Skip to content

Data Analytics For SMEs: Knowing Your Next Move

Team 365 finance

Written by Team 365 finance

Every business, no matter how big or small, produces a significant amount of data. While this data is immensely useful, it can seem very dense and confusing when it’s first generated. In order to extract useful information from raw data, you’ll need to learn more about data analytics.

Data analytics is useful for any business owner, and although it may seem intimidatingly technical, it’s a crucial skill if you’re looking to expand your company. Through data analytics, you can vastly increase the efficiency (and therefore revenue) of your business.

In this article, we’ll define data analytics for Small and Medium Enterprises (SME), explaining what the four kinds of SME data analytics are and how they can help you. Then, we’ll look at exactly how a business can start identifying inefficiencies in the workplace using data analytics. Read on to learn more.

 

What is SME Data Analytics?

To understand data analytics, you first need to understand the concept of “big data”. This is the massive volume of data a business creates simply by existing. There are two kinds of big data:

  • Structured data: Structured data is highly organised and easily searchable, so it doesn’t take much to extract information from it. Examples include point-of-sale data (like a transaction receipt) or information stored on customer relationship management (CRM) systems.
  • Unstructured data: In contrast, unstructured data is raw, unorganised and more challenging to collect, process and interpret. Most qualitative data is unstructured, so things like customer feedback, client emails, and social media posts are all types of unstructured data. Additionally, images, as well as audio and video content count as unstructured data.

Both kinds of big data can be useful, but it’s much more common for SMEs to use structured data in their data analytics simply because it is easier. Combing through unstructured data for useful information is hugely time-consuming as it usually requires someone to manually examine each data point for specific insights.

However, in the modern business world, SMEs can use AI (artificial intelligence) to automate unstructured data analytics. This helps businesses access many sources of data that would have been otherwise unavailable to them.

 

What Are The Four Types of SME Data Analytics?

In data analytics, big data is the subject of the analysis. But how do businesses actually perform data analytics? We’ve described the four primary methods below:

1. Descriptive Analysis

The goal of descriptive analysis is to answer questions about what happened, so it typically involves looking back at a previous event. For example, you would perform descriptive analysis when trying to discover how successful your business was during last year’s summer sales.

Descriptive analysis starts with data collection (collecting receipts and totalling sales figures, for instance) and then moves on to the examination and visualisation of that data. So once you have your sales data, you would try to identify important details (like best-selling products) and visualise that data in a useful way, such as in a spreadsheet or graph.

 

2. Diagnostic Analysis

Sometimes, things go wrong, and you’ll need to find out what you can do to prevent that in the future. Or perhaps a strategy succeeded beyond expectations, and you’re looking to replicate that success. In these instances, you’ll employ diagnostic analysis, which is used to figure out why something happened.

It starts much the same way as descriptive analysis (i.e. with data collection), but the processing and analysis of your results are much more important here. With diagnostic analysis, you’re looking for dependencies, trends and patterns in the data sets you build. Doing so helps you connect the dots between your actions and the relevant business success or failure.

 

3. Predictive Analysis

It’s important for small businesses to look ahead to the future, as having plans and strategies in place well before they’re actually needed helps prevent disasters. For example, even in the height of summer, you’ll want to start thinking about Christmas market ideas. To help plan ahead, small businesses may want to employ predictive analysis.

This type of SME data analytics looks to answer what will happen. It builds on the previous two methods: once you’ve identified trends using diagnostic analysis, predictive analysis involves using statistical modelling or AI-powered predictions to determine if these trends are likely to occur in the future. This technique is commonly used to reduce risk and identify new opportunities.

 

4. Prescriptive Analysis

The first two types of analysis help determine what happened and why, while the third predicts whether it will happen again. Next, you’ll want to employ prescriptive analysis to figure out what to do about it.

One of the most common uses of prescriptive analysis is in modern social media algorithms, where content is shown to the user based on their past preferences. The algorithm employs prescriptive analysis by examining previous content, identifying patterns (e.g. content categories that are often viewed, liked, or searched for), and then displaying similar content to the user in the future.

Small businesses may struggle to perform in-depth prescriptive analysis because it requires a lot of computing power. However, a simple version might involve looking at best-selling product categories, identifying why they’re popular (through diagnostic analysis) and then producing new products with similar features so you can continue to capitalise on the original success.

How You Can Use Data Analytics

Social Media Analytics

Social media is a critical part of modern marketing. As such, analysing social media engagement, return on investment, and ideas for future strategy is a very common use case for data analytics.

However, useful data can only be extracted using the right methods. If you attempt analysis simply by reading through your social media, you’ll be sifting through heaps of unstructured data before you find anything insightful. Instead, consider using analytics tools like Buffer or Sprout Social. These tools can produce structured data from your social media activity, allowing you to complete the analysis at your leisure.

 

Identifying Profitable Market Segments

Not all customers are alike. Some will spend more, return to your store more often, and require less customer service than others. Data analytics can help you identify these super-customers in your existing audience, which can then help you identify and capture them in your potential audience too.

You’ll need a lot of customer data to help with this task, such as purchase history, customer service interactions, and data on the customer journey (e.g. how they found your business and how they interact with it). Analysing this data helps reveal the demographics and interests of your super-customers, which, through prescriptive analysis, can help you market to other consumers who have the potential to be super-customers, too.

 

Identifying Inefficiencies in The Workplace

Another common use case for data analytics is identifying inefficiencies in the workplace. It’s especially prevalent in manufacturing businesses, but can also help with inventory management and customer service.

Let’s look at inventory management as an example. You’ll need to gather data on the number of products you store, how many sell, and how many fail to sell and need to be destroyed or thrown away. The timing of high and low-sales periods is also important. Using this data, you can ensure you’re stocking exactly the right number of products, as opposed to wasting space by overstocking or losing revenue by not having enough.

 

Funding Data Analytics With 365 finance

Data analytics is free for the most part. As long as you have the data, the only thing it costs is time. However, what will you do with the conclusions of your analysis? The inefficiencies that data analytics reveals can only be solved with additional funding — that’s where 365 finance comes in.

With 365 finance, you don’t have to worry about funding any of the changes your prescriptive analysis recommends. Our Rev&U product allows fast and easy access to funding for any business investments you need to make, as there are no spending stipulations. As a revenue-based finance option, Rev&U is also low-risk and comes without the pressure of high monthly payments.

At 365 finance, we can provide long- and short-term financial solutions, with revenue-based funding available from £10,000 to £400,000 in capital. Apply for Rev&U today without affecting your credit score, or speak to our team to find out how we can help your business. To find out more, head to our website.