The rise of Big Data is transforming the way we think about marketing in almost every field. What are the industries using data science now? Nearly all of them.
The term “Big Data” refers to the enormous amount of information that modern technology has made available. Every transaction, every click on a website, every post on social media creates data that can be stored and tracked.
However, this huge amount of data is a problem before it’s a solution. How do you even begin to make sense of that much information? Data analytics is the science (and art) of turning raw data into real insights that can result in better business decisions.
For a marketer, data analytics means “connecting the dots” to get the right messages and the right offers to the right customers. An online masters in digital marketing and data analytics combines “traditional” online marketing disciplines with the even more technical skills of data analytics, making you the “total package” as an online marketer.
Whatever industry you are in, data analytics is becoming more and more important to successful marketing.
The Big Picture: What Data Analytics Adds to Your Marketing Efforts
The specifics will vary from industry to industry, but broadly speaking here are some of the ways that data analytics leads to more successful marketing:
- Anticipating customers’ wants and expectations. Historical data concerning customers’ past behavior provides a way of predicting what they will want in the future. Data analytics makes it possible for marketers to customize offers and messages accordingly, whether in wide-reaching campaigns or in highly-focused, individualized offers.
- Website design. Web analytics is a vitally important sub-field of data analytics. Using tools such as Google Analytics, data analysts are able to understand every aspect of the customer’s online experience. Those insights inform the choices of the team’s web designers.
- Bringing together disparate data. If you pursue a masters degree in data analytics and digital marketing, you won’t just bring technical know-how to a marketing team, but creative thinking. A good data analyst finds useful correlations between types of data that haven’t traditionally been associated with one another.
- Customer segmentation. Resources are never infinite for a marketing team. So it is important that teams maximize their resources by reaching out to the prospects who are most likely to buy. Customer segmentation is one of the most important ways data analysts identify those prospects.
- Social media analytics and voice of the consumer analytics. With every passing year, social media is more and more integrated into the fabric of everyday life. In many ways, online life is just regular life. Keeping up with that much information is a Big Data function, requiring the expertise of a data analyst.
- Automation. Effective marketing requires very repetitive, precise, voluminous, 24/7 activity that is beyond the scope of human ability and attention spans. Marketing automation takes much of that work out of human hands and sends the right messages to the right people at the right time.
If you pursue an online digital marketing and data analytics masters, you could find yourself on a marketing team in almost any industry. Below are some examples of ways in which the ideas above play out in specific industries.
Data Analytics in the Restaurant Industry
Restaurants are a high volume, low margin industry. Data analytics is an important tool both for increasing volume and improving margins.
In the restaurant industry, the menu is one of the most important marketing tools of all. Data analytics provides tools for “maximizing” menus--identifying both the most popular items as well as the items that provide the greatest profit.
Geo-targeting is an increasingly important marketing tool for restaurants. The phrase “restaurants near me” is searched twenty-million times a month. Data analytics provides restaurant marketers with the tools to get their share of those hungry customers.
The era of Big Data means a restaurant can know more about its customers than ever before. What have they eaten in the past? That information gives a creative marketer lots of ideas for offers to get diners back into the restaurant or to cross-sell or up-sell once they’re there.
In the field of predictive analytics, sophisticated sales forecasting can play an important role in restaurant marketing. The marketing push can start before sales soften rather than waiting until it’s too late.
The subfields of social media analytics and voice-of-the-consumer analytics are exceedingly important in the restaurant industry. The essence of branding is to understand and shape the stories that people are telling about your brand. And people love to tell stories about their dining-out experiences.
Social media analytics and voice-of-the-consumer analytics allow marketers to “listen” to their customers and to join the conversation in constructive ways. McDonald’s provides an excellent example of the power of social listening.
In 2016, an episode of the Rick and Morty Show called for a return of the Szechuan sauce that McDonald’s had briefly served in 1998 as part of a tie-in with the movie Mulan. The pro-Szechuan sauce movement started trending on social media. McDonald’s listened and brought it back — first for a one-day promotion, and then for a longer and highly successful run.
