A complete guide to data analysis and how to keep your business competitive in 2022

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Everyone knows data is important, and every company has a lot of it. But let’s be honest: data analysis is hard.

For one, you need the tools to clean, store, and manage the data you collect. But for many companies, data analysis is more of a people issue. Significant obstacles like silos within your organization, centralized teams, and poor governance contribute to bottlenecks in the data analysis process. Unfortunately, these limitations make it virtually impossible to understand the customer experience holistically.

To stay competitive in 2022 and beyond, you need to empower your marketing teams to turn data into insights that matter. Luckily, data analysis solutions and strategies have evolved to solve these complex challenges. As you read this guide, you will learn what data analysis means, what benefits can be gained from it, and how to use it effectively.

What is data analysis?

Data analysis is the process of gathering, organizing, and interpreting data to extract insights that inform decisions. To support these decisions, data analysis often includes condensing, extrapolating, and visualizing data.

When you conduct data analysis, you are looking for patterns. For marketers, this might mean examining engagement rates among new versus returning customers and measuring how that changes during a particular time of the year or within a specific segment of the customer population. The idea behind data analysis is that by slicing and dicing the data, you can start to understand current trends enough to predict and speak to future business needs.

Why data analysis is important: Better decisions for better customer experience

Data analysis is crucial to all aspects of your organization. It can help uncover areas needing improvement or underscore what is going well so you can do more of it. Ultimately, the key to unlocking better customer experiences that drive higher acquisition, retention, and loyalty is the ability to access and use the right data at the right time.

For example, BJ’s Wholesale Club noticed a younger, more digitally savvy segment of their customer base starting to grow. The data was there, but it took thorough data analysis to discover what this new group wanted and needed most. Data analysis enabled BJ to create new customer journeys for this audience, launch new hit services, and customize their messaging to encourage repeat purchase behavior and enhance brand loyalty.

Data analysis leads to insights. Actionable insights lead to loyalty.

Here are just a few specific examples and key benefits of robust data analysis:

Seven types of data analysis

There are many ways to do data analysis, and the best way to go about it will depend on your data and how you will use your insights. Let’s take a look at seven of the most common types of data analysis in order of difficulty, from easiest to most complicated.

1. Descriptive data analysis

In descriptive data analysis, you are trying to determine what happened after you implemented or changed something. It often involves merging several datasets from various sources and then describing what occurred with percentages, frequencies, means, and more. If you have created a KPI dashboard for your department before, you have done descriptive analysis. Typically, descriptive data analysis serves as a jumping-off point for more complex types of data analysis.

2. Diagnostic data analysis

Whereas descriptive data analysis answers the “what happened” question, diagnostic data analysis answers the “why did x happen?” question. At its core, diagnostic data analysis uses statistical methods to find the root cause of something, like a sudden spike or decline in a KPI. Many companies refer to this in practice as “root cause analysis,” or RCA. Once you know the underlying cause of a problem or miracle, you can take steps to fix or reproduce it.

3. Exploratory data analysis

Just like it sounds, the intent of exploratory data analysis is to reveal something new. Going into this type of analysis, you may not even have a hypothesis. But by combining and investigating the relationships between multiple datasets, distinct patterns may start to emerge. This type of analysis is beneficial when you are starting a new vertical or brainstorming new growth or go-to-market initiatives. Unless you know what is out there, you cannot tie it together. Exploratory analysis joins data that may not seem related and turns it into a cohesive, innovative strategy.

4. Inferential data analysis

Inferential data analysis juxtaposes two or more variables to find a correlation. For example, you might want to know if customers in different age groups prefer different products or if men are more likely to have a larger basket size than women. If there are strong correlations, it is probably a sign that you can rest easy extrapolating these trends to the larger customer base. Popular methods of performing inferential data analysis are estimation theories and hypothesis testing. We are starting to enter the more difficult data analysis territory, as both of these methods require a stronger statistics background to carry out and interpret.

5. Predictive data analysis

Now we are cooking with gas, because predictive data analysis is one of the more exciting forms of data analysis because it centers around the question, “What will probably happen in the future?” Usually, predictive analysis unites several demographic and behavioral datasets to predict future customer actions, like downloads, referrals, purchases, and more. While this is not exactly like looking into a crystal ball, it can get close. And the more data you can combine, the better you will get at predicting future outcomes.

6. Prescriptive data analysis

Prescriptive analysis takes predictive analysis a step further. Beyond predicting what will happen in certain scenarios, prescriptive analysis anticipates how to counterbalance or enhance the outcome. Basically, it recommends what the business could or should do next. Doing this takes a lot of horsepower and know-how. Artificial intelligence and machine learning, in which algorithms learn from past behavior, are hallmarks of prescriptive analysis.

7. Text data analysis

Text data analysis is the most advanced data analysis technique, because it involves natural language processing (NLP) and machine learning. As you might guess, text is exceptionally difficult to interpret because humans’ communication is not consistent (everyone speaks and writes slightly differently). But with NLP and machine learning, human language can go from unstructured to structured data that is ripe for analysis. Text data analysis is almost like knowing what your customer is thinking — you can detect sentiment in customer feedback in customer service sessions, in marketing surveys, or in product reviews.

