Azerbaijan. The research evaluated service levels across 8 criteria on a 5-point scale. On average, stores scored 3.41 out of 5 for the 6 criteria related to staff service levels. Perfumery stores had the highest average score of 3.95 out of 5. The report provides detailed ratings for each sector and makes comparisons across outside parameters, domestic parameters, staff appearance, staff behavior, and attitude toward customers.
The document summarizes the results of a research report on the lifestyle of residents in Baku, Azerbaijan. Over 600 residents of Baku were surveyed across 11 districts using random sampling. Key findings include:
- 28 Mall was identified as the most popular shopping center, while traffic issues were seen as the main problem facing Baku.
- Zara was the most commonly cited clothing brand. Most respondents said they do their clothing shopping at Sadarak/Bina Shopping Center.
- Respondents were asked what first comes to mind when they think of Baku - most associated it with terms like "capital", "homeland", and "Caspian Sea".
The research report summarizes the results of a survey of 619 residents of Baku, Azerbaijan conducted in January 2016. Some key findings include:
- The Capital Bank was identified as the most reliable bank by 33.1% of respondents.
- Nearly half of respondents expect the dollar exchange rate to rise over the next month.
- 60.5% of respondents reported decreasing their monthly expenses, primarily related to food and personal belongings.
- Nearly half of respondents think their financial situation will improve over the next year, while 30% think it will get worse.
The research report summarizes a study on the lifestyle and consumer behavior of university students in Azerbaijan. Over 1,000 students from 10 universities participated in the survey. Key findings include:
- McDonald's was the most popular fast food restaurant.
- 16% of students reported doing online shopping in the last month, mostly on eBay.
- The most used mobile phone brands were Samsung and iPhone, and the most popular network was Bakcell.
- 20% of students reported smoking, mostly the Kent brand.
- 66% of students preferred buying clothes at special stores, mostly Romantic.
- 18% of students owned installment cards, mostly from Unibank.
The document reports the results of a lifestyle survey of 803 inhabitants of Baku, Azerbaijan aged over 20. Some key findings include:
- Television is the main source of information (70.1%), while internet is used by 27.5%
- Satellite dish is the most common way to access TV (43.2%)
- ATV is the most watched TV channel (30.6%)
- Half of respondents went on vacation in the past year, most often visiting regions within Azerbaijan like Quba (16.9%) or foreign countries like Turkey (44.8%)
- Nokia is still the most commonly used mobile phone brand (46.6%), while Azercell is the most popular network provider
1. MART 2016 | Business Insight
BAKI SAKİNLƏRİNİN
HƏYAT TƏRZİ
ARAŞDIRMA HESABATI
2. Business Insight ESOMAR Ümumdünya Marketinq və Araşdırma
Mütəxəssisləri Assosiasiyasının üzvüdür
Business Insight bütün marketinq, sosial araşdırmaları və promo
kampaniyaları ICC/ESOMAR-ın Marketinq və Sosial Araşdırma
Təcrübəsi üzrə Məcəlləsində öz əksini tapan ən yüksək peşəkar və etik
standartlara və ISO 20252 beynəlxalq standartının tələblərinə müvafiq
olaraq həyata keçirir.
4. Məqsəd? Hədəf Qrup? Seçim Nümunəsi?
Əhatə Dairəsi? Vaxt? Metodologiya?
Araşdırma haqqında
Respondent sayı: 640
Layihənin başlıca məqsədi paytaxt
sakinlərinin həyat tərzini öyrənməkdir
Bakı sakinlərinin reprezentativi (ağırlıq mərkəzinə
yaxın)
Tədqiqat vahidi kimi təsadüfi seçim metodundan
istifadə etməklə ailə təsərrüfatları götürülüb.
