Out of the campaign analysis the most important contribution is that Data-Driven Marketing makes sense only once it is already part of the marketing plan. So the team preparing the marketing plan defines the goals and sets the proper measurement matrix according to those goals. It enables to adjust the marketing plan to extract more value, watch the execution and do adjustments if necessary and evaluate at the end of the campaign.

The marketing possibilities on the Internet is growing and so are social media marketing. The budget devoted for marketing activities on social media is constantly increasining every year and the time users are spending on social media is also increasing. Among the increasing activities comes a vast amount of data which create endless of opportunities for companies to optimize their marketing activities. In marketing the most important has always been to know your customers and how to reach out to them. The Internet and data that comes with it has made it possible for companies to get to know their customers even better and to reach them with more precision if data is correctly used.   A gap was identified from the litterature search which showed that it is not always clear how to utilize social media for marketing and it is not easy to analyze and interpret the data derived from social media. This has lead to a lack of knowledge on how data can be used for social media activities. From the identified gap regarding data usage in social media marketing, a research question was formulated:   “How is data used in brand’s strategies for social media?”   A qualitative research design conducting semi-structured interviews was used to examine the research question. A purposeful sample of eleven respondents, defined as experts within the research field, from ten different companies was selected. A pilot study was carried out to get insights in the identified gap, to set a base for the theoretical framework, and to optimize the interview questions. All respondents represented agencies except for the respondent in the pilot study.   Academics and business communities are interested in how data is used in marketing purposes and therefore it was elaborated further in this thesis to how data can be used in social media activities. Branding activities are becoming more engaged with its customers, thus marketers need to keep up to date with the new and emerging trends. Furthermore, the aim was to explore how data is used in social media marketing and how data affect decisions in social media strategies.   The results found in this study shows that data is used to define audiences on social media and to enable a greater reach of the messages for the audiences. The audience is defined by data analysis mostly based on consumer behavior in social media. To achive reach marketers use programmatic buying tools, which are based on data and ultimatley enables conversions among the audience. Data is also analyzed by opinion mining where data insights can show what topics customers are engaged in. Data insights can further give direction on how content can encourage engagement among the targeted audience. Lastly, the result shows that it is important to have knowledge about how to analyze, interpret, and use data insights in order to create successful social media activites.

In marketing, behaviour of users is analysed in order to discover which channels (for instance TV, Social media etc.) are important for increasing the user’s intention to buy a product. The search for better channel attribution models than the common last-click model is of major concern for the industry of marketing. In this thesis, a probabilistic model for channel attribution has been developed, and this model is demonstrated to be more data-driven than the conventional last- click model. The modelling includes an attempt to include the time aspect in the modelling which have not been done in previous research. Our model is based on studying different sequence length and computing conditional probabilities of conversion by using logistic regression models. A clickstream dataset from an online store was analysed using the proposed model. This thesis has revealed proof of that the last-click model is not optimal for conducting these kinds of analyses.

By means of data-driven marketing as well as big data technology, this paper presents the investigation of a case study from travel industry implemented by a combination of propensity model and a business model “2W1H”. The business model “2W1H” represents the purchasing behavior “What to buy”, “When to buy”, and “How to buy”. This paper presents the process of building propensity models for the application in behavioral targeting in travel industry.     Combined the propensity scores from predictive analysis and logistic regression with proper marketing and CRM strategies when communicating with travelers, the business model “2W1H” can perform personalized targeting for evaluating of marketing strategy and performance. By analyzing the business model “2W1H” and the propensity model on each business model, both the validation of the model based on training model and test data set, and the validation of actual marketing activities, it has been proven that predictive analytics plays a vital role in the implementation of travelers’ purchasing behavioral targeting in marketing.

Market attribution is the problem of interpreting the influence of advertisements onthe user’s decision process. Market attribution is a hard problem, and it happens to be asignificant reason for Google’s revenue. There are broadly two types of attribution models- data-driven and heuristics.This thesis focuses on the data driven attribution modeland explores the viability of using Bayesian Network as market attribution models andbenchmarks the performance against a logistic regression. The data used in this thesiswas prepossessed using undersampling technique. Furthermore, multiple techniques andalgorithms to learn and train Bayesian Network are explored and evaluated.For the given dataset, it was found that Bayesian Network can be used for market at-tribution modeling and that its performance is better than the baseline logistic model. Keywords: Market Attribution Model, Bayesian Network, Logistic Regression.

The term electronic commerce (e-commerce) refers to a business model that allows companies and individuals to buy and sell goods and services over the internet. The focus of this thesis is on online pharmacies, a segment of the ecommerce market. Even though internet pharmacies are still subject to the same stringent rules imposed on pharmacies that limit the scope for their market growth, it has shown a notable increase in the past decades. The main goal of this thesis is to develop a data-driven marketing strategy based on a Swedish based online pharmacy’s daily sales data. The methodology of the data analysis includes exploratory data analysis (EDA) and market basket analysis (MBA) using the Apriori algorithm and the application of marketing frameworks and theories from a data-driven standpoint. In addition to the data analysis, this paper proposes a conceptual framework of a digital marketing strategy based on the RACE framework (reach, act, convert, and engage). The result of the analysis has led to the following data-driven marketing strategy: Special attention should be paid to association rules with a high lift ration value; high gross profit margin percentile (GPMP) products should have a volume-based marketing strategy that focuses on lower prices on subsequent items; and price bundling is the best marketing strategy for low GPMP products. Some of the practical ideas mentioned in this thesis paper include optimizing keyword search for a high GPMP product type and sending reminder emails and push alerts to avoid cart abandonment. The findings and recommendations presented in this thesis can be used by online pharmacies to extract knowledge that may support several decisions ranging from raising overall order size, marketing campaigns, to increasing the sales of products with a high gross profit margin.

Rapid digital transformation, accelerated by covid-19 and a younger, more digital workforce, has changed the B2B sales environment affecting customer behavior, business practices and technologies. To adapt to this change B2B companies need to create new endeavors that connect marketing and sales activities, and identify how these can be efficiently enhanced by technology and digital tools. There is a need to identify effective tactics in different phases of the marketing and sales process. Consequently, more academic research is needed to investigate critical issues that technology may have in the B2B buying process. This paper thereby aims to establish a framework for B2B companies on how to optimize the customer journey by supporting the sales process with digital inbound marketing. This is examined by looking at how B2B companies can optimize the sales process by targeting customers’ needs with relevant actions throughout the customer journey with the help of technology and digital means. The results of this exploratory single case study demonstrates that the customer journey is complex with several touchpoints, and to create optimized processes, the customer journey must be adapted to each specific customer segment. Further, the study also contributes to the literature by demonstrating the importance of the marketing and sales departments being integrated and working together to create efficient processes. The presented framework for a customer journey can be used by managers to visualize the sales process, and identify at what stages the process can be made more efficient by digital means.