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Data-driven marketing 101: benefits, examples and implementation
Learn everything about data-driven marketing, get inspired by real-life examples, and discover your next steps.

Back in the old days, marketing was ridden with a lot of guesswork. Sometimes, unexpected campaigns brought new leads and converted prospects into customers. Other times, the best-designed campaigns flopped, the market remained unmoved and all you could hear after the launch of a campaign was silence.

Data-driven marketing rose from the pains of this insecurity and took on the overwhelming growth of data for its support. It refers to strategies, processes and campaigns in marketing, all of which are guided by the data collected from customers via digital analytics, market research, qualitative insights, and customer behavior.

Benefits of data-driven marketing

Data-driven marketing offers multiple benefits:

1. Better business results. From cutting costs to producing higher returns on ad spend, using data to drive your marketing often leads to a healthier bottom line.

2. Improved operational clarity. Data improves operational clarity in three ways: 

a) It’s clear whether or not a campaign was successful. In data-driven marketing, you evaluate campaign performance by measuring the success with data: has the number of new potential customers, or prospects converted to customers, reached the desired goal? 

b) It takes the guesswork out of the game. Whenever you set up a marketing campaign, there are multiple ways to hit your goals. In data-driven marketing, you don’t have to argue about which creative is better - just choose your top contenders and let the market decide. From A/B experiments to machine-learning algorithms embedded in ad-buy software, data-driven marketing takes multiple creatives and uses data to decide which one works best for our customers.

c) Strategic alignment. Before turning to data to determine our future marketing efforts, leaders had to guesstimate which channels were the best performers. Some felt that TV commercials brought in the customers, while others argued it was the multiple conferences that they attended. With data-driven marketing, you can measure the effectiveness of each of your channels by attributing leads and purchases to them directly. This clarifies which channels you should double down on in your future marketing strategy.

3. Refined customer experience. Data-driven marketing offers information that helps you to tailor your campaigns to every customer. By personalizing the creatives, marketing messaging and value propositions based on data, customers find that your marketing speaks to them on a personal level and so are more responsive to your products and services. Personalized marketing is not just more effective, but also more satisfying. The improved customer experience leads to higher trust and loyalty, which in turn results in customers loving your brand and repurchasing in the future.

The transformational potential of data-driven marketing

Data-driven marketing boasts a wealth of benefits. However, it’s value also runs on a far deeper level: it changes how companies are structured and how they run their operations. 

Instead of egos and HiPPOs (highest paid person's opinion) running your strategy, you decide to give your customers a voice when it comes to shaping and developing your brand, communications, and product. 

This opens the door to more customer-driven business development, as well as empowering employees to voice their ideas. Any idea can be tested and win over the customer's heart when data - not HiPPOs - is the ultimate judge.

Examples of data-driven marketing

Data-driven marketing can take on many different forms. We’ll now take a look at a couple of areas and showcase what data-driven best practices can be implemented to boost your marketing growth.

Data-driven marketing examples for digital advertising

  1. Personalize your ads. It’s simple to use data to personalize ads to your target audience. For example, Google Ads uses keyword insertion for search ads. By using the same keywords, like the ones that your potential customer just typed, you tailor the ad to their search, thus increasing the chances of them interacting with it.
  2. Retarget qualified website visitors. Google Analytics allows us to track consumer behavior online. Combine Google Analytics with your advertising platforms to target only those website visitors who have demonstrated an interest in your offerings (e.g. read 3+ blog posts, spent a significant amount of time online, etc.).
  3. A/B test your creatives for maximum results. Create multiple creatives (e.g. ads), then use machine-learning algorithms embedded in ad-buy software (such as Facebook ads) to run A/B tests and determine which one is the best.

Case Study: personalized ad targeting

GreenPal - the ‘Uber for Lawn Care’ - is a lawn mowing service. Their Google ads were performing well, but they wanted to improve them with personalization. By analyzing the local prospect user base, they identified a gap between the communication of their digital ads and the needs of the customer base. Namely, customers from different regions of Nashville were more attracted to the low price of the service than the usual value proposition, i.e. the ease of having someone else mow your lawn.

GreenPal segmented the ZIP codes of the price-sensitive customers and localized the targeting of their ads with a personalized ad headline: ‘The Cheapest Lawn Mowing in Nashville. Lawn mowing from $20’. This boomed their ad success with a “200 percent lift in click-through rate and a 30 percent lift in on-page conversion”.

