Service Category: Analysis

The basics of customer insights: leveraging your data for real customer understanding

In today’s competitive business landscape, understanding your customers is key to driving growth and success. Fortunately, with the abundance of data and advanced analytical techniques, businesses can now unlock valuable customer insights like never before. In this article, we will some of the basics around how data and analytics can help you delve into customer behaviour and make informed decisions to optimise your strategies. Let’s dive in!

Segment Composition: Unveiling Customer Diversity

One of the first steps in understanding your customers is segmenting them based on various characteristics. By leveraging data and analytics, you can identify distinct customer segments and tailor your marketing strategies accordingly. Whether it’s demographics, psychographics, or behaviors, segmenting your customer base allows you to personalize your communication, offerings, and experiences. This level of customization can significantly enhance customer satisfaction and drive stronger engagement.

For example, by analysing data such as age, gender, location, and purchase history, you can create targeted campaigns that resonate with specific segments. Understanding the unique needs and preferences of each segment empowers you to deliver personalised messages that truly speak to your customers, ultimately fostering stronger relationships and brand loyalty.

Let’s look at the process involved in delivering an initiative like this:

1 - Identify key customer characteristics
Develop a list of the relevant demographic, psychographic, and behavioral factors that differentiate your customer base. It's ok if this isn't grounded in data yet.
2 - Collect customer data
Gather information such as age, gender, location, purchase history, and any other relevant data points. This might be in your existing systems, or might require new systems. Try to use what you have first for the quick wins.
3 - Analyse the data
Now you'll use your collected data to identify patterns or clusters within your customer segments. This'll likely be on the parameters you identified in Step 1.
4 - Create customer segments
Based on the analysis you've performed, group customers into distinct segments that share similar characteristics or behaviours. You may need tool to help you develop these customer segments, depending on how you plan on targeting them with your efforts (e.g. marketing vs sales).
5 - Refine your efforts
With each segment's unique needs and preferences front of mind, you can trial and iterate on the most effect mechanism to affect outcomes from your customers. This might be different messaging or pricing points, which will resonate better with some segments than others.

Retention Rate: Enhancing Customer Loyalty

Customer retention plays a crucial role in sustaining long-term success. Data analysis can help you measure and understand your customer retention rate, providing valuable insights into customer loyalty. It sounds basic – sure- but by analysing patterns and trends in customer behaviour, you can identify factors that influence customer churn and take proactive measures to improve retention.

Through data-driven analysis, you can uncover potential pain points, such as poor customer service or product issues, that might be causing customers to leave. Armed with this knowledge, you can address these concerns promptly, improve your offerings, and enhance the overall customer experience. By focusing on customer retention, you can increase customer lifetime value and establish a strong foundation for sustainable growth.

1 - Define your retention metrics
Determine the key indicators of customer retention for your business, such as repeat purchases, frequency of purchase, subscription renewals, or customer engagement.
2 - Collect relevant data
Gather data on customer behaviour, interactions, and purchase history to track retention metrics. You may need need implement new data gathering systems to assist, or find ways to extract this data from your existing systems.
3 - Analyse the customer behavior
There's many ways to look at retention - many are driven by strong statistical models in the background. Unfortunately, good analytical work here isn't easy -- you might be best off to engage a partner to ensure you're getting the whole picture.
4 - Identify improvement areas
Identify potential pain points or factors that may contribute to customer churn, such as poor customer service, product issues, or lack of engagement. You can do this by looking at which customer segments (which you identified earlier) have lower retention than others -- and ask yourself, why?
5 - Take proactive measures
Address the identified issues promptly, improve your offerings, enhance the customer experience, and implement retention strategies tailored to retain customers and foster loyalty.

Customer Lifetime Value: Maximising Customer Potential

Customer lifetime value (CLV) is a metric that helps businesses measure the potential profitability of individual customers over their entire relationship with the company. By leveraging analytical and modelling techniques, you can calculate CLV and segment your customers based on their value (or view the CLV of customers in an existing segment). This allows you to allocate resources effectively, prioritise high-value customers, and tailor your marketing efforts accordingly.