Sorğular Bakı şəhərinin 11 rayonunda həyata
keçirilib
MART 2016
Nizami;
10,5
Nəsimi;
10
Xətai; 9,5
Sabunçu;
9,5Yasamal;
9,5Qaradağ
; 9,2
Nəriman
ov; 8,9
Suraxanı;
8,8
Xəzər; 8,3
Binəqədi;
8
Səbail;
7,8
5. Respondentin vətəndaşlıq vəziyyəti (%)
Respondentin cinsi (%)
18-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-65
18,4
16,3
14,4
12,5
9,8 10,5 9,7
5,6
2,8
36
Respondentin yaşı (%)
Orta Yaş
Demoqrafik xarakteristika
Ali Orta ixtisas Orta
44,8
16,3
38,9
Respondentin təhsil səviyyəsi (%)
Evli; 71,2
Dul/
Boşanmış; 2,5
Kişi; 50Qadın; 50
7. Sorğuya cəlb olunanların 43,1 faizi işlədiyini qeyd edib
Bəli; 43,1
Xeyr; 23,6
Tələbə; 9,8
Evdar
xanım; 20,0
Hazırda işləyirsinizmi? (%)
Baza: 640
Bina ;
60
Həyət
Evi; 40
Siz neçənci mərtəbədə yaşayırsınız? (%)
13,7
19,6
16,4
14,8
10,5
5,6 5,6 5,9
3,8
,8 ,5 1,1
,3 ,3 ,8 ,3
Baza: 384
Baza: 640
8. Cavab verməyə çətinlik çəkirəm
Digər
Neft Buruqları (Neft)
Bayraq Meydanı
Şəxsi problemlər
Bakı Bulvarı (Bulvar)
Xəzər dənizi (Dəniz)
Qədim şəhər (İçəri şəhər, Qız Qalası)
Küləklər şəhəri
Gözəl şəhər
Anadan olduğum şəhər (Vətən,
Azərbaycan)
Paytaxt (Mərkəz)
1,9
5,9
1,9
2,2
2,2
3,4
5,5
6,9
10,2
14,7
18,4
26,9
Bakını «Neft buruqları» ilə assosasiya edənlər cəmi 2 faiz təşkil edir
Bakını nə ilə assosasiya edirsiniz, Bakı deyiləndə ilk ağlınıza gələn nədir? (%)
Baza: 640
Alov qüllələri/Flame Tower 0,9
Bakı Dövlət Universiteti 0,8
Tarqovu/Nizami küçəsi 0,8
Turizm şəhəri 0,6
Tıxaclar 0,6
Gözəl günlər/Macəralar
şəhəri
0,6
Bakı Olimpiya Stadionu/2015
Avropa oyunları
0,3
Əhalinin sıxlığı 0,3
Bakının kəndləri 0,3
Zibilxanalar 0,2
Böhran yaşayan şəhər 0,2
Palçıqlı yollar 0,2
Teleqüllə 0,2
Perception
9. Tıxaclar Bakı şəhərinin həllini gözləyən ən vacib problemidir
Bakı şəhərinin ən vacib hansı 2 problemi var? (%)
Baza: 640
Bilmirəm/ Problem yoxdur
Digər
Gənclərin normal istirahəti üçün yerlər azdır (Uşaqlara uyğun meydançalar yoxdur, gənclər şəhərciyi
yoxdur)
İçməli su təmiz deyil
Radarlar çoxdur
Bahalı şəhərdir
Yol hərəkəti qaydalarını göstərən işarələr kifayət qədər deyil (Radarlar azdır, svetaforlar azdır)
Piyada keçidləri azdır (Tratuarlar yoxdur)
Bakıda hava ağırdır (Hava rütubətlidir, havası çirklidir)
Bakının memarlıq görünüşü korlanır (Tikintilər çoxdur, qanunsuz tikililər çoxalır, hündür binalar çoxdur,
köhnə binalar çoxdur)
Nəqliyyat İnfrastrukturu (Avtobuslar vaxtında işləmir, sayı azdır, sürücüləri qaydalara riayət etmir,
dayanacaqları bir-birinə uzaqdır, ünvanlara bir neçə marşrutla çatırıq, metroların sayı azdır)
Yaşıllıq azdır (yoxdur)
Antisanitariya (Süpürən yoxdur, zibillik çoxdur, zibil qutuları azdır)
Kommunal problemlər (Hava soyuq olanda kommunal xidmətlər-işıq,qaz,su dayandırılır, istilik verilmir,
xidmətlər bahadır)
Kanalizasiya problemi (yağışda, qarda küçələr su ilə dolur, binaların zirzəmiləri su ilə doludur)
Əhalinin sıxlığı (Avtobus, metroda sıxlıq, rayondan gələnlər çoxalıb, urbanizasiya)
Yol infrastrukturu (Asfalt çəkib bitəndən sonra yenə qazırlar, palçıq yollar, qışda yolların buzu əridilmir,
şəhərkənarı yollar abadlaşdırılmır)
Tıxaclar (maşınların çoxluğu)
11,4
1,9
0,8
1,1
1,3
1,3
2,3
2,5
4,1
5
5,2
8,4
11,4
13,1
15,3
18,9
27,7
33,9
Pis vərdişlər çoxalıb 0,3
Xəzər dənizinin çirkliliyi 0,3
Abidələr azaldılıb (məhv edilib) 0,2
Binalarda lift işləmir 0,2
Küçələrin qaranlıq olması 0,2
Xəzərin nefti, təbii sərvətləri
istismar olunur
0,2
Turizmin inkişafı zəifdir 0,2
Restoranların çoxluğu 0,2
Hüquq məsləhətxanalarının az
olması
0,2
Əlillər üçün metroda, avtobusda
şərait yoxdur
0,2
10. Çətinlik çəkirəm
Heç biri
Digər
8 km topdan bazar
Aygün City
Port Baku Mall
Laçın TM
Metropark
"Торговая" (Nizami küçəsi)
Park Bulvar
Sədərək/ Binə TM
28 Mall
6,1
,2
4,7
1,3
1,7
1,9
3,6
5,2
6,3
14,8
22,3
32,0
Bakının ən yaxşı alış-veriş mərkəzi hansıdır? (%)
Baza: 640
Baku Mall 0,8
Lotos TM 0,8
Sahil TM 0,6
Life Center 0,6
Suraxanı TM 0,5
Xəqani TM 0,3
Diqlas TM 0,2
Bakıxanov TM 0,2
Nərgiz TM 0,2
Xalqlar Dostluğu TM 0,2
Amay TM 0,2
Əhmədli TM 0,2
Günel TM 0,2
«28 Mall» Bakının ən bəyənilən alış-veriş mərkəzi kimi seçilib
ANALİZ
CİNS Təhsil YAŞ
Kişi Qadın Ali Orta 18-30 31-65
28 Mall 30,1 34 35,9 28,9 47,6 22,3
Sədərək/
Binə
23,8 20,9 15,7 28,0 10,2 30,0
Park Bulvar 17,2 12,5 15,3 14,4 19,1 12,2
"Торговая" 7,5 5,0 8,0 4,8 2,4 8,6
Metropark 4,1 6,2 6,3 4,2 6,5 4,3
28 Mall Park Bulvar MetroPark Tarqovu Sədərək/Binə
Binəqədi 35,4% 10,4% 2,1% 4,2% 29,2%
Xətai 23,0% 16,4% 1,6% 6,6% 28,0%
Xəzər 25,4% 16,5% 3,8% 3,8% 25,5%
Qaradağ 31,8% 15,2% 3,0% 3,5% 40,4%
Nərimanov 29,7% 13,5% 14,5% 3,7% 16,2%
Nəsimi 45,3% 18,8% 6,3% 10,9% 15,6%
Nizami 32,4% 14,7% 11,8% 2,9% 20,6%
Sabunçu 29,9% 13,4% 9,0% 9,0% 16,4%
Səbail 30,0% 28,0% 2,0% 14,0% 14,0%
Suraxanı 32,1% 5,4% 0,0% 1,8% 21,4%
Yasamal 34,3% 11,4% 4,3% 7,1% 18,6%
11. Paytaxt sakinləri arasında «Zara» ən populyar geyim brendidir
Baza: 640
