JLL Detroit Industrial Insight & Statistics - Q3 2016Harrison West
Metro Detroit’s industrial absorption has driven down vacancy numbers and left the region with a lack of space for tenants to move into, especially in regards to Class A properties.
Performed extensive analysis to classify if the Buy is good or not using various classifiers (KNN, SVM, Decision Tree, Random Forest, AdaBoosting, Bagging, Deep Neural Nets – Tensor Flow).
Performed Data visualization using Tableau, Optimized Overfitting using SMOTE, Hyperparameter tuning using PCA, Optimized Bias and Variance.
Learn about the automotive aftermarket data standards: what they are, why you need them, and how SPEEDcat can help you create standardized datasets all by yourself!
Today\'s automotive aftermarket environment demands accuracy and completeness in product data in order to conduct business. AAIA\'s ACES and PIES product data exchange standards help manufacturers connect and sell with their customers.
JLL Detroit Industrial Insight & Statistics - Q3 2016Harrison West
Metro Detroit’s industrial absorption has driven down vacancy numbers and left the region with a lack of space for tenants to move into, especially in regards to Class A properties.
Performed extensive analysis to classify if the Buy is good or not using various classifiers (KNN, SVM, Decision Tree, Random Forest, AdaBoosting, Bagging, Deep Neural Nets – Tensor Flow).
Performed Data visualization using Tableau, Optimized Overfitting using SMOTE, Hyperparameter tuning using PCA, Optimized Bias and Variance.
Learn about the automotive aftermarket data standards: what they are, why you need them, and how SPEEDcat can help you create standardized datasets all by yourself!
Today\'s automotive aftermarket environment demands accuracy and completeness in product data in order to conduct business. AAIA\'s ACES and PIES product data exchange standards help manufacturers connect and sell with their customers.
The global market for automotive dyno is expected to grow from $ 220.2 million in 2021 to $ 238.4 million in 2026. The market is expected to grow at a CAGR of 1.6% over the forecast period (2021-2026). Some of the market's key participants are AVL, Froude Hofmann, HORIBA, KAHN, MTS, Meidensha, Mustang Advanced Engineering, NTS, Rototest, SGS, Schenck, Sierra Instruments, SuperFlow. This report intends to identify significant growth areas and to explore relevant market strategies. This in-depth analysis delves into the global market for automotive dyno. The primary goal of this research is to examine the potential growth areas, significant trends, and the market's impact on the industry. The report also reviews the adoption of automotive dyno in both established and emerging markets.
Which car fits my life? Mobile.de’s approach to recommendationsinovex GmbH
Description
As Germany’s largest online vehicle marketplace mobile.de uses recommendations at scale to help users find the perfect car. We elaborate on collaborative & content-based filtering as well as a hybrid approach addressing the problem of a fast-changing inventory. We then dive into the technical implementation of the recommendation engine, outlining the various challenges faced and experiences made.
Abstract
At mobile.de, Germany’s biggest car marketplace, a dedicated team of data engineers and scientists, supported by the IT project house inovex is responsible for creating intelligent data products. Driven by our company slogan “Find the car that fits your life”, we focus on personalised recommendations to address several user needs. Thereby we improve customer experience during browsing as well as finding the perfect offering. In an introduction to recommendation systems, we briefly mention the traditional approaches for recommendation engines, thereby motivating the need for sophisticated approaches. In particular, we explain the different concepts including collaborative and content-based filtering as well as hybrid approaches and general matrix factorisation methods. This is followed by a deep dive into the implementation and architecture at mobile.de that comprises ElasticSearch, Cassandra and Mahout. We explain how Python and Java is used simultaneously to create and serve recommendations.
By presenting our car-model recommender that suggests similar car models of different brands as a concrete use-case, we reiterate on key-aspects during modelling and implementation. In particular, we present a matrix factorisation library that we used and share our experiences with it. We conclude by a brief demonstration of our results and discuss the improvements we achieved in terms of key performance indicators. Furthermore, we use our use case to exemplify the usage of deep learning for recommendations, comparing it with other traditional approaches and hence providing a brief account of the future of recommendation engines.
