The main advantage that big data provides is an enhanced knowledge of customers. While in the past marketers were making decisions based on intuition and experience, nowadays their guesses could be confirmed by using data science.
As the path to an effective marketing plan becomes increasingly technical in nature, the responsibilities of chief marketing officers are changing. To stay ahead, CMOs need to leverage new technologies and data to get the right message in front of the right customer at the right time and measure the outcomes of their marketing efforts.
But how can they ensure these tasks are being accomplished effectively?
The answer is ‘Data Science’.
Marketing is now an inherently data-driven field, and data-driven marketing is more widely available than ever before.
Over the past few years, the consumption of online information has drastically shot up due to the wide affordability of the World Wide Web. It is estimated that there are more than 6 billion devices connected to the internet right now and around 2.5 million terabytes of data are generated every single day. By 2020, for every single person, there will be 1.7 MB of data created every second.
For marketers, this staggering amount of data is a gold mine. If this data could be properly processed and analyzed, it can deliver valuable insights which marketers can use to target customers. However, decoding huge chunks of data is a mammoth task.
This is where data science can immensely help.
The main goal of every marketer is to derive maximum ROI from their allotted budgets. Achieving this is always tricky and time-consuming. Marketing campaigns are broadly distributed irrespective of the location and audience. As a result, there are high chances for marketers to overshoot their budget. They also may not be able to achieve any of their goals and revenue targets.
Things don’t always go according to plan and efficient budget utilization is not accomplished.
However, if they use data science to analyze their data properly, they will be able to understand which locations and demographics are giving them the highest ROI.
By analyzing a marketer’s spend and data acquisition, a data scientist can build a spending model that can help utilize the budget better. The model can help marketers distribute their budget across locations, channels, mediums, and campaigns to optimize for their key metrics.
A cookie is a file that a website deposits on a user’s machine. It’s basically the website’s way of writing itself a reminder note about something. Sometimes cookies are used to facilitate the login process on any website. When a user registers on your website, you can drop a cookie on their device that remembers the username they registered under. Generally, cookies collect information about a customer’s activity on a certain website.
For example, a clothing retailer’s website cookie file could contain such information as customers’ clothing and shoe size, his preferred colors, styles, brands, and his estimated income level, based on the price range 39 choices.
This data is gathered while the customer is searching for a new product to purchase, and the more data is collected, the more accurate decision marketers could make. Cookies are also the primary method of collecting data from social media. The owner of the social media platform can sell the first-party cookies to companies or place the third-party cookies directly in the platform.
All the features mentioned above allow marketers to create personalized promotions for individual customers. In the past, a shoe company would design a single advertisement for all the 20-year-old female university students living in Helsinki. Now, each of these students would get an individual ad, because one of them might prefer white Adidas sneakers, like the majority of the members of this group, while the minority would like black high Dr. Martens boots.
Not a single customer would be left unnoticed, thanks to big data, and therefore the effectiveness of a promotional campaign will increase. Continuing the story of Amazon, the pioneers of personalized advertisement, a relevant example from a customer point of view will be discussed.
Amanda Zantal-Wiener, a Marketing Blog staff writer, shared her experience with Amazon while discussing the brands which use personalized marketing in the best way possible. She describes herself as a person with a ‘borderline obsession with hip hop’ and attaches a screenshot of the Amazon main page, offering her different products related to her interest. She then notes that personalized ads also help companies provoke unplanned purchases.
Big data is not only a virtue for those concerned with online marketing. It helps with the most common and basic decisions as well, for example, pricing. Companies are used to determining the price considering the cost of the product, competitors’ prices, and value of the product to the customer, for example. Sometimes, when the business is not going well, marketers adapt the seemingly easiest way to boost sales – a 10% discount.
Baker et al. suggest that with big data capabilities it becomes possible to use many more factors to make a better decision. Those could be data from individual deals, decision-escalation points, incentives and performance scoring data, for instance. They stress the importance of approaching any price decision as an individual, especially in the B2B sector, as circumstances may vary from one deal to another.
There are more than a few ways to practically apply data science techniques in marketing, we discussed a few but more examples are:
Data Science is a growing field, and full of potential and possibilities. So, without a doubt, Marketing will keep benefiting from big data and more practical ways to enhance marketing processes will keep emerging.
As a result of the progressive needs of customers and their expectations for more personalized experiences. Predictive and artificial intelligence marketing, as well as data-driven solutions, are becoming essential parts of successful marketing campaigns.
If you want to arm your marketing strategy for 2020 with unambiguous behavior-driven intelligence, you must have the basic knowledge of being a marketer with data analytics skills.
We invite you to check out our Data Science courses for a structured program to help you learn the skills you’ll need: