Issues

Journal & Issues

Volume 14 (2022): Issue 1 (May 2022)
Conscious Consumption

Volume 13 (2021): Issue 2 (November 2021)
Brand Activism

Volume 13 (2021): Issue 1 (May 2021)
The Dark Sides of Digital Marketing

Volume 12 (2020): Issue 2 (November 2020)
The Reputation Economy

Volume 12 (2020): Issue 1 (May 2020)
Crowd Innovation: Hype or Help

Volume 11 (2019): Issue 2 (November 2019)
AI and the Machine Age of Marketing

Volume 11 (2019): Issue 1 (May 2019)
The Future of Retailing

Volume 10 (2018): Issue 2 (October 2018)
IoT - Consumers and the Internet of Things

Volume 10 (2018): Issue 1 (May 2018)
Brand Risk Matters

Volume 9 (2017): Issue 2 (November 2017)
The Connected Consumer

Volume 9 (2017): Issue 1 (May 2017)
Digital Transformation

Volume 8 (2016): Issue 2 (November 2016)
Marketing and Data Science

Volume 8 (2016): Issue 1 (May 2016)
Responsible Marketing

Volume 7 (2015): Issue 2 (November 2015)
Marketing Meets Product Design

Volume 7 (2015): Issue 1 (May 2015)
Truly Accountable Marketing

Volume 6 (2014): Issue 2 (November 2014)
Social Brand Engagement

Volume 6 (2014): Issue 1 (May 2014)
Emotions in Marketing

Volume 5 (2013): Issue 2 (November 2013)

Volume 5 (2013): Issue 1 (May 2013)

Volume 4 (2012): Issue 2 (November 2012)

Volume 4 (2012): Issue 1 (May 2012)

Volume 3 (2011): Issue 2 (November 2011)

Volume 3 (2011): Issue 1 (May 2011)

Volume 2 (2010): Issue 2 (November 2010)

Volume 2 (2010): Issue 1 (May 2010)

Volume 1 (2009): Issue 2 (November 2009)

Volume 1 (2009): Issue 1 (May 2009)

Journal Details
Format
Journal
eISSN
2628-166X
First Published
30 May 2019
Publication timeframe
2 times per year
Languages
English

Search

Volume 8 (2016): Issue 2 (November 2016)
Marketing and Data Science

Journal Details
Format
Journal
eISSN
2628-166X
First Published
30 May 2019
Publication timeframe
2 times per year
Languages
English

Search

9 Articles
Open Access

Editorial

Published Online: 28 Oct 2016
Page range: 3 - 3

Abstract

Open Access

Data, Data and Even More Data: Harvesting Insights From the Data Jungle

Published Online: 28 Oct 2016
Page range: 10 - 17

Abstract

Abstract

Increasing global digitalization brings huge amounts of data. Finding a successful way to handle all this data and to transform it into real insights will be a critical success factor in the future. The biggest challenge for data science applications in marketing is that many marketing people studied marketing because they no longer wanted to do mathematics. A good marketing campaign will still have to be creative, touch emotions and build a brand, but more and more marketing managers will also need technical and analytical skills. It will more than ever be necessary to determine real causal effects to pull the right levers. Consumer insights have always been considered a major driver for growth, but in the digital world, successful growth can also come from improved technical and analytical implementation and skillful application of new tools and methods.

Keywords

  • Data Science
  • Big Data
  • Analytics
  • Growth Hacking
  • Online Advertising
Open Access

Marketing and Data Science: Together the Future is Ours

Published Online: 28 Oct 2016
Page range: 18 - 23

Abstract

Abstract

The synergistic use of computer science and marketing science techniques offers the best avenue for knowledge development and improved applications. A broad area of complementarity between the typical focus in statistics and computer science and that in marketing offers great potential. The former fields tend to focus on pattern recognition, control and prediction. Many marketing analyses embrace these directions, but also contribute by modeling structure and exploring causal relationships. Marketing has successfully combined foci from management science with foci from psychology and economics. These fields complement each other because they enable a broad spectrum of scientific approaches. Combined, they provide both understanding and practical solutions to important and relevant managerial marketing problems, and marketing science is already very successful at obtaining unique insights from big data.

Keywords

  • Data Science
  • Marketing Science
  • Computer Science
  • Big Data
  • Quantitative Analysis
  • Modeling
  • Machine Learning
Open Access

On Storks and Babies: Correlation, Causality and Field Experiments

Published Online: 28 Oct 2016
Page range: 24 - 29

Abstract

Abstract

The explosion of available data has created much excitement among marketing practitioners about their ability to better understand the impact of marketing investments. Big data allows for detecting patterns and often it seems plausible to interpret them as causal. While it is quite obvious that storks do not bring babies, marketing relationships are usually less clear. Apparent “causalities” often fail to hold up under examination. If marketers want to be sure not to walk into a causality trap, they need to conduct field experiments to detect true causal relationships. In the present digital environment, experiments are easier than ever to execute. However, they need to be prepared and interpreted with great care in order to deliver meaningful and genuinely causal results that help improve marketing decisions.

