Now that a vast majority of businesses are online, there are a range of different data sets that they can analyse. This is great! It means they can really take the time to understand how their business is doing, they can also learn more about their customers.

However, this isn’t the easiest process if you don’t know how to accumulate all the data and what tools are needed to sanitise and format the data into a usable state.

At its core, data analytics is the way in which we examine data to uncover insights that help to make informed decisions and grow your business. This means using data to understand customer behaviour, identify market trends, and develop marketing strategies.

This article dives into the essential aspects of data analytics, to take a closer look into what it is, how it can help businesses grow, as well as the best practices and tools for data analysis.

Whether you’re looking to improve your customers’ experience, simply gain insights into how your business is performing, or to drive growth, this article will show you how data analytics can be your key to growth!

In this article:

• The role of data analytics
• The 4 types of data analysis
• Data analysis: Best Practice
• Advanced analysis tools
• Custom analysis tools

The Role of Data Analytics

Data analytics plays a crucial role in modern businesses as it provides essential insights and tools to enhance various aspects of your operations, customer experience, and more.

Here’s how data analytics can help:

Identifying Trends

By analysing historical data, businesses can uncover different patterns and trends. This gives companies the opportunity to gain a deeper understanding of their market and also their customers preferences.

For instance, someone with an ecommerce business could track relevant purchasing trends to determine which items are popular during specific seasons or periods. They can then stock up on these products to meet future demands.

Improving Decision-Making

You might analyse data in order to figure out which products to promote. Whereas before you might have made that decision on a whim, or pushed all of your products, by using data analytics you can pinpoint which ones are performing well, and either improve their promotion more, or focus on other products that are struggling to make conversions. Either way, your decision making will be backed by evidence.

The 4 types of data analysis

Data analytics refers to the process of examining data to draw conclusions about the information they contain. This involves several stages including data collection, processing, and analysis, and more as a means to uncover patterns, and insights that can help you make informed decisions regarding your business .

So what are the different types of data analysis?

There are a few different data analysis types, such as descriptive analytics, and predictive analytics, these data types look at the same data but draw different conclusions. Here’s how:

Predictive analysis:

This looks to analyse data as a means to figure out potential forecasts and future outcomes. It answers the question, “What could happen?” by looking at previous data.

For example, if last year your business had an increase in sales over Christmas, you might use predictive analytics to then prepare you for that season the following year.

The data the businesses will need to gather for this will be:

• Historical data – simply put, this will be data that you would have previously gathered, you can look at sales records or even website traffic.

• Customer focused data – This includes things like previous interactions with clients, for example, their responses on a post.

• Current data – Look at any recent sales data, based on your past information, and your current info on sales, you might then make some predictions, or adjust predictions as new data comes in (For example, if a product sells faster than expected, you could predict the need to restock is sooner than intended)

Descriptive analysis:

This looks at historical data to identify trends and patterns. It answers the question, “What happened in the industry?”

If you had a drop in conversions, this data looks at trends, and patterns to give a closer understanding of what happened during this time.

What data would you need for this analysis?

• Qualitative data – For example look at customer feedback – is there anything your customers are giving negative feedback on?

• Time data – this is looking for patterns in sales, so, you might look for any increases or decreases in sales across different seasons.

Diagnostic analysis:

This explores data to understand the reasons behind past outcomes. It answers the question, “Why did it happen?”.

You might find that ever since you changed payment methods on your website, conversions dropped. You might then find customer reviews or feedback that back up that data.

• Comparative data – to gather this you would look at comparing your data, for example your sales before and after a product price change. Or, analysing your website during any promotional events(periods)

• Correlation Data – For this you might look at any correlations in your data, for example are there similarities between sales and customer satisfaction?

Prescriptive analysis:

These analytics suggest actions you can take. It answers the question, “What should we do?”

To summarise, these data analysis types help businesses pinpoint 4 key areas: What happened, Why did it happen, what was occurring during this time, and what should we do?

• Historical data – Once again, taking a look at historical data, such as sales over the past X years to find any peak periods. You might then alter your marketing accordingly.

• Market data – For this you would look into your market, whether its the markets condition, its competitors or any industry trends.

• Customer data – Understand your customer preferences and behaviours, what is their demographic?

Data Analytics: Best Practices for business Growth

There are a range of ways that businesses can analyse data, we put together the best practices to help businesses navigate analysis and ensure you’re getting the most out of your analytics and decision making…

1. Define Clear Objectives: Before diving into data analysis, it’s essential to define what you want to achieve, or what you want to find and why. Clear objectives guide the entire process and ensure that you focus on what really matters for your business.

If your goal is to increase sales, your objective might be to identify which products are most popular and why. This helps you ask the right questions and choose the right data to analyse.

