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What it Takes to Build a Marketing Data Strategy

There is more data than ever at the fingertips of marketers. The time for an integrated marketing data strategy is firmly upon us. The concept of a marketing data strategy is not new, but it is often misunderstood. Simply looking at data while formulating a campaign, reviewing an email blast, or promoting an event is not enough.

What’s the secret sauce? While a data-driven marketing strategy is crucial, a marketing data strategy isn’t solely focused on making the right choices for your marketing campaigns based on the data you have. It’s about acquiring, managing, enriching, and employing the right data for your marketing department. In short, this type of strategy starts before the marketing strategy and aims to utilize the most useful and insightful sets of data for the marketing campaign.

Why is Having a Data-Driven Marketing Strategy Important?

A marketing strategy without data is little more than a shot in the dark, which is why roughly seven out of ten leading marketers report that their organizations use data to support decision making at every level. In the same report, two out of three marketers stated that CEOs value insightful data over gut instinct. Data is, simply put, the foundation of your marketing department’s success. It encourages strategic decision-making, provides clear answers to questions of success and failure, and allows marketers to get buy-in from the top when implementing new strategies.

Blending Data through Centralized Data Management

Your strategy starts with a data hub – a place where your organization can merge and blend every data source into one location. For most marketing departments, the problem isn’t a lack of data, but what to do with that data once it’s acquired. Or, more specifically, how to correlate, blend, and balance all of the various data sources together to build a cohesive picture. Figuring out which data sources are informative isn’t easy if they’re spread across multiple platforms, though. Even if your marketing data strategists understand your organization’s goals and KPIs (key performance indicators), they’ll still want to analyze all of your data in one, centralized location. That way, you can start to see how certain metrics compliment and stack up next to others, which ones can be merged, and which ones are the most valuable to your marketing strategy.

Read next: How to Build a Data-Driven Content Strategy for your Business

The Power of Data Communication

According to Google, 86% of senior executives agree that removing silos within organizations is crucial to data- and analytics-driven decisions. Additionally, 75% of marketers said that the biggest barrier they face in using data is a lack of education in data analytics. Practically speaking, this means marketers understand the value of data (and so do executives) but tend to experience trouble communicating data insights to departments that may not prioritize data management and training.

There’s no over-the-counter cure for a lack of training and education, but one of the best ways to communicate the importance of data across all departments is to deploy relevant and easy-to-understand information. This means you’ll need to:

  • Share relevant and useful data with senior executives
  • Keep sales teams in the loop with data-driven KPIs
  • Hold your marketing team accountable to communicating data often and effectively
  • Present data in a digestible format that speaks to non-analysts

The key is communicating data across all departments to ensure that everyone is working toward the same goal. Your organization’s marketing data strategy exists to make sure this happens, and to make sure that the strategy for marketing your organization is aligned with these KPIs as well.

Understanding what data to share with each department might seem like a challenge, but is actually quite simple. Start by understanding your organization’s priorities and the metrics that reflect those goals. This will require meetings with stakeholders, mutually agreed upon goals, and explicit connection of metrics to those goals. Then, determine how each department contributes to those goals and identify the data sources that build the KPIs that reflect their progress and success. In the end, you should have a set of varied KPIs with the same purpose: the success of the organization.

You Don’t Have to Be a Data Expert to Understand Data
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An analyst uses a computer and dashboard for data business analysis and Data Management System with KPI and metrics connected to the database for technology finance, operations, sales, marketing

Everyone has goals and (whether they realize it or not) a multitude of metrics and data sets to measure the success of each goal. On the one hand, every facet of your organization should have a data-centric perspective. On the other hand, it is unrealistic to expect every team member to be as data-savvy as your analytics or marketing team. The solution? Finding a way to communicate crucial data points across all departments in a way that is accessible, easy to access, and easy to understand.

The best way to do this is through data visualization. Data visualization “speaks the language” of every department in your organization and is one of the fastest, most efficient ways to communicate KPIs. By using a data visualization tool like a dashboard, you can collect and distribute data quickly and in a way that translates data to every member of every department. Additionally, dashboards facilitate interdepartmental communication, which is crucial to the success of your marketing campaign. In the end, a medium that makes metrics accessible to team members with every level of data training is invaluable.

Gather, Analyze, Rinse, and Repeat

Data analytics can be broken down into two components: descriptive and prescriptive. Generally speaking, descriptive analysis seeks to understand historical data and understand why certain metrics led to specific results. On the other hand, prescriptive analytics takes the analysis a step further and predicts what will happen in the future. In short, prescriptive analytics attempts to answer the question “Based on what we know, what is likely going to happen moving forward?”

Your marketing data strategy should use both descriptive and prescriptive analytics. While descriptive analytics are the building blocks of any data initiative, prescriptive analytics are what provide truly meaningful, actionable, and valuable. Prescriptive analytics are particularly useful when trying to target marketing campaigns. Use demographic data for the people that have responded and converted on your marketing materials in the past to more accurately target on your next campaign. Your reach may be small, but so will your spend, and you should see a lift on conversion rates and ROI.

Once your data strategy is in place, set benchmarks to re-assess your strategy. Marketing is always a moving target, so staying on top of your results on a regular basis is mission-critical. Why was the strategy successful, and in what ways? How can it be improved? Then, using the historical data you’ve gathered, determine key areas for improvement and adjust your strategy to aim for future success.