Data-driven marketing examples for email campaigns

  1. Personalize content. Export the email addresses from your email marketing tool and match them with other information you have inhouse (e.g. past purchases from your CRM, social media, sign-ups to your conferences, etc.) in a single place. Use the enriched information for personalized content creation. Personalization can be as simple as using your inhouse information to automatically fill slots in an email. For example: “Dear {first_name}, I am reaching out to see how happy you are with your {last_purchased_product}?”.
  2. A/B test email flows. Customer behavior is hard to predict. Will they prefer the email with the beautiful design or the one which is in plain text? Which email will lead to more click-through rates and purchases? Instead of guessing, run a small A/B test on a sample of your customers. The winner of the A/B test can automatically be sent to the other email addresses.
  3. Use predictive customer behavior analytics. Analyze your current customer base and customer data for information about purchasing behavior. Use the data analysis to understand which products are usually purchased together. Find customers who purchased one of those products, but not the others, and send them an email campaign to try and sell them the other products. Using machine-learning approaches and micro-segmentation can yield 3-times more purchases than non-data-driven product suggestions.

Case Study: A subject line worth $2 million dollars

Obama’s presidential campaign may have been almost two mandates ago, but the methods used will go down in history for their incredible success. The majority of the campaign funds were raised via email, and in order to make their outreach successful, they conducted experiments with the email to optimize it.

Concretely, multiple subject lines were shortlisted as the best contenders and emails were sent out to smaller batches of the email list for an A/B test. The winning email of those experiments was then sent to the remainder of the newsletter list. 

How impactful were those tests? According to Toby Fallsgraff, Email Director, and Amelia Showalter, Director of Digital Analytics, the difference between the best and the worst email equated to $2 million dollars. This is why testing pays off.

Data-driven marketing examples for social media

  1. Get inspired by search results. Use web analytic tools, such as Google Search Console, to understand the things that people search for before landing on your website. Use the search queries with the most hits to inspire your social media posts. If a lot of your web visitors searched for “How to do X”, be sure to write a social media post about it. The same strategy can be used for better content marketing.
  2. Follow the trends. Consumer behavior is notoriously hard to predict, but you do not need advanced predictive analytics to optimize your social media. Analyze the engagement levels (likes, comments, shares) on different posts and determine which topics and post types produce the highest social media activity among your target audience.

Case Study: research your audience to make your social media work for you

Dove, one of the leading beauty product providers, heavily researched their target audience outside of the domain of cosmetic usage, with the aim of better understanding who they are as people, not just consumers. 

In the research, they identified that 80% of women were exposed to negative body talk online.

To address this online undermining of women, Dove launched a campaign under the #SpeakBeautiful hashtag, which focused on positive body talk. The campaign did not sell Dove’s products directly or inform potential customers of their offerings.

Instead, Dove gained recognition as a positive brand. The results?

  • #SpeakBeautiful was used more than 168,000 times
  • 800 million social media impressions came from the campaign

Data-driven marketing examples for strategic decision-making

  1. Segment your customers. Knowing your customer is paramount for surviving the cut-throat competitive landscape. Segment your customers to tailor to their different needs via separate marketing efforts. For example, if you sell beverages, have different marketing funnels for different segments (for example, tea lovers vs. coffee lovers).
  2. Tap into predictive analytics. Predictive analytics allows you to anticipate customer behavior. We spoke above about recommender systems that outperform general recommendations when it comes to product purchases. But you can also predict repurchase times and shorten the length between repurchase cycles. How? By targeting those customers who are predicted to repurchase soon.
  3. Market research your target audience. You do not need big data to make data-driven decisions. You just need to target the right people. Take a look at what your competitors are doing and how they are approaching potential customers. Learn from their mistakes and copy their successful strategies. Because of the similarity between operations, the marketing insights you get from your competitors can quickly be transferred to your own target market.

Data-driven marketing examples can be tempting. They sound exciting, they’re novel, and any marketer worth their money is itching to try them. But as with every other activity, being fast to market or having good ideas is not enough. To nail your marketing, you need to deliver consistently. Execution is everything. To execute successfully, implement a data-driven marketing strategy to drive your efforts to fruition.

Become data-driven without needing a data engineer or code. Start your free trial.

How to implement a data-driven marketing strategy

Whether you work in a B2B niche or run a B2C e-commerce site, the general steps of implementing a data-driven marketing strategy are consistent across organizations of various sizes, verticals, and levels of data maturity:

1. Start by setting business objectives. Every marketing strategy, including a data-driven one, begins with setting business objectives. The business objectives should be formulated as S.M.A.R.T. goals to make them effective:

Specific: the goal needs to be specific enough for people to immediately understand what the bottom line is without any ambiguity. Are you trying to raise ROAS by 12%? Hit a specific revenue goal? Increase the average weekly number of new leads by X amount? Are these goals for all products, or just specific ones?

