Why is Data Driven Decision Making Important and How Does it Impact Your Business Growth?

The role of a digital marketer has evolved over the years. Now, one of the most highly-sought after skills is proficiency in data science.

This shift is because efficient data management has become one of the top competitive differentiators between businesses. If you want to improve your strategies and successfully build your company, you need data.

Why is data driven decision making important?

When you actively transform your raw data and marketing metrics into actionable insights, you stay one step ahead of the competition at every turn. Not to mention, you gain a deeper understanding of your audiences and industry.

Throughout this discussion, we’ll look at the different ways that data empowers your decision-making and helps you grow your business rapidly.

Why is Data-Driven Decision-Making Important?

Data-driven marketing uses metrics and other information to inform decisions. Data shows you what works, what doesn’t and the steps you can take to improve your campaigns or business strategies.

These insights allow you to make consistently smarter and more accurate decisions in less time.

Here are just a few of the powerful advantages of data-driven marketing.

Time & Clarity

With the right tools and systems in place, businesses can swiftly filter out all the noise to focus on the most critical data. This provides exceptional clarity into the data and metric changes most relevant to your goals and strategies.

Segmentation

Use your marketing data to create better audience categories. You can learn the different types of customers that interact with your brand and make strategic decisions on how to best service their needs.

Personalization

Through deeper customer segmentation, you can deliver experiences and messages that are more personalized to each customer’s preferences. Personalization is a powerful way to improve your marketing success and increase brand loyalty.

Customer Experience

One of the reasons that personalization is so crucial is the effect it has on your customer experience. People are drawn to brands that offer exceptional experiences. Data-driven marketing allows you to tap into feedback forms, survey data and other sources to learn how to develop these types of strategies.

Product Development

As you learn what customers want from brand experiences, you’ll also improve your understanding of products. Data-driven marketing helps businesses develop better products that align with what consumers actually want.

Multi-Channel

Modern marketing takes place over many different channels. The data-driven approach helps you strategize the best channels to use and the right messages, content and strategies to publish on each one.

How To Implement Data-Driven Decision-Making in Marketing

Naturally, you can’t have a data-driven approach without data!

Brands produce tons of data in the Digital Age, whether generated from contact forms and surveys, in-person transactions or online interactions. Even a simple digital marketing campaign can produce mountains of data regularly.

This substantial amount of information holds valuable insight into customer behaviors, competitor strategies, market trends and more.

Let’s dive into the process of implementing a data-driven approach to marketing decisions.

Step 1: Obtaining & Analyzing Data

Data helps businesses develop accurate customer profiles and optimize strategies to achieve the best results. You can apply data to understand the preferences of each customer better and even predict their behaviors and spending.

There are several ways that businesses collect data. Here are a few standard methods:

• Gathering data directly from customers through registration and subscription sign-ups, contact and feedback forms and similar tactics.
• Acquiring website data from cookie tags, analytics tools, etc.
• Collecting engagement data from your ads, blogs and social media content.

Once you gather the data, it needs to be analyzed. This step is arguably the most challenging because you have to overcome the size and complexity of your data.

The goal of the analysis is to make sense of the raw data. You need to know what all those numbers in your spreadsheets really mean!

Step 2: Predicting Consumer Behavior To Drive Sales

When you deeply understand your customers, marketing becomes much easier because you know exactly what they want.

Data analysis helps you tap into the products, topics, websites, apps, etc., that customers prefer. You can use this data to put your best foot forward every time you interact with a customer.

By analyzing and predicting these audience interests, you can directly increase your sales and marketing success.

Step 3: Strategically Align Customer Behaviors To Marketing Goals

Once you have a clear understanding of your audience and their behaviors, you can begin optimizing your strategies and how you present your products.

Again, it’s about putting your best foot forward for every customer. You also need to think about your business and its goals.

For example, let’s say you run a new clothing boutique. You want to improve awareness for your business. By analyzing social media data, you discover that many customers use Instagram to browse new styles or fashion inspiration.

Thanks to this insight, you include Instagram in your content marketing to get your brand in front of these Instagram browsers.

However, if your goal is to increase sales, you want to explore customer spending habits and purchasing behaviors.

Step 4: Customer Segmentation

As you explore your customer data, you’ll discover that not everyone acts the same or carries the same interests and preferences.

You need to segment customers into groups with similar interests and behaviors. These smaller groupings enable you to make more accurate assumptions about what behaviors and interests each segment carries.

There are a few ways that businesses segment their customers.

• Demographic Segmentation
• Psychographic Segmentation
• Behavioral Segmentation
• Geographic Segmentation

You want to utilize more than one segmentation approach to create the most accurate audience groups.

Step 5: Develop Lookalike Targeting

The last step of the process is to use what you’ve learned throughout these stages to develop lookalike audiences.

Lookalike targeting uses the data and insights you’ve acquired about your current customers and applies it to finding new audiences that fit a similar profile.