Event: PyData Berlin 2017
Speaker: Dr. Florian Wilhelm (inovex), Dr. Arnab Dutta (mobile.de)
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Tech-Blog: https://www.inovex.de/blog/
This presentation contains an elaborate (Porter's) Five-Forces Analysis of Car2Go in Frankfurt am Main as a "Free-floating Car-sharing" provider. Additionally, you can find a detailed S-W-O-T Analysis and followinh strategic recommendations for the defined market in Frankfurt, Germany. It has been a strategy project for university so all used information and content is publicly available.
The automobile industry is facing demands on all fronts, including the need to optimize manufacturing and streamline supply chains and logistics and the demand for newer, higher-performing cars. 3D printing Bangalore is one technique that is assisting in meeting these difficulties.
3D printing Bangalore is being investigated in more and more fields of vehicle manufacturing. Aside from fast prototyping, the technology is often used to manufacture tooling and, in some cases, end components.
With the number of automotive 3D print online applications, here are some of the most compelling examples of automakers using the technology to improve production:
https://makenica.com/
Car seat covers market 2019 segmentation, application, technology, opportunit...GeetaBajaj4
The report titled “Global Car Seat Covers Market” has covered and analyzed the potential of Worldwide Car Seat Covers Industry and provides statistics and information on market dynamics, growth factors, key challenges, major drivers & restraints, opportunities and forecast.
Sensors for robotic vehicles 2018 report by yole developpement i-micronewsYole Developpement
High end industrial sensors will win in the emerging robotic vehicle industry.
THE ROBOTIC VEHICLE SUPPLY CHAIN IS NOW STARTING
Announcements are piling up from companies like
Waymo, Uber, Lyft, Baidu and their automotive
manufacturing partners such as Fiat Chrysler
Automobiles, Mercedes, BMW and Renault-
Nissan. 2018 will most probably be the initial launch
year for robotic taxis in several cities around the
globe. The move has direct consequences for
technology providers in high-end sensing and
computing equipment. The vehicle count that we
can collect suggests several tens of thousands of
vehicles on the road worldwide before end of
2022. As far as we know, each robotic vehicle will
be equipped with a suite of sensors encompassing
Lidars, radars, cameras, Inertial Measurement
Units (IMUs) and Global Navigation Satellite
Systems (GNSS). The technology is ready and
the business models associated with autonomous
driving (AD) seem to match the average selling
prices for those sensors.
As Germany’s largest online vehicle marketplace mobile.de uses recommendations at scale to help users find the perfect car. We elaborate on collaborative & content-based filtering as well as a hybrid approach addressing the problem of a fast-changing inventory. We then dive into the technical implementation of the recommendation engine, outlining the various challenges faced and experiences made.
28th Workshop on Information Systems and EconomicsYunkun Zhao, PhD
Extended Abstract (Full Paper Available Upon Request):
Zhao, Y.K., Goh, K.Y., Hou, L.W., “Evaluating the Effectiveness of Online Customer Touchpoints in Omni-channel Marketing Environments on Purchase Behaviors”, Completed-Paper, 28th Workshop on Information Systems and Economics (WISE 2017), Seoul, South Korea, December 13-15, 2017.
PhD Thesis_Digital Media Advertising AttributionYunkun Zhao, PhD
Author: ZhaoYunkun
PhD Thesis @ Department of Information Systems & Analytics
<<Chaper-2>>
Zhao, Y.K., Goh, K.Y., Hou, L.W., “Evaluating the Effectiveness of Online Customer Touchpoints in Omni-channel Marketing Environments on Purchase Behaviors”, Completed-Paper, 28th Workshop on Information Systems and Economics (WISE 2017), Seoul, South Korea, December 13-15, 2017.
<<Chapter-3>>
Zhao, Y.K., Goh, K.Y., Hou, L.W., “Cross-Channel Impacts of Online Advertising, Salesforce and Product Line Strategies in Online-to-Offline Retailing Environments”, Completed-Paper, 2018.
More Related Content
Similar to Online to-offline commerce in automobile industry
The global market for automotive dyno is expected to grow from $ 220.2 million in 2021 to $ 238.4 million in 2026. The market is expected to grow at a CAGR of 1.6% over the forecast period (2021-2026). Some of the market's key participants are AVL, Froude Hofmann, HORIBA, KAHN, MTS, Meidensha, Mustang Advanced Engineering, NTS, Rototest, SGS, Schenck, Sierra Instruments, SuperFlow. This report intends to identify significant growth areas and to explore relevant market strategies. This in-depth analysis delves into the global market for automotive dyno. The primary goal of this research is to examine the potential growth areas, significant trends, and the market's impact on the industry. The report also reviews the adoption of automotive dyno in both established and emerging markets.