It was also after listening to customers on social media that McDonald’s decided to start serving breakfast at night. Giving customers what they want the way they want it is a key component of marketing. Data analysts keep getting better at listening to what customers want, in all industries using data science.
Data Analytics in the Retail Industry
Data analytics isn’t just for online retailers. One of the most exciting tasks of a marketer with an online digital marketing and data analytics masters degree is to integrate all the platforms and channels that retailers use. Data analysts are always looking for ways to understand the customer’s journey, whether it happens online, on mobile devices, or in a brick and mortar store.
The consumers who walk into a retail store are the same consumers who shop on that retailer’s website. Many of those same customers connect with that store on social media and post pictures of the products they bought at that retailer. When they drive past that retailer’s physical store, or walk past it in the mall, they are carrying a smartphone that knows their location.
All of those channels produce data that a marketer can use in all the other channels. Websites can be dynamic, creating a distinct experience for every user who visits. Effective data analysis is also the basis of a personalized experience on social media, delivering highly targeted ads and other content to consumers.
But creative data analysts are also finding ways to improve the in-store, real-world experience of retail customers through foot-traffic analysis and market-basket analysis. Market-basket analysis is the method by which data analysts figure out which items are most often bought with other items. Such analysis creates cross-selling and up-selling opportunities and also provides valuable insights for the members of the marketing team who are responsible for merchandising and store design.
A lot of the daily shopping habits of a consumer are just that — habits that don’t require a lot of conscious thought by the consumer. Marketers are always looking for those “inflection points” in consumers’ lives when they do think about what they buy. From newlyweds and new parents to people who have recently moved, data analytics can help a marketing team identify people who are likely to change their buying behavior.
An online digital marketing and data analytics masters will prepare you to tackle interesting problems like these as an important member of a marketing team. Few jobs combine creativity and scientific precision in such a balanced way.
Data Analytics in the Banking and Financial Services Industry
Banking and financial services are among the most data-intensive industries in the world. Your bank knows information about your spending habits that no other business knows. As banking moves increasingly online, there is even more information. It should come as no surprise that data analytics is growing rapidly in the banking and financial services industry.
Customer “churn” is one of the most expensive problems that banks and financial firms face. It takes a lot of resources to gain new customers, and it takes a lot of resources to replace them if they leave for another institution. While it is important to keep new customers coming in the front door, it is even more important to keep current customers from going out the back door.
That’s why, for marketing teams in the banking and financial services industry, improving customer experience is especially important. Banks and other financial institutions know vast amounts about their customers’ financial habits and history. That puts them in a great position to use predictive analytics to make the kind of recommendations that can truly help their customers and solidify their loyalty.
In the banking and financial services industry, predictive analytics isn’t just a tool for selling, cross-selling, and upselling. It’s a tool for truly serving customers. “Customer experience” in this case includes greater financial stability and a higher standard of living for the customer.
Also, in the banking and financial services industry, it is especially important to move out of the realm of intuition and anecdotal evidence and into the realm of real data. An article by McKinsey & Company described a bank in which private bankers were offering big discounts to attract high-value customers. Their assumption was that they would more than make up for those discounts with other, higher-margin business once they got those customers in the door.
But data analysts discovered that the numbers didn’t bear out the private bankers’ assumptions. They dropped the big discounts as a marketing strategy, and revenues soon increased by 8%. Data analysts bring precision and genuine insight to fields that have often been shaped by fuzzy thinking and a vague sense of the way things have always been.
One of the great things about earning a digital marketing and data analytics masters is that you put yourself in a position to prove your value with real, verifiable results. For data analysts in marketing teams, the numbers speak for themselves.
Data Analytics in the Real Estate Industry
One of the most interesting things about working in data analytics is discovering new insights by putting together data sets that haven’t been put together before. This kind of creativity has been especially fruitful in the real estate industry.
