Social platforms have become a large part of today’s society and there is large scientific interests in what creates a successful social platform. There is plenty of research available on how to acquire users to a social platform, but there is significantly less available on how to retain users on a social platforms. It is easy to forget that acquiring users is only half the battle, users that do not return is of little use. Thus, there is demand for research on how to retain users on social platforms, which is something that are of interest of both developing and established social platforms. This study aim to answer two research questions: “What drives continued use of social platforms?” and “How can CRM and Gamification tools be used to retain users on social platforms?” The factors that are investigated in this paper were derived from current literature. Based on the literature review, a conceptual framework was built from existing theory. The conceptual framework was used as the starting point for our data collection. Data was collected in the form of ten interviews of students currently enrolled at instutiotions of higher education. Later, the primary data were contrasted with the secondary data in order to draw conclusions. The results suggests that previous research have neglected some externalities such as trends and context, and the fact that the drivers for use differ depending on the social platform in question. Finally, a revised conceptual model is presented, it is a modified version of the conceptual model, where the new findings are added in order to create an illustration of how our research added to the existing research on the subject. The findings can be used by management on social platforms to develop user retention strategies. Further, the findings from this study can be used as a foundation for future research on the topic.

Even though growth hacking is a new concept, it has become a buzz-word among entrepreneurs and start-ups. Various startups that have achieved extensive growth, such as Dropbox, Uber, Airbnb, have been sharing their success stories. However, to date, the focus of this concept was more on the practicalities instead of the theoretical research. With so many start-ups that fail to grow, it is important to research growth methodologies, that can help young entrepreneurs to successfully establish themselves. This paper studies growth hacking concept, by trying to understand how the growth hacking strategy works and how it could be used to retain a user-base in a start-up. With the help of an extensive literature review, interviews with the entrepreneurs and a case study analysis, this research provides (1) insights into the theory of growth hacking and retention marketing,(2) examples of its practices, and (3) an implementation of suggestions made based on the findings. The results of this study indicate that growth hacking is a broad concept and has numerous interpretations. Growth hacking framework has been applied at the early stages of start-ups, however, the growth hacking concept has not been defined as well as it has not been determined whether it is a relevant method to improve user retention. In this study, it was discovered that growth hacking practices can improve to set up theuser retention strategy. However, growth hacking strategies must be tailored and adapted to the entrepreneurial venture’s business model. / Även om growth hacking är ett nytt koncept, har det blivit ett buzz-word bland företagare och nystartade företag. Olika nystartade företag som använt growth hacking och uppnått omfattande tillväxt t.ex Dropbox, Uber och Airbnb har alla förmedlat sina framgångshistorier. Men hittills har fokus legat mer på det praktiska i stället för den teoretiska forskningen. Med så många nystartade företag som inte lyckas växa, är det viktigt att undersöka tillväxtmetodik, som kan hjälpa unga företagare att framgångsrikt etablera sig. Denna uppsats studerar growth hacking konceptet, genom att försöka förstå hur denna strategi fungerar och hur det kunde användas för att behålla en användarbas. Med hjälp av en omfattande litteraturgenomgång, intervjuer med företagare och en fallstudie, ger denna forskning (1) insikter i teorin om growth hacking och retention marknadsföring, (2) exempel på dess praxis, och (3) en implementering av förslag har gjorts baserat på resultaten. Resultaten av denna studie visar att growth hacking är ett brett begrepp och har många tolkningar. Growth hacking ramverk har tillämpats i tidiga stadier på nystartade företag, dock har growth hacking koncept inte definierats, liksom har det inte fastställts huruvida det är en relevant metod för att förbättra användarbevarande. I denna studie upptäcktes det att growth hacking metoder kan förbättra utvecklingen av strategier för bibehållning av användare. Dock måste growth hacking strategier vara skräddarsydda och anpassade till det entreprenöriella företagets affärsmodell.

