Data-Driven Marketing: Drive Performance With Customer Data
When it comes to customer data, there’s no shortage of it. But data alone is not enough. For your data-driven marketing strategy to work, processes need to be in place so end users get relevant insights quicker.
In this post, we’ll explore the benefits and pitfalls of data-driven marketing and how it works in practice.
- Data-driven marketing tracks user engagement and behavior to tailor marketing strategies to the needs and preferences of the ideal target customers.
- Using data, organizations provide customers with a personalized experience, which leads to a better return on marketing investment (ROMI) and improved key performance indicators (KPI).
- Data-driven marketing has a lot of challenges. Many organizations get overwhelmed with marketing data, which leads to errors and inconsistencies.
- One way to solve this common problem is to use a data consolidation platform that automatically stores, cleans up, and prepares the data for analytics and reporting.
What Is Data-Driven Marketing?
The meaning of the term "data-driven marketing" lies in the name itself. Data-driven marketing means collecting and using data to drive your marketing decisions.
That's it, very simple.
If we dive into the details, data-driven marketing uses strategies driven by the insights extracted from customer, marketing, and sales data, which we use to make predictions and decisions, run tests, and adjust marketing efforts to match the end customers' needs.
This type of marketing requires teams to digest the data they've already gathered, know how they can aggregate new data, and find ways to harmonize, organize, analyze, and apply it to their marketing strategies.
Traditional vs. Data-Driven Marketing
To understand why marketers care about data and analytics, we need to go back in time and review how marketing efforts were structured a few decades ago.
Basically, companies used a spray-and-pray approach. This means marketers spread their messaging and communication to a broad audience and would simply pray that the message hit the target audience.
Advertisers had near-to-zero information about their audiences, customers' preferences, engagement level, and so on. That's why their decision-making process was based solely on very broad parameters, like age, gender, location, and so on.
Here's a great example of how misleading demographic data can be.
Companies could hardly understand what triggered customers to purchase their products or services. Traditional (or legacy) marketing was based on hunches, guesses, and assumptions about what customers want and need. Relying on intuition and someone's experience was the only option because businesses didn’t have all the relevant data at their fingertips.
The path of trial and error could cost a fortune, and failing marketing campaigns could seriously blow the company's budget or brand image.
Nowadays, marketers possess an incredible amount of data. Real data-driven marketers know the prospects' decision-making behavior, hobbies, how they interact with the brand, what touchpoints they use throughout the customer journey, and more.
Data-driven marketing allows customers to build buyer personas around customers' problems and challenges, rather than their gender or wealth.
Benefits of Data-Driven Marketing
Businesses that lean on data win. As the path to purchase becomes more complex each day, data provides quantifiable proof of what customers want. So, when your organization implements a data-driven marketing strategy, it benefits the business in many ways.
One-size-fits-all marketing is history. Consumers expect a personalized experience as they navigate their buyer journey. When you know your customers' preferences and behaviors, personalization is the next logical step.
Studies show that personalization significantly contributes to business profitability. It improves lead quality, helps win more sales, and generates a higher ROMI.
A well-rounded view of your ideal customer
Data-driven marketing takes you beyond best practices and towards marketing strategies specific to your target customers’ needs and wants.
Combined data from different marketing platforms provide insights into your customers’ behavior at different touchpoints—and ultimately reveal what drives conversions.
All these data-driven marketing insights make you better informed on what prospects need to hear at each stage of the customer journey. You can then tailor your messages to fit their needs and interests.
Better marketing budget allocation
With data powering your decisions, you'll know exactly to which channels you should allocate more of your marketing budget. Simply review past campaign data and identify which activities delivered a higher increase in ROI, better-quality clients, or higher retention.
Just as importantly, having data to back up the success of your marketing campaigns is a great way to get internal buy-in for more activities, such as content marketing.
Precise data-driven attribution
Attribution is one of the key marketing levers for making big leaps in performance. When you laser-focus your marketing activities on channels that have been statistically proven to generate better engagement, you reach your revenue goals faster.
Data-driven marketing allows you to consolidate data from different channels, recreate the customer journey, analyze every customer interaction with your brand, and evaluate which touch points lead to purchase.
Let’s say a prospect clicks an ad on LinkedIn, signs up for a lead magnet on your website a week later, and then clicks an affiliate link before making a purchase.
