Service Category: Insights

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.

Developing your data-driven culture: practical strategies for unconventional industries

Data is increasingly the fuel that powers decision-making; businesses that embrace data find it easier to adapt to changing markets and stay ahead of the curve. However, fostering a data-driven culture isn’t always easy, especially in non-typical industries where the use of data in decision-making isn’t embedded in the culture.

These environments are more numerous than you might think — real estate, fast-moving consumer goods, consumer services, defence industry, just to name a few. But with a lack of strong historical precedence, fostering a data-driven culture in these environments can feel like an insurmountable challenge.

In this blog post, we’ll discuss some practical approaches to jumpstarting this culture in your organisation.

1. Establish business goals and their drivers

Before you can become data-driven, you need to define what you want to achieve. It’s important to highlight — this isn’t what you want to achieve with data. It’s what your organisation wants to achieve as a business.

It’s crucial to set your business objectives and ensure your team thoroughly understand them, and the drivers behind them. If your team doesn’t understand why we need to achieve X, then even with all the data and insights in the world, they might still make poor decisions as they misunderstand the impacts down the chain.

Only once these business goals are set, understood, and embedded can we start to think about how data can assist in achieving those objectives.

It’s also timely to say that, with more data available, it’s entirely likely you’ll revisit what those objectives are. That’s okay. With more data, you’ll be better placed to understand and express the drivers behind your business objectives, and that can unlock entirely new business objectives.

With more data, you’ll be better placed to understand and express the drivers behind your business objectives, and that can unlock entirely new business objectives.

2. Identify and collect relevant data

Once you have defined your goals, you need to identify the data that will help you achieve them. We’ve already spoken at length about how all organisations generate data in the course of their operations, whether they think so or not.

Now we will acknowledge that, in non-typical environments, finding these data sources can be a challenge. But it is there – we just need to think outside the box.

In such environments, it’s important to divorce the concept of “data” from the concept of your “customer”. Some of the most valuable data you have may be entirely unrelated to your customer. While Facebook and Google may collect data on you as a person, the real valuable data is in what you do and how you use their apps. They use that to fine-tune development, forecast future actions, and ultimately tune their business objectives — and the same applies to you.

Think about your data across a few key pillars:

  1. Operational data: This includes data related to the day-to-day operations of a business, such as production rates, inventory levels, and supply chain data. This information can help you identify bottlenecks in your operations, optimise your production processes, and reduce costs.
  2. Financial data: Financial data includes information related to your financial performance, such as revenue, expenses, and profits. This information can help you identify areas where you can cut costs, improve profitability, and make strategic investments.
  3. Employee data: Employee data includes information about your workforce, such as employee turnover rates, employee satisfaction surveys, training compliance and effectiveness, and performance metrics. This information can help you identify areas where they need to improve their employee engagement and retention efforts.
  4. Industry data: Industry data includes information about broader economic and industry trends, such as market share data, industry growth rates, and competitive landscape analysis. This information can help you stay competitive and make informed strategic decisions.
  5. Niche industry data: This includes data specifically generated by your organisation in the course of your business. For example, an organisation delivering property maintenance could collect and use data based on the type of jobs they do, the materials required to complete, the routing/triage of their jobs, and the time taken.

Once you’ve identified your data sources — whether you actively collect them or not — you can start to overlay them with your business processes to identify where they could support decision-making.

3. Overlaying data sources with business process

To start, map out your business processes and highlight where data can be used to optimise your decision-making. This can be game-changing or incremental support.

For example, when ordering stock, you’d certainly be empowered to make a better decision if you had access to forecasted demand levels. When negotiating your electricity and gas contracts, you’d be empowered to make a better decision if you had access to forecast pricing and demand over the contract period.

Ultimately, you’re trying to answer the question: “What could I do better, if only I knew more?”. You should be encouraging your staff to always ask this question of themselves and their work too.

What could I do better, if only I knew more?

Again, it’s important not to get caught up here in the specific mechanism about how that data would get to the appropriate decision maker in the right format — the data management aspect of the solution.