A data analysis use case: The Adobe Holiday Shopping Report

To demonstrate the value of data analysis, let’s look at a real-world example: Take the Adobe Holiday Shopping Report created using Adobe Analytics. The data analysis in this report informs marketers, retailers, and commerce leaders of current holiday shopping trends. Not only does this analysis help upper management tackle the 2021 holiday season, it previews challenges coming in 2022 that they can prepare for and address head-on.

However, organizing this data is easier said than done. For this report, data analysts use Adobe Analytics to stitch together retail site visit data, SKU data, shipping data, and survey data to extract insights regarding consumer behavior and attitudes. Luckily, Adobe Analytics has exploratory analysis tools that pick out distinct patterns and differences in the data this year compared to 2020. As you can see in the report, the rise of ecommerce, curbside pickup, logistics and supply chain issues — as well as new payment structures like buy now, pay later — all have the potential to affect shopping around the holidays and into next year.

But the analysis doesn’t stop there. Adobe Analytics also has descriptive analysis capabilities that can determine what happened last year and highlight what’s been happening this year. The report even goes a step further, using sophisticated diagnostic, inferential, and predictive data analysis to answer questions like “why is x trend happening,” “what variables could affect x outcome,” and “what could this data potentially mean in the future.” And because this report is meant to be informative, Adobe actively stayed away from being prescriptive, leaving this type of analysis to brands themselves.

The data analysis process

Data analysis like this does not happen overnight, and that is a good thing. You want your analysis to be as accurate and precise as possible. Generally, there are eight basic steps to the data analysis process:

  1. Set goals and objectives. Set a specific goal before you dive into the data. Think of it like a science experiment 一 you need a hypothesis before you start.

  2. Collect data. It’s much easier to narrow down the data you need to collect when working towards an objective. Usually, there’s a dataset that very obviously must be part of your analysis, but don’t forget about other more tangential data that could add color to your analysis. And it’s even better if you can combine different datasets together, to get a more holistic view.

  3. Clean data. The data you need isn’t always structured properly, so work with data engineers or analysts to ensure that the data you plan to use is optimized for your analysis.

  4. Analyze data. Now, for the fun part: analysis. Decide on the type of data analysis you want to do, with the questions you want to answer in mind.

  5. Interpret data. With your results in hand, it’s time to translate them into layman’s terms. What does your analysis mean? How can that be applied in your department? Often, data interpretation requires additional context that only certain stakeholders know, or other data that may not yet be a part of your analysis. Using advanced analytics tools that support multiple datasets and consulting with colleagues can give you a clearer picture of what’s happening.

  6. Visualize data. Sometimes, a picture is worth more than words. Consider putting your data into graphical form to show, rather than tell, your results. It’s important to note that interpreting and visualizing data might overlap, since some tools help visualize data to clarify interpretations and conclusions.

  7. Share your insights and get feedback. When you’re the only one looking at your analysis, you’re bound to overlook something. Data analysis is truly a team sport, requiring input and knowledge from various sources. Sharing your analysis with others and asking for feedback will only improve your ability to apply insights to your work. Colleagues might have an alternate perspective on your interpretation or suggest other datasets that could strengthen your conclusions.

  8. Act. What good is data analysis without putting it to use? Acting on the insights you’ve discovered is the last, and perhaps most important, step. Monitoring who uses those insights and how can be a helpful way to decide how to approach data analyses in the future as well.

The Adobe Holiday 2021 Report: Data analysis process in action

Let’s look again at the Adobe Holiday Report to see this process in action. This goal (step 1) in this case is pretty straightforward. To provide businesses insight into the holiday shopping season by offering a holistic view of shopping trends throughout November and December.

When it comes to collecting and cleaning data, the holiday report is a great example of how and why diversifying your data set is important. In this case, because the majority of the top U.S. online retailers us Adobe Analytics, Adobe is able to combine and clean anonymized data (with permission) from over 1 trillion visits to U.S. retail sites.

The webpage (and subsequent PDf report) offer powerful stats using diagnostic and inferential data analysis. A visualization accompanies text to further illustrate shifts in the data. And there is always a sentence or two interpreting what the results of data analysis mean in business terms for marketing, supply chain operations, financial incentives like discounts or BNPL, product sales, and more.

Overall, this report connects the dots between data points, explains their possible causes, and predicts their potential impact on future business outcomes. This interpretation allows leadership to understand trends and pass on actionable insights to their teams, who translate them into acquisition, retention, and loyalty strategies. Once published, Adobe continues to collect feedback from readers and track patterns in holiday shopping to produce an even more robust report in 2022.

Getting started with data analysis

Without data analysis, your team is flying blind. And guessing just doesn’t cut it anymore.

To capture the right customers, marketers need to access and use the right data at the right time. Using data analysis to unearth actionable customer insights helps them attract new customers and nurture the current ones. Simply put, data analysis is the key to unlocking better customer experiences that drive higher acquisition, retention, and loyalty.

The Adobe Holiday Shopping Report is a fantastic benchmark for organizations just starting to flex their data analysis skills. Not only does it serve as a great model for running through each step of data analysis, but it demonstrates how you can conduct and visually represent various types of data analysis as well. You can start learning from it by viewing the insights today.

But reviewing the Holiday Report is only the first step to getting acquainted with data analysis. The same technology behind the Holiday Report is available for your unique, specific business needs. Adobe Analytics has become a market leader by enabling marketers to drive real-time personalization on every channel and measure their success by analyzing the customer journey end-to-end.