Subscription-based services are becoming more popular in today’s society. Therefore, any company that engages in the subscription-based business needs to understand the user behavior and minimize the number of users canceling their subscription, i.e. minimize churn. According to marketing metrics, the probability of selling to an existing user is markedly higher than selling to a brand new user. Nonetheless, it is of great importance that more focus is directed towards preventing users from leaving the service, in other words preventing user churn. To be able to prevent user churn the company needs to identify the users in the risk zone of churning. Therefore, this thesis project will treat this as a classification problem. The objective of the thesis project was to develop a statistical model to predict churn for a subscription-based service. Various statistical methods were used in order to identify patterns in user behavior using activity and engagement data including variables describing recency, frequency, and volume. The best performing statistical model for predicting churn was achieved by the Random Forest algorithm. The selected model is able to separate the two classes of churning users and the non-churning users with 73% probability and has a fairly low missclassification rate of 35%. The results show that it is possible to predict user churn using statistical models. Although, there are indications that it is difficult for the model to generalize a specific behavioral pattern for user churn. This is understandable since human behavior is hard to predict. The results show that variables describing how frequent the user is interacting with the service are explaining the most whether a user is likely to churn or not. / Prenumerationstjänster blir alltmer populära i dagens samhälle. Därför är det viktigt för ett företag med en prenumerationsbaserad verksamhet att ha en god förståelse för sina användares beteendemönster på tjänsten, samt att de minskar antalet användare som avslutar sin prenumeration. Enligt marknads-föringsstatistik är sannolikheten att sälja till en redan existerande användare betydligt högre än att sälja till en helt ny. Av den anledningen, är det viktigt att ett stort fokus riktas mot att förebygga att användare lämnar tjänsten. För att förebygga att användare lämnar tjänsten måste företaget identifiera vilka användare som är i riskzonen att lämna. Därför har detta examensarbete behandlats som ett klassifikations problem. Syftet med arbetet var att utveckla en statistisk modell för att förutspå vilka användare som sannolikt kommer att lämna prenumerationstjänsten inom nästa månad. Olika statistiska metoder har prövats för att identifiera användares beteendemönster i aktivitet- och engagemangsdata, data som inkluderar variabler som beskriver senaste interaktion, frekvens och volym. Bäst prestanda för att förutspå om en användare kommer att lämna tjänsten gavs av Random Forest algoritmen. Den valda modellen kan separera de två klasserna av användare som lämnar tjänsten och de användare som stannar med 73% sannolikhet och har en relativt låg missfrekvens på 35%. Resultatet av arbetet visar att det går att förutspå vilka användare som befinner sig i riskzonen för att lämna tjänsten med hjälp av statistiska modeller, även om det är svårt för modellen att generalisera ett specifikt beteendemönster för de olika grupperna. Detta är dock förståeligt då det är mänskligt beteende som modellen försöker att förutspå. Resultatet av arbetet pekar mot att variabler som beskriver frekvensen av användandet av tjänsten beskriver mer om en användare är påväg att lämna tjänsten än variabler som beskriver användarens aktivitet i volym.

The growing popularity of smartphones in recent years has led to an increase in mobile application development and use. However, a large number of mobile applications are only used once before being removed. For companies and organizations to spend time and money on application development only to achieve low user retention rates is unsustainable. During their first interaction with a mobile application it is crucial that users find functionality and value quickly to avoid discontinuation of use. User onboarding is often implemented in mobile applications to aid in first time interaction, making onboarding processes subject of investigation for effect on user attitude towards continued use of mobile applications. The study examined mobile onboarding processes and their effect on user attitude towards continued use of applications as well as the difference between onboarding processes effect on user attitude towards continued use of applications. The study was conducted within-subjects through a survey consisting of interaction with two prototypes with different onboarding processes and a questionnaire based on the technology acceptance model in order to investigate the variables of interest. The results of the survey were analyzed to measure the effects of the onboarding processes on the factors of the technology acceptance model and to investigate the differences between the onboarding processes. The results showed that user onboarding has a positive influence on perceived usefulness, attitude towards use and intention to use. There was no significant difference between the different types of onboarding patterns effect on attitude towards continued use. The positive effects on attitude and intention to use confirmed that implementing onboarding processes in mobile applications could be beneficial for value proposition and user retention. The perceived usefulness proved to be the determining factor on attitude and intention to use.