Geyim markalarını düşündüyünüzdə, ağlınıza gələn ilk geyim markasının hansı olduğunu deyin. (%)
Qeyd***: Bu cədvəldə 1 respondent 1 cavab verib
Xatırlamıram
(11,5%)/Sədərək, Binə
TM (6,5%)
Digər; 28,8%
Ustop; 2,2%Armani; 2,2%Bershka; 2,5%
Lady Sharm; 3,3%
LC Waikiki; 3,6%
Mustafa Tayat; 3,8%
Mango; 4,4%
Koton; 4,7%
Romantic;
12,0%
Zara; 14,7% Karolina Valiant 1,7%
Dolce Gabbana 1,7%
NewYorker 1,4%
Osmanbey 1,4%
Adidas 1,4%
Colin`s 1,4%
Pierre Cardin 1,4%
Gucci 1,3%
X-Lady 1,1%
Çinici 0,9%
Chanel 0,9%
Emiland 0,8%
Network/ Next 0,6%
Rodi Mood 0,6%
Waggon 0,6%
Versage 0,6%
Lacoste 0,6%
Mothercare 0,6%
UCB 0,5%
Kiğılı 0,5%
Damat 0,5%
Polo 0,3%
Femina 0,3%
Valentino 0,3%
Calvin Klein 0,3%
Nike 0,3%
Zilli 0,3%
H&M 0,3%
Albatros 0,3%
Fendi 0,3%
LTB 0,3%
Philipp Plein 0,3%
Brioni 0,3%
Pull and Bear 0,2%
Emporium 0,2%
Etam 0,2%
Jacadi 0,2%
Sementa 0,2%
Givenchy 0,2%
Massimo Dutti 0,2%
Boss 0,2%
Saksafona 0,2%
PaşaTürk 0,2%
Kiki Riki 0,2%
Suvari 0,2%
Karaca 0,2%
Rebecco 0,2%
Guess 0,2%
Marks & Spencer 0,2%
Ahu 0,2%
Lui Co 0,2%
Cecil 0,2%
Viqano 0,2%
Mavi 0,2%
Hatemoğlu 0,2%
LOUIS VUITTON 0,2%
Roberto Cavalli 0,2%
Max Mara 0,2%
Kenzo 0,2%
Miss Bahar 0,2%
Inspector 0,2%
12. Geyim markalarını düşündüyünüzdə, ağlınıza gələn geyim markalarının hansılar
olduğunu deyin? (%)__Qeyd***: Bu cədvəldə isə 1 respondent bir neçə cavab verib
Geyim markası % Geyim markası % Geyim markası %
Zara 30,5% Femina 0,6% Max Mara 0,2%
Romantic 28,8% Valentino 0,6% Təkbir 0,2%
Mango 18,4% Emporium 0,6% Avantaj 0,2%
Koton 13,9% Zilli 0,6% United Sport 0,2%
LC Waikiki 13,6% Sisley 0,6% Oysho 0,2%
Lady Sharm 12,2% Defacto 0,5% Bebetto 0,2%
Bershka 11,6% Gap 0,5% Chicco 0,2%
Mustafa Tayat 10,8% Calvin Klein 0,5% Rebecco 0,2%
NewYorker 10,8% Dior 0,5% Marks & Spencer 0,2%
Osmanbey 9,5% Massimo Dutti 0,5% Vivian 0,2%
Çinici 8,8% H&M 0,5% Darkwin 0,2%
Ustop 7,8% Prada 0,5% Tom Klaim 0,2%
Karolina Valiant 6,9% Albatros 0,5% Ahu 0,2%
Rodi Mood 5,5% Guess 0,5% Lui Co 0,2%
Waggon 5,5% Damat 0,5% Karen Millen 0,2%
Dolce Gabbana 4,2% Philipp Plein 0,5% Max&Co. 0,2%
Armani 4,1% Etam 0,3% Selio 0,2%
Network/ Next 3,6% Boss 0,3% Cecil 0,2%
Gucci 3,0% Suvari 0,3% Viqano 0,2%
Colin`s 2,8% Karaca 0,3% Stradi Varius 0,2%
Emiland 2,7% Las Vegas 0,3% LOUIS VUITTON 0,2%
X-Lady 2,5% Fendy 0,3% Aldo 0,2%
Versage 2,5% Tom Ford 0,3% Roberto Cavalli 0,2%
Adidas 2,3% Mavi 0,3% Max Mara 0,2%