Keywords

  • Correlation
  • Causality
  • Field Experiments
  • Field Tests
  • Causal Inference
Open Access

Tell Me Where You Are and I’ll Tell You What You Want: Using Location Data to Improve Marketing Decisions

Published Online: 28 Oct 2016
Page range: 30 - 37

Abstract

Abstract

Location data has become more and more accessible. Smartphone applications such as location-based services collect location data on a large scale. Up to now, most approaches have relied on past data, but new developments in machine learning and artificial intelligence will soon enable more dynamic real-time use of location data. Companies that embrace these technologies will be able to create competitive advantages. Location data offers great potential to improve a variety of marketing decisions such as targeted pricing and advertising, store locations and in-store layout. Location based advertising is currently the most common application. It allows targeting all customers within a certain distance of a store. Besides advertising, location data can be used for dynamic pricing decisions. Customers close to competitor’s locations can be charged a lower price for particular products via discounts in order to reduce switching costs. Indoor tracking can help to optimize store design or the positioning of categories and brands. Granular location data about consumers’ movements hence further allows for minimizing potential offline transaction costs based on the distances to stores.

Keywords

  • Location Data
  • Location Intelligence
  • Decision Support Systems
  • Mobile Targeting
  • Mobile Analytics
Open Access

Using Big Data for Online Advertising Without Wastage: Wishful Dream, Nightmare or Reality?

Published Online: 28 Oct 2016
Page range: 38 - 43

Abstract

Abstract

Big data contains lots of information about consumers and allows companies real-time and data-assisted decision making to gain significant competitive advantages. Digital advertising is an important application for tailoring services to individual needs. Customized advertising is expected to be more effective, cost less, and better received by society. But what looks deceptively simple when it succeeds is frequently quite difficult to implement in practice. It is difficult to judge and validate the quality of automatically generated data. And besides quality, there are other aspects that make it tricky to determine the value of the data. A reasonable price for data depends on the context of its application and the potential cost savings it generates. And not only the price per impression is unclear. The number of contacts is also less obvious than it seems at first glance. Primarily third party data providers often incur problems with the monetization of big data and many are struggling to survive. They depend on the fairness of the data buyer and a successful business model has yet to be developed.

Keywords

  • Online Advertising
  • Big Data
  • Third-party Data Provider
  • Personalization
  • Wastage
  • Retargeting
Open Access

The Art of Creating Attractive Consumer Experiences at the Right Time: Skills Marketers Will Need to Survive and Thrive

Published Online: 28 Oct 2016
Page range: 44 - 49

Abstract

Abstract

New technologies have made today’s marketing faster, more mobile, more location-based, more digital, more virtual, and more automatized than ever. In this new world, marketers need to be “real-time relevant” – to gain awareness, to change perceptions and to spur action. They need to have their content in the right channel, format, time and context – from a consumer’s perspective. Only then do they at least have a chance of the consumer attending to the information and being influenced by it. In such an environment new skills and competences are required. The amount of available data has virtually exploded. To gain any perspective or apparent “control” in these environments, successful managers must embrace the complexity and learn to analyze, integrate and interpret all this data. A critical skill for marketers will be to identify the metrics that best reflect the desired outcomes of the organization and that sufficiently reflect specific indicators of critical processes. Furthermore, insights from other disciplines such as architecture, design, information-processing, biology or engineering will be important for creating customer experiences. The marketer of the future will need to be supremely curious and creative and to balance and integrate different worlds. It will all come down to delivering memorable and lasting experiences in a constantly and fast changing environment.

Keywords

  • Marketing Skills
  • Customer Experience
  • Consumer Experience
  • Big Data
  • Key Metrics
  • Causal Models
  • Real-Time
  • Deep Knowledge
  • Creativity
Open Access

Data Analysis Trumps Specialist Advice: How Direct Banks Function

Published Online: 28 Oct 2016
Page range: 50 - 55

Abstract

Abstract

Low interest rates and sluggish economic growth are not exactly ideal conditions for the financial services industry. Almost daily we are confronted with reports of inadequate capital bases, declining earnings, and layoffs at banks. But while many traditional retail banks are struggling with a business downturn, the direct bank market is enjoying steady and respectable growth despite a challenging environment. Dr. Schmidberger, Fully Authorized Representative at ING-DiBa Germany, offers us a glimpse behind the curtains of this direct bank. We will learn how data technology is used so that bank customers are (more) satisfied.