Understanding CLV enables you to identify opportunities for upselling or cross-selling to existing customers. By recognising their purchasing patterns and preferences, you can offer relevant products or services that align with their needs. This personalised approach not only drives additional revenue but also strengthens customer loyalty and advocacy.

1 - Gather customer data
You'll need relevant customer data, including purchase history, transaction values, and ideally customer interactions. Try to get as much history as possible.
2 - Model your CLV
CLV is best served by a statistical model that predicts the true CLV, because you can't actually measure this directly. Your retention metrics from above is one of the key inputs -- so make sure you have a good grasp on your retention before attempting to measure a CLV.
3 - Overlay segments with CLV
You can see which segments have higher CLV or even build segments based on CLV alone.
4 - Use CLV to allocate resourcing
Allocate your marketing and customer service resources based on the value and potential of each customer segment. You may invest more to grow the low-CLV segments, or use the information to invest more in your advertising campaigns, knowing your cost-per-acquisition stacks up.
5 - Take proactive measures
Address the identified issues promptly, improve your offerings, enhance the customer experience, and implement retention strategies tailored to retain customers and foster loyalty.

Monetisation Opportunities: Extracting Revenue Potential

Data analysis also uncovers monetisation opportunities within your customer base. By examining customer behaviour, purchase history, and interactions, you can identify potential revenue streams and optimise your strategies accordingly.

For instance, personalised offers or recommendations based on customer preferences and past purchases can significantly increase the likelihood of conversion. By leveraging data and analytics, you can identify cross-selling or upselling opportunities that align with your customers’ needs, thereby driving incremental revenue.

1 - Understand your customer segments
Follow the above steps to form a solid understanding of the behaviour of your customers.
2 - Identify cross-selling and upselling possibilities
Analyse customer preferences, purchase patterns, and complementary product/service offerings to identify cross-selling and upselling opportunities. Are there products that are more commonly bought in one segment and not another? How does price sensitivity vary by segment?
3 - Test, measure, learn
Implement targeted efforts to quantify how your segments respond to up/cross-sell efforts. By measuring the conversion rate on your efforts, you'll also know how much you can invest upfront to still get a net-positive ROI at the end.

In today’s data-driven world, understanding your customers is the key to unlocking the full potential of your business. By leveraging data and analytics, you can delve deep into customer behaviour, uncover valuable insights, and make informed decisions to drive growth and enhance customer experiences. With the help of Vaxa Analytics and their expertise in data analysis, you can embark on a journey to unleash the power of customer understanding.

Remember, segmentation allows you to speak directly to your customers, tailoring your offerings to their unique needs and preferences. Retention strategies enable you to build loyal customer relationships that stand the test of time. Customer lifetime value empowers you to prioritise and allocate resources effectively, maximising the potential of your most valuable customers. Monetisation opportunities open up new revenue streams that will make your business flourish.

So, don’t miss out on the opportunity to better understand your customers. Start your data-driven journey today and witness the transformative impact it can have on your business.

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Getting started with data & analytics as a small business

Starting a small business can be challenging, but with data and analytics, you can make data-driven decisions that will help your business grow. But where do you begin? The idea of data and analytics can be overwhelming, but with the right guidance, it can be a valuable tool for any small business.

Step 1: Identify Your Goals

The first step in getting started with data and analytics is to identify your goals. What do you hope to achieve by using data and analytics? Some common goals include:

  • Improving customer retention
  • Increasing sales
  • Reducing costs
  • Identifying new business opportunities
  • Understanding productivity and efficiency

Once you know what you hope to achieve, you can start to think about how data and analytics can help you reach those goals.

Step 2: Collect and Organise Your Data

The next step in getting started with data and analytics is to collect and organise your data. This may include sales data, customer information, and website analytics. It’s important to ensure that your data is clean, accurate, and up-to-date – things that a data governance approach will address.

To collect your data, you may need to set up tracking systems such as Google Analytics to track website traffic and behaviour. You can also use tools such as a CRM (customer relationship management) system to collect customer information and sales data. Tools like ERP (enterprise resource management) systems manage information on your operations and inventory levels.