For decades, real estate marketers depended on the same old tried-and-not-especially-true metrics for making huge decisions: comparable prices from recent sales, average price per square foot, ZIP code comparisons, etc. Or maybe they didn’t so much depend on those metrics as start with those metrics and depend on intuition and estimation from there.
In recent years, however, data analytics has traded in those blunt instruments for much more refined tools. While nobody has a crystal ball, the predictors are quite a bit more predictive than the old neighborhood comps. And the proof has been in the results.
One of the most interesting and exciting uses of predictive analytics in real estate marketing is the combining of data from unexpected data sets to find correlations that wouldn’t otherwise be obvious. In the 2010’s for instance, apartment buildings in Seattle appreciated much faster if they were within a quarter-mile of a Whole Foods or a Trader Joe’s.
That relationship between specialty grocery stores and higher real estate prices may not be surprising (though it’s helpful to have data and not just a hunch). But further analysis reveals something less intuitive: where there are more than four such stores within a quarter-mile, actually correlates to lower apartment prices. Data analytics pushes past intuition and “the way it’s always been done” to actionable, quantifiable results.
Working with publicly available data sets can also help a real estate marketer find prospects who are ready to buy. Marriage records, divorce records, death records, for instance, identify people who are at transitional life stages and much more likely to make big real estate decisions.
One of the great things about data analytics in real estate marketing is that it can motivate both buyers and sellers. Predictive analytics gives all involved parties greater confidence that they understand the true value of a property. If you are considering pursuing an online digital marketing and data analytics masters, that’s the kind of exciting work you can look forward to.
Data Analytics in the Hotel Industry
The winter of 2013-14 was unusually cold. It was the kind of cold that resulted in a lot of canceled flights. That gave somebody at Red Roof Inn a brilliant idea. Data analysts combined publicly-available weather data, flight data, and mobile phone location data to target people whose flights were canceled.
Throughout the United States that winter, 90,000 were stranded in airports every day. When those travelers pulled out their phones to search for hotels, they saw an ad for the nearest Red Roof Inn. Red Roof Inn saw a 10% increase in sales.
That’s a great example of how data analytics has become one of the most-transformed industries using data science.
Another crucial way that data analytics has changed the hotel industry is “yield management.” Yield management is the delicate art of balancing prices and occupancy rates. When prices go up, occupancy rates go down. As prices go down, occupancy rates go up, but that raises the possibility that the hotel is missing out on a lot of revenue.
Data analytics helps a hotel manager find that sweet spot where prices are low enough to ensure high occupancy, but high enough to maximize profits. Furthermore, predictive analytics allows marketers to forecast seasonal events that will affect occupancy rates and plan campaigns accordingly.
As in so many other industries using data science, data analysts in hotel marketing teams improve customer satisfaction by more fully understanding what guests want and expect. Predictive “next-to-buy” algorithms allow hotels to anticipate customer behavior and market to them appropriately.
Thanks to contemporary methods of data collection, customer lifetime value is a metric that is more easily calculated than in decades past. Data analytics allows a marketer to see beyond a customer’s behavior in a single visit. To quote from Forbes.com:
“For example, a high-rolling customer spending money like it is going out of fashion in the hotel casino may be on a “holiday of a lifetime” following retirement, and unlikely to behave in this way every day. Meanwhile a frugal business customer taking an economy room and spending very little on extra services may be a traveling businessman who will potentially return frequently if the hotel meets his needs, and therefore have a higher lifetime value. Big Data analytics can help make this distinction.”
Once again, data analytics moves marketing away from the anecdotal and toward meaningful data-driven insights.
Find Your Path to Data Analytics
Data analytics is a fast growing field with a very bright future. Whether you’re looking into the exciting world of data analytics or you’re already a marketing professional, an online masters degree in digital marketing and data analytics from Emerson College will provide you with the comprehensive knowledge you’ll need to become a true innovator in marketing.
There’s no GMAT or GRE required to apply, and you can graduate in as little as one year.