What Is Data-Driven Marketing (+5 Business Examples)

Patrick Foster
Patrick Foster  |  February 25, 2019

Big data is a hot topic in the business world at the moment.

The wealth of data at your fingertips provides valuable insight into every aspect of your business from product development down to personalized marketing.

But what exactly does it entail? And what benefits and challenges does your business face as it adopts a data-driven marketing strategy?

Read on to discover more, with six examples of businesses doing big data right.

What is data-driven marketing?

There are countless definitions of data-driven marketing online, each one more complex than the last.

In its most simplest form, data-driven marketing is:

Data-driven marketing definition

“The analysis of big data that provides insights into consumer behavior, which is then used to inform future marketing strategies.

Let’s break that down.

What is big data?

Big data refers to vast amounts of complex data collected from a variety of sources.

For example, a business might collect data on what a customer buys, how often, at what time, how they paid for it — the list is virtually endless.

This data is collected digitally through the analytics of your web-store, email, social ads, and so on. It’s plentiful and granular, and offers businesses deep insight into their customers.

Why use data-driven marketing?

Like anything, data-driven marketing has its pros and cons. Here are four of the key benefits of big data-led marketing, plus a few challenges.

Pros

1. More precise consumer segments

Your customer segments are one of the most important aspects of your marketing. Big data fleshes out your segments with greater depth and clarity.

As a consequence, you can identify with precision the customer data that matters. You might even identify new customer segments that have gone unnoticed until now.

2. Hyper-personalized marketing strategies

Data-driven marketing allows you to create personalized and hyperlocal marketing campaigns that are tailored to specific consumer segments.

By appealing to known customer information, you can generate more leads and conversions, aided by a deep understanding of your consumers on an individual level.

3. Enhanced customer experience

As well as using big data analysis to improve your marketing efforts, you can use it to enhance your overall customer experience.

Data-driven marketing highlights areas of poor performance. For example, customer service channels that have been neglected. Identifying what hooks your customers — and what repels them — on a granular level helps you finesse your CX.

4. Informed product development

Product development is a long, complex process. But by spotting customer trends in big data, you can see what products will be popular.

Not only does this shorten the ideation process considerably, but it provides data-backed development of products with a higher likelihood of profitability.

Cons

1. It requires time and effort

Data-driven marketing is not a side project. It is a full-time commitment, requiring enormous amounts of time, effort, and labor. Consequently, undertaking such a strategy should not be done half-heartedly.

2. It needs staff skilled in big data analysis

Big data is still a relatively young field — it’s new, niche and, for the uninitiated, nebulous.

Consequently, businesses might struggle to find recruits with the requisite skills and experience needed to implement a successful big data-driven marketing strategy.

3. It requires careful integration across systems and processes

As mentioned earlier, big data is collected from a wide variety of sources: marketing analytics, referral programs, social media, and so on.

While this is good news in terms of data, it becomes fraught with complications when it comes to integrating that data with a business’ systems.

Examples of businesses doing big date-driven marketing right

While the theory of data-driven marketing is useful, it can only get you so far.

Let’s look at six examples of businesses using data-driven marketing in the real world.

1. Coca-Cola

Loyalty programs are an effective way to acquire and retain customers for any business, and soft drink giant Coca-Cola is no exception.

Coca-Cola had a problem. Its MyCoke loyalty program provided it with reams of big data about its customers. But nothing lasts forever, and, eventually, only the most dedicated Coke fans stayed in the program.

However, because Coca-Cola still had that data, it was able to reach out to those customers with personalized marketing to offer alternative products. Coca-Cola was able to expose ‘lost’ customer segments to other products, such as Sprite or Minute Maid – increasing profits.

In a similar vein, the soft drink giant created Cherry Sprite as a direct result of this big data analysis gleaned from, of all places, the self-service drink machines found in fast-food chains. Through analysis of the most popular flavors, Coca-Cola was able to develop a new product backed by hard data.

TIP: Big data reveals a great deal about consumer personas and behaviors. Analyzing big data to learn more about your customers lets you deliver tailored marketing messages and develop new products that are virtually guaranteed to be profitable.

2. Netflix

Have you ever received a push notification recommending the latest Netflix series to you? Even if you haven’t, you’ve almost certainly browsed the ‘Because You Watched…’ section of the streaming service.

Every time you watch something on the service, Netflix takes note of it. It reviews search queries, which device a user views a program on, what date they watched it on, when a program is paused, how much it is re-watched — everything.