Once you have collected your data, it is important to organise it in a way that makes it easy to analyse. With multiple systems, this may include creating a data warehouse or data lake, where you can store and organise all of your data in one place. You may also need to perform data cleaning to ensure that your data is accurate and consistent. Custom data warehouses directly address your need, but turnkey solutions exist for many popular platforms that can provide a good starting point while you up-skill.

Step 3: Analyse Your Data

Now that you have your data organised, you can start to analyse it. This may include looking at sales trends, customer demographics, and website analytics. You can use tools ranging from Excel, Google Analytics, and Amplitude, right through to custom Python code to help you with this step.

When analysing your data, it’s important to keep your goals in mind. For example, if your goal is to increase sales, you may want to focus on analysing sales data and identifying trends or patterns. If your goal is to improve customer retention, you may want to analyse customer data and identify factors that lead to customer churn.

There are a variety of tools and techniques that you can use to analyse your data, such as descriptive statistics, data visualisation, and machine learning. Data analyst specialists – like my team at Vaxa Analytics – can help you with this step and provide you with more advanced analytics techniques like predictive modelling, A/B testing, and customer segmentation.

Step 4: Use Your Data to Make Decisions

Once you have analysed your data, you can use it to make decisions. This may include changes to your marketing strategy, changes to your products or services, buying new equipment, or even hiring new staff.

While these steps may seem simple, they can be challenging to implement on your own while running your core operations. Consider the balance of implementing these steps yourself versus engaging help; we help many small businesses who’d rather focus on their operations to take their first steps and extract value from their data.

In conclusion, data and analytics can be valuable tools for small businesses, but it can be overwhelming to get started. By identifying your goals, collecting and organising your data, analysing it and using the insights to make decisions, a small business can gain a competitive edge. And when the going gets tough, consider engaging an analytics partner to guide you through the process and help you make the most out of your data.

QTAC logo

A data-first financial model for a new public-facing product, delivering exceptional results

The Queensland Tertiary Admissions Centre (QTAC) is a non-profit organisation and Queensland’s trusted leader in tertiary admissions for more than 40 years. QTAC provides a one-stop shop for 17 higher education providers in Queensland and northern New South Wales, across both undergraduate and postgraduate admissions. 

Vaxa Analytics was engaged to assist QTAC in the development of a financial model regarding a new public-facing product. The Vaxa Analytics team worked to find and integrate a range of third-party datasets and relevant industry benchmarks to build the model under several scenarios.  

The team were able to project the growth of the product under those scenarios, accounting for various revenue stream, growth profiles and sensitivities over time. 

Additionally, Vaxa Analytics provided advice at a strategic level on the analytics and data-driven development of the new product, what QTAC should be looking to integrate into the product to understand which features users are using, and how that impacts long-term product retention. 

“Curtis and the Vaxa Analytics team helped us determine what data we should be accessing and how best to use it to get the most accurate results and insights,” QTAC’S Head of Strategic Growth, Kristie Fankhauser, said. 

“It was great having a specialist external team work on this project rather than impacting our internal team who were extremely busy during a peak time for our business. 

“Vaxa Analytics delivered over and above my expectations, were professional, transparent in everything they did, and easy to work with. I have already recommended them to many people and hope to continue our working relationship on this project and future ones.” 

The outcome 

The financial model was reviewed by QTAC’s engaged forensic accountant who agreed with the assumptions presented in the model and the data used. The model was an important part of the work required to progress further development of the product. Work is continuing. 

Dashboard reports

Building accessible, relevant and succinct reporting to give your team the info to make the right decisions. ​


No more sifting through endless spreadsheets and graphs to find the information you need. Our dashboards and reports give you a clear, concise view of your data in real time.

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Actionable insights.

Our team of experienced analysts will help you identify key trends and areas for improvement, so you can take informed action to drive your business forward. We’ll even help you implement and optimise it.

Customised to your needs.

