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 (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.
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.
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.
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:
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.
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.
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.
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.
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.
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:
Choose solutions that cause the least amount of disruption and require the least training
Remember, your end-users aren’t analysts, nor should they be. The solution will need to provide insights, not just raw data.
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:
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.
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.
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.
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.
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.
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
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.
A data strategy is a plan for how an organisation will use data to drive growth and improve efficiency. It differs from a business strategy in that it focuses specifically on how data can be used to support and achieve the organisation’s business goals.
Business strategy vs data strategy
Business strategy and data strategy are closely related, but they are not the same thing. Business strategy is a plan for how an organisation will achieve its overall business goals, while data strategy is a plan for how data can be used to support and achieve those goals.
For example, a business strategy might include goals such as increasing sales, expanding into new markets, or improving efficiency.
A data strategy might include goals such as improving data quality, integrating data from multiple sources, or using data to inform decision-making to enable teams to track where increases in sales are coming from or to enable the team to focus and execute on the opportunities with the best ROI.
Who is responsible for data strategy?
In an organisation, data strategy is typically the responsibility of the chief data officer (CDO) or a data strategy team. The CDO is responsible for leading the development and implementation of the data strategy, and for ensuring that the organisation’s data assets are aligned with the business strategy.
The CDO may be the same as the CTO and occasionally the CISO due to the similar fields, but often it makes the most sense to utilise the specialist knowledge of virtual (or fractional) CDO. We help many clients in this capacity to great success.
Steps to develop a data strategy.
Here are the steps involved in developing a data strategy:
Define your business goals: The first step in developing a data strategy is to define your business goals. This will help you determine how data can be used to support and achieve those goals.
Assess your current data assets: Next, you’ll want to assess your current data assets, including data sources, data quality, and data infrastructure. This will help you understand what you have to work with and where there are opportunities for improvement.
Identify data gaps and opportunities: Based on your business goals and assessment of your current data assets, you can identify any gaps or opportunities for improvement. This might include identifying new data sources, improving data quality, or building new data infrastructure.
Develop a roadmap: With your data gaps and opportunities identified, you can develop a roadmap for addressing them. This might include a timeline for implementing new data sources, a plan for improving data quality, or a strategy for building new data infrastructure.
Communicate the data strategy: It’s important to ensure that your data strategy is clearly communicated to all relevant stakeholders, including leadership, data professionals, and business users. This will help ensure that everyone is on the same page and working towards the same goals.
Monitor and review: Your data strategy should be a living document that is reviewed and updated regularly. This will help ensure that it remains aligned with your business goals and responsive to changing needs and opportunities.
Here are a few examples of how a data strategy can be used to drive growth and improve efficiency in an organisation:
Improving customer experience: A data strategy can be used to improve the customer experience by laying the foundation for a long-term project to integrate data from multiple sources, such as CRM systems, web analytics, and social media, to create a comprehensive view of customers and their needs.
Optimising marketing efforts: A data strategy can be used to optimise marketing efforts by ensuring the appropriate data to target marketing efforts towards the most likely buyers is available and to set up the parameters for tracking the effectiveness of different marketing campaigns.
Improving operational efficiency: A data strategy can help an organisation improve operational efficiency by analysing data on business processes and identifying areas for improvement over the long term and highlighting targeted areas for improvement or blindspots.
A data strategy is a critical component of a successful business. It allows organisations to use data to drive growth and improve efficiency and is essential for competing in today’s data-driven world. By following the steps outlined above, you can develop a data strategy that supports your business goals and helps your organisation make informed data-driven decisions.
So why do things piecemeal?
We’ll help you develop the right data strategy to underpin your organisation’s aspirations.
Data literacy is the ability to understand and use data to make informed decisions. It’s a critical skill for today’s businesses, as data is increasingly being used to drive growth and improve efficiency. But building data literacy within an organisation can be a challenge. That’s where our data literacy training services come in.
Assessing your organisation’s data literacy.
To assess your organisation’s data literacy, you should consider the following questions:
Do your team members understand how to access and use data to inform their decisions?
Do they know how to effectively communicate insights and findings from data to others in the organisation?
If you’re not confident that your team has the necessary data literacy skills, it may be time to invest in training.
Step-by-step training roadmap.
A typical data literacy training roadmap may include the following steps:
Identify your goals: The first step is to establish what you hope to achieve through your data literacy training. This will help you determine which skills and techniques are most important to focus on.
Assess your team’s current skills: Next, you’ll want to assess your team’s current data literacy skills. This will help you understand what they already know and what they need to learn.
Develop a training plan: Based on your goals and assessment of your team’s skills, you can develop a training plan that includes a mix of formal training, hands-on exercises, and real-world projects.
Implement the training: Once you have a training plan in place, we’ll help you implement it by delivering the training to your team. This may involve us delivering the training ourselves, engaging an appropriate external trainer, developing internal training materials, or using a combination of the above.
Evaluate the training: After the training is complete, it’s important to evaluate its effectiveness. This will help you understand what worked well and what could be improved upon in the future.
Continual improvement: Even after the first round of training, the professional development of data literacy isn’t over. Learning is a lifelong journey.
Common data literacy myths.
There are a few common myths about data literacy that can be misleading:
Only data scientists and analysts need to be data literate.
Data literacy is a continuous learning process. It’s important to regularly refresh and build upon your team’s skills to ensure that they stay up-to-date.
Data literacy is only about technical skills
Data literacy involves both technical and non-technical skills, such as the ability to communicate insights and findings from data effectively.
Data literacy is a critical skill for today’s businesses. It empowers teams to make informed decisions using data and is essential for driving growth and improving efficiency. By investing in data literacy training, you can equip your team with the skills they need to succeed in the data-driven world.
Ready to empower your team with data literacy skills?
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:
Modern analytics deserves to be tightly-coupled with business strategy
Data can and should be used to inform and guide decision-making in real-time
Organisations should take advantage of the increasing availability and quality of data
Move beyond just analysing what’s happened in the past. Instead, look towards the future.
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 onwho are you spending the most money with → Use this to determine where cost optimisation efforts could be placed
Data onwho is spending the most money with you → Use this to determine where account management efforts could be placed
Data onhow 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 onhow 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 onhow 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 onwhat 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
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 onthe 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 onwhich 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
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.