With adequately managed and consolidated data, you can develop a data-driven attribution model that fits your marketing goals based on these actions.
Improved cross-channel advertising
Recent research by Fluent has found that 62% of customers who interact with the brand via 10+ channels make purchases at least once per week. However, according to London Research, only 38% of businesses have synchronized the customer journey across all physical and digital channels. This means a lot of companies are missing a competitive edge and additional revenue by overlooking omnichannel advertising.
Omnichannel marketing ensures that your company sends consistent, aligned messages that reach the right users at the right time. However, aligning cross-channel marketing data is hard without proper data tools. ETL marketing solutions help marketing organizations to:
- Aggregate data from all their marketing and sales resources to get a holistic view;
- Unify disparate data to avoid making decisions based on low-quality data;
- Create a single source of truth to foster alignment between departments;
- Cut time-to-insight and optimize campaigns on the fly.
With organized data processes and all marketing data in one place, advertisers can reduce repetitive work and concentrate on marketing analysis rather than manual data operations.
Minimized Failure Risks
With data-driven advertising and an enhanced understanding of the market, companies minimize the risk of failure when launching new products or new product lines to the market.
With granular insights into their prospects and the market as a whole, marketers conduct initial testing, identify potential customers beforehand, and guarantee a successful product launch.
Data-Driven Marketing: Pitfalls to Watch Out For
When the marketing department adopts a data-driven strategy, it creates ripples through the entire business. Data becomes the central asset from which marketing and business decisions are drawn.
However, all this data can be overwhelming. And if an organization does not have processes in place, pitfalls happen. Businesses should be mindful of them when adopting a data-driven marketing culture.
Poor data quality is one of the most common reasons executives don't trust data. It also results in bad marketing decisions that lead to lost deals, confused or angry customers, and loss of revenue—among many other negative outcomes.
To have good data quality, we must ensure that it has these seven traits: accuracy, completeness, consistency, validity, uniqueness, timeliness, and integrity.
Data privacy regulations affect the workflow of data-driven marketers. Non-compliance with GDPR or CCPA regulations may lead to million-dollar fines. Quite a penalty for making the user experience more personalized.
Additionally, Apple's recent privacy changes dealt a serious blow to the marketing world. For example, Snap stock fell 22% after reporting its third-quarter revenue. The company lost its revenue after its advertising business was disrupted by Apple's new privacy policies.
Cookies also threaten user data privacy. Deloitte's study shows that only 26% of cookies across six different industries were secured. The rest of the personal data was left unsecured. In a nutshell, companies have to take data privacy very seriously.
Cookies are no longer a reliable source of information for omnichannel analytics. Only around 30% of users consented to cookies in 2021. This means marketers have to search for new ways of tracking attribution, such as cookieless attribution.
Different departments within an organization sometimes collect their own data, with their own software, and with their own data processing rules. Some of this data isn’t shared with other departments, which creates data silos.
Data silos create gaps in campaign reports. This means marketers do not get a holistic view of the customer journey.
One effective way to stop data silos is to make transparency part of the company culture. Also, you should consider investing in a data warehouse and following an ETL process that automatically connects multiple data systems into one database.
Lack of alignment between the marketing and sales departments
Businesses that align their sales and marketing departments close 38% more deals and generate up to 208% more revenue.
Unfortunately, in many organizations, marketing and sales work independently of each other. This often means each department creates its own data. But the reality is that the lines between the two are blurred, and alignment is key to organizational and business success.
To encourage cooperation, marketing and sales departments must share data, have an open line of communication, and strive towards a common goal.
Problems during data organization and management
Data needs to be processed before it becomes usable to the people in your company. It's a vital but fragile part of a data-driven marketing strategy.
Many things can go wrong during this process, such as data duplication, loss of information, or privacy vulnerability. To prevent errors in data organization and management, you should put a data governance strategy in place. This ensures that data engineers and scientists are clear on the queries and metrics that are important for the end user.
This is understandably a complicated and time-consuming process. You can make this easier by using an ETL platform that automatically extracts data from various data sources, transforms it, and loads the results to a final destination.
Data-Driven Marketing in Action
What does data-driven marketing look like in practice? How can it affect the way you run your business?