Once you’ve identified these areas where data can be used to support decision-making, you must determine how to integrate the data into your processes. There’s no one size fits all solution here, as it depends on your existing technology, budget, and approach, but there are a few key guiding principles:

  1. Choose solutions that cause the least amount of disruption and require the least training
  2. Remember, your end-users aren’t analysts, nor should they be. The solution will need to provide insights, not just raw data.
  3. Timeliness and trust in data are critical – things that are very hard to correct after the fact.

4. Develop a data-driven culture

Developing a data-driven culture is a critical step towards leveraging data to support decision-making in non-typical environments. However, this can be a significant challenge as decision-making in these environments may be based on intuition or experience, rather than data.

To develop a data-driven culture, it’s important to first educate your employees on the importance of data and how it can help them make better decisions. This can involve training sessions, workshops, and ongoing communication about the value of data-driven decision-making. It’s also important to provide employees with the tools and training they need to access and analyse the data effectively.

It’s crucial to emphasise that data is not there to highlight employee shortcomings or replace them. Rather, it’s about empowering them to make the best decisions possible and to improve overall organisational performance. By providing employees with the necessary tools and training, you can create a culture that values data and uses it to inform decision-making.

Creating a data-driven culture won’t happen overnight. It may be more effective to take a team-by-team approach rather than trying to implement it organisation-wide all at once. Additionally, leadership commitment and buy-in are essential to ensure the success of a data-driven culture. When leadership is committed to the value of data and supports the necessary changes to create a data-driven culture, employees are more likely to adopt these changes and drive positive outcomes.

In terms of specific programs to foster your data-driven culture, several types of support can be made available to employees. These include:

  1. Training: One of the key aspects of developing a data-driven culture is to provide training and education to employees. This can include training on data literacy, how to access and analyse data, and how to use data to make better decisions.
  2. Data governance: Data governance ensures that data is managed effectively, including data quality and security. Establishing a data governance program can help to ensure that data is accurate, timely, and trustworthy. It’s also important for your staff to understand where the data comes from, where it’s made available, and the limitations of that data set for a specific application. We’ve spoken about how to start with data governance as a SME, if of interest.
  3. Access to tools: Access to data analysis tools such as data visualisation software, statistical software, and data analysis platforms is critical to enable employees to access and analyse data effectively — but remember, they’re not there to be analysts, just users.
  4. Analytics support: Organisations can provide analytics support to employees by having dedicated analytics teams or experts who can help employees analyse and interpret data. This can include data scientists, business analysts, or data analysts. They need to have an intimate appreciation and understanding of the business operations to be effective.
  5. Communication and collaboration: Ultimately, like all initiatives in an organisation, effective communication and collaboration with staff are key. This can involve sharing data insights across teams, encouraging cross-functional collaboration, and establishing regular data review meetings.

In conclusion, while data increasingly becomes a crucial component of decision-making across all industries, fostering a data-driven culture is not always easy, particularly in unconventional industries where data may not be embedded in the culture. To develop a data-driven culture, businesses must define their business objectives, identify and collect relevant data, overlay data sources with business processes, and integrate the data into decision-making processes. While developing a data-driven culture is a challenge, it is also an opportunity for businesses to gain a competitive edge, stay ahead of the curve, and achieve new business objectives.

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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. 

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SMEs and data governance: an approachable guide

Small-medium enterprises are constantly looking for ways to improve their business operations and make data-driven decisions. However, many SMEs struggle with understanding and implementing effective data governance practices – the very practices which underpin all data-driven decisions. In this beginner’s guide, we’ll take a look at what data governance is, why it’s important for SMEs and some best practices for getting started.

What is data governance?

Data governance is the set of policies, procedures, and standards that govern how data is collected, stored and used within an organisation. It ensures that data is accurate, consistent, and compliant with regulations and industry standards. This includes the management of data quality, data security, data privacy, and data lineage.

At Vaxa Analytics, we group data governance under our Data Management offering which, together with Data Strategy and Data Analysis, form the foundations for extracting actionable insights from your data and helping you answer the questions you didn’t even know you should be asking.

Why is data governance important, even for SMEs?

Data governance is essential for SMEs to make sure that their data is accurate, consistent, and compliant with regulations and industry standards. Without effective data governance, SMEs risk making decisions based on inaccurate or unreliable data, which can lead to costly mistakes and negatively impact their bottom line. Additionally, with increasing regulation on privacy and data management, it’s more important than ever for SMEs to have a strong data governance program in place.

