Have you ever come across the term Big Data without fully comprehending its meaning?
Big Data analytics, the importance of Big Data, Big Data-based marketing strategy and a thousand other terms increasingly flooding the web.
According to a recent analysis conducted by the company Domo, which has been publishing the report, ”data never sleep” for eight years, the amount of data produced per minute worldwide is steadily increasing.
In 2020, 64.2 Zettabytes of data were created or replicated, forecast to hit the 180 Zb ceiling in 2025.
But how and why is data about yourself used by expert marketers?
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What is the definition of Big Data?
The term Big Data refers to a collection of data so large in volume and so complex that traditional software is not able to collect, manage and process it in a reasonable time and it is therefore necessary to use specific software to extract value or knowledge.
The data that is produced is manifold. From the data of users connecting to a website, a social network, or a certain app, to data produced by sources such as GPS sensors, IoT, RFID, data related to online financial transactions, and so on.
Analyzing such a large amount of data requires not only specific skills but, above all, advanced technologies capable of supporting such large files to extract useful and reusable information.
Big Data, as many may mistakenly think, does not only concern the IT sector.
They are needed and useful in the most diverse businesses: automotive, medicine, commerce, astronomy, biology, pharmaceuticals, finance, gaming, etc.
No sector in which there is marketing can consider itself unscathed by the Big Data revolution.
What are the characteristics of Big Data?
Data can be divided into data:
- structured data, i.e. data that is predefined and formatted in a rigid structure before being stored. Examples are data that we enter in precisely defined fields, addresses, and credit card numbers.
- unstructured data, i.e. data stored in their native formats such as emails, social media posts, and chats.
The characteristics of big data can be encapsulated by the following scheme:
- volume: the size of Big Data varies between terabytes and petabytes;
- variety: data come from highly disparate types of sources and are presented in different formats;
- speed: data must be collected through web scraping activities, saved, processed and analysed at very short intervals, usually in real-time.
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From which sources are Big Data collected?
Before finding out how Big Data can be useful for your business, you need to be clear about where this data comes from. The following is a list of the main sources of this infinite amount of data.
This phenomenon, also known as the Internet of Things (IoT), is the set of data that reaches IT systems from a network of connected devices. Companies can collect this type of data and decide which to analyse now and which to keep as they require subsequent analysis.
Social Media Data
Data from social network interactions are becoming increasingly interesting, especially for marketing, sales, and customer support.
Facebook, with its 2.85 billion members, can even tell when a love affair has reached a critical point.
Based on the status updates on the message boards, the company can predict whether or not the relationship in question will last with disturbing accuracy.
Not to mention Twitter, a platform where around 350,000 tweets are published every 60 seconds, and which has developed an API (Application Program Interface) that allows third parties to access each of these, as they are, by design, all public.
These are unstructured data, which can interpret the emotions contained in textual information, helping decision-makers (corporate and political) to understand public taste, thinking and preferences.
Available public sources
A further large amount of data comes from so-called open data sources.
Open Data are public data collected within the PA, they must be available, reusable, and made available to the community.
An example is European Union Open Data Portal.
The Google search engine has an impressive knowledge of what we search for online. For this reason, Google can recognize our generalities, and profile us according to our surfing modes and preferences to offer us targeted advertisements, tailor-made for us.
But it is not only Google, Facebook or Twitter that track any of our digital actions: also Bing, Yahoo, Amazon, and any Internet provider that at all times knows the pages we visit (even when we think we are doing it in hidden mode).
Big Data is also collected when, trivially, a loyalty card is swiped, a phone call is received from a call center, and also thanks to all the traffic of opinions and thoughts passing through the various CRM (Customer Relationship Management) systems, a system that effectively manages the relationship between current and potential customers.
How can we use Big Data to refine digital marketing strategies?
In today’s scenario, unlike many technological fads, Big Data is not a trend but a real management necessity. And they are so for any type of organization.
What is the one thing all digital marketers need to know?
Having complete and accurate data is essential for making effective marketing decisions.
Here we can see what exactly Big Data can be used for in Digital marketing.
Finding the right keywords
Using the right keywords is essential when setting up a campaign on Google Ads or social networks when writing a blog page or an article.
The use of Big Data can help identify what potential customers are searching for on the web if they are looking for a particular product or service, or with which keywords they came to find our website.
By analyzing this information, we may discover new related keywords (words semantically connected to the main keyword we have entered in the search query) to focus on those that drive the most traffic.
So when we are going to start a campaign or identify the right keywords to put on our site, by implementing a keyword strategy, we can focus on those words that are most used by users to find the very product/service we want to offer.
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Defining our target consumers
Every day, millions of display ads (banner advertisements with images, text, gifs or videos) are offered to Internet users.
Campaigns defined as Display can be carried out either through affiliate marketing or through the use of Programmatic Advertising. In the latter case, advertising campaigns can only be targeted at customers who are most likely to be interested in the article/service proposed in the banner
How can all of this happen?
