So, you’re thinking about becoming a data scientist.
That’s not a bad idea at all.
In fact, that might be one of the best decisions you’ll ever make.
A data scientist is in the top 3 best jobs in the U.S., with a median base salary of more than $107,000 and decent job satisfaction levels.
The situation in Ireland? It’s also getting more interesting.
Ireland has the potential to create up to 21,000 jobs in the field of data science within just 6 years. If you go to the highest level and get an executive-level position, expect to earn between €100,000 and €150,000.
But quickly-growing salaries aren’t the only thing that attracts people to data science. You can work on projects like the Starbucks recommendation engine or Airbnb’s machine learning models. The nature of the job itself, is, indeed, something quite unique and meaningful: researching human behaviour by using analytics, machine learning, and pattern interpretation.
But how exactly do data scientists find that intricate balance between humanity and numbers to bring businesses and technology together? In this article, you’ll find the answer to this question, and, hopefully, it’ll help you understand what kind of impact can data scientists have on people and businesses.
How Does Data Science Add Value to Businesses and Their Customers?
Are you a Starbucks fan?
If the answer to this question is “yes,” then chances are that you also use their official loyalty app to pay for the coffee.
By the way, so do more than 27 million people.
With a tremendous amount of transactions every week, the app supplies the coffee giant with tons of customer data to inform sales, marketing, and other business decisions.
For example, by learning a user’s drink and food buying habits, the app can generate personalized product recommendations, discounts, rewards, as well as the entire user experience.
Buying an espresso every workday in a specific location, for example? Expect to see some discounts for sandwiches with that espresso in that store once in a while. The app has provided the data on your buying habits and now the folks at Starbucks know your coffee preferences better than some of your family members.
All of this, of course, would not have been possible without large-scale data analysis.
But the applications of machine learning don't end there. By using advanced data science, Starbucks can even do such amazing things like recommending new store locations based on the analysis of the impact on the area.
“Through a system called Atlas, Starbucks links to as many external and internal APIs as possible, connecting the data with R to build cannibalization models that can determine the impact to existing stores if a new store enters the area,” said Patrick O’Hagan, director of market planning at Starbucks.
Fascinating! There might not be a need to go out and survey people or do other fieldwork.
Businesses now have access to technology that makes turning terabytes of information into something they can use to advance themselves.
The role of data scientists here is major. Here are just some of the most important points.
4 Ways Data Scientists Can Make Data a Competitive Advantage
1. Define Your Real Target Audience
What do you know about predictive audience segmentation?
Chances are, not a lot.
It’s okay for now, but soon it will replace traditional audience research and marketing campaigns. Simply put, predictive audience segmentation is the type of analytics that uses AI to identify a target audience with the highest potential for conversion to a lead or a customer.
It does so by analyzing various behavioural patterns and combining the results with demographic data. If there’s a pattern that a company can explore and use to appeal to a group, the AI-based algorithm finds it. Typically, this pattern helps to inform the marketing effort to make customer communication more relevant and engaging.
Of course, the process of audience targeting is much more complicated - that’s why they call it data science - but the outcome might actually be amazing for companies: increased website traffic and more effective marketing.
2. Personalize Brand Experience
“The demand for content related to data science and content personalization has been rising consistently,” says Dorian Martin, a senior writer at WowGrade. “Personalization is something that each business can benefit from, so a lot of people are learning how to use data insights to deliver that.”
Indeed, and it seems like large companies are leading the way.
We touched upon this topic with the example of Starbucks already, so let’s now talk about personalization in a little more detail. Personalization of customer experience with brands is a hot topic these days because businesses can now detect more patterns, preferences, and interests on a much bigger scale.
By using machine learning models, brands like Starbucks are working tirelessly to make customer experience one-of-a-kind.
Here are some of the ways that customer insight analysis helps the coffee giant to achieve that:
- Send personalized product recommendations based on purchasing and browsing history
- Re-engaging passive customers with enticing offers based on previous interactions
- Suggesting seasonal offers based on the customer’s location
- Offer discounts and reward points for products customers have bought in the past
- Using GPS technology to trigger relevant in-app offers when a customer is in the area of a store
According to Mckinsey, one retailer that uses these personalization techniques in its official app has been able to increase incremental sales by 10 percent as well as the size of each transaction by 5 percent.
3. Prevent Potential Fraud
eCommerce fraud is one of the most pressing issues faced by businesses now. The annual value of fraud losses on UK-issued credit cards, for example, exceeds £394 million and is rising constantly. One of the newest weapons to reduce the number of fraudulent transactions is data science.
Data scientists build special models designed to define suspicious patterns in transaction data. By looking for these red flags and signs indicating fraud, they can identify suspicious operations, cancel them, and prevent them in the future.
Once again, most of this wouldn’t be possible without the amazing work of data scientists.
4. Increase Sales
The most well-understood big data-powered tool for increasing sales is recommender engines. They help businesses sell more by learning the buying and browsing habits of customers, and many retail websites are already using them. For example, when you visit Amazon’s website repetitively, you’ll notice that it shows products that are similar to those you viewed.
By the way, Amazon’s recommendation engine is a real hit. It’s responsible for 35 percent of their total sales.
Data scientists are a big help here because big data is behind these recommendations. To teach a recommender system to generate relevant offers, they use tons of data, including:
- On-site customer activity: search logs, product items, page views, and clicks
- Product details: names, descriptions, price ranges, categories, characteristics
- Contextual data: user location, language, device used to access a website.
To get a bit technical, here are the types of recommendation systems data scientists create:
- Content-based filtering. In this system, the algorithm compares the features and attributes of products to define recommendations
- Collaborative filtering. This approach relies on the data from people who bought similar products, therefore, share similar needs or interests
- Hybrid systems. As the name suggests, this one combines the two previous models, i.e. generates recommendations based both on product features and characteristics of products/content preferred by other customers.
What Do You Think?
So, here you go, that’s some of the things that data scientists do. One could definitely say that they are the link between businesses and this exciting new technology. In the near future, more and more businesses will see data science as an incredible source of competitive advantage, so the demand for these professionals should only increase.
So what did you think? Does data science sound like something you’d be interested in? If yes, I invite you to check out the latest data science job positions on Next Generation to see clients that are ready to provide for this skill.
About the Author:
Estelle Liotard is an accomplished blogger and a guest writer on many well-known websites. She blogs about content marketing, artificial intelligence, and big data. Currently, Estelle works as a senior writer at BestEssayEducation and as an editor for Trust My Paper and Studicus. In her free time, she likes riding her red bike with friends and taking personality quizzes.