Why Does Your Company Need Data Analysis?

Data is the new gold. That is what the biggest in the business say, knowing the value of the data, the analyses, and the conclusions they bring. If you are wondering why your company needs data analysis and what value it could bring to your business, this article is for you.

Many companies have used financial data analysis to boost revenue, cut costs, and increase profits. This approach is still valuable, but today's technology lets you easily capture and use much more information to unlock great value for your business. By analyzing data, you can make smarter decisions that can transform your business.

Your customers' interactions, like orders, product questions, and returns, all hold valuable insights. Even potential customers searching online can provide useful data. Though it might seem overwhelming, tools like SQL and Python can simplify data analysis.

So, how can data analysis benefit your business? I’ll share real-life examples of how I’ve used data analysis to help clients and employers make impactful strategic decisions. Let's dive in.

Understanding Your Customers and Their Needs Better With Data Insighsts

A business exists because your customers have a certain need that you are able to fulfil better than your competitors. This is the absolutely fundamental definition of entrepreneurship. Whoever understands it, wins.

Customer centricity and a good understanding of these needs can help you succeed, create value for your customers, and make good money in the process. But how can you use data to understand this?

Collecting relevant data enables organizations to identify customer needs and preferences, leading to better business decisions. By analyzing this data, businesses can predict scenarios that could potentially cause losses and devise recovery and mitigation strategies.

For instance, say you want to understand which features of the smartphones your company manufactures are the most loved by your customers. Why do they choose your product? You can ask your customers to rate each feature on a scale of 1-5 and compile the results.

Here, we have the feedback stored in a database table called customer_ratings.

customer_ratings table:

A simple SQL query can help you generate summarized results for feedback with just a few lines. Don’t worry if you don’t understand how to write the SQL query at this stage. If you want to learn, I recommend the SQL Basics course from LearnSQL.com. Learn the basics, practice writing simple SQL queries, then come back here for tips for your business.

Sample Query:

SELECT   model_name,
	   avg(camera_rating) camera-rating,
	   avg(performance_rating) performance-rating,
	   avg(battery_rating) battery-rating,
	   avg(looks_rating) looks-rating
FROM 	   customer_ratings
GROUP BY model_name;

Sample Output:

Now, once you have summarized the feedback, you can understand clearly which features to improve in each model to increase the sales of the next version. For instance, the “Milkyway” model needs a better battery, and the “zPhone” could use a better camera quality. You can even group the reviews by customer age to see which age group of customers like your phones the most. This can become your target segment for higher penetration in the market.

Even if you do not have millions of rows of data, your database can be quite useful, especially if your business is new or if you have just started collecting data. For specific use cases relating to small data sets, you can read this article.

Since we touched on marketing, let’s take a look at a good use case on how data analysis can aid marketing efforts.

Targeting Your Marketing Activities Better With Data Analysis

It is important to get your marketing channels right to get the biggest bang for the buck. And even with the right marketing channel, you need to target your customers accurately. Data analysis can help you improve the return on your marketing investment by helping you make the right decisions. A data analyst can help clean and analyze vast datasets to optimize marketing efforts.

Here is an example. Let’s say you pay $0.10 for each ad click for your social media channels. You realize that a lot of people are clicking the ads but only a few are actually converting (completing a purchase on your website). If you pay per click, this may not be a great situation for you.

If about 100,000 people clicked your ad and only 1,000 converted, this is a conversion rate of 1%. If you earn a $10 margin from each customer, your total earnings are $10,000. But you actually net $0, since you are also billed $10,000 by your marketing channel!

So how can you change this? You can do data analysis and then choose the right channel and prospects for your targeting.

You have the following data at your disposal. The table below displays the details of all users who clicked your ad and visited your website.

prospects table:

You can use SQL again to see the details by marketing channel.

Sample Query:

SELECT    source_website,
	    count(prospect_name) number_of_prospects,
	    sum(conversion_flag) no_of_conversions,
	    sum(total_order_value) order_value
FROM      prospects
GROUP BY  source_website

Sample Output:

In this SQL query, I used a GROUP BY statement.

You can clearly conclude that, while you get the most clicks and visits to your website from Facebook users, YouTube results in the highest conversions per click (10%). If you are spending the most money on Facebook and the least on YouTube, it makes sense for you to change your strategy and advertise more aggressively on YouTube for a better return.

You can also see the age groups that respond the most to your ads and then target them in the channels.

The next step is to understand why the conversion rates are lower from other channels and if the issue is actually with your landing page or product. In fact, if you use Google Analytics in your website or application, do read this article to understand how you can analyze it with SQL.

This is just an example of an ROI (return on investment) analysis. You can use SQL for many other marketing situations. You can analyze the ad results, the reach of specific posts, the demographic profiles of those who read your blogs, or the power of a specific keyword for you to match the content more precisely. The possibilities are endless!

Optimizing Orders and Offers

Your order history data is a gold mine. You can do multiple analyses on this data to uncover trends and increase your sales from insights generated by observing those trends. Data analysts use various techniques to analyze order history data and uncover trends that can increase sales. Understanding different data analysis techniques, including quantitative and qualitative methods, is crucial to transforming raw data into meaningful insights. To give you a better understanding, let’s consider how a startup can use data analysis to attract more investments.

Do you know what key metric many investors look for in a startup before investing? It is the number of repeat orders. If the same customers come back to order again and again, it is a clear sign they really value your service or product.