2. Track Relevant Ecommerce data: You can collect data on things that are directly related to your ecommerce goals in a range of different areas.

This includes:

• Sales: Revenue, conversions, and sales growth.

• Customers: Customer acquisition, customer behaviour.

• Website: Traffic, conversion rates, and bounce rates.

• Products: Inventory levels, product return rates, and top-selling products.

To approach these tasks, start by familiarising yourself with the tools you’ll be using. Set up Google Analytics and explore its features to track sales and website data. You could also look into your ecommerce platform’s reporting tools.

3. Analyse Customer Behaviour: Dive into customer behaviour data to understand the individuals that are buying your products or services better. For example, you might look into what drives purchases and what may be causing cart abandonment.

This helps you tailor strategies to improve the customer experience.

Some ways you can do this include…

• Review data from Google Analytics to track how customers navigate your site, which pages they visit, and where they might drop off in the checkout process.

• Look into customer reviews or comments – if multiple customers mention that they find the buying process on your website too complicated, you might simplify it by reducing the number of steps or offering more payment options.

4. Monitor Competitor Performance: Analyse data from competitors to understand their strategies and performance. For example, competitor websites offer a wealth of information; you could observe their product offerings, pricing, and promotions.

Similarly, social media platforms provide insights into their marketing strategies, such as the types of content they post, engagement rates, and more.

This can help you find opportunities for improvement and can even help you to identify market trends.

5. Leverage Customer Feedback: A simple option that is often missed, you can collect and analyse customer feedback to gain insights into their experiences and preferences.

To gather this data, you could look at reviews on your product pages or third-party sites like Yelp to gather more opinions. Social media also offers a way to see what customers are saying about your brand through comments and direct messages.

Once you have collected feedback, analyse it to identify common themes and issues. Look for patterns in responses to see what customers like or dislike about your products or services.

You could then:

• Quantify feedback by counting how often certain issues or compliments are mentioned, and…

• Examine customer suggestions for new features or products.

6. Regularly Review and Adjust Strategies: Remember, data analysis is not a one-time task but an ongoing process. Regularly check your data to make sure you’re on track with your goals. Use the insights you gather to make informed decisions, like changing your marketing strategies or optimising your website. By staying engaged with your data, you’ll be better equipped to grow your ecommerce business.

Data Analytics Tools

Data analysis is becoming more and more important for businesses, with the rise of businesses online, to the vast amounts of competition, and audience preferences it has become more and more significant to analyse your businesses data. As we have mentioned previously, while having that data is crucial, if you don’t actually know what to look for and how to analyse and improve from that data things can get a little more complicated.

This is where Data analytics tools come in. These are designed to help you get deeper insights and more in depth analyses, going beyond the more basic data collection side of things.

Let’s say you run an online store and want to improve your sales. You might have collected a lot of different data from various sources, including your website, Google Analytics, and social media analytics (such as meta insights). Instead of looking at this data in separate reports or spreadsheets, you might instead use an advanced analytics tool to bring it all together in one place. Doing so will streamline your business operations and make analysing data that much easier.

So what tools can you use?

There are a range of different tools with different use cases, Power BI for example gathers data from different sources and formats it in a way that’s’ easier to understand.

And while using tools like these can be beneficial, there still needs to be a good understanding of what data to gather, and knowledge of what this data is telling you.

A good alternative in this instance is a custom data analysis tool.

Custom Data Analysis

One thing that businesses can do is approach their marketing teams, developers etc, and ask them to build them a data tool. This approach means that the data tools they get will be more bespoke, tailored for that business, and therefore will gather more specific findings. It also means that professionals will be able to assist and gather more specialised data that really matters.

The benefits of custom tools

For businesses with unique or highly specific data sets, custom data tools can become essential. A prime example is Rotala Hub, a bus management company dealing with extensive daily data. We developed a tailored data analysis tool for Rotala Hub that consolidated their various data sources into a cross-system report, this brought together all of the relevant information they needed into one accessible place.

Click here to learn more on the data tool we built for Rotala-hub.

This is why custom tools are often more beneficial. For a business like Rotala Hub, it is likely that other more general tools won’t cut it, and they might instead need a data tool that is more specific to them.

Final thoughts

All businesses should be using data analytics to understand how their business is performing. A lot of businesses get a website, or, get a following on social media, and leave it at that, but instead they should be continuously analysing their performance, particularly ecommerce businesses.

Afterall, by effectively analysing data, you can uncover trends, and patterns which all work towards enhancing every aspect of your businesses approach, this might be as simple as identifying popular products – this data can help you both find these products, learn where/what you need to improve, whether that’s with the product itself or even your marketing strategy.

But, the key thing to remember is that data analysis is not a one-time task but an ongoing process, and if you struggle to analyse data there are a range of tools available that you can use to make the process easier.