Big data has transformed how brands develop and market their products to the extent that marketers back in the ‘90s would have never imagined. If you’re a marketer, chances are you’re already utilising big data for your marketing campaigns.
What is big data marketing?
Big data marketing leverages the vast and complex sets of information generated in today’s digital world. This information comes from various sources, including social media platforms, customer websites, and even direct customer interactions.
Big data marketing encompasses both structured data, like numbers in a spreadsheet, and unstructured data, like text in a social media post. It can also include things like images and videos.
By analyzing this massive amount of data, marketers can gain a deeper understanding of their target audience, their preferences, and their behaviors.
This allows them to make more accurate and timely decisions than ever before. Big data marketing empowers marketers to personalize campaigns, optimize marketing spend, and ultimately achieve better results.
Why is big data important in marketing?
Three out of every four marketers stick to data-driven decisions as they are far more effective than gut instincts. Additionally, data-driven marketing campaigns have an exceptionally high return on investment (ROI) – especially the ones that leverage data-driven personalization – a whopping increase of 5-8x ROI.
Big data analytics (BDA) enables marketers to analyse massive amounts of structured and unstructured data. For instance, BDA can be used to detect pictures of a newly adopted dog on Instagram, which marketers can use to offer the owner discounts on pet products.
Types of big data in marketing
Three primary categories of big data are crucial for marketers: customer data, financial data, and operational data. Each data type originates from multiple sources within an organisation and is often stored in various locations. Let’s examine the three types of big data used in marketing.
- Customer data: This data delves into customer behaviour, attitudes, and transactions. It originates from various sources like marketing campaigns, sales records, website interactions, surveys, social media activity, and loyalty programs.
- Operational data: This data focuses on the internal efficiency of marketing processes. It includes objective metrics such as resource allocation, campaign execution, asset management, and budget control. Analyzing this data allows marketers to identify areas for improvement and optimize their marketing operations.
- Financial data: This data reflects the financial performance of marketing efforts. It typically resides within the organization’s financial systems and includes metrics like sales figures, revenue streams, and profit margins. Analysis of this data allows marketers to calculate the return on investment (ROI) for their campaigns, informing future resource allocation strategies.
How Is Big Data Used in Marketing?
1. Crafting Better Products
Big data can be used to determine the products customers love and hate. Unlike traditional methods that rely on surveys and customer feedback, big data analytics enable brands to recognise the positive-negative aspects of a product by accumulating data from social media networks, ratings and reviews, and reverse logistics (the process of returning a product).
Big data also makes it easier to make better pricing decisions. By considering complex macroeconomics indicators such as inflation rate, GDP growth rate, interest rate and more, brands can define the most profitable and acceptable price ranges for their products.
2. Planning Marketing Strategies
With segmentation, customers can be sorted into multiple target groups, and marketers can run specific campaigns for each. As the preferences of each group differ, marketers can tweak timings, content, and platform of choice for better results.
Marketers can also eliminate the guesswork of crafting user personas by considering data such as consumer preferences and behaviour, purchasing patterns, and demographics.
3. Measuring ROI
With multiple marketing strategies performed across various platforms, it is natural for marketers to lose sight of their goals and budgets – if not for big data analytics.
Big data solutions make it feasible to analyse multiple parameters, such as customer response, click-through rates (CTRs), actions taken, and conversions, to estimate the ROI.
4. Embracing Loyalty
It is common knowledge that the minority of the customer base will be responsible for the majority of sales and profits – known as the 80-20 rule (aka Pareto Principle). With big data analytics, brands can effortlessly identify customers with high customer lifetime value (CLV) and invest more in retaining them.
5. 360-Degree View of Customers
With data collected from virtually every platform where users converse about a brand, marketers will have a 360-degree picture of their customers, empowering them to serve customer-specific content in the most effective platform, and at the most desirable timings.
6. Personalization
As previously mentioned, personalization can work wonders when it comes to improving sales and loyalty. With the application of big data in marketing, brands can provide hyper-personalised shopping experiences to customers by analysing their previous purchases, preferences and demographics.
7. Understanding Competitors
Just like understanding your customers, knowing your competitors is critical to gaining a competitive advantage. That is something big data analytics is extensively used for. By accumulating and analysing data relating to what drives customers to competitors and what consumers like (and dislike) about them, marketers will have more actionable insights.
8. Lower Customer Acquisition Costs
With customer analytics (use of customer behaviour to make business decisions), brands are 23 times more likely to acquire new customers. Big data in marketing also allows marketers to determine the factors that influence customer loyalty.
