Intro
Ever wonder how after online shopping at American Eagle you are somehow getting ads of American Eagle on your instagram now? Or how Spotify made you an AI-generated playlist with songs that are similar to the ones you have been recently listening to? Well, in this blog post I will be discussing chapter 3 of Rajat Paharia’s book “Loyalty 3.0″Chapter 3 is all about big data, which is the information that companies use to get to know all about you and your preferences. There are several types of methods and means of which companies utilize this, which will be further discussed.
What is Big Data?
Big data is a newer type of data that is collected by businesses and organizations; however, it is more than just name, email, or phone number–it includes things like IP addresses, an individual’s location, technology patterns, and more. It is classified as being an extremely large amount of data (more than we can even imagine), moving at a very rapid rate of speed, with multiple different elements. The main difference between this data and other data is that big data is analyzed by computer systems in order to optimize the information. Businesses mainly utilize big data to gain specific information about people so that they are able to market to them specifically.
Types of Big-data Collection
There are several methods that are used for the collection of this data. Cluster analysis is the first type, and it takes big amounts of data and organizes them into smaller groups. This type of data collection is beneficial for separating customers by purchase habits and target marketing. Another type of collection is A/B testing, which is primarily comparing a group A and a group B to identify responses to verify that the marketing techniques are working. Crowdsourcing, according to Paharia (2013), is “outsourcing of work to a distributed group of people who aren’t known ahead of time, aka ‘the crowd’” (p 44). Predictive modeling, another popular collection method, includes utilizing math to help analyze the results of data and predict future data. Another data-collection type is Sentiment Analysis, which is focused on analyzing text to determine what the results will be and how it will be received. Stream Processing is the reading of data in real-time as it comes in. Outlier Direction and similarity search are a newer type of data collection, where softwares and systems will help identify the problems and solutions in order to streamline recommendations for users. Finally, the last common data-collection method is Cohort Analysis, which essentially divides users into groups based on their user history.
How are Businesses using Big Data?
There are five primary ways that businesses are utilizing this big data on the day-to-day. The first one is Microsegmentation, which is the separation of consumers based on preferences, browsing history, and overall activity. Next is In-Store Behavior Analysis, and this is the tracking of an individuals’ location, or tracking within stores, store layouts, etc. An example of this is the Old Navy fitting rooms, they have a computer device (it is actually an iPad), that scans the RFID in the tags on the items when someone enters the fitting room. This is a huge collector of big data as it can provide information about customer preferences, frequency of purchase, consistency, etc. Thirdly is Real-time Pricing Optimization, which is simply where businesses alter their prices based on certain events like time of year, holidays, inventory, trends, and more. Next is Social-Media Monitoring, which is pretty self-explanatory: gaining data from social media and what people are actively viewing. Last is Recommendation Engines, which is one of the most common forms. Companies will utilize previous data that will help predict other items that an individual would want. Some great examples for this is Spotify and Netflix, where they will give you recommended songs, playlists, or shows based on previous listening or watching.
Conclusion
After reading chapter 3 and discovering more of what big data actually is, it has become evident to me how much information it truly being received and the speed of which it is being analyzed. And although it feel overwhelming and incomprehensible, I could not help but think about how much more God knows about His children. As it says in Psalm 147:5, “Great is our Lord and mighty in power; His understanding has no limit” (NIV). He knows how many hairs we have on our heads, He knows what the inside of our minds look like, He knows what we spend time looking at on social media or the internet, and He knows far more than these companies will ever know. How comforting is it to know that the one who knows the most about us, out Maker, holds us and keeps us close to His heart?
References
New International Version. (2011). BibleGateway.com. https://www.biblegateway.com/versions/New-International-Version-NIV-Bible/#booklist.
Rajat Paharia. (2013). Loyalty 3.0 : how big data and gamification are revolutionizing customer and employee engagement. Mcgraw-Hill Education.
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