Big Data in Marketing Analytics: Emerging Trends for 2021

13 min readDec 25, 2020

The global big data and analytics market is forecasted to grow up to $274.3 billion by 2022 at a CAGR of 13.2%. During the forecast period, analytics market is projected to grow up to USD 33.8 billion by 2021, at a CAGR of 15.5%.

Factually many marketers treat data as a huge asset for their business. In many ways, it has proven them right. However, for an instance, imagine the customer data collected by businesses 20 years ago — point of sale transaction data, responses to email campaigns, season offer purchase details, etc. In contrast, imagine the data (big data) collected by businesses today — online purchase data, click-through rates (CTR), Search Engine Marketing (SEM) results, browsing behavior, social media interactions, mobile device usage, geolocation data, etc. Essentially there is no comparison point whatsoever. Therefore, forward-thinking companies need to embrace the change; adopt new technologies to deal with big data coming from different sources for business success.

In the current scenario, big data in marketing analytics is taking a big leap forward, which is beyond volume, velocity, variety, and veracity. If you’re a new/existing business owner of any industry and planning to invest in big data and analytics, this blog will answer most of your queries, walk you through the development around the world of data, trends and significantly help you connect the dots to decipher rapidly evolving big data adoption in marketing analytics.

Let’s start from here…,

Story on Big data as a hero!

In marketing analytics, big data is helping businesses to identify insights into which content type can be effective at each stage of a sales cycle and lucrative ways of investments in Customer Relationship Management (CRM) setup that leads to convincing business outcomes (for example — improvement in developing strategies for better conversion rates, prospect engagement, revenue generation and customer lifetime value). Similarly, for cloud-based enterprise software companies, big data helps to identify insights into how to lower the Customer Acquisition Cost (CAC), Customer Lifetime Value (CLTV), and manage other customer-driven metrics essentials to operate a hassle-free and successful cloud-based business.

Global Analytics Market size

From USD 25.4 billion in 2019 to USD 33.8 billion by 2021, at a CAGR of 15.5%.

North America is predicted to dominate the global analytics market, whereas Asia Pacific (APAC) and Latin America regions are expected to grow at the highest CAGR during the forecast period. The confrontations faced by different industry verticals, such as BSFI, Manufacturing, retail, and eCommerce are now expected to efficiently process, manage, and store large data sets leading to the adoption of analytics solutions in the North American region.

In the last 5 to 8 years, enterprise organizations have rapidly started adopting new ideas of marketing analytics with big data while enabling the advancement of machine learning and artificial intelligence parallelly to scale bigger in the marketing activities. However, during the course of this time period, the promise of these technologies can sometimes get lost in the reality of implementing them in the real-world enterprise workflow setup. There might be several reasons for the downfall, and it could vary from business-to-business. However, comprehending few crucial aspects such as identifying worthy and effective big data sources, valuable metrics of big data in marketing analytics, and how to use big data to generate better marketing analytics results can get you proven marketing results and substantial business outcomes.

Worthy and effective big data sources

Variety of data flow in the form of structured, unstructured, and semi-structured from various sources. But Customer, operational, and financial data sources are considered as crucial assets for marketing activities followed by analytics methodologies.

Customer: The customer data types include behavioral, attitudinal, and transactional metrics collected from different sources like — marketing campaigns, points of sale, websites, customer surveys, social media, online communities, and SEO/SEM activities.
Operational: Operational data types point towards objective metrics used to measure the quality of marketing initiatives, which in turn denotes marketing operations, resource allocation, asset management, budgetary controls, etc.
Financial: Essentially stored in an organization’s financial systems, which include sales, revenue, profits, and other elements of data types used to measure the financial health of the business.

Valuable metrics of big data in marketing analytics

Marketers can collect a different piece of information through big data but identifying valuable metrics of big data in marketing analytics is pivotal, which ought to pave the following touchpoints.

Management reporting: Marketing teams can use analytics reports out of huge data sets from respective campaigns, and marketing activities to create a consolidated report for decision-makers of the organization. The insights of these reports help them understand the key highlights of the marketing campaign results and identify the new ways to reach the target customer base.
Content optimization: Various types of information collected from customer responses and certain actions performed during the advertising of the content. The real-time feedback can be used to improve the marketing efforts to ensure the approach is optimized in all forms of the content.
Encouraging customer loyalty: Thinking beyond marketing campaign implementation and identifying loyal customers is paramount to strengthen the customer relationship. With the help of analytics in marketing, you can identify loyal customers and encourage them with offers to improve the customer relationship.
Search engine optimization and Search engine marketing: Data analytics reports are useful to refine your search engine strategies both organically and inorganically(paid) to achieve greater prominence and relevance to reach potential customers using various techniques and tools.

