It’s no secret that consumer behaviour is shifting. It shifted during and after the pandemic, and it’s moving again as wallets tighten. For businesses, this is undoubtedly a cause for concern – and rightly so. When consumer behaviour changes, it leads to uncertainty in revenue and all the logical flow-on effects. But how does your business grapple with these changes and regain control of its revenue?

The answer lies in understanding two things: how the consumer’s behaviour has changed and how that is impacting your business, and using that to hypothesise the drivers behind the shift. Once those two things are well-understood, the business can begin to correct course by implementing initiatives that directly address these drivers. And the secret to understanding those two things? Your data.

In this blog post, we’ll explore the crucial role of data in deciphering changing consumer behaviour, and provide insights on how your business can more effectively utilise the data it already collects to navigate the shifting tides of consumer behaviour and seize opportunities for growth and success in a difficult market.

Tapping into your own data

We’ve spoken at length about the basics of customer insights and how businesses should be using this information to understand their customers — so if you haven’t given this a read yet, we recommend you do. But the gist of it is, your own data already tells you plenty about your customers.

You just need to take a good hard look at it.

Now, the exact way you analyse this is going to depend on the type of business you run, and your goals/objectives — there’s no getting around that. Generally speaking, there are a few core metrics that will neatly describe the overall health of your relationship with your customers using just basic transactional data that every business has:

• Frequency: how often are your customers returning to buy from you?
  • A reduction in frequency may mean a few things, including your sales/marketing process becoming less effective, your customers’ wallets tightening, or other issues that mean your purchasing funnel has become more difficult to traverse.
• Recency: how recently did your customers make a purchase?
  • Even if your frequency metric looks okay, it mightn’t capture the full picture — because some of your customers may be disengaging with your brand. Mapping out the recency of your customers’ purchases can shine a light on this.
  • If you have a large cohort of customers who haven’t purchased in X days, then it’s likely that these people are disengaging from your brand. You should ask yourself why this may be — the driver (informed by the metrics above and below) — and what potential intervening action you can take to remediate the issue.
• Retention Rate: what percentage of customers continue to engage with your brand over a specific period?
  • Now if you’re a subscription-based business, this is much easier as you can clearly measure when a subscription cancellation happens. You should probably be measuring this as a churn rate (either revenue churn or member churn) — we recommend the formula defined in this post by Recurly.
  • If you’re not a subscription-based business, you’ll need to use a statistical model to describe the retention of your customers. The entertainingly-titled family of models called “Buy ‘Till You Die” are a great example of this. They do require much more custom code to run, so we’ll be posting a guide on this in the coming weeks – stay tuned!
• Customer Lifetime Value: how much value does each customer generate over their lifetime?
  • Once you have a good grasp on your retention rate, you can form a picture of the lifetime value of each of your customers — are they spending $100 over their lifetime or $1000?
  • This directly speaks to how much you should be spending on your acquisition costs – Cost Per Acquisition must be lower than your lifetime value — but it also directly speaks to your long-term expectation of revenue from your existing customer base. If your LTV-over-time metric is dropping, this usually isn’t a good thing (unless you’re targeting a new/lower-value segment) so you need to establish why it’s dropping. Basically, it’s either your retention is getting worse, or the average transaction value is getting worse (or both) — but the reason for that is something you need more research to make an informed decision. See the section below for more details.
• Segmentation: categorising your customer base into distinct groups based on various attributes or behaviours.
  • We’ve also spoken a lot about segmentation in the past too, so we won’t spend too much time explaining it again here. The gist of it is, by establishing your key segments, you can overlay the above metrics and form an even better picture of how customer behaviour is shifting. Why is frequency stable overall, dropping for segment X but improving for segment Y? It might be down to changes in your pricing, or perhaps your marketing has shifted more towards targeting segment Y — but you need to see how the numbers stack up before you can make an informed decision.

Looking externally

Your business doesn’t operate in a vacuum, and neither should your analysis. You need to consider external influences on your business and industry to truly understand the shift in consumer behaviour.

You should be monitoring – at a minimum – metrics from the ABS on household spending in your sector. This is new and built off direct access into the bank’s transaction dataset so it’s incredibly accurate and up-to-date. You can access this information here.

You should also investigate what industry research or publications exist that may speak more directly to your challenges — for example, peak bodies often conduct research on behalf of their members. If you’re a member, reach out to your peak body and see what data they may have collected or who they can put you in touch with.

Your competitors are also a useful source of insight. Have they changed tact recently — pricing, promotion, product? That can speak to not only their challenges but potentially how they’ve pivoted to address them. Use that insight to inform how you might address some of your own challenges. Not necessarily by copying, mind you, as there’s no guarantee that their changes will work for your customers.

In some industries, you can also directly get data on your competitors by monitoring inventory levels and the like. There’s a whole field of competitor analytics you can tap into. We’ll be talking more about competitor analytics in a future blog post — so stay tuned for that.

For now, you should also be keeping across industry trends in general. Changing consumer behaviour may also be caused by innovations disrupting your sector — there’s no better example than Uber storming into the market and disrupting taxis. That insight wasn’t sitting in the taxi’s datasets — it was sitting in the industry datasets.

Identifying the drivers of behaviour shifts

Once you’ve established your internal and external views on how consumer behaviour has changed, we need to establish the drivers.

There are a few options to explore here — and you probably will need to do multiple:

• Surveys: particularly suitable for large datasets. Consider the sample size (you’ll need high hundreds if not thousands to form a good picture), the mechanism (how do people respond) and the incentive (they probably won’t do it for free).
• Customer interviews: perhaps more effectively, you can speak directly with your customers through an interview. While you won’t get as much rock-solid data, you will gain a deeper understanding of your customer’s behaviours. Don’t be afraid to speak with your customers. It’s critical.
• Social listening: consumers may be expressing their sentiments through social media platforms and other venues. Off-the-shelf tools can help you listen in on these online communities, or for more bespoke applications we develop solutions to do this for you. In any case, understanding what’s being spoken about here can inform your view of the drivers of the behaviour shift.

Once we have these drivers — the next question is quite obvious: how do we intervene and correct the course of your business?

We’ll be talking about how you can easily devise and implement interventions in our next blog post. Stay subscribed for more details by entering your details below.

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.

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. 

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 

Recap

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.