Do you regularly visit Amazon.com? Then their product recommendations won’t come as a surprise to you. But that isn’t even the most exciting thing about Amazon. It isn’t a big deal to record the products that you viewed recently and recommend similar and supplementary products.
Did you know that Amazon.com has more than 2 billion visitors each month? The number of visits peaked at 2.7 billion in December 2019. The real magic is that Amazon manages to provide meaningful and personalized recommendations to every one of these 2 billion+ visitors. Amazon’s recommendation system works based on predictive analysis algorithms. It considers past visits, regional trends, and seasonal trends.
If you are not already leveraging big data, keep reading to find out how it can help your business.
How can Businesses benefit from Data Science
Organizations are using Data Science for business analysis in view of improving various key areas such as marketing, sales, revenue, and logistics.
Make Smarter Decisions
In the past, say a decade or two ago, many companies measured their transactions in hundreds or thousands. Statistics and math were used along with business analysis to understand transaction data. The transition to online channels has recently allowed most businesses to grow a thousandfold. Analyzing transactions in this magnitude is nearly impossible without robust data processing tools.
Business Intelligence (BI) consists of strategies, technologies, and tools used to analyze data of businesses, usually in large capacities. The data analyzed through these BI tools is then conveyed to decision makers through data visualization and reporting tools. Without such tools, decision-makers will be left unable to quantify transactional data accurately.
The essence of Data Science is to identify the nature of the problem, gather relevant data, process this data through data manipulation tools, and translate the resulting information into insights that can facilitate decision making. All this becomes possible with the help of Data Science. It can explain past performance as well as predict what could happen in the near future. This is essential for decision making in the current dynamic business world.
Optimize your Products
One of the main problems that pop up when serving millions of customers is that they expect a large variety of products. This trend is especially true in industries like mobile phones, fashion, and entertainment.
Many companies like Samsung, Dolce & Gabbana, and Netflix use big data to conceptualize the types of products they need to send to the market. Many mobile phone manufacturers use big data to identify features and price ranges that are preferred by various market segments so that they can optimize their products. The end result is higher sales and more revenue through products that customers prefer.
A rather unseen aspect of using Data Science to optimize products takes place on advertising and social media platforms. To the surprise of many, the products of these companies are insights regarding the behavior of their uses. These insights are sold to other businesses for the purpose of optimizing their products. Facebook and Google are two of the main platforms that follow this business model. We’ll be discussing them in more detail later in this article.
Measure KPIs to Assess Business Health
Key Performance Indicators (KPI) are a tried and tested method for measuring the performance and health of a business. However, unlike in the past, it is no longer a task that can be performed manually due to the sheer magnitude of data, making Data Science crucial to track and understand KPIs.
Businesses track many types of KPIs, such as marketing and sales performance, employee engagement, and logistics. These amount to thousands of data points with millions of records each. A Data Science business analyst uses specialized tools for data cleaning and munging. Google Analytics is a good way of tracking usage statistics of your online platform, especially if it is related to e-commerce. It is just one of the ways to track things like the most popular products.
Early detection of non-performing areas allows organizations to take necessary corrective actions. AI and Machine learning tools can assess data and identify trends such as regions in which certain products perform well, or even unsuitable products which can then be taken off the market. Data Scientists use Business process modeling techniques to train Machine learning tools.
Identify opportunities through Predictive Analysis
Netflix is one of the best examples of using predictive analysis to identify opportunities. Whether they are creating new content or deciding to extend existing ones, they heavily rely on deep learning to analyze and track viewer preferences.
By definition, Predictive analysis is the use of historical data to provide predictions regarding future outcomes. This is another area in which data science for business becomes vital. SAS, IBM SPSS, SAP HANA are just some of the machine learning tools used in Data Science.
Predictive analysis provides predictions regarding many aspects of a business, such as sales forecasts, seasonal trends, market analysis, and risk assessments. This is one of the most significant differences when considering data science vs. business analysis. Unlike in typical business analysis, data science provides estimations and recommendations through Data Science tools trained through deep learning models. This type of analysis will allow managers to take corrective and mitigative actions.
How can Data Science be applied to Real-world problems
We looked at some of the ways we can use Data Science for business growth. But there’s no better way to understand how important it is other than looking at some of the biggest success stories in today’s business world.
Targeting Advertisements to optimize sales
Many of us use Search engines and Social media without the slightest idea of how they work. Despite many common misconceptions, users and user insights are the main products of these businesses. They track user behavior and analyze it to arrive at insights that are useful for advertising, both internally and by third parties.
Google is a more obvious candidate for this type of targeted advertising. It uses your search and browsing history to show advertisements that may interest you. The advertised products don’t belong to Google, but to third parties that use Google’s advertising platform, “AdSense.”
Another business in this category, Facebook, uses deep learning tools to analyze photos and text from posts made by its users – an average of 1.7 billion individuals that log in each day. These insights are then used within the platform to suggest friends and pages that may interest users, as well as to make product suggestions through advertisements.
However, Facebook’s primary revenue stream is providing insights to third parties regarding various demographics. Its revenue grew from $777 million in 2009 to $70,697 million in 2019 (9,000% growth) as a result of its intelligent use of Data Science.
Both Google and Facebook are typical examples of heavy use of deep learning tools to analyze user data, resulting in targeted advertisements for billions of users. It is essential that you are familiar with Data Science for business in order to use this technology to your advantage.
Innovations in Product Development
If you have hosted a property on Airbnb, then you would be familiar with their pricing suggestions. If you are new to hosting properties and do not know how much to charge for your property, Airbnb has you covered. Airbnb’s predictive analytics models provide suggested seasonal pricing to hosts based on nearby properties and regional trends.
Airbnb fixed another interesting real-world problem using Data Science. Deep learning tools were able to identify that certain neighborhoods were seeing a high percentage of bounced views. Users were viewing neighborhood data and images and then not booking. Airbnb mitigated this issue by replacing neighborhood links with top travel destination links, resulting in a 10% improvement in conversions.
These are the amazing ways in which data scientists are solving real-world problems. Amazon is an innovator in more than one aspect. It uses Data Science in many ways, but not as much as it does for developing products and new markets.
Amazon Fresh, the retail giant’s specialized sales channel for groceries and fresh produce, uses big data analysis to understand market conditions before moving into new markets. A process that would otherwise take months or years of market research is now taken care of with Data Science. These tools tell Amazon exactly which markets to move in to and the best products to promote.
Product Recommendations to increase engagement
Spotify and Netflix are two of the most popular on-demand entertainment platforms in the audio and video formats, respectively. Both of these platforms employ all the well-known approaches to Data Science, like using deep learning and predictive analysis to provide recommendations on music and movies.
In addition to these techniques, both of these platforms are popular for Collaborative filtering, which uses preferences of similar profiles to make recommendations. In this way, they introduce users to content that they will like, but weren’t already aware of, with an incredible rate of accuracy.
Netflix, however, has gone an extra step in producing its own content based on user preferences. One of the best examples of this is the 2020 blockbuster “Extraction,” starring Chris Hemsworth and written/directed by the Russo brothers, who are all part of Marvel’s Avengers movies. The movie’s starring actors and directors have added to its popularity. On top of catering to an audience that thrives for action movies, Extraction also attracts the South Asian market, which is a new and upcoming segment for Netflix.
In a time when COVID-19 has almost completely shut down movie theatres, Netflix continues to profit from a well-planned $65 million direct-to-DVD movie. This is the power of Data Science for business. It allows you to identify opportunities in new markets, viewer preferences, and which segments to target.
Detecting Fraud and Assessing Risk
Banks and the finance industry have greatly benefited from Data Science. Analyzing customer behavior and preferences to recommend products and services have become commonplace.
The Bank of America (BoA) is an example of taking Data Science to another class. Its well-rounded approach to Data Science has resulted in an industry first in the form of a virtual financial advisor – Erica. More than 45 million customers around the world make use of Erica to enhance their portfolios through recommendations that take into consideration investment trends, regional financial patterns, and preferences of similar customers. The use of Data Science has increased the assets of the customer, resulting in increased returns for the bank.
Many banks like BoA use big data analysis to perform data mining on millions of data records and flag anomalies and irregularities. One of the best examples of this is when banks call you to verify why your credit card was used in a different region or country.
The UOB Bank of Singapore recently introduced a risk management system that brought their application processing time from an average of 18 hours down to a few minutes. This is possible through AI and machine learning tools that identify trends and flag irregularities based on predefined business process models.
Optimizing Supply Chain Management and Logistics
Managing Supply Chains and Logistics requires a lot of planning. It is vital to identify regional and seasonal trends in order to ensure profits in these processes. Pepsi Co. is a global giant in cold beverages and heavily uses Data Science to identify the right products and the optimum quantities for each retailer. It uses a combination of tools, including Tableau and a Hadoop platform to analyze inventory and sales data from all regional stores on a regular basis. The result has been a 90% reduction in analysis time, which has resulted in reduced overheads and increased revenue.
DHL is probably the most popular courier company in the world. It maintains this reputation by using Data Science in three main ways. Firstly, DHL uses predictive analysis to understand future warehousing needs, ETAs of packages, and even more unorthodox things like potential clients that may leave. Some of these insights are provided to partners in the form of prescriptive analysis.
Route Optimization, however, is the most effective use of Data Science for DHL. This is evident in the fact that it delivered an average of 4.6 million parcels and 59 million letters on each working day. Both historical data and predictions are used to decide on the most efficient routes, select the mode of transport, prioritize between cost and time, and how to consolidate. All this happens in an automated process monitored by its large data science business analyst team. The positive effects on revenue are apparent with these tools in place.
We looked at how Data Science can be used to enhance businesses by increasing sales and revenue through methods like targeted marketing and optimizing products. We also looked at ten of the biggest companies in the world that utilize Data Science in innovative and exemplary ways to solve real-world business problems.
What’s important to understand is that all this is possible not only because of the various tools such as predictive analysis, machine learning, and AI but mainly through the skills and expertise of Data Scientists. So it is essential that you learn about programming, database, and data manipulation tools to master Data Science for business.
Feel free to subscribe to our Data Science for Business course if you like to learn more about Data Science and its applications.