Every business is unique, and we understand that one size does not fit all. Our dashboards and reports are tailored to your specific business needs and goals, so you can get the most value out of your data.

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How do dashboards and reports help?

Dashboards and reports provide a wealth of information that can help businesses run more effectively and efficiently. They can help businesses:

  • Monitor key performance metrics: Dashboards and reports allow businesses to track key performance metrics in real-time, providing a clear picture of how the business is performing.
  • Identify trends and patterns: Dashboards and reports can help businesses identify trends and patterns in their data, allowing them to make informed decisions about how to improve their operations.
  • Make better decisions: By providing real-time insights into key performance metrics, dashboards and reports can help businesses make better decisions about how to allocate resources, set goals, and grow their business.

Why invest in automated reporting and dashboards?

There are several reasons why it can be important to automate reporting and dashboards:

  1. Time-saving: Automating reporting and dashboards can save time by eliminating the need to manually compile data and create reports. This can allow businesses to focus on other tasks and be more productive.
  2. Improved accuracy: Automating reporting and dashboards can improve accuracy by reducing the risk of human error. This can lead to more accurate and reliable insights.
  3. Real-time insights: Automating reporting and dashboards can provide real-time insights into key performance metrics. This can allow businesses to make better decisions and respond more quickly to changes in their environment.
  4. Customisation: Automating reporting and dashboards can allow businesses to customise the data and metrics they track, ensuring that they are getting the insights they need to make informed decisions.

Overall, automating reporting and dashboards can provide numerous benefits and help businesses run more effectively and efficiently.

Practical examples.

A retail company could use our dashboards and reports to track sales, customer demographics, and product performance. This could help them identify best-selling products, target marketing efforts, and optimise inventory management.

A manufacturer could use our dashboards and reports to monitor production efficiency, cost trends, and supplier performance. This could help them identify bottlenecks in their production process, reduce costs, and improve supplier relationships.

A healthcare organisation could use our dashboards and reports to track patient outcomes, staff productivity, and resource utilisation. This could help them improve patient care, reduce costs, and allocate resources more effectively.

A non-profit organisation could use our dashboards and reports to track fundraising efforts, program impact, and volunteer engagement. This could help them measure the success of their programs, identify areas for improvement, and engage donors and volunteers more effectively.

These are just a few examples, but the possibilities are endless.

Our team of experienced analysts can work with you to understand your specific business needs and goals and develop custom dashboards and reports that meet your unique requirements.

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Data models and predictions

Predicting the future using your data and the most advanced machine learning and AI techniques.

How we work.

Our team of data scientists and analysts use a variety of machine learning and AI techniques to build predictive models that are customised to your business. Whether you want to forecast sales, predict customer behaviour, or identify trends in your data, we have the tools and expertise to help you achieve your goals.

Here are a few examples of the types of models we can build:

  • Regression models: These models predict a continuous outcome, such as sales or revenue. They are useful for forecasting trends and identifying patterns in your data.
  • Classification models: These models predict a categorical outcome, such as whether a customer will churn or whether an email will be spam. They are useful for identifying patterns and making decisions based on those patterns.
  • Clustering models: These models group similar data points together, helping you identify trends and patterns within your data. They are useful for segmenting customers or identifying areas for improvement.

Prescriptive analytics is your goal.

Dashboards and reports are good, but they often only tell you what’s happened — not what is going to happen, or what you should do to realise your business strategy.

Your analytics should be doing these things. We call that “predictive” and “prescriptive” analytics. There are many benefits to reaching these more advanced levels of analytics:

  • Make informed decisions: By understanding the trends and patterns in your data, you can make better-informed decisions that drive business growth.
  • Stay ahead of the competition: By predicting the future, you can stay ahead of the competition and be better prepared for changes in the market.
  • Improve efficiency: By identifying patterns and trends in your data, you can optimise your operations and improve efficiency.
  • Increase revenue: By predicting customer behaviour and forecasting sales, you can make targeted marketing efforts and increase revenue.

Are you ready to start using predictive and prescriptive analytics to drive your business forward?

We’ll help you see past the present and into the future.