Open Access

Big Data in Market Research: Why More Data Does Not Automatically Mean Better Information

Published Online: 28 Oct 2016
Page range: 56 - 63

Abstract

Abstract

Big data will change market research at its core in the long term because consumption of products and media can be logged electronically more and more, making it measurable on a large scale. Unfortunately, big data datasets are rarely representative, even if they are huge. Smart algorithms are needed to achieve high precision and prediction quality for digital and non-representative approaches. Also, big data can only be processed with complex and therefore error-prone software, which leads to measurement errors that need to be corrected. Another challenge is posed by missing but critical variables. The amount of data can indeed be overwhelming, but it often lacks important information. The missing observations can only be filled in by using statistical data imputation. This requires an additional data source with the additional variables, for example a panel. Linear imputation is a statistical procedure that is anything but trivial. It is an instrument to “transport information,” and the higher the observed data correlates with the data to be imputed, the better it works. It makes structures visible even if the depth of the data is limited.

Keywords

  • Big Data
  • Market Research
  • Information
  • Representativeness
  • Data Integration
  • Data Imputation
9 Articles
Open Access

Editorial

Published Online: 28 Oct 2016
Page range: 3 - 3

Abstract

Open Access

Data, Data and Even More Data: Harvesting Insights From the Data Jungle

Published Online: 28 Oct 2016
Page range: 10 - 17

Abstract

Abstract

Increasing global digitalization brings huge amounts of data. Finding a successful way to handle all this data and to transform it into real insights will be a critical success factor in the future. The biggest challenge for data science applications in marketing is that many marketing people studied marketing because they no longer wanted to do mathematics. A good marketing campaign will still have to be creative, touch emotions and build a brand, but more and more marketing managers will also need technical and analytical skills. It will more than ever be necessary to determine real causal effects to pull the right levers. Consumer insights have always been considered a major driver for growth, but in the digital world, successful growth can also come from improved technical and analytical implementation and skillful application of new tools and methods.

Keywords

  • Data Science
  • Big Data
  • Analytics
  • Growth Hacking
  • Online Advertising
Open Access

Marketing and Data Science: Together the Future is Ours

Published Online: 28 Oct 2016
Page range: 18 - 23

Abstract

Abstract

The synergistic use of computer science and marketing science techniques offers the best avenue for knowledge development and improved applications. A broad area of complementarity between the typical focus in statistics and computer science and that in marketing offers great potential. The former fields tend to focus on pattern recognition, control and prediction. Many marketing analyses embrace these directions, but also contribute by modeling structure and exploring causal relationships. Marketing has successfully combined foci from management science with foci from psychology and economics. These fields complement each other because they enable a broad spectrum of scientific approaches. Combined, they provide both understanding and practical solutions to important and relevant managerial marketing problems, and marketing science is already very successful at obtaining unique insights from big data.

Keywords

  • Data Science
  • Marketing Science
  • Computer Science
  • Big Data
  • Quantitative Analysis
  • Modeling
  • Machine Learning
Open Access

On Storks and Babies: Correlation, Causality and Field Experiments

Published Online: 28 Oct 2016
Page range: 24 - 29

Abstract

Abstract

The explosion of available data has created much excitement among marketing practitioners about their ability to better understand the impact of marketing investments. Big data allows for detecting patterns and often it seems plausible to interpret them as causal. While it is quite obvious that storks do not bring babies, marketing relationships are usually less clear. Apparent “causalities” often fail to hold up under examination. If marketers want to be sure not to walk into a causality trap, they need to conduct field experiments to detect true causal relationships. In the present digital environment, experiments are easier than ever to execute. However, they need to be prepared and interpreted with great care in order to deliver meaningful and genuinely causal results that help improve marketing decisions.

Keywords

  • Correlation
  • Causality
  • Field Experiments
  • Field Tests
  • Causal Inference
Open Access

Tell Me Where You Are and I’ll Tell You What You Want: Using Location Data to Improve Marketing Decisions

Published Online: 28 Oct 2016
Page range: 30 - 37

Abstract

Abstract

Location data has become more and more accessible. Smartphone applications such as location-based services collect location data on a large scale. Up to now, most approaches have relied on past data, but new developments in machine learning and artificial intelligence will soon enable more dynamic real-time use of location data. Companies that embrace these technologies will be able to create competitive advantages. Location data offers great potential to improve a variety of marketing decisions such as targeted pricing and advertising, store locations and in-store layout. Location based advertising is currently the most common application. It allows targeting all customers within a certain distance of a store. Besides advertising, location data can be used for dynamic pricing decisions. Customers close to competitor’s locations can be charged a lower price for particular products via discounts in order to reduce switching costs. Indoor tracking can help to optimize store design or the positioning of categories and brands. Granular location data about consumers’ movements hence further allows for minimizing potential offline transaction costs based on the distances to stores.

