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How Big Data in Marketing Shapes the Future of Advertising

Big Data is a phrase that has been thrown around a lot in the last few years. Tech-savvy people use this term here and there and keep mentioning that it’'s the future of everything. But why exactly is Big Data on everyone’'s A-list? How can it help marketers create new customer experiences and reach out to the right audiences?

According to the recent research by McKinsey, marketing and sales specialists from all major industries claim that Big Data and analytics have significantly influenced their workflow and accelerated their performance.

How different industries use Big Data

Big Data in different industries
Source


Learn how much various industries spenton paid ads this year.

What’'s more, 69% of companies are exploring new possibilities with customer analysis. Adopting a data-driven culture has allowed 49% of companies to decrease their expenses while opening new revenue and growth opportunities.

Big Data initiatives and success rate
Source

The percentage of companies that successfully implemented Big Data

There’s no denying that Big Data adoption can be a challenging and time-consuming process. That’'s why, in this article, we’ll help you find out why this onion is worth peeling and how Big Data can help you optimize your digital strategy.

How Big Data has changed digital marketing

Big Data is essential in marketing since it allows marketers to figure out their prospects’' behavior and intentions. The global trend of predictive analytics has taken over many industries, and marketing is no exception. By 2022, the global market for predictive analytics is projected to reach $10.95 billion.

The high demand for predictive analytics and ever-growing amounts of generated data creates a strong need for new approaches and technologies for data analysis.

According to Chiefmartech, in the marketing field alone, Big Data gave birth to 8,000 different solutions that leverage marketing processes in various domains, such as:

  • Advertising and promotion
  • Content and experience
  • Social and relationships
  • Commerce and sales
  • Data
  • Management


Marketing technology landscape
Source

Marketing technology landscape


Besides, Big Data spurred the development of particular marketing approaches and accelerated the growth of MarTech companies. 

Examples of how Big Data changed the MarTech landscape.

Let’'s take a look at account-based marketing (ABM), a strategic approach taken by marketing and sales departments when they collaborate to target ideal-fit accounts and convert them into customers.

Account-based marketing approach relies on two key components:

  • Processing huge B2B contacts databases
  • Optimization of algorithms that learn at scale (machine learning)

The main assumption behind ABM is that all accounts are created equal. So, if marketers can get to know their ideal-fit contacts better than their competitors do, they will win deals at a much faster rate. However, it would be impossible to analyze thousands of contacts quickly and accurately without implementing Big Data algorithms and in-depth analysis. That’'s why companies, such as RollWorks, that provide extensive databases of contacts and process them with machine learning algorithms are now on the rise.

How Amazon uses Big Data

Speaking of well-known brand names, Amazon is a company that uses Big Data to accelerate sales and marketing performance on a giant scale.

Amazon utilizes Big Data to improve the customer’s experience and personalization. Unlike most brands, Amazon uses a far more comprehensive approach. They have a much larger client base and various services that need their procedures.

It is no wonder that Amazon is reaping the rewards of its extensive data usage, as it is driving a significant amount of sales. Their machine learning algorithms also synchronize with data to improve the efficiency of ratings and customer reviews.

How companies apply Big Data in digital marketing

As we can see, Big Data plays a crucial role in data reports and automated marketing strategies. But how do companies apply Big Data as an integral part of their digital strategy? 

To get the most out of Big Data, companies need to embrace new technologies and make sure there are enough resources for further analysis. In this section, we’'ll look at the different ways in which Big Data is used in marketing and how it benefits businesses.

AI-driven marketing companies rely on Big Data

With the rapid development of AI in marketing, the demand for Big Data has also surged. The reason for this is pretty straightforward:. AI applications require large data sets to train their algorithms.

How Jarvis use Big Data

Take a look at Jarvis, for example. It’'s an AI-based tool that assists withwriting blogs, social media posts, and other types of content. Jarvis is based on a GPT-3 autoregressive language model created by OpenAI. This neural network has been trained on millions of words of content to output human-like text for content marketers.

JarvisAI example

Here’s an example of how JarvisAI expands a few words into a meaningful piece of text.