Customer segmentation

Everyone’s unique.

We understand that not all customers are created equal. That’s why we offer customised customer segmentation services to help our clients better understand and target their unique audiences.

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Deep dive.

Using advanced algorithms, we’re able to take a deep dive into your customer data and identify distinct groups of individuals with similar characteristics, behaviours, and preferences. This allows you to tailor your marketing efforts and provide more personalised experiences for each segment, ultimately driving engagement and increasing sales.

Not just numbers.

But we’re not just about the numbers. Our team of plain-English analysts will work with you to come up with fun and memorable names for each segment, so you can easily reference them in your campaigns and reports. Whether you have “The Early Birds” who always make their purchases first thing in the morning, or “The Night Owls” who prefer to shop in the wee hours of the night, we’ll help you bring your customer segments to life in the best marketing automation platform for your organisation.

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The evolving landscape of customer segmentation.

In recent years, the field of customer segmentation has evolved significantly, driven in part by advances in data analytics and machine learning.

Here are some ways in which modern customer segmentation differs from a few years ago:

  1. Increased complexity: Modern customer segmentation often involves analysing large and complex data sets, including both structured and unstructured data. This can provide a more comprehensive and nuanced view of customers and help businesses identify more granular segments.
  2. Personalisation: Modern customer segmentation often focuses on personalisation, using data to create customised experiences and campaigns for different segments of customers. This can help businesses better engage and retain customers.
  3. Machine learning: Machine learning algorithms can be used to automate and optimise customer segmentation, allowing businesses to identify and target segments more effectively.
  4. Increased focus on customer experience: Modern customer segmentation often takes into account the customer experience, including both online and offline interactions. This can help businesses create more seamless and consistent experiences for different segments of customers.

Overall, modern customer segmentation is more sophisticated and data-driven than in the past, and it focuses on personalisation and the customer experience to drive business growth.

Practical examples of customer segmentation.

Some practical examples of using customer segmentation include:

Personalised marketing campaigns: By dividing customers into distinct segments based on their characteristics, behaviours, and preferences, companies can create targeted marketing campaigns that are more likely to resonate with each segment. This can help increase engagement and ultimately drive sales.

Customised product development: Customer segmentation can help companies identify specific groups of customers with unique needs and preferences. This information can be used to develop products and services that are tailored to these segments, increasing customer satisfaction and loyalty.

Improved customer service: Customer segmentation can also be used to provide more personalised experiences in customer service. For example, a company might use segmentation to identify high-value customers and provide them with dedicated support teams or priority access to services.

Increased efficiency in operations: By understanding the different needs and behaviours of each customer segment, companies can streamline their operations and allocate resources more efficiently. For example, a company might use segmentation to identify segments that are more likely to respond to online advertising and allocate a larger portion of its marketing budget to those channels.

So why settle for a one-size-fits-all approach?

We’ll help you get to know your customers on a deeper level and watch your business soar to new heights.

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Machine learning

We understand the potential that machine learning has to revolutionise businesses of all sizes and industries.

That’s why we’re dedicated to providing our clients with high-quality machine learning services that can help them unlock new growth opportunities and drive success.

What is machine learning, in simple terms?

Machine learning is a type of artificial intelligence that allows computers to learn and make decisions without being explicitly programmed. It can help businesses unlock the power of their data to make better decisions and improve operations.

It helps solve traditionally difficult problems, where an exact solution definition isn’t available. Instead, machine learning algorithms can be fed bulk amounts of data and build their own solution to the problem at hand.

There’s a broad range of applications when it comes to machine learning, including:

  • Predicting customer behaviour: Machine learning can help businesses predict how customers will behave, allowing them to tailor their marketing efforts and improve the customer experience.
  • Detecting fraud: Machine learning can help businesses detect fraudulent activity, improving security and reducing the risk of loss.
  • Improving operations: Machine learning can help businesses optimise their operations by identifying patterns and trends in their data and making recommendations for improvement.

What is needed for machine learning?