Here is an interesting use case. Say you run an online food business, and you offer uniquely made dishes using a special nutrient-rich ingredient developed by your scientists. You observe that, while a lot of new customers order because of the buzz, only a few come back and reorder.

One of the analysts in the team presents the following data to you about the first orders.

first_order information:

What do you observe

Clearly, customers who are trying a greater number of items in their first order are more likely to come back and reorder. In this case, 5 seems like the magic number – we see a massive jump in the reorder rate for customers with more than 5 items in the cart of their first order.

You might offer a 10% discount on the first order with more than 5 items or waive the delivery charges to incentivize those who order 5 or more items. This in turn may increase your repeat order rate. Insights like this give you an idea of how to make offers in line with your business objectives. Of course, this is a very simple example and just the tip of the iceberg.

While this analysis was based on data collected internally by the company, you can also benefit from analyzing external data to understand changing market trends.

Understanding Market Trends Through Data Analysis

The market is continuously evolving, and so are the customer needs. This can be an opportunity or a problem depending on how you tackle it.

A company that can observe market trends and act accordingly will always be successful; a company that doesn’t is bound to lose. Take smartphones for example. Nokia, which at one time was a leader in the mobile phone market, lost market share as it could not envisage the massive trend of smartphones. With Kodak and digital cameras, the case was similar.

So how can data help here? Every industry or product follows a life cycle, all the way from the launch to the eventual decline. By analyzing historical data, companies can make predictions, identify patterns, and understand past outcomes to recommend actions that optimize future outcomes. This is where data science comes into play.

Data science encompasses a broader spectrum of skills and techniques, such as machine learning and predictive modeling, to derive actionable insights and solve complex problems. By leveraging data science, companies can analyze market trends more effectively and make informed decisions.

For instance, the e-commerce industry is in a growth phase as the revenue/sales as well as profits are steadily going up. Similarly, you can graph the data trends of a product or an industry and find out which markets are profitable to enter and which are not.

Source: Investopedia

Analyzing the trend in sales data for particular products or even industries can help you gauge whether they are reaching a plateau or declining.

Some interventions like launching product variants or offering heavy discounts may help alleviate the situation for a period of time. However, it is better to focus on customer needs and on whether disruptions are killing an industry to give rise to a new one.

One point of caution here is that such analysis should be performed over long periods of time (5-10 years) and not over a short period, since it may not really make much sense otherwise. That said, even analyses over shorter periods can be useful. Thanks to SQL, you will be able to catch small signs of upcoming changes in the market and prepare for them in advance.

Making Strategic Decisions Using Data Analysis Techniques

As a business, you may be able to survive in the market based on gut or instinctive decision making for some time. However, if you want long-term success, you need data-backed decisions. Decisions that have a strong basis are more likely to yield great results.

Your decisions may be tactical or strategic in nature. A strategic decision is a longer-term plan to achieve a company’s vision or goals. A tactical decision, on the other hand, is a comparatively short-term execution-based decision. For instance, a company may strategically choose to become a low-cost provider of goods and tactically implement cost control measures to pass on the benefit of the improvement to the customer.

Business intelligence plays a crucial role in analyzing data to identify areas of improvement within an organization, guiding decisions for higher profitability, gaining a competitive advantage in the market, and using insights to improve business processes and operations. 

Statistical analysis techniques, such as descriptive and inferential statistics, are crucial for interpreting data and making informed strategic decisions. Tools and programming languages like R and Python are often used for statistical analysis, emphasizing its importance in data analytics and data science.

It is important that tactical plans align well with the strategy. For example, say you are a luxury furniture seller and charge a premium to your customers. It may not be a great idea for you to reduce the cost of your product by reducing quality or premium features.

The good thing is that data analysis can aid better decision making for both strategic and tactical decisions. Some examples I have described in this article, like understanding customer needs or analyzing market trends, can lead to insights that can help in strategic decision making. In contrast, examples like offer optimization or marketing channel optimization are more tactical.

Ready to Implement Data Analysis in Your Company?

If you have read the article this far, you now have a clear understanding of how important data analysis can be to the business. The examples in this article, though important, are nowhere close to being exhaustive. There are endless business cases and analyses that you can explore with your data. Implementing a structured data analysis process is crucial to transforming raw data into actionable insights.

Want a specific example of a data-driven company that conquered the world? Read the Uber case study. See how the introduction of SQL in its business helped it grow, and how employees of this global giant use databases on a daily basis.

An important requirement to make the best use of data is to be aware of and be skilled in certain tools that help reduce the time to build data views and manipulate information. While a lot of people use Excel spreadsheets for this purpose, I suggest you should also know SQL. Why? Check out Still Using Excel for Data Analysis? See Why SQL Is Better! for at least a few good reasons for using databases and SQL.

In fact, for a tool that doesn’t take too long to learn, SQL is extremely useful. Regardless of your role in an organization, it can come in handy if you deal with a lot of data. You may work in finance, marketing, supply chain, operations, or IT. Even if you are a student looking to freelance or work for a corporation, SQL has a use case for you.

If the analysis of the collected data led you to a strategic decision that you should learn SQL, I have a recommendation for you: the SQL A to Z track. This is a great interactive course where you will find everything you need to squeeze the most value out of your databases. It just pays off. If you've already made up your mind, get in touch with the people at LearnSQL.com. Let them bring the best SQL learning experience to your organization.

Ready to start? Begin learning and applying data analysis today to make smarter, impactful decisions!