How to use big data to generate better marketing analytics results?

In this data-driven world, there is no secret sauce for instant success; but using the right data sets in your marketing activities can do the trick to achieve near-perfect results. Below mentioned techniques could take you a step closer in this regard.

Using big data to extract deeper insights: Data analytics helps you dig deeper and deeper into the big data to extract each layer in order to collect richer insights. These insights are extracted from the initial level of analysis, which can be further explored again for even richer and deeper insights each time. Thus, insights can be used to formulate marketing strategies and actions to drive marketing initiatives successfully.
Providing big data insights to those who can use it: Marketing managers and CMO’s need big data insights from different sources to develop the marketing plan and strategies; so do sales associates and many other teams who rely on data to deal with customers. Thus, it is essential to identify who needs it most, precisely the specific data insights, and act on it accordingly.
Prioritizing key objectives of data: Your approach to use data should be balanced to achieve convincing results out of data collected. Therefore, starting with prioritizing key objectives of data is the smart way to generate improved marketing analytics results. You can get started with it by forming questioners such as what are the focus areas of your marketing activity? Type of data you would need from different sources. And so on. Once you settle on questioners, you can identify which data set you would need to support the related analysis in the form of answers. This process continues for all the key objectives you’ve noted.

How Analytics Drives Customer Life-Cycle Management Vision in the marketing journey?

Big data in marketing analytics is capable of enhancing a marketer’s ability to look beyond campaign execution and focus on how to make customer relationships more successful. The below fine points and representations are a mere walk-through of one such approach that marketers need to follow for successful marketing results.

Aligning life-cycle approach between business and customer

Customer decision making life-cycle vs. Analytics approach

There is always a hairline difference between the way customers walk in the decision-making lifecycle to make the purchase choices in contrast to the businesses developing initiatives centered on acquisition, onboarding, retention, and loyalty efforts. Therefore, it’s crucial to select the suitable analytics techniques, align life-cycle programs with the way customers follow in the decision-making process as shown in the above graphic representation.

Using analytics to drive customer value across the life cycle

Applying appropriate analytics techniques across the decision-making life cycle of the customer journey reveals new opportunities to extend the customer’s lifetime value and prompt value-generating behaviors. With analytics goal to identify customer focus area and behaviors, businesses can:

Optimize the analytics requirements to identify the scope of customer interest and marketing campaign
Gradually acquire more profitable customers through better targeting abilities
Improve cost reduction of acquisition in the early stages of the relationship
Enable cross-sell and upsell to the right customers
Gain the abilities to increase wallet share through loyalty, retention, and recovery programs

Comprehending the profitability vs. customer lifetime value probabilities is the proven methodology to achieve successful marketing results. With proper customer data analytics mechanism in place, you will have the flexibility to derive critical behavioral insights that require to act on and help retain the customer base that responds to your marketing initiatives.

Big data in marketing analytics & emerging trends for 2021

With COVID-19 slowly fading away from the whole world, many big players across the industries are busy in guesswork to identify the new opportunities in 2021 and ahead. Since the big data in marketing analytics was crucial for public and private enterprises in 2020, the year 2021 is a mere paradigm shift of the same. Given the fact that big data and analytics marketing is expected to boost the marketing ROI, all sizes of companies need to know what are the big data trends that they should be most wary of? How to harness data analytics to move businesses towards achieving higher ROI?

A bunch of big data trends impacting the current landscape of marketing analytics mentioned below holds the answers for these questions, and help you see a bigger picture.