Which car fits my life? Mobile.de’s approach to recommendationsinovex GmbH
Description
As Germany’s largest online vehicle marketplace mobile.de uses recommendations at scale to help users find the perfect car. We elaborate on collaborative & content-based filtering as well as a hybrid approach addressing the problem of a fast-changing inventory. We then dive into the technical implementation of the recommendation engine, outlining the various challenges faced and experiences made.
Abstract
At mobile.de, Germany’s biggest car marketplace, a dedicated team of data engineers and scientists, supported by the IT project house inovex is responsible for creating intelligent data products. Driven by our company slogan “Find the car that fits your life”, we focus on personalised recommendations to address several user needs. Thereby we improve customer experience during browsing as well as finding the perfect offering. In an introduction to recommendation systems, we briefly mention the traditional approaches for recommendation engines, thereby motivating the need for sophisticated approaches. In particular, we explain the different concepts including collaborative and content-based filtering as well as hybrid approaches and general matrix factorisation methods. This is followed by a deep dive into the implementation and architecture at mobile.de that comprises ElasticSearch, Cassandra and Mahout. We explain how Python and Java is used simultaneously to create and serve recommendations.
By presenting our car-model recommender that suggests similar car models of different brands as a concrete use-case, we reiterate on key-aspects during modelling and implementation. In particular, we present a matrix factorisation library that we used and share our experiences with it. We conclude by a brief demonstration of our results and discuss the improvements we achieved in terms of key performance indicators. Furthermore, we use our use case to exemplify the usage of deep learning for recommendations, comparing it with other traditional approaches and hence providing a brief account of the future of recommendation engines.
Event: PyData Berlin 2017
Speaker: Dr. Florian Wilhelm (inovex), Dr. Arnab Dutta (mobile.de)
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Tech-Blog: https://www.inovex.de/blog/
This presentation contains an elaborate (Porter's) Five-Forces Analysis of Car2Go in Frankfurt am Main as a "Free-floating Car-sharing" provider. Additionally, you can find a detailed S-W-O-T Analysis and followinh strategic recommendations for the defined market in Frankfurt, Germany. It has been a strategy project for university so all used information and content is publicly available.
The automobile industry is facing demands on all fronts, including the need to optimize manufacturing and streamline supply chains and logistics and the demand for newer, higher-performing cars. 3D printing Bangalore is one technique that is assisting in meeting these difficulties.
3D printing Bangalore is being investigated in more and more fields of vehicle manufacturing. Aside from fast prototyping, the technology is often used to manufacture tooling and, in some cases, end components.
With the number of automotive 3D print online applications, here are some of the most compelling examples of automakers using the technology to improve production:
https://makenica.com/
Car seat covers market 2019 segmentation, application, technology, opportunit...GeetaBajaj4
The report titled “Global Car Seat Covers Market” has covered and analyzed the potential of Worldwide Car Seat Covers Industry and provides statistics and information on market dynamics, growth factors, key challenges, major drivers & restraints, opportunities and forecast.
Sensors for robotic vehicles 2018 report by yole developpement i-micronewsYole Developpement
High end industrial sensors will win in the emerging robotic vehicle industry.
THE ROBOTIC VEHICLE SUPPLY CHAIN IS NOW STARTING
Announcements are piling up from companies like
Waymo, Uber, Lyft, Baidu and their automotive
manufacturing partners such as Fiat Chrysler
Automobiles, Mercedes, BMW and Renault-
Nissan. 2018 will most probably be the initial launch
year for robotic taxis in several cities around the
globe. The move has direct consequences for
technology providers in high-end sensing and
computing equipment. The vehicle count that we
can collect suggests several tens of thousands of
vehicles on the road worldwide before end of
2022. As far as we know, each robotic vehicle will
be equipped with a suite of sensors encompassing
Lidars, radars, cameras, Inertial Measurement
Units (IMUs) and Global Navigation Satellite
Systems (GNSS). The technology is ready and
the business models associated with autonomous
driving (AD) seem to match the average selling
prices for those sensors.