How Viable uses Big Data

Viable is another tool that can help marketers, customer support managers, and sales teams simplify their communication with customers. The software analyzes all customer feedback from different channels and extracts meaningful insights from it. Users can ask straightforward questions, like “"What is our most frequent feature request?”" and Viable will give them an answer based on user requests and feedback.

Viable example

How Viable works


This is not all, however. Many other companies use Big Data to augment their AI-powered marketing strategies and increase customer engagement rates. For instance, LiveRamp uses machine learning algorithms to build audiences from CRM systems in real time.

The list of tools in which Big Data plays a role goes on and on. To cut a long story short, Big Data in combination with AI improves digital strategy in multiple ways by driving sales growth, improving the user experience, and automating the workflow.

Would be cool to add some reference to the list of companies using AI

How Big Data affects marketing reporting

Only a few years ago, it wasn’'t uncommon to see marketers spending tens of hours trying to figure out what went wrong with their campaigns.

Big Data slows down and finally kills traditional reporting

The only things marketers of that time knew for sure were whether their campaigns were running and how much of the budget was left. But when things hit the fan and campaigns didn’t perform well, finding the issue could take an eternity. This gave life to middleware analytics products that saved these teams tons of hours on building reports and helped them adapt to the constant growth of the marketing data.

Today, automated marketing reporting is all the rage. Marketers can now keep an eye on their campaigns 24/7/365 and quickly pinpoint the issues by streamlining all data to dashboards. They have all the necessary data to see what went wrong, why it happened and how big the problem is, all with no manual actions required to collect and prepare that data for reporting.

More channels and tools make the revenue pipeline difficult to maintain

The credit for these advances goes to Big Data in marketing. Rapidly growing amounts of information have brought us new technologies for data organization and consolidation. 

Let’'s take a glance at Improvado. By utilizing Big Data, Improvado offers marketing reporting automation to an infinite number of companies without losing the speed of delivery and flexibility.

At its core, Improvado represents a niche of marketing ETL platforms that help advertisers unify data from disparate data sources, gather it in one place, and streamline it to dashboards. 

ETL scheme

How a marketing ETL system works in e-commerce


What’'s more, automated data normalization and data mapping save hundreds on routine data operations. It allows analysts to focus on the analysis itself rather than manually accumulate and match data fields.

Old-school marketers who use spreadsheets as their universal tool might say that Big Data technologies are redundant. But the truth is, today, Big Data in marketing isn’t a redundancy; it’s a necessity. And companies that can extract actionable insights from their data, will reap the most rewards.

How Klaviyo and RetailRocket use product recommendations in their email campaigns

One of the most powerful use cases of Big Data in email marketing is product recommendations. In a nutshell, product recommendations are a part of the e-commerce personalization strategy that suggests products to customers based on browsing behavior, customer preferences, and other parameters.

We can divide email product recommendations into two different methods:

  • Bulk recommendations
  • Personalized recommendations

Marketers set up bulk product recommendation email campaigns when they don’t have enough customer behavior data. The system might suggest products at a discount, top-rated products, and so on.

Product recommendation example
Source

An example of a product recommendation engine in action


Personalized recommendations are based on the customer’'s preferences and intentions. Usually, they are used to follow-up with clients who abandoned their shopping cart, stopped browsing products, didn’t visit the website for a particular time, or failed to confirm their order.

This marketing strategy jumpstarted the emergence of new businesses. For example, RetailRocket offers an AI personalization engine that helps to deliver personalized offers to customers at the right time. By analyzing customer behavior, the tool can create accurate suggestions and send them to customers via email.

Klaviyo is another tool that tailors product recommendations to email recipients based on their shopping behavior. The system analyzes each customer’'s preferences and recommends products purchased by other clients with similar tastes. If you don’'t have enough data about customers, you can choose suggested products manually.

Klaviyo recommendation engine analysis
Source

Customer actions analysis by Klaviyo

However, this strategy is possible only if you have petabytes of information to train algorithms. Hence, the more customer information your e-commerce platform can gather, the more effective your AI algorithm will be.