Here are a few examples of how organizations use data to drive their marketing operations.
With the buyer's journey becoming more complex, deciding which attribution model to use is increasingly difficult. But using data and machine learning, you can automate this process and choose the data attribution model that best fits your marketing goals.
For example, data-driven attribution is now Google Ads' default attribution model. Whereas traditionally, the last action may be given attribution for a conversion, Google Ads now looks at the whole customer journey and uses machine learning to identify which keywords, ads, and campaigns play the biggest role in helping businesses reach their goals.
Data-driven A/B tests and conversion optimization
By their very nature, A/B tests rely on data to test different website variations to figure out which one resonates more with consumers. It’s a low-cost, high-reward approach to optimizing marketing assets.
Results of A/B tests help marketers identify what messaging resonates with customers and what offers get results.
Data-driven advertising uses AI, machine learning, and automation to show ads personalized to the consumer. Using data, you can better identify the right platforms to reach your audience and the type of content that generates higher ROI.
For example, TikTok already knows the type of videos that will likely lead to a lift in conversions. Also, if you make good use of retargeting ads, you can show different messages to a prospect based on their engagement behavior.
Data-driven content marketing
Content is traditionally a creative pursuit. But data-driven content marketing leans on the analytical and creative skills of content marketers.
This way, you can keep a tab on consumer trends and quickly identify the type of content your ideal customers like to consume.
Geolocation is extremely valuable data for marketers. If you have precise information about your customers' location, you can successfully use it with paid ads campaigns. Google Ads allows marketers to target prospects based on countries, regions, or even a specific radius around a certain location.
Thus, if company data shows that state X has a history of high-converting leads, doubling down on PPC in this region would be an example of the strategic use of data you have on hand.
Steps to Build a Data-Driven Marketing Campaign
Now, it’s time to break down a data-driven marketing process into steps and take a closer look at each of them.
Step 1. Define requirements
Before you proceed with collecting data, you must clearly define the objectives that you want to achieve with data-driven marketing. Analyze your current marketing channels and cross-reference them to understand what metrics you can monitor. You should also set clear KPIs that will help you understand what data you should collect.
Step 2. Data extraction
Take a look at your objectives and make a final list of the data source connectors you need to extract data from. You can extract data manually by invoking APIs during each data update, or you can find a data extraction tool that will automate the whole process.
Step 3. Data normalization and loading
For this step, you need to choose your storage solution, which is where you’ll aggregate your future data. Solutions like Amazon S3, Snowflake, and Google BigQuery are the data warehouses most popular among marketing data analysts.
Before loading your data to a warehouse, you have to cleanse and unify it. Manually eliminating duplicates, excess fields, and columns may take up too much time, and that’s why we recommend that you use ETL systems. While some teams prefer to do mapping and cleansing on their own, experienced data-driven marketers delegate such tasks to software solutions. For example, Improvado offers its MCDM (Marketing Common Data Model), which allows you to transform unstructured marketing data in a way you need and store it in a warehouse.
Step 4. Derive actionable insights with visualization tools
When your analysts have a warehouse filled with analysis-ready data, it’s time to create marketing dashboards and feed them with the gathered data. Tools like Google Data Studio, Looker, Tableau, and others provide you with a granular picture of your marketing performance and highlight previously overlooked customer behavior patterns. A quality ETL system will streamline all your normalized data into a visualization tool without any help.
Step 5. Handle Your Campaign
With all of the required data on the dashboard, you can optimize your existing campaigns or launch new ones. Now, you have to focus on metrics like ROMI, ROI, CPA, CPC, and others to identify the differences and track the progress of your efforts.
Step 6. Monitor Your Marketing Performance
To clearly understand the outcomes of your efforts, your marketing analysts should continuously track performance, calculate ROMI, and monitor marketing reports. Only in that way can you truly assess the effectiveness of data-driven marketing.
Data-driven marketing isn’t always straightforward. With the modern marketing tech stack, businesses have so much data from different sources that they may become overwhelmed and end up not doing anything with the data they have.
Besides, consolidating all this data runs the risk of data inconsistencies, with human error as the leading cause.
To prevent these common data-driven marketing pitfalls, use an ETL platform that automatically extracts data from multiple platforms, transforms it, and turns it into a coherent and helpful report for all stakeholders and decision-makers.