Data-driven analytics in marketing and decision making (2023)

Your company must know the facts, right? Without facts, you can make important decisions on a whim and the results may not be as meaningful as you'd expect. Overall, the chances of success are higher in all respects using data-driven analytics.

Businesses today are becoming more data driven as technology advances and data becomes widely available. Furthermore, a data-driven approach can transform an organization in hugely positive ways, if done right.

However, a company needs to understand what it means to be data-driven, the pros and cons, and the keys to a data-driven culture.

In this article, we'll cover everything you need to know about starting a data-driven business so you can decide if it's the right move for you.

What is enterprise data analytics?

Data analytics can help companies understand their customers,Analyze advertising campaigns, customize, develop products and create content strategies. With that in mind, being a data-driven business has many benefits, most notably creating powerful strategies that increase profits.

Where do the different data sources come from?

Data collection can be as simple as a survey sent to your customer database. Many companies use historical research data or newly obtained information for a specific reason. Data may also be collected from customers and website visitors or acquired from other organizations.

The data that a company collects from its customers is called first-party data. Data collected by a company (competitor) that has collected its own data is called third-party data. Finally, buying data from a company that sells it is called third-party data.

Types of insights from data analysis

The amount of data that companies can obtain thanks to technology is staggering. When reviewing your analytics, you can see your audience's demographics, interests, psychographics, consumer behavior, predictive analytics, and more.

But in general, consumer data can be divided into four sections:

1. Personal data:Personally identifiable information, such as gender and social security numbers. It may also contain your IP address, web browser cookies and device IDs (both mobile and desktop).

2. Engagement data:How customers interact with a company's content. It includes average order value, page likes, shares and comments, how many people use your customer support system, etc.

3. Behavioral data:How much time customers spend liking your content, mouse movements, purchase history, and product usage information such as repeat actions.

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4. Configuration data:Key metrics on purchase criteria, customer satisfaction, product wants, opinions, branding and sentiment.

All of this information can help you target your ads for the best results and create content around what your audience interacts with most.

Where can you see your analytics?

Many data analysis tools give you a 360-degree view of all your analysis sources together. However, if you run a Google Ad, you don't need to pay and download any tools. Checking your ad analytics is as easy as signing up for Google My Business. From there you can see all yourGoogle Analyticson your dashboard.

You can do the same on social media platforms like Facebook and Instagram.

How companies are using data analytics to boost their bottom line

Data-driven analytics in marketing and decision making (1)

Businesses can use data analytics to make timely decisions in a number of key areas needed for long-term success. Here is a summary of the places where data benefits the most:

Use of data analytics in marketing

In the past, companies have lost billions of advertising and marketing dollars simply because they didn't run ads that caught consumers' attention. really appreciated$611 billionare lost each year because of poorly targeted digital marketing campaigns.

Why did this happen?

It is very likely that they skipped the research phase. With data analytics available, companies can create hyper-focused marketing campaigns for their target audience, resulting in far more effective campaigns.

Research includes observing online activity, monitoring point-of-sale transactions and adapting to rapidly changing consumer trends.

Netflix is ​​a prime example of a data analytics use case for marketing. They track their subscribers' search and watch dates to know which movies to suggest. Smart suggestions like these keep subscribers interested and, in return, keep their subscriptions.

Product innovations and development

Every new product idea starts with finding out exactly what the customer needs. Without analytics, product development boils down to the basics of having a vision and executing on it, whether it resonates with your audience or not.

Data-driven companies use analytics to analyze:

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  • Product viability:Using data to verify and strengthen product development concepts.
  • Measuring product progress:Analyze and inform your company which features of a new product are attractive and which should not result in a more versatile final product.
  • User experience information:The data can be used to understand why customers buy the product and what purpose it serves in their lives. Some companies can use this data to see why a customer is choosing a competitor's product.
  • Inspiration for product development:Analytics can inspire innovations or help existing ones remain relevant despite changing consumer needs and trends.

Data is essential for the long-term success of a product and its evolution to adapt to new trends and market changes.

Customer Service and Loyalty

No business can succeed without building a customer base. And with such a customer base, a business cannot ignore competition that can drive those customers away. If a company is slow to learn what customers are looking for, losing customers will have a negative impact on its survival.

Finding ways to keep customers happy and tracking their behavior can be a long shot if you're just guessing.

There's onepredictive analyticsenter the game. Based on historical data and current facts, you can make extremely reliable predictions about the problems your customers have with your service or product.

Performing predictive analytics to resolve customer issues offers a number of benefits to a business as it allows you to identify issues before the customer contacts you. And because you have more time to resolve business issues, the customer will appreciate that your company is attentive and proactively resolving issues.

Run operations more efficiently

Most companies plan to cut costs on their financial statements, but many companies struggle to meet their cost-cutting targets. It can be difficult to know where they are spending money on the wrong things.

How can you achieve sustainable operations that cost money without sacrificing the customer experience or the company's ability to grow?

The most effective way to eliminate errors and external expenses is to look at the data. Data analysis streamlines your process by better understanding what your audience wants. So no time or money is wasted on efforts that don't match your audience's interest.

