The applications of social media in sports marketing

n the era of big data, sports consumer's activities in social media become valuable assets to sports marketers. In this paper, the authors review extant literature regarding how to effectively use social media to promote sports as well as how to effectively analyze social media data to support business decisions. Methods: The literature review method. Results: Our findings suggest that sports marketers can use social media to achieve the following goals, such as facilitating marketing communication campaigns, adding values to sports products and services, creating a two-way communication between sports brands and consumers, supporting sports sponsorship program, and forging brand communities. As to how to effectively analyze social media data to support business decisions, extent literature suggests that sports marketers to undertake traffic and engagement analysis on their social media sites as well as to conduct sentiment analysis to probe customer's opinions. These insights can support various aspects of business decisions, such as marketing communication management, consumer's voice probing, and sales predictions. Conclusion: Social media are ubiquitous in the sports marketing and consumption practices. In the era of big data, these ＂footprints＂ can now be effectively analyzed to generate insights to support business decisions. Recommendations to both the sports marketing practices and research are also addressed

THE ROLE OF DATABASE MARKETING IN THE OPERATIONALIZATION OF THE SERVICES RELATIONSHIP MARKETING

The relationship marketing aims the construction of a durable relation between the enterprise and the final client, identified at an individual level. The particular part of the relationship marketing has two main concepts: individuality and the relation. This paper presents the concepts of relationship marketing, database marketing and geomarketing. We present the importance of implementing a marketing database in a service providing enterprise and its implications on one hand for the client and on the other hand for the enterprise. The paper point out the marketing database instruments and the advantages for the elements of the marketing mix. The implementation of a marketing database will aid the enterprise to better target and attract the client, to transform them into loyal consumers and in the same time it can help refresh the image of the enterprise.relationship marketing, one-to-one marketing, customer relationship management, database marketing

THE ROLE OF DATABASE MARKETING IN THE OPERATIONALIZATION OF THE SERVICES RELATIONSHIP MARKETING

The relationship marketing aims the construction of a durable relation between the enterprise and the final client, identified at an individual level. The particular part of the relationship marketing has two main concepts: individuality and the relation. This paper presents the concepts of relationship marketing, database marketing and geomarketing. We present the importance of implementing a marketing database in a service providing enterprise and its implications on one hand for the client and on the other hand for the enterprise. The paper point out the marketing database instruments and the advantages for the elements of the marketing mix. The implementation of a marketing database will aid the enterprise to better target and attract the client, to transform them into loyal consumers and in the same time it can help refresh the image of the enterprise.relationship marketing, one-to-one marketing, customer relationship management, database marketing

Off-Policy Evaluation of Probabilistic Identity Data in Lookalike Modeling

We evaluate the impact of probabilistically-constructed digital identity data collected from Sep. to Dec. 2017 (approx.), in the context of Lookalike-targeted campaigns. The backbone of this study is a large set of probabilistically-constructed "identities", represented as small bags of cookies and mobile ad identifiers with associated metadata, that are likely all owned by the same underlying user. The identity data allows to generate "identity-based", rather than "identifier-based", user models, giving a fuller picture of the interests of the users underlying the identifiers. We employ off-policy techniques to evaluate the potential of identity-powered lookalike models without incurring the risk of allowing untested models to direct large amounts of ad spend or the large cost of performing A/B tests. We add to historical work on off-policy evaluation by noting a significant type of "finite-sample bias" that occurs for studies combining modestly-sized datasets and evaluation metrics involving rare events (e.g., conversions). We illustrate this bias using a simulation study that later informs the handling of inverse propensity weights in our analyses on real data. We demonstrate significant lift in identity-powered lookalikes versus an identity-ignorant baseline: on average ~70% lift in conversion rate. This rises to factors of ~(4-32)x for identifiers having little data themselves, but that can be inferred to belong to users with substantial data to aggregate across identifiers. This implies that identity-powered user modeling is especially important in the context of identifiers having very short lifespans (i.e., frequently churned cookies). Our work motivates and informs the use of probabilistically-constructed identities in marketing. It also deepens the canon of examples in which off-policy learning has been employed to evaluate the complex systems of the internet economy.Comment: Accepted by WSDM 201

eCRM in the Travel Industry

We are bombarded with Internet forecasts and statistics every day, however there is little doubt that the Internet has permanently changed the face of travel promotion and distribution. While only a minority of consumers are actually prepared to buy online at the present time, this minority is growing and there are large numbers of consumers who wish to use the Internet for information and communication. Travel and hospitality companies are selling an information-rich product and will need to leverage the full range of offline and e-channels to engage their customers in dialogue. The Internet does not have any respect for geographic or organisational boundaries and companies will have to forge new business models, involving partnerships and customer-driven product design, in order to meet the needs of the online consumer. There are major challenges and opportunities for companies wishing to add the e to their CRM strategy

Predicting customer's gender and age depending on mobile phone data

In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain

Next best action – a data-driven marketing approach

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThe Next Best Action (NBA) is a framework that is built in order to assign to each client three (or more) actions that are considered to be the best actions to perform with the client. These actions can range from product offering to pro-active retention actions and upselling recommendations. It can be a useful tool to generate leads for ongoing campaigns but also an excellent tool for analysis and a driver for the creation of new campaigns, being a key element in Customer Relationship Management (CRM) as a Data-Driven Marketing approach. Initially planned as a joint collaboration between a Bank and an Insurance Company to improve the Bancassurance business model, three versions of the NBA were built with the first two being tested on a campaign setting showing promising results. The last version, NBA 3.0, later became a sole project of the Insurance Company due to GPDR compliance policies and due to time constraints could not be evaluated