This method is excellent at generating new business from valuable prospects that are likely to purchase your brands.

Then, you can start the data-driven approach all over again, gathering details about your newest customers. This keeps a steady stream of prospects and leads funneling into your business.

The Most Significant Data-Driven Marketing Challenges

There are plenty of advantages to taking a data-driven approach. It’s a no-brainer, right? Yet, many businesses still unsuccessfully tap into their data to discover customer and marketing insights.

The problem is that becoming data-driven is not easy. There are several challenges associated with analyzing data and discovering insights.

Locating The Right Data

No business has a deficiency of data. Every company generates tons of it daily. While there is plenty of value in data, not all of it is useful or valuable.

You have to remove all of the noise and unnecessary info that distracts you from the data with real value and relevance to your marketing goals.

Many marketers find themselves spending more time filtering their data than actually analyzing it.

Think about your marketing and business goals and which data sets and metrics align with these

Asking The Correct Questions

Before you can even begin filtering your data, you have to know what parts are valuable. In other words, you need to know what data is just noise to ignore and what data is relevant to your decision-making.

Every data analysis starts with a question. You’re hoping to make a discovery or deepen your understanding of a topic. This question will guide what data you need to analyze and what conclusions or results you may receive.

Unfortunately, knowing which questions to ask can be a challenge. When you make incorrect queries, it wastes time and leads you to the wrong conclusions.

After all, data analysis is time-consuming as is; you don’t want to add more work to the equation by failing to ask the right questions.

Normalizing Big Data

Once you’ve filtered out the non-vital data, you’re still left with a substantial amount of information. Often, you’re pulling data from multiple sources at once.

For instance, you may be grabbing data from your CRM platform, website analytics and sales system.

To bring these data sets together, you need to perform some cleaning and processing to normalize the information. This ensures that each data source can communicate with the others.

This is also the point where you check your data over for errors, discrepancies and other problems.

It’s yet another task that is incredibly time-consuming and requires some expertise in data science.

Interpreting The Data

Even when filtered, processed and normalized, marketing data is still complex.

Unless you have a statistical background or strong analytical skills, interpreting your spreadsheets is challenging.

You need to utilize charts, reports and other tools to help simplify the wall of numbers and help you understand the story behind the data.

These tools and methods certainly help, but you still need to have some analysis skills to accurately interpret what’s happening.

If you make the wrong interpretations, you’ll reach the wrong conclusions and make poor decisions. When inaccurate interpretations are used to guide your decision-making, it’s worse than relying on assumptions.

Keeping Up With Real-Time Data

Data helps you respond to the market’s current status and make future predictions to continue prospering tomorrow and the next day.

That level of agility allows businesses to become proactive in their marketing optimization efforts, instead of only reactive. You’ll capture new trends and avoid probable risks sooner than your competitors.

However, to improve decision-making speed and efficiency, you have to be agile yourself. You need to be able to capitalize on the real-time data that your strategies create.

In other words, there’s a time frame for your analysis. The longer it takes you to discover insights, the less valuable the information becomes.

It doesn’t help to detect a trend that’s already come and gone. Similarly, you want to detect issues or risks immediately, not after they’ve turned into much larger problems.

You have to master the speed of your data!

Reporting & Communicating Data

The final challenge is what happens after you discover your findings.

Your marketing strategy doesn’t happen in a bubble; it’s part of a cohesive business strategy. You need to report and communicate your results to the rest of the organization.

To do this, you’ll link the insights you’ve discovered to other known factors. If you’re unable to make any of these connections, the data may not be helpful or actionable.

This exercise tests the validity of your findings, while simultaneously developing a strong data culture. After all, data-driven decision-making needs to be a company-wide approach!

Reporting and communicating your insights can be challenging because you have to cater to the audience. They may not be familiar with the same metrics or concepts as you and your team.

Developing the right way to tell the story of your data is both an art and a science!

PPC Signal: Technology For Data-Driven Marketing

To overcome the challenges of becoming a data-driven organization, you need some outside help. No team can keep up with their data manually. It’s just too fast, large and complex.

You need automation; you need PPC Signal.

PPC Signal leverages AI technology and machine learning algorithms to analyze marketing data to detect changes in your Google Ads account automatically.

If you incorporate PPC into your marketing strategies, PPC Signal is a must-have tool to empower your data-driven decision-making.

By removing the stress and hassle that comes with analyzing your data, you’re left with far more free time and resources.

Instead of funneling countless hours into analyzing your data and discovering the right decisions to make, you can focus on acting on those decisions and consistently improving your business.

How Does PPC Signal Work?

To appreciate the impact that this tool will have on your data-driven decision-making, let’s look at how the system works.

When you access PPC Signal, you’re brought to the main dashboard. This menu displays all of your active signals. Here is an example:

You’ll notice a few helpful features on the PPC Signal dashboard.

