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.
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.
Here are more ways companies can use big data in marketing.
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 stick to just 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 taking into account 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 the customer 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 that 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 gain 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 rate. 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, when the industry average was 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-personalized 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-personalized home pages, the brand aggregated data sets such as customer location data, weather reports of a customers’ location, purchase history, search history, and frequently visited category.
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 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 in developing various user-profiles and later was used to shape 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 back 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 behavioural score data of customers to understand their pre-conversion journey. This helped them identify customers who are more likely to convert. 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 in creating 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 are 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. The strategy boosted the email opening rates by 48% and helped in maintaining the excitement of using the equipment.
7. Airbnb
Image Credit: news.airbnb.com
Airbnb is known for diverse hiring and dedicates the process to be 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 website versions 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 a host and a guest. 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.
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 and 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 to tap into the emotions of a customer throughout their relationship.