How Dynamic Yield and Emarsys use personalization to improve conversion

Every day, millions of people visit different websites. Yet only a handful come back to the same site they visited previously. 

Big Data in digital marketing helps to identify the reasons why customers leave the website more personalized experiences can be delivered.

One of the biggest issues of personalization is that there’'s no easy way to give each customer a unique interaction with your website. Software solutions like Dynamic Yield provide a comprehensive toolkit to offer a unique customer experience. The platform supports multiple data collection methods to build an omnichannel touchpoint history for every user that interacts with your brand. 

Dynamic Yield example
Source

Multi-touch campaigns by Dynamic Yield

As for the personalization itself, Dynamic Yield helps to set up triggers for different user actions, automates A/B tests, provides an advanced personalization engine, and more.

Emarsys also helps to establish one-to-one experiences with customers. The platform consolidates and turns offline and online data into personalized offerings, product recommendations, and precise predictions about user behavior. With the help of AI and Big Data in marketing, Emarsys drives better website conversions.

Another Big Data marketing strategy is AI-driven pop-ups. 

These simple, yet effective, elements improve customer retention rate and drive conversions by engaging with your website visitors at the right time. For example, Octane AI and AiTrillion assist in creating conversational pop-ups triggered by various actions. Besides, these solutions learn when it’'s better to show a particular pop-up.

 Example of Octane AI’s Big Data marketing approach
Source

Octane AI pop-up editor

Big Data in social media marketing

Big Data in social media marketing utilizes social listening capabilities that help marketers to analyze what customers like or dislike about their brands. This information helps to improve customer service, increase loyalty, and build a community.

Social listening helps companies to find out what their customers are saying about the brand by analyzing different social networks. Solutions like Brandwatch utilize Big Data marketing techniques to highlight which brand-related topics are currently trending on social media. Additionally, it accumulates data from millions of online conversations and draws up reports that can be used for market research, competitors analysis, crisis management, and other purposes.

Brandwatch social listening example
Source

Brandwatch’s market analysis dashboard

Advertisers that use social media as one of their main touchpoints with clients need solutions that automate content publishing and track the performance of existing content. Hootsuite is probably the most popular solution on the market, helping leverage Big Data marketing to monitor the performance of posts, measure results across all channels, and promote both organic content and paid ads across the network.

SproutSocial also brings the advantages of Big Data marketing to businesses focusing on social media networks. The platform streamlines all social media metrics and creates a unified view for engagement efforts. It also helps marketers to plan and create social media content. They can also get a report on the content’s performance and optimize campaigns according to the results.

SproutSocial dashboard example
Source

SproutSocial’s engagement dashboard

If you’d like to display creatives from multiple social media channels in one dashboard, or merge your social media acquisition channels data with others, you need an ETL platform. For example, Improvado stores all social media creatives from various platforms so you can later display them on the dashboard and track their performance individually. 

Improvado paid creatives

How Improvado handles social media creatives

What’'s more, the automated data pipeline allows you to merge social media data with metrics from other channels to create a comprehensive picture of your marketing efforts and identify the best-performing channels.

Report in Improvado reportingg-1.png

Example of Improvado’s cross-channel report

How content tools became irreplaceable in maintaining a healthy content marketing strategy

Creating helpful and engaging materials for customers helps to increase customer satisfaction, brand awareness, and loyalty. 

At the same time, content specialists have to keep in mind that the content should achieve particular business objectives. Eventually, it becomes difficult to combine all of these elements and deliver quality content that serves its purpose on a regular basis, and that’s why marketing teams often rely on Big Data-driven content assistants.

Surfer is one of those tools. The software provides numerous features for content creation, such as a keyword research tool, SERP analyzer, SEO audit, and so on. One of the key features of this product is the content editor. It analyzes your content and scouts the web for the top-performing content from competitors. After this in-depth research into your competitors’ content, it provides you with hints on how to make your piece better than others.

Surfer Big Data marketing example

Surfer’s automated content planner

Previously, we mentioned Jarvis. This AI-based tool writes content for you based on millions of analyzed articles, blog posts, etc. Even though the algorithm might not always produce top-quality texts, it certainly provides inspiration to content specialists who may be experiencing writer’s block.