Chanel 2,2% Hatemoğlu 0,3% Özdilek 0,2%
Pierre Cardin 2,2% Brioni 0,3% Kenzo 0,2%
Nike 1,4% Jacadi 0,2% Kids World 0,2%
Pull and Bear 1,1% Ralph Lauren 0,2% Miss Bahar 0,2%
Mothercare 1,1% Sementa 0,2% Promod 0,2%
Polo 0,9% Givenchy 0,2% Ferrari 0,2%
U. C. OF BENETTON 0,9% Saksafona 0,2% Inspector 0,2%
Kiğılı 0,9% PaşaTürk 0,2% Greyder 0,2%
Lacoste 0,9% Garda 0,2% Jack and Jones 0,2%
LTB 0,9% Kiki Riki 0,2% Xatırlamıram (11,5%) /Sədərək Binə TM (6,5%) 18,0% Baza: 640
13. Paytaxt sakinlərinin ən çox geyim aldığı məkan Sədərək/Binə TM-dir
Adi mağazadı
adını xatırlamıram
Lady Sharm
LC Waikiki
Ustop
NewYorker
Mango
Laçın TM
Koton
Romantic
Zara
Sədərək/ Binə TM
2,7%
4,4%
2,7%
3,2%
3,2%
3,7%
3,7%
3,9%
5,4%
6,9%
10,6%
14,8%
Keçdiyimiz 3 ay içində, ən son haradan geyim aldınız? (%)
Bəli;
63,4
Xeyr;
36,6
Son 3 ay içində geyim aldınızmı? (%)
Baza: 406
Bershka 2,2%
Mustafa Tayat 1,7%
Ucuzluq, Univermaq 1,2%
8 km topdan bazar 1,2%
Network/ Next 1,0%
Rodi Mood 1,0%
Çinici 0,7%
X-Lady 0,7%
Defacto 0,7%
Armani 0,7%
Colin`s 0,7%
Pierre Cardin 0,7%
Podium 0,7%
Metropark 0,7%
Osmanbey 0,5%
Və s ......
Baza: 640
Dərziyəm özüm tikirəm/ Alverçidən/ Xaricdən/
Tikiş sexindən/ Qızım alır/ Online sifariş edirəm
Digər-35,5%
Brand Equity
14. Net Promoter Balının Hesablanması (NPS - Net Promoter Score)
Baza: 60
Net Promoter Balının hesablanması metodologiyası, həm yenidən alış həm də istiqamətləndirmə (tövsiyə) ehtimalını nəzərdə tutan tək
bir sual üzərinə qurulmuşdur:
"Bu mağazanı dostlarınıza/yaxınlarınıza təsvir edərkən onlara tövsiyə edib-etməyəcəyinizi neçə bal ilə dəyərləndirərsiniz?
(Müştərilər cavablarını 0-10 şkalaya əsasən qiymətləndirərlər.)
o 0-6 bal = Pisləyənlər: mənfi söz-söhbətlərlə markamıza zərər verə biləcək, məmnun olmayan müştərilər
o 7-8 bal = Passivlər: rəqiblər tərəfindən cəlb edilə bilən, məmnun, amma laqeyd müştərilər
o 9-10 bal = Dəstəkçilər/Loyallar: satın almağa və başqalarını da istiqamətləndirməyə davam edəcək, sadiq müştərilər
Sıfıra yaxın və yaxud aşağı (mənfi) NPS göstəricisi ilə digər şirkətlərə güclü axın təhlükəsi mövcuddur. Şirkət yeni alıcıları, yalnız
reklam kampaniyaları hesabına cəlb edə bilər. Bu o deməkdir ki, narazı və qeyri loyal olan müştərilər yaxınlarını həmin məhsulu
almamağa daha çox inandırır, nəinki almağa.
50 və yuxarı NPS göstəricisi isə reklam kampaniyalarının verilməməsinin mümkünlüyünü və alıcı bazasının öz-özunə artma
ehtimalını bildirir.