To be practical, machine learning typically requires:

  • Large amounts of data: Machine learning algorithms need a large amount of data to learn from, so the more data a business has, the more accurate the predictions and decisions made by the algorithms will be.
  • A clear goal: Machine learning algorithms need to be trained to solve a specific problem or achieve a specific goal, so it’s important to have a clear understanding of what you want the algorithm to do.
  • A machine learning algorithm: There are many different types of machine learning algorithms, and the best one for a given problem will depend on the specific circumstances.

Practical examples.

Some practical examples of machine learning include:

Fraud detection: Machine learning algorithms can be trained to identify patterns and anomalies in transaction data that may indicate fraudulent activity. This can help financial institutions reduce losses from fraud and improve the customer experience by catching fraudulent transactions more quickly.

Customer service chatbots: Machine learning can be used to build chatbots that can handle customer inquiries and provide personalised responses in real time. These chatbots can help reduce the workload on human customer service agents and improve the overall customer experience.

Predictive maintenance: Machine learning algorithms can be used to analyse data from sensors on industrial equipment to predict when maintenance will be needed. This can help companies reduce downtime and improve the efficiency of their operations.

Medical diagnosis: Machine learning algorithms can be trained on large datasets of medical images and patient records to assist in the diagnosis of diseases such as cancer or diabetes. This can help doctors make more accurate and timely diagnoses, ultimately improving patient outcomes.

Music and movie recommendations: Many music and video streaming services use machine learning algorithms to recommend content to users based on their past listening or viewing habits. This can help users discover new content and improve their overall experience with the service.

Less common, but novel examples.

With the power of machine learning, many more “traditionally hard” problems for computers to solve become solvable, including:

  • Document OCR: Optical character recognition (OCR) is a type of machine learning that allows computers to recognise and extract text from images and documents. It can be used to digitise paper documents, making them easier to store, search, and share.
  • Voice recognition: Machine learning can be used to develop voice recognition systems that can understand and respond to human speech. These systems can be used in a variety of applications, including virtual assistants, language translation, and voice-controlled devices.
  • Image recognition: Machine learning can be used to develop image recognition systems that can classify and identify objects in images. These systems can be used in a variety of applications, including security, surveillance, and marketing.
  • Predictive maintenance: Machine learning can be used to develop predictive maintenance systems that can predict when equipment is likely to fail and schedule maintenance before it does. This can help businesses reduce downtime and improve efficiency.

So why not solve the hard problems?

We’ll help you use the latest breakthroughs in machine learning to tackle your organisation’s challenges to more efficiently and more effectively

Analytics isn’t reporting any more

In the not-too-distant past, analytics used to be seen as after-the-fact reporting, dashboarding, charts — understanding what had already happened. Today, analytics is much more tightly coupled with business strategy and product development. It is used to inform and guide decision-making in real-time. 

This shift is due in part to the increasing availability of data. There are now more data sources than ever before, and they are producing more data than ever before. This means data is being generated at an ever-increasing pace, making it difficult for many businesses to keep up, let alone take full advantage of the possibilities that lie at their feet. 

It’s not just the volume of data that has changed, it’s also the quality. Data is now more accurate and timelier, thanks to advances in data collection and processing technologies. This has made it possible to use data to understand not just what has happened in the past, but also what is happening right now and what is expected to happen in the future. 

This shift has had — or, in too many cases, ought to have — a major impact on the way businesses operates. In the past, businesses would make decisions based on their own experience and intuition. Today, they should be increasingly incorporating data-driven insights to guide their decision-making. 

This change has been driven by the need to be more agile and responsive to the ever-changing and competitive market. In the past, businesses could afford to take their time to make decisions. Today, they need to be able to make decisions quickly, based on the latest data. 

This change has also been driven by demands to be more efficient and accurate in investments and use of resources. In the past, businesses would often make decisions based on hunches or guesswork, and there’s a certain “they did the best they could do” asterisk against such decisions that turned out poorly. 

Today, with the high-quality and real-time data available in every business sector globally, a growing expectation is placed on decisions being correct and backed by the data. 