As Germany’s largest online vehicle marketplace mobile.de uses recommendations at scale to help users find the perfect car. We elaborate on collaborative & content-based filtering as well as a hybrid approach addressing the problem of a fast-changing inventory. We then dive into the technical implementation of the recommendation engine, outlining the various challenges faced and experiences made.
Similar to Online to-offline commerce in automobile industry (20)
28th Workshop on Information Systems and EconomicsYunkun Zhao, PhD
Extended Abstract (Full Paper Available Upon Request):
Zhao, Y.K., Goh, K.Y., Hou, L.W., “Evaluating the Effectiveness of Online Customer Touchpoints in Omni-channel Marketing Environments on Purchase Behaviors”, Completed-Paper, 28th Workshop on Information Systems and Economics (WISE 2017), Seoul, South Korea, December 13-15, 2017.
PhD Thesis_Digital Media Advertising AttributionYunkun Zhao, PhD
Author: ZhaoYunkun
PhD Thesis @ Department of Information Systems & Analytics
<<Chaper-2>>
Zhao, Y.K., Goh, K.Y., Hou, L.W., “Evaluating the Effectiveness of Online Customer Touchpoints in Omni-channel Marketing Environments on Purchase Behaviors”, Completed-Paper, 28th Workshop on Information Systems and Economics (WISE 2017), Seoul, South Korea, December 13-15, 2017.
<<Chapter-3>>
Zhao, Y.K., Goh, K.Y., Hou, L.W., “Cross-Channel Impacts of Online Advertising, Salesforce and Product Line Strategies in Online-to-Offline Retailing Environments”, Completed-Paper, 2018.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
1. Cross-Channel Impacts of Online Advertising,
Salesforce and Product Line Strategies in O2O
Retailing Environments
Yunkun Zhao*, Khim Yong Goh* and Liwen Hou**
* National University of Singapore, ** Shanghai Jiaotong University
Contact Author: mozartkun@gmail.com
Motivation
Brand Ad
Product Line
Product Ad
Research Objectives and Hypotheses
• Evaluating the impacts of salesman attributes on effectiveness of
advertising strategies
• Evaluating the impacts of product line length on effectiveness of
advertising strategies
H1A: Brand Ad + Salesman Train
+
Brand Ad + Salesman Tenure
+
H1B: Product Ad + Salesman Train
+
Product Ad + Salesman Tenure
+
H2A: Brand Ad + Length Across
Different Brand -
H2B: Product Ad + Length Across
Different Brand -
H2C: Brand Ad + Length Within
Same Brand +
H2D: Product Ad + Length Within
Same Brand -
Data Background
Data provided by a multi-brand multi-product O2O automobile manufacturer in China,
selling 3 brands, 35 models in 1,980 dealership stores throughout China. We have (1)
Purchase history, (2) Offline visit records, (3) Automobile information, and (4) Salesman
information
Individual Level Analysis
Aggregate Level Analysis
Sample, Dependent (Y) and Independent Variables (X)
Sample: (1) Customer-product-day level data from Jan, 2014 till Jun, 2016;
(2) 551,056 observations from 524,991 customers
Choiceijt Y1: Whether customer i choose to buy product j on day t (=1 yes, =0 no)
BrandAdijt X1: Whether customer i is lead by brand-oriented advertising for product j
into offline official store to visit at day t
ProductAdijt X2: Whether customer i is lead by product-oriented advertising for
product j into offline official store to visit at day t
SpBrandTenureit X3: The number of months the salesperson who serves customer i at day t
has been responsible for the car product j
SpPassTrainit X4: Whether the salesperson who serves customer i at day t passes sales
training program
CarLineDiffBrandit X5: The number of same-line different-brand car models of the car j that
the customer i intends to buy at day t