Grammarly is yet another powerful solution for content marketers. This tool helps you to write mistake-free texts with a simple browser extension. The application provides hundreds of grammar and style corrections so you can produce errorless content from the very first draft. What’'s more, it helps to maintain the specified tone of voice and check texts for uniqueness.

Big Data in content marketing: Grammarly

Grammarly’s text goals and tone of voice

Marketing Analytics

Among all other marketing activities, analytics is the one that lets Big Data technology unfold to its full capacity. Analytics tools, such as Google Analytics, HubSpot, Mixpanel, in combination with data warehouse tools, allow you to gather unlimited amounts of data. With sufficient data at their fingertips, analysts can experiment with different queries to extract new insights that previously were out of their sight.

Holistic marketing reporting is also a significant benefit of big data marketing. Reports not only provide you with the necessary data but also help to visualize your insights in a way that’'s easy for comprehension and allows you to find new ways to improve your marketing campaigns’' performance. 

Due to Big Data algorithms, Improvado can merge data from more than 300+ data channels on a single dashboard. Besides, its MCDM algorithm ensures high granularity and empowers marketers to dig deeper, find valuable insights faster, and experiment with data freely. These all make tools like Improvado irreplaceable partners when faced with the constantly expanding world of Big Data.

MCDM explanation

How Improvado’s MCDM algorithms works

Big Data marketing real-life use cases

Big Data is already being used extensively by marketers. As a result, finding real-world examples of companies utilizing Big Data isn’t difficult. These real-world applications offer us insight into how large companies effectively integrate Big Data in their marketing processes.

Illy’s Marketing Team Efficiently Organized Big Data and Gained All Required Answers From It

The Illy marketing team faced a daunting challenge. They needed to consolidate omnichannel marketing data from more than 140 countries.

According to the company, their campaign was divided into local clusters based on the most common media sources in each nation. This ever-expanding data set became increasingly difficult to analyze, resulting in poor data aggregation and loss of marketing value.

They were able to find success by synchronizing all relevant information in one dashboard, giving senior managers and analysts an easy way to understand their customers' preferences and behaviors across every region around the world. 

Using a robust marketing analytics toolkit, Illy eliminated data silos and automated decision-making, which  improved its marketing strategy and cut more than 100 hours each week on reporting. As a result, Illy’s marketers can now focus on new insights rather than performing manual data operations. 

Illy's Management Team Finally Gets the Answers They Need

OnePlus Uses Social Listening to Hear Its Audience

OnePlus has run several marketing initiatives since the release of the OnePlus 6, including the #OnePlusUnboxing campaign, the OnePlus 6 Marvel Avengers Edition, and others. These campaigns received a lot of attention on social media, so it was critical to keep track of the audience feedback and understand the meaning behind all social media mentions.

However, the team experienced trouble with tracking all messages and mentions of the brand in real-time across numerous channels. That’s why the digital analytics department implemented social listening to address all of the problems and inefficiencies of the manual approach.

With BrandWatch and the implementation of the Big Data marketing approach, OnePlus added context to their brand tracking, which powered their product lines, helped establish new influencer relationships, and improved brand loyalty. Furthermore, the team identified fthe hottest topics in discussions about the new device. The main three were the new processor, the price, and the unique Avenger design.

MediaMarkt Adds Website Personalization to Increase Conversion Rate

MediaMarkt, one of Europe’'s major consumer electronics companies, hopped on the digital revolution train right on time. As a result, both its offline and online presence has become widespread across the continent. Over time, the company decided to merge all its online and offline data to provide a quality customer experience and gain a holistic view of its audience.

Having merged customer data across all channels, MediaMarkt was able to implement a recommendation engine and deliver personalized content to its customers.

Affinity-based product recommendations across the homepage, product pages, and category pages improved the company’s conversion rate. However, MediaMarkt went further and tested different combinations of recommendation strategies to reach a new level of customer engagement.

Eventually, the retailer’'s efforts paid off. The company experienced a 14% revenue growth per user and delivered a whole new customer experience.

