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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, then this article is for you.

Historically, many companies have gone into analytics mainly through financial and accounting data to improve revenue, reduce costs, and eventually increase profitability. And without a doubt, such analyses are still useful.

However, technology today provides easy ways to capture and access a lot of useful information which, if put to good use, can unlock tremendous value for your business. If you can analyze data meaningfully, your decisions can become smarter, and this can be a game changer for you and your business.

Your customers’ interaction with you, like placing orders or asking questions about the products, returns, and warranty claims, are all recorded one way or another. And these can uncover insights that can completely transform your growth trajectory.

Prospective customers, not just your existing customers, may be searching for products or services you provide on the Internet. Even this information can be made available to you through several tools.

It may seem like a daunting task to access and utilize the sheer volume of data available today. However, data querying and analysis tools like SQL and programming languages like Python can make it an easier task.

So, what are the various use cases for data analysis that can definitely benefit your business? I will take you through some of them, with real life examples of how I have used data analysis to make recommendations about strategic decisions that impacted my clients and employers greatly.

Let’s dive straight in.

Understanding Your Customers and Their Needs Better

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?

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:

customer_nameagemodel_namecamera_ratingperformance_ratingbattery_ratinglooks_rating
Alexis30Milkyway3544

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:

model_namecamera-ratingperformance-ratingbattery-ratinglooks-rating
Milkyway4.84.22.44.5
zPhone3.24.13.94

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.

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:

prospect_nameagesource_websiteconversion_flagtotal_order_value
Emily24facebook1$100
Ross28google1$20
Rachel25youtube0$0

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:

source_websitenumber_of_prospectsno_of_conversionsorder_value
Facebook40000100$1000
Google30000200$500
Youtube5000500$4000
Instagram25000200$2000

In this SQL query, I used a GROUP BY statement. If you want to know more about it, check out this article.

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. 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:

number_of_unique_itemsaverage_repeat_orders_per_customer
10.2
20.2
30.4
40.8
53
63.2
>63.4

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.

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.

Data analysis

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

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.

Data analysis

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.

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 Forget About Excel, High Five With SQL 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. Are you wondering how to help with your project? Check out How to Boost Your Company With SQL Courses. 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 for the journey? Start learning and applying data analysis today to make awesome decisions!

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