World full of cloud: By 2022, public cloud services will be a potential prerequisite for 90% of data and analytics innovation and transformations. On the other hand, AI is one of the top workload categories in the cloud, cloud-based AI is predicted to increase 5x between 2020 and 2023. However, the cloud and AI trend occupied the market even before the pandemic, COVID -19’s impact on the enterprise has certainly accelerated usage of these technologies, and early 2021 would witness more power in the market occupancy.
X Analytics: “X” here represents a different type of analytics, such as video analytics, audio analytics, etc. One of the recent reports of Gartner states that by 2025 AI for video, audio, vibration, text, emotion, and other content analytics are in a position to predominantly takeover and transform 75% of Fortune 500 companies’ workflow and innovations. This prediction would pick up a speed in 2021 with new opportunities for analytics as the facilitation and data types denote unexplored areas for most organizations.
Data and Analytics forming new world: Gartner predicts that non-analytic applications bring the capabilities to adopt analytics in the near future, which would make way for 95% of Fortune 500 companies to adopt converged analytics governance in practice to manage broad data areas and increase the analytics governance initiatives by 2023. However, before this, products that didn’t consider machine learning in the initial level plan would start embracing ML model development and scoring around 40% by 2022.
Data occupied marketplaces and exchanges: Marketplaces and exchanges being front face of modern-day businesses, ease to manage the mountain amount of data is need of the hour. In order to suffice this need, advancement in technology adoption is essential. Gartner’s report suggests that advancement in the likes of AI/ML, cloud, and data science would take a front seat to drive the data world in the year 2021, resulting in 35% of the big names in the industry may become potential sellers or buyers of data by 2022.
Matured AI: In contrast to the historical data generated before pandemic would lose its value to the data being generated in the current year. This prediction is backed by 75% of enterprises, who are keen to switch from piloting mode of operation to enabling comprehensive AI operations in the workflow by the year 2024. The maturity of AI is also predicted to boost 5x in streaming data and analytics infrastructures accordingly. Hence, matured AI will introduce new algorithms such as reinforcement learning, deep reinforcement learning, and interpretable learning like explainable AI.
Data stories replacing dashboards: Growing usage of data stories are complimenting AI and ML techniques in the workflow to drive the business intelligence platforms to overcome the manual work of dashboard users to extract deeper insights. Hence, by 2025 data stories are expected to replace dashboard use to engage the analytics processes, in turn, fuel the augmented analytics techniques to generate 75% stories automatically.
Boom in Blockchain use: Blockchain use in data and analytics enhances business-driven initiatives such as smart contracts. Though, blockchains are considered as less secure than alternative data sources, by 2023 organizations using blockchain smart contracts will increase overall data quality by 50%, however reducing data availability by 30%, while creating useful data and analytics ROI as-is.
Decision intelligence: Changing decision logic without the help of programmers was a near-impossible task earlier, but now with decision intelligence in place, it’s made possible. This approach brings a wide range of decision-making techniques, applications like complex adaptive systems, and a framework supporting advanced AI/ML together to deliver effective decision results. As a result, by 2023, 33% of organizations are expected to have analysts (both technical and non-technical) practicing decision intelligence including decision modeling.

Realtime use cases of big data in marketing analytics

In the last couple of years, nearly 90% of the data has been created around the different parts of the world, and big players across the industries have spent more than $180 billion a year on big data analysis. Many of the businesses across the industry believe that collecting timely customer insights in the form of data allows them to analyze, observe various customer-related patterns, and trends to meet the customer expectations accurately. Thus, investing in big data and analytics technologies could be a game-changer for new and existing business owners, but a window to see a few successful use case scenarios would give life to this prediction. To suffice this context, I have mentioned a couple of use case examples below.

Use case example 1, Amazon:

Scenario A — Dynamic pricing

Unlike airline’s strategy to sell plane tickets when the user frequently checks the same tickets over-and-over again, Amazon applies the same logic. Data and analytics-driven techniques are used to change the prices of a certain product based on factors like shopping patterns, competitor’s prices, and whether the product is a common one or not. As a result, Amazon changes its prices up to 2,5 million times a day.

Scenario B — Product recommendations

Every customer move will be captured and stored even when a customer land on the product page or drop the product in the cart for a few seconds — Amazon will use that data. These customer sessions will help them understand likes, dislikes, and recommend the personalized products suggestion based on past data history when they return for shopping. Practicing this tactic Amazon earns 35% of their annual sales.

Use case example 2, Netflix:

Scenario A: Exceptional customer experience

Netflix firmly believing the fact ‘’personalization is key to current and future success.’’ They keep a 93% retention rate when compared to their top competitors. Netflix collects the data of subscribers watching the show/movie if they binge-watched it or the time taken by the viewer with breaks in the middle, whether they have used frequent pauses and if they resumed it after pausing. They use all these data sets to provide an exceptional customer experience for every subscriber.

Scenario B: AI & ML backed trailers

All subscribers watching the same trailer suggestion could be boring. Netflix is planning to suggest fully personalized trailers for its viewers with the help of AI and ML technologies. For example — If a subscribed viewer is interested in watching action movies, then that user would get a movie trailer of non-action genre (which are partially action movies) suggestion if the trailer includes few action sequences. And this whole process backed by data, which will be huge in size and analyzed timely.

Feeling like overwhelmed with a lot of information above?

Well, I’m keeping the bottom lines crisp and constructive. Just like the way data analytics does!

Big data in marketing analytics is bigger than one could envisage. It’s a recipe change from person-to-person and business-to-business but carries similar spices everywhere in a different chef’s hands. Likewise, it’s the marketer’s responsibility in the organization to identify the effective ways of using big data that leads them to achieve convincing marketing results.