Measurable: Every goal needs to be measurable. Some goals are easy to measure, e.g. the number of new prospects, customers, and ROI changes. But consider some of the harder things to measure, such as social media engagement (we could count the number of likes, shares, comments, or all of the above) or brand affinity (hard to measure, unless you implement a sentiment analysis over your customer reviews or check NPS changes). In general, ‘softer’ goals (those which aim to improve customer satisfaction, opinions and values) are harder to measure, but not impossible. 

Achievable: The goal needs to be attainable. If it’s impossible to achieve, you are playing lip service to the data-driven marketing strategy and will probably feel demoralized by the impossibility of it.

Relevant: The goal must be beneficial to the company. This does not necessarily mean increasing the revenue (by acquiring new customers or upselling existing ones); it could mean increasing brand loyalty or customer satisfaction. The latter still affects the bottom line, but on a longer time scale.

Timely: The goal needs to be set against a timeline, with a start and end date. This prevents you from constantly waiting for the results of a campaign. If there are no results, that’s fine - the goal could not be determined as successful and you can move on to other goals.

2. Translate business objectives to marketing campaigns. Every business goal can result in multiple marketing campaigns and strategies. This is where your expertise has the opportunity to shine. Formulate multiple tactics to reach your goal and shortlist the best contenders based on your experience.

3. Establish what data is needed for the launch of the marketing campaigns. Some marketing campaigns require data before they can be launched. For example, imagine you are launching an upselling email marketing campaign - you would need a list with customer information (name, age, previous products, recommender product for upselling for every individual). Other campaigns can only be evaluated using data after the launch, for example, Facebook remarketing ads to convert leads from a landing page blog to paying customers. Establish exactly what data you need to make the campaign great.

4. Implement a data collection process. Once you know the data that you’ll need for your marketing campaign, figure out how to collect it. In some cases, this can be trivial (e.g. Facebook campaigns can be run within the Ads management platform). In other cases, this can result in a cross-departmental chase for engineers. Use data management platforms that help you to aggregate data about your customers from different marketing platforms (e.g. Google Ads, Facebook Ads, Linkedin Ads, Bing Ads, CRM, Email marketing tools). Enrich the data, then analyze it (for instance, use a recommender algorithm to understand which products you should recommend to each customer).

5. Run the marketing campaign.

6. Evaluate the marketing campaign based on data. Evaluate the success of the marketing campaign by comparing it to the SMART goal. Have you reached the predetermined measurable success that you set out to achieve?

7. Automate. When you nail your goals, automate them - there’s no need to manually run the same data collections and marketing campaigns. Rely on software and tools (such as data management platforms), which re-run all of the aforementioned steps for you. This saves you time while guaranteeing a high standard of repeatable marketing processes.

What are the challenges of implementing data-driven marketing?

There are two types of challenges that accompany the implementation of data-driven marketing: organizational and data challenges.

Organizational challenges refer to the expertise and skills that are found in-house. A lack of data engineering and analytic skills (or the lack of time faced by the workers who have those skills) can severely impede your data-driven progress. Trying to collect the relevant data for your marketing efforts by hand, without the experts, can slow you down. Thankfully, there are data management platforms that can do the heavy-lifting for you.

There are also data-specific challenges that arise when implementing data-driven marketing:

  1. Dispersed data. Data is held by different departments in data silos, and a lot of work is required to join it all together.
  2. Stale data. The technological stack that you use to collect, aggregate, analyze, and integrate your data is slow, which causes your data to be stale. Your marketing campaigns are damaged by the outdated data that you use.
  3. Manual work. Your technological solutions for ingraining data into marketing require a lot of manual work and do not offer automation.

Many of these challenges can be solved by relying on a trusted data management platform.

Data-driven marketing with a data management platform

The right data management platform can help you to quickly achieve better results with your marketing.

Keboola is an all-in-one data platform, built to accelerate data-driven company operations:

  1. Keboola connects all of your marketing, sales, email, and software tools in just a couple of clicks, regardless of how the data is dispersed across departments and tools.
  2. The state-of-the-art engineering is optimized to run the entire data pipeline from data collection to data analysis, and fast. This means that you will always be working with fresh data.
  3. The platform allows you to automate your repetitive tasks to avoid manual work and quickly achieve repeatable marketing processes.
  4. Collaboration is integral to marketing success. Keboola allows you to collaborate within the platform itself on the same pipelines, as well as share your data and results with others in a simple manner.

Become data-driven without needing a data engineer or code. Start your free trial.




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