In short, the big data Netflix analyzes is pretty granular. Your viewing habits are part of the brand’s own big data, and Netflix mines it regularly to provide tailored film and TV recommendations just for you.

In fact, 80 percent of what we watch is influenced by Netflix’s big data analysis.

As a result, the brand increases customer retention by giving its customers exactly what they want through highly personalized viewing recommendations — even if the audience didn’t know they wanted it.

TIP: Big data can be used to create highly tailored customer experiences. From recommended products and services to bespoke marketing messages, big data helps deliver an individualized customer experience every step of the way.

3. Rawlings

Email marketing isn’t exempt from big data’s reach. The digital snail mail provides a wealth of customer data, and integrates with a range of business ecosystems – from e-commerce storefronts to content management systems.

Sophisticated email marketing software provides an array of big customer data, from browsed products to preferred shopping times. Consequently, it’s possible to segment customers on a granular level and drive conversions through tailored email marketing.

Sporting goods brand Rawlings uses savvy big data analysis to create effective abandoned cart emails in its own marketing strategy.

By analyzing its big data, Rawlings is able to determine customers that abandon their purchases. It can then follow up with a gentle chaser email promoting those products and reminding them to finish checking out – increasing conversions as a result.

TIP: Using email big data helps target qualified leads who have fallen out of your sales funnel. Create an abandoned cart and first purchase email automation workflow that drives data-backed conversions.

4. Amazon

Big data delivers big insight into big consumer bases. It creates a sort of informational microcosm – a virtual world that can be mined to predict customers’ needs, even before they arise.

AmazonFresh is a fine example of this approach at work. In 2017, the e-commerce giant bought out health food supermarket chain Whole Foods, incorporating it into its own grocery delivery service.

So, what drove this buyout? Amazon used big data to learn that the same customer base that uses its Prime service is also the same demographic that shops at Whole Foods.

Consequently, Amazon was able to make an informed business decision that would be guaranteed to boost its profits. It saw where its customers were shopping and turned a would-be competitor into an asset — clever.

TIP: As well as delivering a better customer experience, big data can help you make savvy decisions as your business scales. Use big data to scope out business opportunities and spot profitable investments.

5. PepsiCo

Warehouse and shipping logistics is a complex area. Coordinating inventory and shipping between warehouses, distributors, and outlets requires careful planning to ensure each step in the supply chain has enough product to fulfill orders.

Multinational food and beverage company PepsiCo has streamlined its entire supply chain through savvy use of big data.

PepsiCo’s clients — the retailers that stock its products — send the brand regular reports detailing current inventory and data from its point-of-sale (POS) systems. Armed with this data, PepsiCo fine-tunes its forecasted production and shipping requirements on a granular level.

Rather than potentially wasting unsold stock, or over-delivering, PepsiCo streamlines each step in the supply chain for maximum efficiency. Warehouses, retailers, and delivery trucks carry all the stock needed with the smallest margin of error possible.

TIP: Your supply chain is one of the most crucial aspects of your business – and the one most fraught with pitfalls. But analysis of your supply chain big data provides valuable insight into logistical shortcomings, highlighting problem areas and allowing you to implement data-backed streamlining solutions.

6. United Overseas Bank

Between trade wars between the U.S. and China and political turmoil in Europe, it’s more important than ever to focus on risk management. Strong risk management can’t always protect your business against unprecedented events, but it can prepare you for them.

Singapore’s United Overseas Bank (UOB) knows the importance of risk management. Risk is a crucial element in the financial sector in order to prevent fraud, laundering, or insider dealing – to name but a few potential risks.

UOB implemented a comprehensive risk management system that mined existing reams of big data to more quickly calculate its value when at risk. Past processes took approximately 18 hours, but using existing big data, UOB was able to reduce that time to mere minutes.

As a result, UOB can better protect its clients’ interests while maintaining its reputation on the global stage as a respected financial institution.

TIP: Big data analysis gives you insight into real-world events. Assessing existing data from past events lets you make accurate predictions about future ones – which, in turn, helps you confidently prepare in advance.

In conclusion

For novices, big data can be a daunting prospect, but it doesn’t need to be.

Through careful implementation and goal-led analysis, your brand can receive a wealth of insight that better guides your business decisions in 2019.

Patrick Foster
Author

Patrick Foster

Patrick Foster is an ecommerce consultant at Ecommerce Tips, which is an industry-leading ecommerce blog dedicated to sharing business and entrepreneurial insights from the sector. Start growing your business today and check out the latest on Twitter @myecommercetips.