It’s simple! Thanks to the use of Big Data, systems dedicated to this type of digital strategy make use of special platforms called Data Management Platforms (DMP).
For example, Google Analytics allows us to understand how people arrive at our site, how many and which pages they visit, and from which page navigation is abandoned.
There is also another tool that can give us useful information on what users do once they arrive on a page of our site: these are heat maps.
Heat maps are a graphical representation of data represented through colors and, in the specific case of the web, are used to understand how users move around your website, where clicks are concentrated, and allow you to record entire browsing sessions on video.
One of the online tools that can enable you to do this analysis is Hotjar.
Implementing Retargeting Strategies
Big Data allows us to collect information about visitors to our site, allowing us to know which products are of most interest to them and perhaps whether the user was about to conclude a transaction but did not complete the purchase.
Retargeting is very effective because it focuses your advertising investments on people who are already familiar with your brand and have recently shown interest.
Improving the content of the website
Knowing what a customer is interested in or appreciates means that you can not only create better web design or more personalized email marketing but also create content that continues to provide better solutions. Content can serve as an initial contact point that channels consumers toward a desired action. Using data, you can discover key information about customer behavior that helps you create quality content and increase audience engagement.
Choosing the right marketing channels
Potential customers buy differently when they are on a mobile device than on a desktop. Analysis of mobile user data will help refine the customer acquisition process and increase mobile customer conversions. The mobile strategy allows you to do this, attracting and engaging potential customers from smartphones, iPads, tablets, smartwatches, and other such devices.
Seeking new customers
Big Data can help you find new audience segments and determine which groups are most likely to buy. When evaluating new potential audiences, it is important to know their intentions. Using tools like Quantcast can help entrepreneurs find a relevant target audience and grow that resource to create a larger customer base.
Monitoring online campaigns
Constant monitoring of the results of the campaign will allow us to check whether to change certain campaign parameters or to stop or enhance them. e.g. again thanks to Google Analytics, we can see which of our campaigns is the best performing and on which channel so that we do not waste precious budgets.
Implementing automated marketing campaigns
Marketing Automation Software allows you to create customized Multichannel Campaigns. This means that a specific group of users is automatically sent a series of communications with a customized cadence and content depending on the actions performed and preset scenarios defined as workflows. This is the classic example of the e-mail we have all received at least once in our lives with a discount voucher on our birthday.
Examples of the use of Big Data in marketing strategies
In digital marketing, the use of Big Data is essential in the construction of so-called recommendation methods.
Netflix and Amazon constantly use big data to make purchase proposals on the basis most relevant to a given customer’s interests.
Netflix does an outstanding job of providing visitors with personalized recommendations based on the films and programs they have watched.
Amazon uses all the data from that user’s browsing, from his previous purchases, from the products he has rated or researched, and then suggests the most suitable products for that particular customer, the ones that tickle his curiosity and prompt him to buy.
Belonging to Big Data are the algorithms that can predict whether a female shopper is pregnant by tracking her web searches and previously acquired items. Once the particular status has been identified, that same user is offered special offers and coupons on products related to her status.
With the help of Big Data, the credit card companies themselves have found almost absurd associations to assess a person’s financial risk. According to data mining research, it is shown that people who are habitual furniture buyers are the best customers for credit institutions because they are more careful about paying back the sums owed on time.
Big Data can also be a valuable tool for retailers. It is estimated that retailers who make use of Big Data can increase their margins by up to 60%. How? By analyzing purchasing behavior, i.e. the receipt associated with the loyalty card and the various interactions with promotions, advertisements, e-mail marketing, and any newsletters received periodically. All this represents a mountain of information to be collected and analyzed to define an increasingly customer-friendly offer.
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We practically understand how any Digital Marketing activity would not be implementable without the use of the infamous Big Data. Although there is still resistance from web surfers to logging online, accepting cookies, and tolerating the idea that we are constantly monitored in everything we do on the web.
However, we should all keep in mind that everything stored on the web about us is anonymous. No one will ever know that Stefano bought an Adidas T-shirt on 2 June. Stefano will simply be a male user, of a certain age and from a certain part of the world identified by a number, among billions of others.
Secondly, it must be remembered that by denying consent to profiling cookies, navigation will not be free of advertising, meaning instead that videos and banners that will be shown to us will not take into account interests but will be random.
So, publicity for publicity’s sake, let’s let the cookies do their job and who knows, maybe tomorrow you’ll be shown that t-shirt you longed for but couldn’t remember on what website you found it. And let’s not forget how many great films we have discovered thanks to the fact that Netflix knows our film tastes better than our boyfriend/girlfriend.
If you found the article interesting and feel fascinated by the World of Big Data and Online Marketing or maybe you would like to work in this field, you can enroll in the Web Analytics Specialist course or a more comprehensive Digital Marketing Specialist course that gives you a comprehensive understanding of how to structure a winning online marketing strategy.
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