Data governance doesn’t get any easy the larger an organisation gets. Indeed, it’s quite the opposite.

Setting solid foundations helps to avoid growing pains in the future. Thankfully, SMEs benefit from their smaller scale, meaning small changes have a relatively large impact on the organisation’s data governance maturity.

Getting started with data governance

An excellent first step in implementing data governance in a small-medium enterprise is to establish clear roles and responsibilities for data governance within the organisation. Assign specific individuals or teams to manage data governance and ensure that everyone within the organisation understands their roles and responsibilities. This is vital as it sets the foundation for effective data governance by ensuring that there is someone accountable for managing data governance within the organisation.

From there, the next logical step is to develop policies and procedures for data collection, storage, and use. This includes auditing your current policies (if any) alongside the data collected from the systems. From there, begin by crafting a data retention policy, data classification policy, data access policy, and data breach response plan. It’s important to physically document these policies (not just assume staff know what to do) because it establishes guidelines and rules for how data should be collected, stored, and used within the organisation, which will ensure that data is accurate, consistent, and compliant with regulations and industry standards. This step also ensures that everyone in the organisation understands and follows the policies and procedures, which will help to minimise the risk of data breaches and other data-related issues.

Now, let’s not kid ourselves – data governance has a wide scope and it isn’t a two-step process! Here are more activities that SMEs should consider — in rough order of priority (and roughly aligned to organisational scale):

  1. Regularly review and update policies: Review and update data governance policies and procedures regularly, such as annually or when regulations change, to ensure that they remain effective and compliant with current regulations and systems in use.
  2. Monitor and measure data quality: Implement processes such as data quality checks, data profiling, and data validation to monitor and measure data quality. Use data quality dashboards and reports to track data quality over time and take action to correct any issues that are identified.
  3. Ensure data security: Implement appropriate security measures such as encryption, access controls, and network security to protect sensitive data from unauthorised access and breaches.
  4. Educate employees: Provide regular training and education to employees on data governance policies, procedures, and best practices, such as data privacy and security awareness training.
  5. Consider getting help: If you’re struggling to implement data governance in your SME, consider engaging a consultant to assist. This is a specialist field, after all. Consultants can help you assess your current data governance practices, identify areas for improvement, and provide guidance on how to implement effective data governance.
  6. Implement automation: Use automation tools to automate data governance processes, such as data masking, data archiving, and data lineage tracking. This will help you to reduce the time spent on manual data governance tasks and increase the accuracy of your data governance.
  7. Implement Data Governance Platform: Implement a Data Governance Platform that will provide a centralised location to manage data governance policies and procedures, monitor data quality, and track compliance.
  8. Establish a Data Governance Council: Establish a Data Governance Council, comprising representatives from different parts of the business, to provide a common forum to discuss and resolve data-related issues and to make decisions on data governance policies. Note that this is more applicable to the larger end of the SME segment.

Cheat sheet for data governance terms

There’s a lot of terminology n the data governance space, and they’ll likely come up in any organisation’s data governance journey. Here’s a quick guide to the most common:

  • Data steward: A person or group responsible for managing and maintaining the quality and accuracy of a specific set of data within an organisation.
  • Data lineage: The ability to trace the origins, history, and movement of data through an organisation’s systems.
  • Data dictionary: A document or system that defines the different data elements and their meanings within an organisation.
  • Data quality: The degree to which data meets the needs of its intended users, is accurate, and is free from errors and inconsistencies, typically assessed under “the 7 data quality dimensions”.
  • Data security: Measures taken to protect data from unauthorised access, use, disclosure, disruption, modification, or destruction.
  • Data privacy: The protection of personal information and sensitive data, including compliance with regulations such as the EU General Data Protection Regulation (GDPR) and Australia’s Privacy Act.
  • Metadata: Data that describes other data, such as data element definitions, data lineage, and data quality rules. Metadata can help classify and categorise data, and therefore help make decisions on the appropriate rules and policies to put in place.

