Key examples of data-driven marketing strategies

Marketing needs to be data-driven in order to compete. Basing your strategies and tactics on robust data leads to more accurate insights, quicker decision making and more effective marketing campaigns.

And there’s a lot of data available. Statista predicts that the world will produce, store and consume roughly 181 zettabytes of data by the end of 2025. To put that into perspective, one zettabyte is enough data to store 30 billion 4k films.

Figuring out how to collect and use the vast amount of available data may seem complicated, but data-driven marketing is well within reach. This post will cover five examples demonstrating what it can look like and how you can succeed by basing your marketing decisions on the numbers. We’ll talk about:

• What is data-driven marketing?
• Top five examples of data-driven marketing strategies
• Data-driven marketing example: Philips crafts a new digital identity
• Benefits of data-driven marketing

What is data-driven marketing?

Data-driven marketing is the practice of using data to improve marketing communications. Traditional marketing tries to understand the target audience based largely on trial and error. With data-driven marketing, concrete details allow you to be certain that you’re optimising every part of your marketing strategy.

Over the last several years, digital transformation has enabled data-driven marketing like never before. It’s also created a demand for this type of marketing, generating a cycle of enablement and expectation. Because so many companies are using data to inform customer experience (CX) management, users have come to expect the kind of engagement that data-driven marketing enables — one that’s highly personalised and interactive.

Top five examples of data-driven marketing strategies

There are multiple ways you can put your customer and company data to work. Here are five examples.

1. Share data across different channels

Customers today rarely engage with a company or brand on a single channel. This means data silos are a major roadblock to any business that uses or is trying to implement an omnichannel marketing strategy. Trends and insights from one marketing channel need to be shared with others so that marketing decisions can be better informed.

Sharing data between different channels helps you to unify customer profiles. For example, how a buyer behaves on your landing pages should inform your social media campaigns and vice versa. If they view a product on social media, that same customer should be able to easily find that same product when they visit your website. When you allow data to be shared across channels, conversations become seamless as prospects move back and forth.

Sharing data also enables you to apply successful strategies from one touchpoint to another. If you find a particular call to action (CTA) effective in your emails, try that same CTA on your website or social media posts. Additionally, if you find certain keywords you’re using for paid advertising result in clicks or conversion, share that data with your social media team so that they can create posts targeting the same audience.

2. Use demographic data to plan campaigns

Another method of data-driven marketing is using demographic data to plan campaigns. Demographic data includes details like:

• Age
• Race
• Income
• Education
• Job title
• Income level
• Marital status

You’ll collect a lot of this data on leads and customers as you build detailed customer profiles. This information can come from social media accounts and form fills and help you to optimise personas and design targeted marketing campaigns.


But publicly available data on general audience segments is helpful as well. You can find quite a bit of demographic data using sources like the U.S. Census Bureau and its surveys — including the American Community Survey or the Current Population Survey.


Look for demographic data to inform your next marketing campaign. Maybe you’re advertising a high-end furniture brand to the entire city but haven’t received many leads. Use demographic data to determine which neighbourhoods are in a higher income tier. Focus your resources on targeting those postcodes and performance will likely improve.

3. Personalise the customer journey

Personalised customer experiences are essential. According to McKinsey, 71% of consumers expect companies to deliver personalised interactions and 76% get frustrated when companies don’t. Additionally, growing companies drive 40% more of their revenue from personalisation.

Customer data is the only way to personalise your content and communications. Go beyond plugging someone’s first name into an email. Data allows you to dig deeper and offer a level of personalisation that’s rich and unique.

There are countless ways to use customer data to create personalised marketing experiences and your team will get more creative with personalisation as they get used to using customer data. There are a few common strategies you can start with.

• Personalised product recommendations. You can easily personalise product recommendations on your landing pages, home page, emails, paid ads and social media posts just by automating data related to recent product views and purchases. Show similar or complementary products or those that other customers with similar purchase histories also bought. Use email or paid ads to retarget customers who started to shop but then left before completing a purchase.
• Personalised discounts. Customer data can be used to offer discounts on abandoned basket items, special discount code “gifts” for birthdays or loyalty programme rewards.
• Personalised messaging. Names in emails and special offers on birthdays are some very simple ways to use customer data to personalise the customer journey.