1,7 1,7
0
3,3
0
10
20
21,7
16,7
3,3
21,7
0 bal 1 bal 2 bal 3 bal 4 bal 5 bal 6 bal 7 bal 8 bal 9 bal 10 bal
36,7% 38,3% 25%
22 23 15
Sədərək/Binə TM
NPS : 11,7
15. NPS - Net Promoter Score
11,6% 32,6% 55,8%
ZARA
NPS : 44,2
0 0 0 2,3 0
9,3
0
11,6
20,9
9,3
46,5
0 bal 1 bal 2 bal 3 bal 4 bal 5 bal 6 bal 7 bal 8 bal 9 bal 10 bal
5 14 24
Baza: 28
Baza: 43
ROMANTİC
NPS : 3,6
0 0 0 0 0
7,1
17,9 17,9
28,6
10,7
17,9
0 bal 1 bal 2 bal 3 bal 4 bal 5 bal 6 bal 7 bal 8 bal 9 bal 10 bal
25% 46,4% 28,6%
7 13 8
16. NPS - Net Promoter Score
36,4% 63,6%
KOTON
NPS : 63,6
Baza: 13
Baza: 22
LC Waikiki
NPS : 7,7
15,4% 61,5% 23,1%
0 0 0 0 0 0 0
13,6
22,7
36,4
27,3
0 bal 1 bal 2 bal 3 bal 4 bal 5 bal 6 bal 7 bal 8 bal 9 bal 10 bal
8 14
0 0 0 0 0
7,7 7,7
23,1
38,5
7,7
15,4
0 bal 1 bal 2 bal 3 bal 4 bal 5 bal 6 bal 7 bal 8 bal 9 bal 10 bal
2 8 3
18. Bəli ; 13,3
Xeyr; 86,7
Geyim markalarına loyallıq aşağı səviyyədədir
Hər zaman eyni geyim markasını satın alma meylinizin olub, olmadığını söyləyə
bilərsinizmi? Yəni hər zaman eyni markadanmı geyim almağa üstünlük verirsiniz? (%)
Baza: 640
CİNS YAŞ
Kişi Qadın 18-30 31-65
61,2% 38,8% 44,7% 55,3%
Bəli: 13,3% - Baza: 85
19. Bilmirəm
Heç biri
Digər
Aqua Mineral
Qax
Erikli
Şahdağ
Jalə
Aqua Vita
Badamlı
Bonaqua
Sirab
Slavyanka
2,8
5,6
8,3
1,9
2,5
2,7
5,0
5,2
5,3
9,4
14,8
17,3
19,2
Şollar 1,3
Damla 1,3
Vita1000 0,9
Evian 0,6
Mor Mor 0,5
Pinar 0,5
Aquşa 0,5
Kəhriz 0,3
Vata 0,3
İpek su 0,3
İvanovka 0,2
Aysu 0,2
Nestle Pure Life 0,2
Mor Şinski 0,2
Xan 0,2
Saf 0,2
El 0,2
Mərcan 0,2
Qızıl Quyu 0,2
Sultan 0,2
Uludağ 0,2
Золотой колодец 0,2
«Sirab» qazsız su markası kimi populyarlaşa bilməyib
Baza: 640
Sizcə ən keyfiyyətli qazsız içməli (süfrə suyu) su
markası hansıdır? (%)
Digər
Erikli
Aqua Mineral
Qax
Şahdağ
Jalə
Aqua Vita
Badamlı
Sirab
Slavyanka
Bonaqua
10,0
7,5
1,1
1,7
2,3
4,4
4,7
6,7
9,7
16,3
16,4
19,2
Siz qazsız içməli suda (süfrə) hansı markaya
üstünlük verirsiniz? (%)
Baza: 640
Vita1000 0,9
Mor Mor 0,8
Şollar 0,8
Pinar 0,6
Aquşa 0,6
Damla 0,5
Nestle Pure Life 0,3
Qəbələ 0,3
Evian 0,3
Kəhriz 0,3
İpek su 0,3
İvanovka 0,2
Aysu 0,2
Xan 0,2
Tutu 0,2
Saf 0,2
Vata 0,2
El 0,2
Gədəbəy 0,2
Sultan 0,2
Fiji 0,2
Золотой колодец 0,2Heç biri/ qaynanmış
/filtirli su
20. Daha yaxşıdır;
7,5
Sabit, dəyişməz
qalıb; 24,5 Daha pisdir;
68,0
Paytaxt sakinləri arasında son 1 il ərzində maddi vəziyyətinin pisləşdiyini iddia
edənlər artıb
Baza: 640
1 il əvvələ baxanda, hal-hazırda maddi vəziyyətiniz daha yaxşıdır, yoxsa daha pisdir? (%)
Cins Yaş Məşğulluq üzrə
Kişi Qadın 18-30 31-65 İşləyir İşləmir Təqaüdçü Tələbə Evdar xanım
Daha yaxşıdır 8,2% 6,9% 12,2% 4,6% 11,4% 3,6% 9,1% 7,9% 6,3%
Sabit, dəyişməz qalıb 24,8% 24,3% 28,9% 21,8% 25,4% 21,2% 18,2% 36,5% 21,9%
Daha pisdir 67,1% 68,8% 58,9% 73,6% 63,2% 75,2% 72,7% 55,6% 71,9%
100,0% 100,0% 100,0% 100,0% 100,0% 100,0% 100,0% 100,0% 100,0%
İşləyən respondentlər
nisbətən daha pozitiv
cavablar veriblər.