Predicting when a customer is about to turn to a competitor can be difficult, yet extremely valuable from a business perspective. The moment a customer stops being considered a customer is known as churn, a widely researched topic in several industries when dealing with subscription-services. However, in industries with non-subscription services and products, defining churn can be a daunting task and the existing literature does not fully cover this field. Therefore, this thesis can be seen as a contribution to current research, specially when not having a set definition for churn. A definition for churn, adjusted to DIAKRIT’s business, is created. DIAKRIT is a company working in the real estate industry, which faces many challenges, such as a huge seasonality. The prediction was approached as a supervised problem, where three different Machine Learning methods were used: Logistic Regression, Random Forest and Support Vector Machine. The variables used in the predictions are predominantly activity data. With a relatively high accuracy and AUC-score, Random Forest was concluded to be the most reliable model. It is however clear that the model cannot separate between the classes perfectly. It was also visible that the Random Forest model produces a relatively high precision. Thereby, it can be settled that even though the model is not flawless the customers predicted to churn are very likely to churn. / Att prediktera när en kund är påväg att vända sig till en konkurrent kan vara svårt, dock kan det visa sig extremt värdefullt ur ett affärsperspektiv. När en kund slutar vara kund benäms det ofta som kundbortfall eller ”churn”. Detta är ett ämne som är brett forskat på i flertalet olika industrier, men då ofta i situationer med prenumenationstjänster. När man inte har en prenumerationstjänst försvåras uppgiften att definera churn och existerande studier brister i att analysera detta. Denna uppsats kan därför ses som ett bidrag till nuvarande litteratur, i synnerhet i fall där ingen tydlig definition för churn existerar. En definition för churn, anpassad efter DIAKRIT och deras affärsstruktur har skapats i det här projektet. DIAKRIT är verksamma i fastighetsbranschen, en industri som har flera utmaningar, bland annat en extrem säsongsvariaton. För att genomföra prediktionerna användes tre olika maskininlärningamodeller: Logistisk Regression, Random Forest och Support Vector Machine. De variabler som användes är mestadels aktivitetsdata. Med relativt hög noggranhet och AUC-värde anses Random Forest vara mest pålitlig. Modellen kan dock inte separera mellan de två klasserna perfekt. Random Forest modellen visade sig också genera en hög precision. Därför kan slutsatsen dras att även om modellen inte är felfri verkar det som att kunderna predikterade som churn mest sannolikt kommer churna.

User retention is a key factor for Software as a Service (SaaS) companies to ensure long-term growth and profitability. One area which can have a lasting impact on a digital product’s user retention is its user onboarding experience, that is, the methods and elements that guide new users to become familiar with the product and activate them to become fully registered users. Within the area of user onboarding, multiple authors discuss “best practice” design patterns which are stated to positively influence the user retention of new users. However, none of the sources reviewed showcase any statistically significant proof of this claim. Thus, the objective of this study was to: Design and implement a set of commonly applied design patterns within a digital product’s user onboarding experience and evaluate their effects on user retention Through A/B testing on the SaaS product GetAccept, the following two design patterns were evaluated: Reduce friction – reducing the number of barriers and steps for a new user when first using a digital product; and Monitor progress – monitoring and clearly showcasing the progress of a new user’s journey when first using a digital product. The retention metric used to evaluate the two design patterns was first week user retention, defined as the share of customers who after signing up, sign in again at least once within one week. This was tested by randomly assigning new users into different groups: groups that did receive changes related to the design patterns, and one group did not receive any changes. By then comparing the first week user retention data between the groups using Fisher’s exact test, the conclusion could be drawn that with statistical significance, both of the evaluated design patterns positively influenced user retention for GetAccept. Furthermore, due to the generalizable nature of GetAccept’s product and the aspects evaluated, this conclusion should also be applicable to other companies and digital products with similar characteristics, and the method used to evaluate the impact of implementing the design patterns should be applicable for evaluating other design patterns and/or changes in digital products. However, as the method used for data collection in the study could not ensure full validity of it, the study could and should be repeated with the same design patterns on another digital product and set of users in order to strengthen the reliability of the conclusions drawn.