4. Target better with predictive analytics

Predictive analytics uses machine learning and advanced statistical modelling to analyse customer data, identify patterns and predict future behaviour. Most enterprise businesses collect far more customer data than they could ever manually organise and use. Predictive analytics helps data analysts use these massive sets.

Predictive analytics accomplishes two primary goals:

• Creating better customer profiles. Predictive analytics factors in details about prospects like industry, company size, funding resources and more to target companies that fit your customer profiles. Keep in mind that the data has to be up to date, accurate and readily available. Any compromise in the quality of your data will negatively affect your customer profiles.
• Identifying better accounts. Predictive analytics monitors behavioural patterns of decision makers at target accounts to determine which are most likely to convert. But it’s important to note that not all intent data is the same.

It’s easy to confuse predictive analytics and artificial intelligence (AI), but there’s a distinct difference. AI is a tool. Predictive analytics is a focused process that uses AI capabilities. An example of predictive analytics in marketing might look like analysing behavioural data to help you to recommend the right content to the right people at the right time.

5. Deepen audience insights with data onboarding

Data onboarding is the process of taking off-line customer data and moving it to online platforms. This off-line data could be any information related to your customers, like their contact details or info regarding in-store purchases. Once you’ve successfully transferred this data to your online environment, you can use it for marketing purposes.

Data onboarding can take a little extra time and effort — especially since any personally identifiable information (PII) will need to be anonymised. But it’s an important process that makes valuable information readily available to your teams.

Onboarding off-line customer data gives you insight into how your customers behave away from their devices — opening a whole new set of data to ensure your ads are reaching interested customers. For example, data onboarding helps increase the relevance of your targeted ads and deepen personalisation.

Data-driven marketing example — Philips crafts a new digital identity

To see how data-driven marketing works in practice, we’ll take a look at how technology and manufacturing company Philips implemented it to meet their complex digital marketing needs.

Philips needed to standardise the way the company’s dynamic content was produced, delivered and localised to increase product and brand awareness. The largest hurdle for the project was the fact that Philips has a digital presence in 79 markets and 38 languages — all with a massive amount of web pages that earn enormous traffic.

To meet this challenge, Philips used a robust data management platform, data analytics software and AI to test, measure and deploy modular content across global sites. Philips also integrated its data-driven marketing suite with a product information management (PIM) system. Analytics and AI allow Philips to continuously test how customers respond to content. This real-time data makes sure buyers receive the right messaging, at the right time, to optimise conversions.

For example, Philips found that by implementing a slide-in call to action (CTA), newsletter signups increased by 635%. Removing auto-play on videos improved product views by 15.85%. All of these decisions were backed by concrete data collected and utilised to make informed content marketing decisions.

Benefits of data-driven marketing

Data-driven marketing offers countless benefits that will help your brand. By incorporating marketing strategies that come from data-based decisions, you can:

• Enable effective ABM campaigns. Account-based marketing (ABM) is easier with advanced data-driven selection, helping inform decisions and boost your ROI.
• Create accurate attribution models and ROI reporting. Attribution will help you to understand the value of each action in a customer’s buying journey, offering visibility into how certain interactions affect conversion.
• Identify the best channels. Understand where your audience is spending their time and what content they interact with most. Without quality data, you’ll never know and your content strategy will be less effective.
• Develop memorable customer experiences. Data is the only way to provide the kind of hyper-personalised customer experience that stands out and turns prospects into brand advocates.
• Empower customer service teams. Providing standardised data to your customer service reps allows them to gain context for complaints and provide better support for customers.

For each of these benefits the end result is the same — increasing your bottom line. As more brands turn to data to help make better marketing decisions, collecting and using your own data will soon be standard practice.

Getting started with data-driven marketing

Data-driven marketing promises to make your marketing campaigns more effective. Get started by gathering your team so you can figure out what your marketing department needs from the company’s data. Remember, data-driven marketing is a means to an end — so start with your goal in mind. Then work backwards as you plan out your campaign and incorporate data where needed.

Adobe Analytics is more than a web analytics software. Benefit from a tool that lets you analyse data from any point in the customer’s journey. Analytics works across multiple digital channels to gather data in one place. Backed by Adobe Sensei, it uses AI for predictive insights based on the scope of your data.

Request a demo or watch an overview video to see how Analytics gives you actionable insights, not just canned reports.

Additional resources

To learn more about data-driven marketing, take a look at these resources:

• Level up your data-driven marketing game