Big Data Marketing Examples
1. Kroger
Image Credit: wane.com
An American retail company, Kroger, wanted to increase in-store visits and coupon return rates. With big data analytics, the company was able to offer the right coupons- at the right time, to the right customers.
But unlike other retailers, Kroger didn’t stick to providing a Pepsi coupon to a Coke buyer. Instead, the brand accumulated and analysed shopping patterns (up to two years of purchase history), involvement in customer loyalty programs, historical data of coupon returns, spending habits, and brand loyalty.
Coupons from Kroger
By personalising their email campaigns and sending highly relevant coupons, the brand witnessed a 70% coupon return rate, compared to the industry average of 3.7%.
2. Very
Image Credit: theverygroup.com
With 71% of consumers preferring personalized ads and twice as many customers tempted to click a website banner if it was personalized, the British online retailer Very wanted to utilise the power of personalization.
For that, the brand utilised big data analytics to create highly personalised home pages that would reflect the interests and preferences of each customer. Within a year of implementation, the brand produced nearly 3.5 million versions of the home page.
To serve fully personalised home pages, the brand aggregated data sets such as customer location data, weather reports of customers’ locations, purchase history, search history, and frequently visited categories.
Personalised homepage by Very. Image Credit: internetretailing.net
So if the weather condition were “rainy” in a particular area, customers visiting from there would receive recommendations for raincoats.
The brand experienced an increase of £20 million in sales by the end of the first year. According to the Very Group chief executive, the company was able to sell relevancy when customers were generally drowning in a sea of irrelevant choices.
3. Nestlé Purina
Image Credit: hps-pigging.com
When Nestlé Purina acquired Petfinder, they wanted to build strong, trusting relationships with their customers. Soon, they realised that sending personalised messages based on a customer’s needs at the right time was one way of doing it.
They teamed up with an analytics company to pull off this feat. The brand had a significant amount of first-party data about its customers’ behaviour but didn’t have the means to utilise it. The data included purchase history, search history, and engagement with the website, to name a few.
Common types of parameters used for customer segmentation. Image Credit: ebcg.com
With the help of analytics, the brand was able to link the data and tools to create accurate customer profiles and segment each user into target groups for smoother predictive marketing actions.
They were also able to understand the content preferences of customers, and as a result, witnessed an increase of 300% in conversion rate at 1/10 of the cost of acquisition as compared to other marketing strategies.
4. The Economist
Image Credit: commons.wikimedia.org
The Economist wanted to grow its opt-ins for digital subscriptions. For that, the head of the marketing and operations team decided to implement a data-driven strategy that would make the marketing efforts more customer-centric.
The company teamed up with an analytics solution provider to collect data from multiple sources and de-anonymise it. The data collected helped develop various user profiles and later shaped the marketing campaigns aimed at finding and attracting new customers.
Each user action adds a specific score. The higher the score, the better qualified the lead is. Image Credit: cyberclick.net
The Economist also used predictive scoring – a lead scoring technique that combines historical and activity data with predictive analytics to identify sales leads who are more likely to convert. The company also served personalised ads based on a customer’s subscription status, behavioural scores and content affinity.
The Economist witnessed an 80% reduction in acquisition costs and a 300% increase in digital subscriptions. The company experienced an overall rise in on-site time as well.
5. The Motley Fool
Image Credit: fool.com
The Motley Fool is a financial and stock investing advice company founded in 1993. As brands are increasingly going digital-first, the company knew, in order to expand its customer base, it must reach the right customer, at the right time with the right message.
The company started using big data analytics with customer behavioural score data to understand their pre-conversion journey. This helped them identify customers who were more likely to convert, and based on that observation, they performed customer segmentation.
Segmentation of customers for delivering personalized offers. Image Credit: shopify.com
The customers who were more likely to convert were segmented into three tiers—silver, gold, and platinum. This helped create bidding strategies in paid marketing channels around each target group, meaning the company allocated more budgets for aggressive marketing on platinum customers (the ones who were most likely to convert).
As a result, the brand experienced a 20% overall drop in customer acquisition costs, mainly contributed by the platinum customers (the ones with the highest customer lifetime value). The company was also able to target similar prospects by creating lookalike audience groups, even if the individuals haven’t interacted with the brand before.
6. Peloton
Image Credit: onepeloton.com
Peloton is an American exercise equipment company that extensively uses email marketing to engage and retain customers. With big data analytics, Peloton was able to craft highly-personalised and relevant emails that included workout schedules and activity recaps.
Personalised emails from Peloton. Image Credit: reallygoodemails.com
According to the brand’s email marketing team, they use user engagement data and data collected from the equipment to personalize emails for each customer. This strategy boosted the email opening rates by 48% and helped maintain the excitement of using the equipment.