Data governance is essential for small-medium enterprises to make sure that their data is accurate, consistent, and compliant with regulations and industry standards. By following these best practices, SMEs can establish a strong data governance program that will help them make data-driven decisions and stay competitive in today’s business environment.

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.

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Data migration

Your business generates vast amounts of data every day, but if that data is inaccurate, incomplete, or hard to access, it’s not doing you much good. That’s where data cleansing and migration comes in.

What is data cleansing and migration?

Data cleansing is the process of identifying and correcting errors and inconsistencies in your data, such as incorrect or outdated information, duplicates, and formatting errors.

Data migration is the process of transferring data from one system or database to another. Together, these processes ensure that your data is accurate, complete, and accessible, so you can get the most value out of it.

There are a few common scenarios when a business may need data cleansing and migration:

  • When you’re transitioning to a new system or database
  • When you’re merging with another company or organisation
  • When you’re consolidating multiple databases or systems
  • When you’re dealing with large amounts of outdated or incorrect data

The data cleansing and migration process

There are a few common myths about data cleansing and migration that we’d like to dispel:

Myth 1: It’s a one-time process

Data cleansing and migration are ongoing processes that need to be regularly monitored and maintained to ensure your data is accurate and up-to-date.

Myth 2: It’s easy

Data cleansing and migration require specialised skills and tools to ensure that the process is thorough and accurate.

Myth 3: It’s not important

Data is the lifeblood of your business, and accurate, accessible data is essential for making informed decisions and driving growth.

So, what does a typical data cleansing and migration process look like? Here’s an overview:

  1. Assess your data needs: We start by understanding your business, goals, and data requirements.
  2. Identify and correct errors: We use specialised tools and techniques to identify and correct errors and inconsistencies in your data.
  3. Migrate your data: We transfer your data to the new system or database, ensuring that all data is properly formatted and accurate.
  4. Test and validate: We thoroughly test and validate the migrated data to ensure that everything is working as it should.
  5. Ongoing maintenance: We provide ongoing support and maintenance to ensure that your data stays accurate and up-to-date.

Our expertise.

Our team of data experts has the skills and experience to help you with all your data cleansing and migration needs.

Whether you’re transitioning to a new system, consolidating multiple databases, or dealing with large amounts of outdated data, we can help you get the most value out of your data.

Ready to unlock the full potential of your data?

We’ll get you from point A to point B in good shape.

A rendered image depicting a hologram-like data flow in a grid shape

Data collection

Capturing your first-party data in all the right places, and finding third-party sources that will work for you while respecting your customer’s right to privacy.

What is data collection?

Data collection is the process of gathering information from various sources to be used for analysis and decision-making.

There are two primary types of data: first-party data, which is data that you collect directly from your own customers or systems, and third-party data, which is data that you purchase or license from external sources.

You’ll also sometimes hear the distinction made between 0th and 1st-party data. 0th-party data is data your customer have directly opted to give to you (customer surveys), while 1st-party data is data you collect on your customers – think website traffic. In most practical scenarios, 0th and 1st party data are very similar.

There are also 2nd-party data and 3rd-party data distinctions. 2nd-party data is data exchanged between two organisations with a trusted relationship, while 3rd-party data is data simply exchanged and sold on a marketplace or via a transactional relationship.

Data collection process.

Gathering data can be a complex and time-consuming process, but we have the tools and expertise to make it easy. Here’s a look at our typical data collection process:

  1. Identify your data needs: We start by understanding your business, goals, and data requirements.
  2. Collect first-party data: We help you capture your first-party data in all the right places, using tools like web analytics, customer surveys, and CRM systems.
  3. Find third-party data sources: We help you identify and evaluate third-party data sources that align with your business goals and data needs.
  4. Integrate and organise your data: We help you integrate and organise your data, ensuring that it is accurate and accessible.
  5. Ongoing support and maintenance: We provide ongoing support and maintenance to ensure that your data stays up-to-date and accurate, and aligned with your organisational data strategy.

Practical examples.

A retail company might use web analytics tools to collect data on customer behaviour on their websites, such as the pages customers visit, the products they view, and the actions they take. This data can help the company understand customer preferences and identify areas for improvement on its website.