Keywords

  • Location Data
  • Location Intelligence
  • Decision Support Systems
  • Mobile Targeting
  • Mobile Analytics
Open Access

Using Big Data for Online Advertising Without Wastage: Wishful Dream, Nightmare or Reality?

Published Online: 28 Oct 2016
Page range: 38 - 43

Abstract

Abstract

Big data contains lots of information about consumers and allows companies real-time and data-assisted decision making to gain significant competitive advantages. Digital advertising is an important application for tailoring services to individual needs. Customized advertising is expected to be more effective, cost less, and better received by society. But what looks deceptively simple when it succeeds is frequently quite difficult to implement in practice. It is difficult to judge and validate the quality of automatically generated data. And besides quality, there are other aspects that make it tricky to determine the value of the data. A reasonable price for data depends on the context of its application and the potential cost savings it generates. And not only the price per impression is unclear. The number of contacts is also less obvious than it seems at first glance. Primarily third party data providers often incur problems with the monetization of big data and many are struggling to survive. They depend on the fairness of the data buyer and a successful business model has yet to be developed.

Keywords

  • Online Advertising
  • Big Data
  • Third-party Data Provider
  • Personalization
  • Wastage
  • Retargeting
Open Access

The Art of Creating Attractive Consumer Experiences at the Right Time: Skills Marketers Will Need to Survive and Thrive

Published Online: 28 Oct 2016
Page range: 44 - 49

Abstract

Abstract

New technologies have made today’s marketing faster, more mobile, more location-based, more digital, more virtual, and more automatized than ever. In this new world, marketers need to be “real-time relevant” – to gain awareness, to change perceptions and to spur action. They need to have their content in the right channel, format, time and context – from a consumer’s perspective. Only then do they at least have a chance of the consumer attending to the information and being influenced by it. In such an environment new skills and competences are required. The amount of available data has virtually exploded. To gain any perspective or apparent “control” in these environments, successful managers must embrace the complexity and learn to analyze, integrate and interpret all this data. A critical skill for marketers will be to identify the metrics that best reflect the desired outcomes of the organization and that sufficiently reflect specific indicators of critical processes. Furthermore, insights from other disciplines such as architecture, design, information-processing, biology or engineering will be important for creating customer experiences. The marketer of the future will need to be supremely curious and creative and to balance and integrate different worlds. It will all come down to delivering memorable and lasting experiences in a constantly and fast changing environment.

Keywords

  • Marketing Skills
  • Customer Experience
  • Consumer Experience
  • Big Data
  • Key Metrics
  • Causal Models
  • Real-Time
  • Deep Knowledge
  • Creativity
Open Access

Data Analysis Trumps Specialist Advice: How Direct Banks Function

Published Online: 28 Oct 2016
Page range: 50 - 55

Abstract

Abstract

Low interest rates and sluggish economic growth are not exactly ideal conditions for the financial services industry. Almost daily we are confronted with reports of inadequate capital bases, declining earnings, and layoffs at banks. But while many traditional retail banks are struggling with a business downturn, the direct bank market is enjoying steady and respectable growth despite a challenging environment. Dr. Schmidberger, Fully Authorized Representative at ING-DiBa Germany, offers us a glimpse behind the curtains of this direct bank. We will learn how data technology is used so that bank customers are (more) satisfied.

Open Access

Big Data in Market Research: Why More Data Does Not Automatically Mean Better Information

Published Online: 28 Oct 2016
Page range: 56 - 63

Abstract

Abstract

Big data will change market research at its core in the long term because consumption of products and media can be logged electronically more and more, making it measurable on a large scale. Unfortunately, big data datasets are rarely representative, even if they are huge. Smart algorithms are needed to achieve high precision and prediction quality for digital and non-representative approaches. Also, big data can only be processed with complex and therefore error-prone software, which leads to measurement errors that need to be corrected. Another challenge is posed by missing but critical variables. The amount of data can indeed be overwhelming, but it often lacks important information. The missing observations can only be filled in by using statistical data imputation. This requires an additional data source with the additional variables, for example a panel. Linear imputation is a statistical procedure that is anything but trivial. It is an instrument to “transport information,” and the higher the observed data correlates with the data to be imputed, the better it works. It makes structures visible even if the depth of the data is limited.

Keywords

  • Big Data
  • Market Research
  • Information
  • Representativeness
  • Data Integration
  • Data Imputation

Plan your remote conference with Sciendo