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In digital marketing, we live in the age of data. We can get a lot of incredible information about each user, practically in real time. But now, our challenge is knowing how to apply all this information to our marketing objectives.
The data science is the discipline that is responsible for processing all this data and turn it into insights that help us improve our marketing. We are still discovering the tip of the iceberg of everything we can do with them, so there is a lot of exciting news awaiting us in the coming years. To get you started, I want to share with you these 10 applications of data science in digital marketing. Do not miss it!
10 applications of data science in digital marketing
1) Dynamic pricing strategy
Most marketers base their pricing strategy on factors such as the product’s cost to manufacture, margins, and competitor prices. More subjective aspects also come into play, such as the positioning we want to give the brand.
Using data science in this process allows us not to have to make estimates by eye and hit the target, adapting the price to what is happening in real time. Thus, we will be able to take into account elements such as global market behavior, individual customer preferences, reactions to previous discounts and a long etcetera.
2) Advanced lead scoring
The lead scoring is a technique that allows us to assign a score to each lead based on their chances of becoming a client, in order to focus our resources on the most profitable contacts.
Thanks to data science, we can create advanced predictive lead scoring algorithms that take into account multiple factors to segment your contacts into lists and give them specialized treatment to multiply the conversion possibilities.
3) Email marketing
Data science can be used to find out which emails are most attractive to which specific customers . For example, we can know how often emails are read, when to send them, what type of content works best with each audience segment, etc. Thus, we can send contextualized email marketing campaigns and reach customers with the best offers for them.
4) Content Marketing
The content marketing requires a major investment of time and effort, so it is essential to ensure that we are creating the right content to our audience reach and influence their behavior.
The data science can help us find our audience data to help us create the best content for each client. For example, if a user has come looking for a specific keyword through Google, we know that we have to use it in the content. Going even further, we can use data science to help us create dynamic and personalized websites, which show different content to each person who visits them.
5) Preparation of user profiles
One of the biggest challenges in digital marketing today is to develop consistent user profiles across different devices, which also take into account that users are interested in different products at different times.
Thanks to data science, we are progressing more and more in this regard. Now we can create buyer personas using user behavior data on different devices and even assign them roles based on what they are doing at the time (for example, according to children’s book illustration styles a user’s online behavior varies during their telework day or when they are looking for options for entertainment).
6) Optimization of budgets
Marketers are concerned with allocating the budget in the most optimized way possible to obtain the highest return on investment.
Data science allows us to analyze spending and acquisition patterns to be able to make predictive models that help us better distribute the budget between different locations, channels, formats and campaigns and achieve the maximum possible efficiency.
7) Customer experience
Analyzing the data helps us decide the right time and channel to communicate with customers. For example, we can know if a user is not very receptive to SMS, but is receptive to emails sent outside of business hours. All this allows us to optimize your experience when interacting with the brand.
In addition, data science also collects and analyzes behavior patterns that predict when a user needs a specific product or service. So we can anticipate your needs and create the most seamless experience possible.
8) Definition of audiences
The audience segmentation is key to achieving the goals of brand marketing spend of the budget without established. The ideal is to send our messages only to the users who most closely resemble our ideal client and who are most likely to convert.
Given that we can now analyze the interactions of each user with our brand, data science can help us to create hyperspecific segments and tailor the messages we send them to achieve maximum effectiveness.
9) Opinion analysis
Digital marketers have an unprecedented opportunity to find out what our target audience thinks. In many cases, we don’t even have to ask them: their opinions are available to everyone online. The problem is in being able to analyze them and correctly attribute the sentiment of the messages.
Fortunately, the evolution of data science and artificial intelligence make it possible to analyze a large number of messages to obtain insights on the beliefs, opinions and attitudes of customers. We can also monitor how they react to marketing campaigns and how they interact with the brand.
Last but not least, we can apply data science to a critical aspect of any business: loyalty .
By drawing up individual profiles for each customer based on data, we can know which offers will interest them the most at all times and send them the right messages through the right channels . Thus, we will develop a long-term relationship that will lead to repeat purchases and recommendations.