Hopefully it’s becoming clear that organisations need to embrace the capabilities made available to them with modern analytics. However, modern analytics isn’t delivered by simply hiring an analyst; organisations need to consider how their analytics function interweaves with the business as a whole. 

In many cases, analytics can neatly sit alongside both the business strategy and operations teams, forming a vital link between these levels of an organisation. Other times, a business partner approach can work well. In any case, an organisation’s analytics function must be underpinned by investment in systems, processes, and most importantly of all, the business culture. 

Business culture means promoting a data-driven mindset throughout the organisation, from the boardroom to the front line. It means everyone in the organisation understands that data should be used to inform their own decision-making. 

It also means creating an environment where it’s safe to experiment, where failure is seen as a learning opportunity rather than a cause for punishment. And it means having the right people with right skills in place. Communication skills and data literacy are as equally important as technical prowess. 

Consider if an internal analytics function is right for the scale of your organisation. In many cases, analytics isn’t a core business skill, and the investment required to manage this in-house isn’t worth it; good analysts and systems don’t come cheap. Quality analytics-as-a-service offerings can deliver great outcomes while remaining cost-effective. 

Ultimately, there is no one-size-fits-all solution when it comes to utilising analytics into an organisation, but there are some key principles that all organisations should bear in mind: 

  1. Modern analytics deserves to be tightly-coupled with business strategy 
  2. Data can and should be used to inform and guide decision-making in real-time 
  3. Organisations should take advantage of the increasing availability and quality of data 
  4. Move beyond just analysing what’s happened in the past. Instead, look towards the future. 

Why “I don’t have any data” is a cop out

An all too common phrase heard across almost every business and industry is “we don’t have any data to analyse”. This couldn’t be further from the truth; I can assure you that almost every business does have data to analyse. You don’t need to be a Google-scale operation – you just need to broaden your horizons on what useful data is. Once you’re done reading through this post, you might find it hard to stop thinking about all the various places you’re generating and collecting useful data already! 

Let’s take a quick step back and lay the foundations first. We want to use analytics to give us actionable insights: “If your goal is X, you should do Y to achieve it”. To get to this (incredibly powerful) state, you’ll first need to have three key pillars firmly in place: 

  • Data strategy: Why are you doing this initiative? How can you use data and its insights to inform, support and execute your business strategy? 
  • Data management: Where and how are you storing the data, validating it, securing it, and making it easily available for quick and useful analysis? 
  • Data analysis: Given the right data in the right format, what actionable insights can be extracted? 

So, when a business says, “we don’t have any data to analyse”, it’s actually often attributable to focusing too much on the analysis instead of the strategy and management. One of the key steps in developing the latter two is to broaden your horizons and truly understand the breadth and depth of data you’re already collecting (alongside future data collection initiatives, when they align with your business strategy). 

Now with those foundations laid, let’s run through some useful datasets that almost every business will be generating and what you can learn from them. 

Financial and customer data 

For most businesses, this is probably going to be the most reliably collected data available. After all, it’s mission-critical; without the data on who owes you money, your business is in a lot of trouble! 

The data you generate managing your accounting and commercials can help form actionable insights like: 

  • Data on who are you spending the most money with → Use this to determine where cost optimisation efforts could be placed 
  • Data on who is spending the most money with you → Use this to determine where account management efforts could be placed 
  • Data on how often and how much your customers are spending → Use this to determine if your clientele is aligned with your strategic goals, or if you need to source a new client base 
  • Data on how customer spend behaviour has changed over time → Use this to see if newer customers are more or less profitable than older ones (and determine if you’re acquiring customers from the right source) 
  • Data on how often a specific customer is usually buying from you → If they’ve missed their timeframe, you could use this opportunity to re-engage with them to win them back (bonus points if you can set up marketing automation to complement this at scale) 
  • Data on what products and services are usually bought together → Use this to understand what bundling options to approach the market with, or perhaps gauge untapped bundles that you should be upselling 

Business sentiment 

Once you get a few customers through the door, it’s likely your customers will start talking about you either directly via feedback, or indirectly via reviews or general online discussions. 