• Active Signals: The main feature on the dashboard is the list of your current signals. Each signal represents a performance change to your PPC account. Naturally, these types of fluctuations happen all the time in advertising. PPC Signal tracks these trends, shifts and other patterns and only presents the most significant ones. We’ll explore more about signals in the next section.

• Filters: Along the left side of the screen, you’ll notice the filtering options. This allows you to look at active signals that fit your criteria. For instance, you can choose to look at changes to your ad clicks exclusively. You can also filter by parts of your account, dimensions, signal types and more. When you have hundreds of active signals available, filters are essential to get to the changes that matter most to your business.
• Bookmarked Signals: If you see a signal that interests you, you can bookmark it. This enables you to return to it later. It’s useful when you find a critical change, but don’t have the time to resolve it right away. Bookmarking ensures that you don’t have to keep looking for the same signal each time you open the tool.
• Actioned Signals: These are the signals that you’ve resolved in the past. You can use it to see the history of changes made and what actions were taken to resolve past signals. If you’re working with a team, actioned signals help you track why certain changes were made to your campaigns.

What Does A Signal Look Like?

To understand what types of changes and data PPC Signal presents to marketers, let’s look at a sample of one of the alerts that a marketer might see on their dashboard.

Each signal is packed with information.

• What’s changing? The first thing you’ll see is the metric that’s changing. The signal displays this in bold, underlined text to make it clear.
• What direction? Is the data moving upwards or downwards?
• Is the change positive or negative? The text describing the direction of the change is highlighted either green or red. Green text signifies a favorable opportunity, while a red represents a risk.
• When did the change start? PPC Signal tells you when the event began and how long the trend or shift has been occurring.
• How significant is the change? PPC Signal determines the current value of your metrics as compared to where it was in the past. The system displays both figures and calculates the percentage change.
• What parts of the account are affected? Below the text, you see what areas of your account are involved in this change. In the sample, you’re shown that the change is affecting mobile device users in the “Forerunner Sales” campaign.
• What does this change look like? A sparkline chart is displayed to present the difference visually. Seeing the data charted is much faster than analyzing the raw numbers alone.

Exploring Your Signals Further

You’ll notice the option to bookmark or explore the signal. As discussed, bookmarking allows you to easily locate this change later.

When you tap Explore, it opens up an expanded view of the data.

This menu has several features and tools to help you investigate the data even further.

• Signal header: The top of the menu is the signal header. This includes all of the basic details about the change. Most of this information is included in the signal itself.
• Take Action: The Take Action button is a valuable feature when you aren’t sure what to do next. It will suggest an action that will help resolve the alert. This makes managing your PPC accounts incredibly easy. It requires minimal effort to act and improve your campaigns.
• Detailed chart: Each signal has a basic line chart included to show you what the data trend actually looks like. From the Explore menu, you see a more zoomed-in view of the data. You can look even closer with the zoom slider. This detailed chart also allows you to add extra metrics, making it easy to see how this change affects other metrics. Adding each metric furthers your understanding and provides context behind the data.
• Export: The signal data can be exported with a button in the top left corner of the chart. If you want to bring data into another environment, like your spreadsheets, this will download a file with the information.
• Table view: Next to the export button is the option to view the data as a table. PPC Signal highlights the most significant data in the table. The table will replace the streamline chart and you can tap the same button to return to the visualization.

All of these features make it easy to make data-driven decisions to improve your Google Ads account performance.

FAQs:

What Is A Data-Driven Strategy?

A data-driven approach is when you’re utilizing data and analysis results to help make strategic decisions. Data-driven decisions help you better serve customers and optimize your marketing campaigns. Instead of using opinions, gut feelings, intuitions, assumptions, etc., you rely on concrete evidence supplied by your data to guide your decisions.

Is Data-Driven Marketing Important?

Absolutely! Without data-driven marketing, your marketing strategies are flying blind. You’re relying exclusively on guesswork. This leads to flawed decisions that can hurt your business more than they help.

How Do You Use AI To Analyze Marketing Data?

Analyzing marketing data is a challenge for many businesses. AI-driven tools help eliminate these difficulties and provide overall better insights into what’s happening in your data. For example, PPC Signal is an AI-powered solution that automatically analyzes your Google Ads data to find essential performance changes.

Wrap Up

Why is data-driven decision-making important? The answer is simple: your customers.

The decisions you make are ultimately designed to better target customers with more relevant experiences. You want to make the best choices for your customers, right?

In turn, happier customers improve your sales, increase brand loyalty and retention, lower your costs and guarantee your marketing success.

Data-Driven decision-making is also a competitive difference-maker. If you aren’t delivering the best possible experiences and meeting their ever-changing needs and behaviors, a competitor will.

You can’t rely on your gut and opinions to guide business decisions when competitors are using data and insights.

Data-driven decision-making is vital to your success, but you need the right tools to simplify the process.

If you’re managing a Google Ads account, PPC Signal is the perfect solution. It helps you make fast, accurate decisions to improve PPC campaign performance.

Simplify your decisions and amplify your success!