The key to building a data-driven culture

"We're not much smarter than we used to be, even though we have a lot more information - and that means the real skill now is learning how to get useful information out of all that noise."

Nat Silver

A data-driven culture incorporates data into decision-making, treating data as a strategic asset for the business. It encourages frequent experimentation to learn, grow and improve. A data-driven enterprise recognizes that a solid foundation of data is critical, including artificial intelligence (AI) and machine learning (ML). It is a culture that consists of a high level of data literacy and uses data to move the company and all its members forward as a whole.

Data-driven companies make data accessible to employees so they can always consult the facts before making decisions. At the same time, data-driven companies are transparent about data access restrictions and governance methods.

A business needs to know that data can only grow so far - people are the real drivers of growth in any data-driven business. Analytics leaders need to find people with data skills, lead by example, and know when relying on data is a mistake.

So how can you, as a leader, create a data-driven culture?

1. Target your leaders first

Your organization's leaders should be the first to build a data-driven culture. These leaders will set examples that will transpire through all levels of management and among employees, leading to lasting organizational transformation.

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2. Implement a mindset shift

A key component in developing a data-driven culture is changing the way your employees view data. Data analysis shouldn't be seen as just another boring task you have to do. Instead, it should be considered the focal point of any decision.

Not only does your entire team need to use data to answer questions, but they also need to regularly review available data and encourage discussion about the results.

3. Build a team of experienced people

Being a data-driven company requires technical staff capable of supporting:data driven culture🇧🇷 These individuals are responsible for maintaining and building the company's database. Its primary focus is using technology and analytics platforms to better organize data (by eliminating Excel spreadsheets and manual calculations) and making data more readable for employees.

Your technical team will also be the people driving the data-driven culture, helping other departments structure and understand data.

4. Make data analysis a standard decision-making practice

Your entire team needs to understand that no strategic decision should be made without first analyzing the data. Additionally, your team needs to understand why data plays such an important role in business decisions.

While the answer to the question of why data is analyzed in the first place may differ from company to company, some of the main reasons are that decisions are made based on facts, which increases the likelihood of a positive outcome and, at the same time, it increases credible leads for your company.

Once you implement these steps, your organization will be well on its way to refining a data-driven culture. It's important to note that you can only have a fully data-driven culture when everyone in your organization is on board.

This shift can be especially difficult if your company has used other methods for years. Changes in corporate culture must be practiced and will be achieved over time.

Which brings us to the challenges of a data-driven business.

Challenges faced by organizations during the transition to data governance

As more companies realize the benefits of a data-driven way of working, they're investing in the tools and people they need to do it. However, if it were that easy to be data driven, there wouldn't be so many doubts about it and companies would do it without hesitation.

Challenges that hold companies back include:

1. Difficulty adopting a data-driven culture

Startups and new businesses are adapting to a data-driven culture because of the benefits it brings. However, much of the industry does not know how to take the first steps towards leading a data-driven approach into their existing corporate culture.

Cultural shifts may be more evident in certain types of organizations than in others. Let's take a manufacturing and manufacturing company as an example. This type of company is familiar with traditional ways of doing business and may struggle to leverage data.

2. Lack of leadership in the data-driven approach

As mentioned above, if your business is truly data-driven, everyone needs to be on the same page.

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Having everyone on the same page is only possible when you have specific people preaching culture to everyone in the company. Most importantly, leaders consistently reinforce the culture and serve as role models for all.

Lacking this leadership can result in a partially data-driven company, causing conflict between disparate teams and unreliable results.

3. Be wary of data

Did you know that71%of people don't trust the quality of the data?

Most people analyzing data don't know where big data analytics comes from, when it can be useful, and how timely it is. Confusion starts most when data insights contradict a long-held norm.

Mistrust of data often causes executives to resort to making decisions they believe they can trust, leaving the data aside and following their instincts.

4. Transfer data insights to where they add the most value

You have the data. And now?

These data could provide better results for theMarketing Team, the customer service team or the operations team? Knowing where to send data can be difficult and is often misused.

The best way to ensure that data goes to the right place is to make it accessible to all teams in your organization. This allows teams to share valuable insights and see where the data can fit.

5. Find and recruit analytical staff

It's unrealistic for everyone in an organization to be comfortable using big data and advanced analytics, especially if you've recently transitioned to a data-driven business. Many companies struggle with the imbalance between data professionals and non-technical workers because professionals always end up having the final say, leading to demotions and potential workplace disputes.

Interactions between data specialists and non-technical employees are critical to avoiding problems. This simply requires good communication between teams during meetings and data analysis.

Your team must learn from each other, regardless of their level of expertise, and be open to discussion.

final thoughts

A data-driven business analyzes data before making big decisions. Everyone from management to employees must be on the same page. Companies that are data-driven typically see much better growth, as every business decision they make has evidence of its potential success.

While moving to data governance has great benefits, companies that choose to do so also face challenges that slow or even halt the change. To determine if a data-driven business is right for your business, it's important to first analyze your business model. Some companies are better off using traditional methods, while others have more opportunities to change.

You also need to check if you have enough experienced staff or if you need to hire and train all new technical staff. Overall, it's worth the effort because a data-driven business gives you a competitive advantage and incredible business opportunities.

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