As this is a qualitative research, my main method will be acquiring and analyzing information from as many different sources as possible.
My target is to gain information in the field of online marketing, mobile marketing and mobile technology. These are new fields that are constantly evolving and changing. For this reason, all information must be recent and up to date in order to be relevant. There are only a few academic publications on the subject. Most of the information I have gathered comes from blogs and articles found online. The problem with such sources is that their authority could be hard to evaluate. To do this, I used criteria similar to the ranking algorithms of search engines.
Search engines such as Google need to know the value, authority and popularity of a website in order to rank it in search results. They use complex algorithms that employ a number of factors in order to calculate rankings. The higher a website is ranked, the more authoritative and valuable it is supposed to be. Some websites, however, use SEO (Search Enigne Oprimization) tricks in order to bypass these criteria. This is why I could not rely only on Google's rankings, I had to ensure for myself that the information on the website was valid.
These are the criteria I followed when searching for blogs and articles:
- Publication date. I looked for articles published in the last two years. If the year of publication was earlier than 2012, I usually disregarded the article as not relevant anymore. The ideal case was if they were published in 2014.
- Age of the blog/website. This is a very important factor for Google, but it can easily be bypassed by buying an aged domain and placing a new website on it. Therefore, I checked for the launching date of the actual blog: how far back the publications go in time. I looked for blogs and websites that are at least an year old, preferably . This proves that the author is experienced in writing about the topic.
- Author biography. I always checked for a snippet of information about the author. If available, I looked up other publications by them, or pages about them to ensure they are a certified expert in the field they were writing about. If this information was not available, I would likely disregard the article.
- Number of followers. This criteria is a subjective one, considering the number of followers can easily be influenced. Thousands of social media followers can be purchased on websites such as Fiverr for a very affordable price. Moreover, valuable blogs might have few followers simply because the topic is too technical. I used this factor in conjunction with the other criteria; it was not a determining factor on its own.
Aside from individual blogs, a good and reliable source of information is whitepapers by major companies in the field. Such whitepapers can be found on Slideshare and company blogs.
I looked up major companies in the field of digital marketing and used their corporate blogs, if available. I also looked them up on Slideshare to see if they have any whitepapers or presentations available. A lot of these proved valuable resources and gave me great insights about the topic.
A website called the Social Science Research Network aided me greatly in finding more scientific publications when researching the underlying psychological factors of brand loyalty.
Finally, I have encountered a couple of relevant academic publications on the subjects using Google Scholar. The main criteria (aside from relevancy) was the date of the publication, as the mobile ecosystem develops quickly, and publications that are more than several years old may not be relevant anymore. Most of the works I found were rather specific for local markets (such as India and China), but the conclusions are global and can be used in my research.
Methods of the App Analysis
Another way to gather information was to find popular, successful retail apps and analyze their features. Unfortunately, there was no certain way to evaluate the profits these apps are bringing for the businesses. Thus, I used other criteria in determining which retail apps are successful: their rankings and reviews in the App Stores, as well as reviews on tech blogs.
Using this criteria, I will pick out and analyze 5 retail applications. I will look at the way they leverage m-commerce and the mobile shopping experience, but I will also focus on additional features that engage customers and build loyalty.
Methods of the Interviews
Besides desk research, I have conducted interviews with professional working in the mobile marketing field. This way, I can gain hands-on, practical insights into what marketing techniques companies are using right now.
I was looking for experts in the fields of marketing, sales and mobile technology - or, preferably, a combination of these fields. Using my network and connections, I arranged for interviews with two such professionals. One is a digital marketing consultant for Desigual, a Spanish clothing brand with stores all over Europe. The other is a manager of mobile projects at La Caixa, one of the biggest banks in Spain with headquarters in Barcelona.
Methods of the Survey
In order to come up with a clear, effective survey questions, I did a small separate research on survey methods. My goal is to make the questions easy for users to answer, while still getting enough information to support my research. The goal of the survey is to investigate how likely users are to purchase products on mobile, and what are the factors that influence their decisions. Moreover, I am exploring the negative factors that prevent users from shopping on mobile, so I can come up with solutions.
While the desk research focuses on describing mobile marketing techniques, the questionnaire is focused on gathering data specifically on shopping behavior in order to determine which of these techniques would work in practice.
This research is of descriptive nature but the information from the survey will help me give advice as to how mobile retail could be improved - what are the threats and the opportunities, what should be avoided and what should be emphasized on.
The survey is divided into four parts:
- Online Shopping. The data gathered from these questions will be used to divide the users into three groups: online shoppers, mobile shoppers and physical shoppers. Mobile shoppers are the most interesting group. My hypothesis is that this percentage of consumers is small but expanding at a stable rate, and m-commerce will soon be able to compete with or overtake e-commerce.
- Mobile Behaviour. This data will give us an insight on what different user groups do on their mobile devices, and what marketing techniques would be the most beneficial for brands going mobile. If, for example, the mobile shopper group indicates they are playing a lot of games, this indicates that brands should go into gamification - launch game app related to the brand, invest in in-game advertising in other popular gaming apps, or gamify their mobile website.
- Shopping Behaviour. These questions relate both to physical shopping and online shopping, and will be used to evaluate how the different user groups behave when shopping in general. If we find that users that like shopping at stores located near each other are also very mobile, we can suggest that brands use location-based mobile marketing such as an app that gives push notification whenever a brand store is near - or, for braver and more innovative brands, beacon technology. If we find that mobile users tend to shop during sales or use discount cards and loyalty points, the conclusion is that brands should lean toward loyalty marketing, and launch an app or website where users can check in at the store and collect points for their purchases. Moreover, if this user group is also found to use their mobile device for playing games (questions above), the process could be gamified to engage them further. If we find that mobile users like shopping with friends, and are more likely to buy products recommended by friends, we can conclude that brands should go into social media, and also humanise themselves so that the user is more likely to perceive the brand itself as a friend.
- Online Shopping Behaviour. The data from these questions will give an idea of things that can be improved (if disliked by users) or emphasised (if liked by users). If users indicate that they cannot judge the quality of products when shopping online, brands can improve this by including videos of products and customer reviews. The last question will be used to determine whether users tend to shop online more if their friends do it too. If so, we can suggest that brands lean toward social media and showing the user how many of their friends have already made online purchases.