Some actionable insights that can be extracted from this data include: 

  • Data on the sentiment of the feedback / online discussions → Use this to understand if your customer engagement and experience is actually in line with your strategic goals 
  • Data on which parts of your business are associated with positive/negative feedback → Use this to find any focus areas where you need to improve your customer engagement 
  • Data on “black spots” where customers using a specific part of your business aren’t engaging → Use this to analyse and better service customers using this product 

Marketing data 

If you’ve taken the leap and started actively marketing your business (if not, we’d be happy to chat), then you’re certainly generating valuable data that can steer your marketing efforts towards long-term success. 

Most digital marketing campaigns will collect: 

  • Data on how often your customers are being contacted (i.e. your marketing schedule) → Use this to understand the optimal frequency to contact customers (once or twice per week, or once a month) 
  • Data on when customers engage with your content → If you’re a B2B business then you may be better off sending your marketing emails during the working day, while B2C may suit after-hours emails – but this isn’t a hard and fast rule! Your engagement data – like open and click-through rates – can tell you what’s working for your specific customer base already 
  • Data on the content that resonates with each segment (a good base-level approach) and each person (a more advanced analytical approach) → Use this to send the relevant types of content to each segment/person to maximise their conversion to your product and overarching marketing goals 


Hopefully, I’ve been able to demonstrate that the saying “we don’t have any data to analyse” usually couldn’t be further from the truth. Once you broaden your horizons on what useful data can be, you’ll find most businesses sit on a wealth of data. It often just needs a little attention – via the development of a sound data strategy and data management practices – before powerful actionable insights can be extracted. 

I’d encourage setting aside some time to reflect on the above and, equally, contemplate what data your business generates by virtue of being the unique business that it is – it’s a competitive necessity in an increasingly data-driven world!

Contact us today for more information on how we can work together to create data-driven, actionable insights to help your business succeed.

Chart to success: spurious scaling

We live in a time when data is king. Data is everywhere – and it’s tempting to believe that, the more data we have, the more accurate our decisions will be. But regardless of the data, even the most simple graphs can easily mislead you. Even when using the best tools available, your conclusions may not be as sound as they seem if you can’t spot some fundamental issues. 

In this series, we’ll be taking bite-sized looks at some of the most common data analysis mistakes made when building and interpreting graphs. 

Spurious scaling 

With graphs – as with all your business decisions – context is critical

Your perception of a situation is easily skewed if there isn’t a representative baseline. This is usually because your axes don’t start at 0, or you’re missing a comparative value. 

Take the graph below for example. It shows the 2016 Olympics 100m sprint final times. You might look at this and initially think that Usain Bolt was simply dominant in the field. Did you notice the axis scale though? 

That’s right, we’re only comparing the sprinters over 0.25 seconds! The competition looks much closer when you look at exactly the same dataset but with an appropriate baseline:

Imagine if you were shown the two bar charts above comparing different project budget options – would you have made the right choice?

Even still, would you know if a 9.8 second 100m sprint is a good result? Let’s anchor the value with the sprint times of the average non-athlete:

Now we can really see just how close the competition is amongst these elite athletes!

Unfortunately, some news networks are renowned for this exact scaling problem – and if you’re not keeping an eye out for it, it’s very easy to be misled!


When viewing a graph, ask yourself questions like:

  • what number do the axes start from, and why?
  • is there another value that could be included for context?

This will ensure you actually take the time to understand the scale of the graph and avoid any spurious scaling issues.

If you’re using graphs to make data-driven decisions, it’s important that they are accurate and reliable. Graphs can be misleading if the graph type, colours or scales are poorly chosen, so look out for these details before making a decision.

There is no such thing as a “perfect” graph – every graph has limitations! However, there are many ways in which we can improve our understanding of any given dataset by carefully considering all aspects of the analytical process.

Vaxa Analytics offers free analytics audits to help elevate your business intelligence with insights from experienced professionals who understand what makes a good analysis tick.

Contact us today for more information on how we can work together to create data-driven, actionable insights to help your business succeed.