A healthcare organisation might use patient portals and electronic medical records to collect data on patient visits, diagnoses, and treatment plans. This data can help the organisation track patient outcomes, identify trends, and optimise resource utilisation.

A non-profit organisation might use surveys and focus groups to collect data on the effectiveness of its programs, donor attitudes, and volunteer engagement. This data can help the organisation measure the impact of its programs, identify areas for improvement, and engage donors and volunteers more effectively.

A manufacturer might use sensors and IoT devices to collect data on production efficiency, equipment performance, and resource utilization. This data can help the company identify bottlenecks in their production process, optimise its operations, and reduce costs.

The data collection process involves gathering information from various sources, and the specific sources and methods used will depend on the needs and goals of the organisation.

Collect it before you need it. Don’t drop your data on the ground.

Network servers racks with light

Data warehousing

Consolidating multiple sources of data into one consistent, accurate, and accessible location.

Your business generates vast amounts of data every day, but if you’re not storing and organising it properly, you’re not getting the full value out of it. That’s where data warehousing and data lakes come in.

What are data warehouses, data lakes, and data lakehouses?

A data warehouse is a centralised repository of structured data that is used for reporting and data analysis. It allows organisations to store large amounts of data in a single location, and to access and analyse that data quickly and easily.

A data lake is a centralised repository of structured and unstructured data that is used for big data analytics. It allows organisations to store and process large amounts of data in a single location, and to access and analyse that data using a variety of tools and techniques.

A data lakehouse is a hybrid approach that combines the features of a data warehouse and a data lake. It allows organisations to store and process both structured and unstructured data in a single location, and to access and analyse that data using a variety of tools and techniques.

For most small-to-medium organisations, a data warehouse is the right fit. Data lakes and data lakehouses tend to fit better for large enterprises, generating volumes of data that would overwhelm most data warehousing solutions.

When data warehouses make sense for you.

There are a few common scenarios when it might be the right time to invest in a data warehouse or data lake:

  1. When you have large amounts of data: If you’re dealing with large volumes of data that are difficult to manage and analyse, a data warehouse or data lake can help you store and organise your data in a centralised repository, making it easier to access and analyse.
  2. When you need to integrate data from multiple sources: If you’re working with data from multiple systems or databases, a data warehouse or data lake can help you integrate and organise your data in a single location, making it easier to analyse and get insights.
  3. When you need to support advanced analytics: If you’re looking to use advanced analytics tools and techniques, such as machine learning or big data analytics, a data warehouse or data lake can provide the storage and processing power you need.
  4. When you need to improve decision-making: If you’re looking to improve your decision-making process by getting better insights from your data, a data warehouse or data lake can help you analyse and visualise your data in a way that makes it easier to understand and act on.

Ultimately, the decision to invest in a data warehouse or data lake will depend on your specific business needs and goals. We’ll carefully evaluate your data requirements and determine if a data warehouse or data lake is the right solution for you.

Practical examples.

Consider our friend ACME Manufacturing Inc. who, like many manufacturers, runs multiple operating systems to manage their workload.

  1. The manufacturer is dealing with large volumes of data from multiple systems, such as production data, financial data, and customer data. This data is difficult to manage and analyse, and it is hindering the company’s ability to make informed decisions.
  2. The manufacturer decides to invest in a data warehouse to store and organise its data in a centralised repository. The data warehouse integrates data from multiple systems and allows the company to access and analyse its data more easily.
  3. The manufacturer uses the data warehouse to identify bottlenecks in its production process and optimise its operations. By analysing data on production efficiency, cost trends, and supplier performance, the company is able to reduce costs and improve efficiency.
  4. The manufacturer also uses the data warehouse to identify new product opportunities and target marketing efforts. By analysing data on customer demographics, sales trends, and product performance, the company is able to identify new products and markets to pursue.
  5. The manufacturer sees a significant ROI from its data warehouse investment. The company’s improved decision-making and optimisation efforts lead to increased sales, reduced costs, and improved customer satisfaction.

This is just one example, but the possibilities for using a data warehouse to drive ROI are endless. By storing and organising its data in a centralised repository, almost any type of business can gain insights and make informed decisions that drive growth and improve efficiency.

So why only see part of your picture?

We’ll help you bring together your data sources, and help you soar to new heights.