7. Airbnb
Image Credit: news.airbnb.com
Airbnb is known for diverse hiring and dedicates the process to being an essential part of its growth. With big data analytics, the company discovered that customers in Japan, Korea, China and Singapore had a different customer journey as compared to the rest of the world.
It all started back in 2014 when the company noticed that the bounce rate when visiting the homepage was higher in certain Asian countries. Upon further analysis, the team discovered that users belonging to the region with a higher bounce rate would click on the “Neighbourhood” link, browse some of the photos and never return.
These insights were shared with the website engineers, who soon redesigned the websites version for users from that area. By doing so, the company experienced an increase of 10% in conversion rate.
Graph showing the relationship between acceptance rate and checkout gaps. Image Credit: neilpatel.com
Airbnb is also known to use big data and machine learning algorithms to create matching models between hosts and guests. For this, they analyse data sets like checkout gaps preferred by hosts, online behavioural aspects of the guest, dimensional factors such as language and device used, sentiment analysis, and weather conditions.
8. Intel
Intel uses big data analytics to gather and analyze vast amounts of customer data from various sources, including social media, website interactions, and customer feedback. This data helps Intel understand customer needs, preferences, and buying behaviors.
By segmenting their market more accurately, Intel can tailor their marketing strategies to target specific groups more effectively, enhancing the relevance and impact of its campaigns.
The company also employs predictive analytics to forecast future trends and consumer demands. This allows them to develop new products and technologies that meet the anticipated needs of their customers. Predictive models also help Intel in planning their production and inventory, reducing overproduction and minimizing waste.
They have also been analyzing the effectiveness of different marketing channels and campaigns using big data analytics. This includes measuring the impact of various advertising campaigns across different platforms to determine which are most effective at reaching their target audience and generating leads. This information helps them optimize their marketing spend and strategy, focusing resources on the most productive initiatives.
How Does Big Data Affect Marketing Strategy?
In the past, marketing strategies were often based on assumptions or limited data sets. Today, the vast amount of information available through big data has revolutionized the way companies approach marketing. This sea of data offers marketers a powerful tool to truly understand their target audience.
Big data isn’t just about understanding customers; it’s also about creating personalised experiences.
In the manufacturing industry, for example, big data can be used to personalise after-sales service recommendations based on equipment usage data.
Similarly, IT companies can leverage big data to identify customers who might benefit from specific software upgrades based on their current usage patterns.
Big data in marketing empowers marketers to optimise their efforts and identify new opportunities. Real-time analysis of campaign performance allows marketers to see what’s working and what’s not, enabling them to adjust their strategies on the fly for maximum impact. They can leverage this data to design loyalty programs that cater directly to customer preferences, fostering stronger relationships and long-term brand loyalty.
In short, big data has transformed marketing from a one-size-fits-all approach to a highly targeted and data-driven discipline, allowing businesses to achieve superior results.
Importance Of Big Data Training For Companies
Big data training is a must for companies as it enables them to effectively manage and analyze vast amounts of data, unlocking opportunities for innovation and competitive advantage.
By equipping employees with big data skills, companies can improve their capabilities in areas such as predictive analytics, machine learning, and data governance.
This ensures that organizations are not only adept at extracting valuable insights from large datasets but also proficient in ensuring data accuracy and security.
As a result, companies that invest in big data training are better positioned to respond to market trends, optimize operations, and drive sustained growth through informed data-driven strategies.
Case Study on How Data Science in Marketing Drives Industry Success
A media company, heavily reliant on ad revenue from loyal users, faced the challenge of engaging new users and converting them into long-term subscribers. They implemented an AI-powered recommendation system to personalise user experiences by analysing initial content interactions and engagement patterns.
The AI system examined user behaviour, such as content types new users engaged with, and engagement patterns, like the time of day they were most active. Based on this data, the AI recommended similar content, content by popular creators, and time-based suggestions to keep users engaged.
This approach led to a 30% increase in the time new users spent on the platform and significantly boosted conversion rates as these engaged users turned into loyal subscribers.
To learn more, read our detailed case study on how data science in marketing can transform user engagement here.
Big Data = Bigger Marketing Opportunities
It is safe to say the days when marketing decisions were based on intuition and experience are long gone and will soon be forgotten. With big data analytics offering the capabilities to process large data sets (unlike traditional systems), it is becoming more prevalent across industries, from healthcare to small-to-medium retail stores.
Brands utilising big data in marketing can be viewed as customer-obsessed businesses with a customer-centric approach. With data collected from multiple sources—both online and offline—big data analytics helps organisations tap into customers’ emotions throughout their relationships.
