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The Data Analysis Process for Better Decision Making
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The Data Analysis Process for Better Decision Making

By Jake Stevens

Making critical business decisions that affect an entire organization is both daunting and challenging. However, big decisions are easier to make when there are results and evidence to support your case. Understanding how to use and interpret your organization’s data is the first step in better decision-making. There are advantages and limitations of data-driven decision making but knowing each will allow for an enhanced ability to act when the time comes. The advantages can be seen when you’re able to solve data analysis problems and interpret patterns in data. Understanding how your organization can use data to settle on a plan of action is a key step on your journey to utilizing data analysis.

"Data-driven organizations are not only 23 times more likely to acquire customers, but they’re also six times as likely to retain customers and 19 times more likely to be profitable."
Mckinsey Global Institute

The Importance of Data-Driven Decision Making

With data analysis, you’re able to make decisions based on hard data rather than observations, assumptions, or gut feelings. Data is concrete, compared to observations and instincts, which are subjective to each person. Analyzed and verified data allows you to take the guesswork out of decision making and have confidence you’re moving in the right direction.

"Data analytics makes decision-making 5x faster for business."
Better Buys

Techniques for Optimizing Data and Improving Conversion Rates

Cost optimization is a universal goal companies strive for across the globe. So, what’s the best way to keep costs low while maximizing business value? Data.

There is a lot of data that is available to members of an organization, but the difficult part is how you use this information to create a strategy and drive decisions. Here are four techniques for improving conversion rates and optimizing costs using data.

A/B Testing

A/B testing is a method of comparing two versions of a campaign, ad, web page, or app against each other to determine which one performs better. This technique requires three steps: deciding on what to test, creating a testing hypothesis, and acquiring data. In this test, there are no assumptions made. All the insights are backed by consumer behavior data.

You first must decide what to test. Our example tests the effectiveness of a new marketing campaign. Next, we will create a testable hypothesis for our campaign. This involves a few steps, such as identifying business objectives and determining target metrics. Acquiring data is a big part of this process and can be difficult if you’re unsure where to look. The data needed to test our hypothesis was gathered from an analytics provider but could have also been collected from a customer survey or other source. After gathering data, you can begin to test your hypothesis. Finding your control and test groups is the next step. Running a test for an extended period of time will give you more accurate results. Analyzing your results will help your team determine whether to fully implement the new marketing campaign, revise the campaign or kill it.

"During the 2012 Obama for America campaign, their team was able to use A/B testing and generate actionable insights that increased donation conversions by 49%."

Cart Abandonment Analysis

This technique analyzes the customers who went all the way through the customer experience process and did not convert. In other words, this is when a visitor to an eCommerce site visits a product page and adds a product to their shopping cart, and either leaves the site or does not complete the order. Studies show over ¾ of shoppers choose to leave the site without completing a purchase. By analyzing this information, you can better understand the intentions of the visitors to your site. After gathering this information, you can implement a strategy to increase your conversion rate.

Customer Journey Analysis

The journey it takes for a customer to first recognize your organization to become a recurring buyer is how the customer journey is represented. Every customer has a different journey, and to better understand how customers transition from awareness of your company to being a loyal customer starts with data. To gather this data, it takes research to find previous and current consumer experiences using survey information. Previous customer experiences will help you understand the steps taken by customers to go from initially recognizing your company to a recurring customer. Surveys help determine whether your customers are fulfilled with your product or site. Current customer experiences can provide insights on their perspective of your product or website and their buying experiences. After all of this information is collected, you can create a strategy to meet consumer needs and achieve company goals.

"13% of customers will tell 15 or more people about their bad experiences. 72% will tell six or more people about good experiences."
Annemarie Bufe

Optimizing the User Experience

Consumers feel more engaged with a brand when they think their experience is customized to meet their needs. Being able to tailor experiences to customer needs starts with a goal. Most companies want to increase interaction on their website or increase conversion rates. Personalizing a user experience can be an excellent way to help improve interactions on a website. In fact, a study by Entrepreneur shows better UX design as a result of user testing can increase a company’s conversion rate by 400%. Segments to personalize can include location, transactional data, and device usage. Like the other techniques for optimizing data and improving conversion rates, optimizing the user experience needs to be tested and updated as your goals change.

Turning Data into Actionable Insights

When data is analyzed properly, it can be turned into insights that can be used when making key business decisions. As the amount of available data increases exponentially, companies and organizations must have an end goal, so they don’t get lost in information. A company with a large amount of data from customer surveys can transform it into actionable Insights to explain customer experiences and interactions with a company.

Making Decisions Based on Insights

Quantifying data can help organizations draw conclusions from actionable insights. After concluding the insights, making decisions based on the findings is supported by hard data. In a perfect world, all business decisions would be the correct decision supported by concrete evidence and facts. As we know, we don’t live in a perfect world, so all decisions made won’t be right, but we can make smart decisions that are supported by evidence. For this reason, companies must focus on improving data analytics and science skills. Improving these skills will allow for more efficient use of data and, in turn, make this data analysis process for better decision making more effective. Once you begin to make decisions based on supported data, it will become apparent how vital using data is to your organization.

At Hahn Agency, we help companies improve internal efficiencies by changing the culture surrounding data. We help companies transition to a data focused culture, with an end goal of analyzing that data to generate actionable insights to make data driven decisions. A data driven culture allows for more confidence in decisions, and speeds up processes. It can be a daunting task changing the culture surrounding data. Our team has experience helping companies during these transitions and strive to make this process efficient and personable. At Hahn Agency we also have experience generating actionable insights by performing different techniques to optimize data and improve conversion rates.


More Lab Intelligence

Why Data Validation Matters to Businesses

Data validation means checking data for correctness and completeness. Data validation can be described as a series of tests and rules on the data to certify its quality and integrity.

By Jake Stevens