Trello Implements a Big Data Social Media Marketing Approach to Address all Support Issues

Trello’'s team discovered a problem with how the brand’s Twitter content was being displayed. When people accessed @Trello through the Twitter app, they were faced with support-related discussions and replies instead of tweets promoting their blog content.

The team realized that the Twitter profile might lose new followers and prospective clients. That’s why they decided to launch a new @TrelloSupport account that addresses all support questions and takes the load off the main Trello social page.

By implementing social listening and smart social inbox tools, Trello managed to create a flawless user experience on social media. Over time, the team improved the response rate by 16%, answered more than 2,000 customer inquiries, and achieved a 97% response rate to support tweets within 24 hours.

How to develop a successful Big Data marketing strategy?

The key to a successful Big Data marketing strategy is the use of marketing analytics. Today, marketing is more about data than about the marketing itself. The most successful marketers are the ones who use customer data to create brand-related value, target audiences better, and improve ROI.

Implementing marketing analytics across your organization is a cornerstone of sustainable growth. However, the analysis of omnichannel marketing efforts becomes a real nightmare without proper automation and data management tools.

We’'ve already mentioned a large set of tools in this article that can significantly save marketing analysts’' time and improve the outcomes of marketing campaigns. Still, it’'s important to highlight the vital need for third-party tools.

Many companies think that they can implement cross-channel analytics on their own, without third-party vendors. However, when it comes to the execution of this plan, the final solution might have a lot of bottlenecks, data discrepancies, and limitations.

To decide whether you need third-party software, ask yourself, “Why is it better than doing it yourself?” Let’s consider product recommendations engines. It’s not a secret that the development of a machine learning recommendation algorithm from scratch will take a lot of your software engineers’' time and effort.

Build vs buy

However, it’'s not simply a question of time, as there is also the efficacy of the invested efforts to consider. If the in-house solution won’t produce the required results, all the time and resources invested will be wasted in vain. So, why not use the recommendation engine that has been tested with numerous clients?

The same thing applies to data management. This process lies at the core of Big Data marketing, and unfortunately, it’s often neglected. Instead of streamlining the process, many companies aggregate all marketing data manually. This results in data inconsistencies, data silos, and lots of wasted time.

With ETL systems, Big Data marketing can unleash its full potential. Keeping all insights in a centralized data warehouse, marketing analysts can reach them at any time, analyze them with BI tools, or conduct new experiments with Big Data algorithms.

Let’s consider Improvado’s ETL system. By utilizing ClickHouse storage, the company achieved superb performance in terms of data processing. The data processing speed reaches up to 30GB/s, making Improvado one of the most efficient solutions for querying data and experimenting with different reports. Unlike popular solutions, such as Redshift or Snowflake, ClickHouse doesn’'t charge for each query, making Improvado also a cost-effective storage solution for marketing analysts.

ClickHouse vs. Redshift comparison
Image source

ClickHouse vs. Redshift query execution speed benchmark.

Check out our blog to find more insights on other ETL systems, their performance, and benefits for businesses.

Takeaways

To sum everything up, we can highlight three key takeaways that will help you build a perfect Big Data marketing strategy:

  • Organize. When Big Data marketing is not properly managed, it can turn into a mess.
  • Automate. Don’t hesitate to integrate third-party solutions. They will save your time, money, and effort when it comes to operations with Big Data in marketing.
  • Experiment. Big Data brings numerous opportunities and uncovers insights that were previously out of your sight. However, to utilize these opportunities, you have to experiment with different Big Data tools, techniques, and approaches.

Improvado can help you organize your Big Data by consolidating all marketing and sales insights from 300+ sources. The ETL solution automatically harmonizes your data and stores all information in a dedicated data warehouse. Due to our one-hour data synchronization frequency, your AI-driven algorithms will always have enough data to train on. Schedule a call to learn how you can improve your Big Data marketing strategy with Improvado.

Our recommendation:

Check out How to Streamline data from Snowflake to Tableau [Two Easy Ways]

Learn how to gather actionable insights for your Big Data marketing strategy with Improvado.

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