CarLineSameBrandit X6: The number of same-line same-brand car models of the car j that the
customer i intends to buy at day t
Model Specification:
Pr( 1) ( )ijt ijtChoice X
0 1 2 3 4 5 6
7 8 9 10
11
=
*
*
ijt ijt ijt ijt it it it
it it it ijt it
ijt it
X BrandAd ProductAd DeciLevel SpMidSchool SpCollege SpGraduate
SpAge SpBrandTenure SpPassTrain BrandAd SpBrandTenure
BrandAd SpPassTrain
12 13
14 15 16 17
18 19
* *
ln( ) ln( )
ln( )
ijt it ijt it
jt jt jt j
j j i j t ijt
ProductAd SpBrandTenure ProductAd SpPassTrain
CarLineDiffBrand CarLineSameBrand CarPrice CarDisplacement
CarFuelEconomy CarSeats
Sample, Dependent (Y) and Independent Variables (X)
Sample: (1) Car-week level data from Jan, 2014 till Jun, 2016;
(2) 2,493 observations from sales of 35 unique car models
Transactionjt Y1: The total number of transactions of car model j on month t
Visitjt Y2: The total number of offline store visits of car model j on month t
TotalBrandAdjt X1: The total number of brand-oriented advertising exposures of car
model j on month t
TotalProductAdjt X2: The total number of product-oriented advertising exposures of
car model j on month t
CarLineDiffBrandjt X3: The number of same-line different-brand car models of the car j
at month t
CarLineSameBrandjt X4: The number of same-line same-brand car models of the car j at
month t
Model Specification:
andln(1 )jt jtTransaction X ln( )jt jtVisit X
0 1 2 3 4
5 6
7 8
=
* *
*
jt jt jt jt jt
jt jt jt jt
jt jt
X TotalBrandAd TotalProductAd CarLineDiffBrand CarLineSameBrand
TotalBrandAd CarLineDiffBrand TotalProductAd CarLineDiffBrand
TotalBrandAd CarLineSameBrand T
9 10 11
12 13 14
*
ln( )
ln( ) ln( )
jt jt
jt jt jt
j j j t jt
otalProductAd CarLineSameBrand
RivalBrandAdSType RivalProductAdSType CarPrice
CarDisplacement CarFuelEconomy CarSeats
Results and Findings
Individual-Level Aggregate-Level
Variables (1)
Logit
Choice
(2)
Probit
Choice
BrandAd 0.743*** 0.362***
ProductAd 0.428*** 0.259***
SpBrandTenure -0.002 -0.002
SpPassTrain 0.153*** 0.080***
BrandAd ×
SpBrandTenure
-0.005 -0.003
BrandAd ×
SpPassTrain
-0.098 -0.067
ProductAd ×
SpBrandTenure
-4.19e-04 -8.59e-04
ProductAd ×
SpPassTrain
0.231*** 0.092***
Controls √ √
Constant -10.21*** -4.177***
BIC 7099.215 8359.542
Observations 551,056 551,056
Note: Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1
Variables (1)
Ln(1+Transac
tion)
FE
(2)
Ln(1+Transa
ction)
RE
(3)
Ln(Visit)
FE
(4)
Ln(Visit)
RE
TotalBrandAd 1.57e-04 9.86e-04*** 1.52e-03*** 3.02e-03***
TotalProductAd 7.57e-05 5.37e-04*** 8.97e-04*** 1.70e-03***
CarLineDiffBrand -0.011 0.156*** 0.014 0.293***
CarLineSameBrand 0.032 0.098*** 0.477*** 0.751***
TotalBrandAd ×
CarLineDiffBrand
-5.02e-05 -2.50e-04*** -2.70e-04*** -5.80e-04***
TotalProductAd ×
CarLineDiffBrand
1.08e-05 7.82e-06 1.03e-05 -1.17e-05
TotalBrandAd ×
CarLineSameBrand
1.24e-04*** 1.64e-04*** 3.38e-05 -8.32e-06
TotalProductAd ×
CarLineSameBrand
1.51e-05 -1.12e-04*** -2.04e-04*** -4.16e-04***
Controls √ √ √ √
Constant 0.286 14.51*** 3.437*** 25.69***
Overall-R2 0.282 0.532 0.319 0.497
Observations 2,493 2,493 2,493 2,493
Identifications and Robustness
Instrument
Variable
Estimation
Non-
randomly
Assigned
Salesman
Endogeneity
Concern of
Product
Line Length
Simultaneity
Alternative
Model
Hierarchical
Linear
Bayesian
MCMC
Estimation
√ √ √ √ √ √ √
Contributions
• Contribute to the literature on the interdependencies between online
advertising and offline salesman interactions by granularity testing
• Contribute to the literature on the interdependencies between online
advertising strategies and product line management strategies
• Managers should evaluate trade-offs of different advertising strategies, offline
salesman training and product line management
