Data science is the result of the digital era. Despite the fact that statistics have been present for centuries, the first references to data science did not appear until 1964.
Nowadays, more data than ever before is produced by our mobile devices, creating new hurdles for storage and processing. Every day, exabytes (one million terabytes) of data are generated. It is getting more and harder to get relevant marketing information from this ever-expanding database.
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In this blog, we will go over how data science is helping the mobile marketing industry.
What is Data Science?
Data science is basically the merger of three fields: business, math, and computer science.
Each of these topics is difficult enough to challenge even the most brilliant minds constantly. When used together? They create much more difficulties.
Data scientists design intricate algorithms and computational systems that execute statistical analysis on sizable unstructured data sets to accomplish a corporate objective. They achieve this by utilizing computer science approaches.
Additionally, data science is not confined to any particular sector or field of study. Data science has already been shown to be useful in a number of fields, including marketing, healthcare, energy, economics, and criminal justice.
How Data Science is Changing Mobile Marketing
One industry that has benefited from data science is mobile marketing.
To maximize their marketing efforts, businesses of all sizes, including internet behemoths like Facebook and small startups, use data scientists.
In order to build recommendation engines that anticipate and optimize user behavior, businesses continue to feed their advanced machine-learning algorithms with user data.
We must admit that you are very predictable. Companies like Netflix and Amazon can forecast your preferences and offer suggestions based on what you've already viewed or bought.
One excellent example is Amazon's Prime Now service.
4 Examples of Data Science in Marketing
You might be surprised to learn that major firms have used data science for years.
In order to monitor its fleet of more than 60,000 US vehicles and carry out preventive maintenance, UPS, for instance, has been utilizing predictive analysis since 2000. Additionally, UPS reduced driver routes by 85 million miles, saving 8.5 million gallons of fuel.
People and organizations of all sizes can use data science. In reality, smaller businesses are increasingly able to use their power.
Four instances of data science being used successfully in marketing departments of both large and small businesses are shown below:
Target's Pregnancy Score
Target's use of predictive data science is among the most recognizable. After studying consumer and prior purchase data, Target discovered that pregnant women purchase similar products throughout their trimesters, such as unscented lotion and magnesium supplements.
Target is able to assign a pregnancy score to each client because of this data.
Target's revenues rose from $44 billion in 2002 to $67 billion in 2010, a 52% rise over the preceding 8 years due to the company's efforts to target pregnant mothers using data analytics.
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Walmart's Hurricane Gain
By examining previous transactions, Walmart was able to examine purchases made in 2004 that had to do with the weather. What exactly did they notice?
The week before a hurricane, torch sales increased. But indeed that is obvious?
Somewhat less obviously, Pop-Tart sales increased. In particular, strawberry Pop-Tarts were seven times more likely to be bought before a hurricane.
Walmart now keeps Pop-Tarts next to the door as storms approach for a simple 7x increase in sales.
Airbnb Search Engine Optimization
In order to identify the most attractive neighborhoods inside one of more than 81,000 cities, the data science team at Airbnb encountered a unique set of difficulties when analyzing listing data.
When reservations were made for a specific property, Airbnb ran into an issue as it worked to improve its search engine because it could not get any additional search data while the guest was there.
The data science team deployed neural networks to determine visitor preferences for a particular place. The customer path, which starts with a search and ends with a booking, is how the model learns these preferences.
While You Weren't on Twitter
The data science team at Twitter has been depending more and more on data to direct their product development activities.
As Twitter's VP of Engineering says, "It's rare for a day to go by without running at least one experiment." Alex Roetter believes that experimenting is engrained in the product development DNA.
The data science team at Twitter could apply machine learning to identify which tweets were pertinent and would be interesting to specific users. The "while you were away" function was born from the need to tell users when they return to a product after a break.
Common Data Science Strategies in Marketing
Dynamic Pricing
Applying data science to mobile marketing has made more advanced pricing models possible. Apps like Uber and Lyft, for instance, are able to adjust pricing points because of their exceptional capacity to analyze supply and demand in real time. Think back to the days before data science, when taxi drivers could not adjust their pricing to match the supply and demand curve.
Demand Forecasting
Can it accurately forecast the need for inventory to lessen cash flow issues?
Accurate demand forecasting is possible through data analytics to use historical data and data science to create predictive models.
As a result, Amazon's working capital requirements are reduced, and their cash conversion cycle is shortened to 14 days, as opposed to the norm of 30 days for most retailers.
Churn Forecasting
Data scientists create forecasting models to foretell when the demand for resources or goods will rise, but they can also offer odds of customer churn.
Customer Segmentation
Every user encounter is a worthwhile chance to gain more knowledge about them.
Your clients will have a wide range of backgrounds and life experiences, even within a niche market, resulting in various responses to your marketing materials and user interfaces.
Data science may assist in creating relevant experiences for those segments by helping to understand how customers perceive information on a deeper level.
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