22. Bilmirəm/Yoxdur
Digər
Pal Süd
Azərçay
Gilan Holding
Naz Lifan
SOCAR
Embawood
Emiland
Jalə
Bizim Tarla
Azərsun
25,8
30,0
2,3
2,7
2,8
3,0
3,3
4,1
5,5
6,4
6,7
7,5
Ən məşhur uğurlu Azərbaycan brendi «Azərsun» hesab edilir
Baza: 640
Sizcə ən uğurlu Azərbaycan brendi/markası hansıdır? (%)
Badamlı 2,0
Sirab 1,9
Gazelli 1,7
Azərcell 1,6
Sevimli Dad 1,6
Milla 1,6
Bakcell 1,1
Azərlight 1,1
Nar Mobile 0,9
Unibank 0,9
Ulduz 0,9
Femina 0,8
Səba Toyuq 0,8
Bazar Store 0,6
Azərsüd 0,6
Palmali 0,6
23. Hamı yerli mal alsın! Xeyri vətənə qalsın!
Baza: 640
Bəli; 85,9
Xeyr; 14,1
Bazarı canlandırmaq, yerli istehsalı artırmaq və xaricdən idxal olunan (alınan) məhsulların sayını azaltmaq üçün
sadəcə yerli malları/məhsulları istifadə etmə kampaniyası olsa iştirak edərsinizmi? (%)
24. Bilmirəm, yoldaşım alır
Tort yemirik
Sosial şəbəkələrdən sifariş edirəm
Marketlər şəbəkəsi
Digər
Tatlı
Ləzzət
Z-Style
Fantaziya
Ləziz
Kardeşler
Bon apetit
Çudo peçka
Azza
Özümüz bişiririk
0,9%
0,6%
0,2%
3,1%
10,3%
1,1%
1,3%
1,3%
1,6%
2,0%
5,0%
5,2%
11,4%
15,8%
41,6%
Baza: 640
Özəl günlərdə tortu haradan sifariş edirsiniz/alırsınız? (%)
Tort istehsal edən mağazalar arasında «Azza» bazar lideridir
BazarStore 0,8%
Araz Market 0,6%
Bizim market 0,6%
Favorit 0,3%
Grand market 0,3%
Ballı Market 0,2%
Xəzər Market 0,2%
Rahat Market 0,2%
Adi marketlərdən 1,1%
Şirniyyat evi 1,1%
Oven 0,6%
King Smart 0,5%
Cam şirniyyat evi 0,5%
Viva 0,5%
Truffle 0,5%
Vinni Kulinariya 0,5%
Gürcüstan şirniyyatı 0,3%
Alov 0,3%
Güllüoğlu 0,3%
Şir-şirniyyat evi 0,3%
Az Cake 0,3%
Land Mark 0,2%
Nazlı 0,2%
Şərq şirniyyatı 0,2%
Vanilla 0,2%
Biskvit Fabriki 0,2%
Ev sifarişi 0,2%
Akulin Kulinariya 0,2%
Avon 0,2%
Belissina 0,2%
E-Love 0,2%
Başkent 0,2%
Azur 0,2%
Hacıoğlu 0,2%
Azon 0,2%
Lemanca 0,2%
Usta Mübariz 0,2%
Kulinariya 0,2%
Klassika 0,2%
Kod Deazur 0,2%
Mado 0,2%
Final 0,2%
Fairmont Otel 0,2%
От Жанны 0,2%
Brand Equity
25. Tel.: (+994 12) 430 30 42 / 430 00 70 / 511 24 58
Fax: (+994 12) 430 17 77
E-mail: office@businessinsight-az.com
www.businessinsight-az.com
Business Insight
araşdırmalara verdiyiniz dəyərə görə Sizə təşəkkür edir.