In the world of digital marketing, data is everything. It helps you understand your customers, optimize your campaigns, and make smarter business decisions. Not to mention, it's a critical prerequisite for using AI in your marketing.
There’s a ton of data out there, ready to be gathered from social media, websites, mobile devices, and the Internet of Things (IoT). But merely collecting data isn’t enough. You have to know what to do with the information. Successful marketers are able to harness and interpret ‘big data’, incorporating it into their processes.
In this post, I’ll explain exactly what big data means, and how you can use it to improve your marketing strategy.
What Is Big Data?
Big data is the term for large, diverse datasets that are growing at an exponential rate, and are too large or complex to be dealt with by traditional data-processing software. However, it can also refer to the challenges and capabilities of storing and analyzing these huge data sets.
Big data is typically defined by three elements:
- Volume: the amount of data being received and processed.
- Velocity: the speed at which data is accumulated.
- Variety: the different types of data and its sources.
Sometimes, a fourth “V” is added: Veracity, which describes the quality and accuracy of the data.
Data may be either structured (usually stored in a data warehouse) or unstructured (stored in a data lake). It usually comes from three different sources:
- Customer data: this can include browsing behavior, social media interactions, online purchase data, click-through rates, mobile device usage, and geolocation data.
- Financial data: your sales and marketing statistics, costs, and margins, plus financial data about your competitors.
- Operational data: any data relating to business processes, such as shipping and logistics or CRM systems.
Big data provides marketers with valuable insights into customer preferences and behaviors, which can be used to optimize virtual marketing strategies. But you need the ability to analyze it properly, which is where big data analytics comes in.
Big Data Analytics
Before the advent of big data analytics (BDA), companies would let information pile up in their databases with no clear idea of how to use it. But if the data isn’t interpreted correctly, your strategies and decision-making will be way off base.
However, with big data analytics tools and techniques, data professionals can extract information such as hidden patterns, correlations, and market trends from huge and complex sets of data. Unsurprisingly, the concept is growing in popularity, with worldwide BDA revenue expected to reach $274.3 billion this year.
BDA makes use of predictive models, statistical algorithms, and machine learning to provide insights and conclusions. For example, Apache Kudu is a big data engine intended for structured data. Apache Kudu use cases include real-time analytics for fast data.
How Do You Collect Big Data?
Businesses should already be keeping track of their financial and operational data, but there are a variety of ways to gather big data about customers. For example, if you would like to gather big data about how effectively your business's help desk is answering customer queries and resolving any problems they may have, you could use surveys.
Let's explore various methods of collecting big data about your customers.
Customers will fill in their basic details every time they make a purchase on your website or sign up to receive discounts or newsletters. This instantly gives you their location and contact information, as well as an insight into the products they like.
You can also ask for data in the form of a survey, which is useful for profiling customers and understanding their preferences.
Loyalty schemes, in which customers collect points and receive rewards, are another good way of gathering data about their purchasing habits.
Social media activity gives you all sorts of clues to a customer’s personality, and there are various applications that help you track data from their posts, comments, and follows. It's therefore advisable to include tracking this data as a part of your social media content marketing strategy.
Companies can track whether or not an email has been opened and read, and whether the recipient has clicked through to the website or social media. You can use email finder software to ensure you’re sending to valid addresses.
Cookies (small text files stored in a web user's browser directory or data folder) enable online browsers to track and store data from a user’s search history or other online activity.
It’s possible to purchase customer data from third-party companies, but be careful—you could be contravening privacy laws such as GDPR, and customers are not keen on having their information sold in this way.
How Does Big Data Help Your Marketing Strategy?
Whether you conduct your marketing in house or use a marketing agency, big data is an essential part of modern marketing. With customers expecting personalized messaging and maximum convenience on their journey with your brand, you have to learn everything you can about them and the way they behave online.
Finding Your Audience
Before you embark on a new campaign, you need to know who you’re marketing it to. Whether you’re targeting new leads or existing customers, you need an accurate (preferably real-time) picture of your intended audience, and big data can help with that.
You can gather information on the latest trends by analyzing popular search terms, and see which channels deliver the best results for competitors in your niche. You can identify quality leads by tracking their online behavior, and micro-segment your customers in order to send the right messages to the right people.
If you’re involved in B2B marketing, big data can be used to identify businesses that could benefit from your agency’s skills (here’s a marketing agency proposal template that you can send out to potential sources).
In order to keep customers engaged, you need to truly understand their needs, wants, and preferences, and prove that you understand them by offering relevant products and services. You also have to encourage loyalty by giving them the best possible experience of your brand.
You should have plenty of data on your existing customers, based on their previous activities, purchases, and interactions. Big data helps you capture new information in real-time, enabling you to target each customer with personalized content.
For example, machine learning-based SageMaker models enable your website to predict which products to suggest to a particular shopper, while sentiment analysis highlights which customers need a little extra care.
Predicting The Future
Speaking of predictions, big data analysis and machine learning help you to determine what customers are likely to do next, so you can adapt your marketing efforts accordingly. If certain users always respond well to specific offers on your site, then you’ll know it’s worth sending them similar offers in the future.
Big data can highlight browsing and buying patterns (“people who bought A also viewed B”), which help you recommend products to customers and also inform how you design future versions of your website.
Armed with accurate knowledge on what your customers did in the past, what they’re doing in real-time, and what they’re likely to do in the future, you can put together the ideal marketing campaign. You’ll be able to see which platforms work best for your ads and messages. For example, if you find that barely anyone clicks through from emails, you can find other ways to connect.
Big data also helps you optimize online content, analyzing user feedback so you can create content your audience wants to see. It enables you to optimize for SEO, and discover which content is most effective at each stage of a sales cycle. With predictive analytics, you can identify the optimal marketing spend across multiple channels.
Although the marketing team doesn’t actually set product prices, they can collect valuable information through big data and pass it on to the rest of the company. For instance, behavioral tracking and sentiment analysis may show that a majority of customers are put off by current pricing. There’s the potential to adjust prices in real-time, and for marketers to send targeted discount offers to valuable customers.
Challenges of Using Big Data
Siloed Data Systems
In the omnichannel era, data comes from many different sources, and most companies have a variety of systems for storing and processing their data. This makes it harder and more time- consuming to carry out comprehensive data analysis. It’s best to keep all your data in one central place where everyone in the company can access it.
Most of the data we generate is unstructured, which means it comes in different forms and sizes including documents, social media posts, images, video and audio streams. This type of data makes up around 80-90% of the overall digital data universe.
Unstructured data can be difficult and expensive to manage, but you can safely store, retrieve, and analyze it with an Azure data lake (click here for an Azure data lake architecture diagram).
Big data must be properly managed in order to minimize the risk of a breach and protect sensitive information. It’s best to be transparent about your data collection and usage, informing customers of what you’re collecting and why you’re doing it. As the chart above shows, many of them are concerned enough about privacy that they would welcome more regulations.
Global data creation (the total amount of data created, captured, copied, and consumed) will be more than 180 zettabytes by 2025. That’s a lot of information to handle, but the more you learn about your customers, the better your marketing strategy will be.
Once you know how your customers behave online, and what they think of your brand, you can optimize your campaigns to target the right people, achieve better engagement and loyalty, and make informed decisions for the future.
Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks.