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Your customer data are gold dust — you can use them to transform your performance
This article is not about marketing strategies or technologies. Nor is it about sales techniques. It’s about how to analyse the data your business generates on its customers to make more sales.
Those customer data are gold dust — they tell you lots of valuable things about what real customers like and how they behave. The best part is, only you have the data on your customers. So it would be wise not to miss the opportunity — by learning from your data you can transform your business performance.
I’ve written the article for small business entrepreneurs who don’t have a background in sales or marketing. I’ve focused on some essential techniques, metrics and ratios and provided practical examples of how to use them.
Key ratio analysis is a vital part of running a small business successfully. The aim of this article is to help you extract valuable metrics and ratios from your customer data and use the results to take your business to a new level of performance.
The benefits you’ll get from applying customer data analysis
Customer data analysis, often referred to as customer analytics, is the process of gathering and analysing the hard data you have about your customers’ behaviour in order to take better business decisions about which customers to target and how to do it. It’s a vital contributor to the process of designing a marketing strategy for your small business.
By analysing your customer data, you should be able to:
Identify who your most profitable customers are and then work out which common characteristics they share. What demographic do they belong to? How did they find your brand? What types of product do they buy? By asking these questions you can build a deeper understanding of your best customers and what makes them tick.
Use these insights to offer your best customers more of what they like. That could mean adjusting your product range, the marketing channels you prioritise or other factors. In a nutshell you can discover ways to appeal more strongly to them.
The aim is to attract, convert and retain more of the customers who contribute most to your profits and reduce your focus on customers who are less profitable. You may even find you’re servicing customers who are unprofitable, in which case you can drop them altogether.
Customer analytics should also pick up changes in customer trends. For example, are your new customers buying less than your existing ones? Are your existing customers becoming inactive (‘churning’) at an increasing rate? By spotting these trends early you will give yourself the best chance of capitalising on emerging opportunities or adapting to looming challenges.
What’s the evidence that using customer analytics works?
Here’s an extract from a 2016 report by McKinsey & Company. They asked senior managers about their use of analytics and their assessment of their profit and sales growth performance compared with their competitors. Managers at companies making extensive use of analytics were far more likely to believe they were outperforming their peers.
Source: McKinsey & Company (2016). Why customer analytics matter.
What do you need to get started?
Established businesses employ dedicated software solutions to gather, organise and analyse their customer data (their purchases, which channel they found you through and so on). I’ve written more on the technology side of customer analytics later in this article.
If your business is still at an early stage, don’t worry. Your customer and sales volumes are probably still small enough for you to apply the concepts in this report without any whizzy software. Simple analysis using a spreadsheet should yield the insights you need to make the right choices on key decisions. You can upgrade to software later when your business is more established.
Let’s turn to some of the common metrics and techniques used in customer analytics to see how you can apply them in practice. I’ll start with sales per customer.
Sales per customer
Sales per customer sounds like a simple metric but it’s surprising how much insight you can gain from it.
One way to use the metric to generate insights about your best customers is to carry out a segment analysis — a concept we’ll come back to several times. Just follow these steps:
From your CRM solution or your accounting system, extract sales values for all your customers for the period you want to study. The best period to choose depends on the type of business you’re in but the last year will often be fine.
Rank your customers on sales from highest to lowest. Only include customers who were active in the period (i.e. the ones who bought something from you). If any customers were inactive in the period then exclude them.
Split your active customer population into four quarters or ‘quartiles’. The first quartile (or ‘top quartile’) is the 25% of customers with the highest sales in your chosen period. The second quartile is the 25% of customers with the next highest sales, and so on.
Look for common characteristics or behaviour patterns among the customers in each quartile. Focus most of all on spotting similarities among the customers who buy the most. What do the customers in the first quartile have in common?
When studying your top quartile customers, try considering these factors:
Channel. What marketing channel did you most commonly reach them through? How did they find your company or your brand?
Product category. Do they tend to buy more of one category of your products than others?
Price point. Do they buy your cheaper products or your more expensive ones? Just because they spend a lot with you doesn’t mean they buy your priciest products. It might be that your biggest customers love your cheaper products and just buy lots of them.
Location. Do your top customers cluster in any particular region or, if you sell abroad, in any particular overseas country?
Age or gender. If you’re a B2C brand, is age a common factor in explaining why some customers buy more than others? Do women buy more of your products than men or does it make no difference?
Company size and industry. If you’re a B2B seller, are your biggest customers usually larger or smaller businesses? Do they operate in one particular industry?
And so on.
Once you’ve analysed your top performing customers from all the angles you can think of, draw your conclusions and act on them.
What does that mean in practice? Mainly, you should do more of what is working.
For example, if the data show you reach your best performing customers through a particular marketing channel then allocate more of your spending to that channel and reduce your spending on your other marketing activities.
If your best customers tend to live in the south east of England, or they’re males aged between 31-40, or they’re businesses with a turnover of more than £50m annually, then reallocate more of your marketing budget towards generating leads in those categories.
So far I’ve assumed that your best customers are the ones who buy the most from you.
Be careful — this assumes that all your products generate similar profit margins. For many companies that will be true but for some it may not. Customers who buy a lot from you might be buying your lowest margin products and might not be that profitable after all.
If that’s the case, don’t worry. You can easily segment your customers in different ways that better capture their profitability. For example, if you sell multiple categories of product that have very different profit margins, then try organising your active customers into four quartiles according to how much of your most profitable products they buy. Then continue as above.
This kind of granular investigation might seem like hard work. But it’s by sifting through your proprietary customer data that you can dig up nuggets of information about how your most profitable customers behave. This can generate ideas for finding more of them and for encouraging your lower yielding customers to behave more like your best ones.
A second way to use the sales per customer metric to generate insights into what’s driving your business is to calculate the average figure across all your customers, monitor the trend over time and investigate any changes in it.
To get started, you calculate average revenue per customer like this:
Total sales in the period / Total active customers in the period
Once again, only include customers who made a purchase in the period — don’t include all customers who’ve ever bought from you.
Many young companies should expect their average sales per customer to increase gradually over time. That’s because new customers may buy a basic product or service first to try out a brand or supplier without putting too much money at risk.
The task for the seller is to win the trust and confidence of first time buyers and later persuade them to make follow-on purchases of bigger or more highly specified products or services that carry higher price tags (upselling).
If your average sales per customer value is steadily increasing, that’s a good sign. It can signal that you’re succeeding with upselling tactics (or just finding better customers who value your products more highly or have deeper pockets).
But if your average sales per active customer is declining, that’s a more concerning indicator.
It could mean your upselling tactics aren’t working effectively. In this case, you need to investigate those tactics, diagnose what’s going wrong and fix it.
Alternatively it could mean that you’ve exhausted the pool of customers who really value your product or service highly. Your USP is a perfect match for these initial customers — you solve a painful problem for them or help them obtain an important benefit.
If that’s the case, the new customers you win in future may only be willing to purchase simpler products with lower specifications or basic services without frills. They’re going to be less profitable than the customers you’ve won so far.
Even if that’s true, all is not lost. But you’ll need to revisit your strategy and examine how you can adjust it to prosper in the future. For example:
Would it make sense to launch a wider range of lower priced products so that you would at least capture a higher share of this lower-tier customer segment?
Should you redirect more of your product development spending or service design effort to the lower tier product segment to find ways to deliver to customers the benefits they need more cheaply? That could reduce the drag on your margins as lower tier customers become a bigger share of your total sales.
Either way, you should keep a close eye on your customer acquisition cost to check that the amount you’re spending to acquire more price conscious customers will be compensated for by the profits you can make from selling to them. I’ll discuss customer acquisition cost in detail below.
The key point is this. You won’t spot that your average sales per customer is declining if you only monitor your total sales growth. Your headline sales might keep on growing strongly for a while even though, under the surface, the quality of the new customers you’re winning has started declining.
It’s only by digging into the detail and tracking your average revenue per customer that you’ll spot trends like this early and give yourself time to adapt.
Average transaction value (i.e. average value per customer purchase)
It’s possible to take your analysis of average sales per customer a step further. You can do this by splitting average sales per customer into two constituent parts.
Here’s the formula:
Average sales per active customer = average transaction value x average purchasing frequency of active customers
For example, if your customers spend an average of £25 when they purchase something from you and they make an average of three purchases a year, then your average revenue per customer will be £75 per year.
Let’s dig into average transaction value (ATV) first. In the retail industry ATV is often called ‘average basket size’ but it’s just as applicable in non-retail businesses.
Here is how you calculate ATV:
Total revenue / total number of customer transactions
(Note that the number of transactions is not the same as the number of products sold — one transaction might include several products).
Let’s consider an example of how you analyse your ATV to improve your performance.
Compare your ATV across your whole customer population. Do your customers mostly spend a similar amount when they shop with you or do some customers spend much more than others when they buy? Try ranking your customers on ATV and splitting them into quartiles like you did with sales per customer. Usually you’ll find that some customers spend a lot more than others.
Ask questions like these: Which products do your higher-spending customers buy the most? Do they buy the same products each time or different ones? Do they tend to buy some products in combination?
Can you work out the reasons behind any patterns you find? Does your analysis suggest any insights you could use to nudge your lower-spending customers to buy more?
For example, if your lower-spending customers buy a product that high-spending customers often buy in combination with others, could you offer a discount to incentivise your lower-spending customers to try out the complementary products?
Customer purchasing frequency
The second driver of average revenue per customer is average customer purchasing frequency.
Here is how you calculate the metric:
Total transactions in the period / total active customers in the period
To generate ideas for boosting customer purchasing frequency, try doing the same ranking and segmentation exercise you did for sales per customer and for ATV. Once you’ve identified the customers who purchase most often, look for patterns again.
If some customers purchase much less frequently than others, is it possible they are buying the products they buy from you from another brand or supplier as well? Could you prompt them to re-order from you instead by offering them a special benefit or price discount after a certain period of time has elapsed since their last order, when they may be running low on the product?
Share of sales to new vs returning customers
Here’s another way to generate fresh ideas to improve your business performance. Try analysing the mix of your revenues between sales to customers who made a first purchase in the period and sales to returning (i.e. existing) customers who have bought from you previously.
The calculation for your new customer share is:
100 x sales made to customers who made a first purchase in the period / total sales
(The remaining share is of course your share of sales to returning customers).
When you first launch your business, all your sales are to new customers. Over time, as your customer base grows, your share of sales to returning customers will naturally increase.
What is a good value for the share of sales to new or returning customers? There’s no right answer to this question — it depends on the type of business you’re in and how young or mature your company is. But there’s a balance to be struck.
Here are a few issues to think about:
On one hand, you should always be trying hard to win new customers. New customers aren’t the only source of a company’s growth, but they’re usually an important part of it. Besides, it’s inevitable that you’ll lose some existing customers from time to time. So you need to keep winning new customers to compensate.
On the other hand, generating repeat sales from existing customers is nearly always more profitable than making first sales to new customers because it requires limited extra marketing. So sales to existing customers are good for your profit margins.
Here are some examples to illustrate what can go right and wrong:
If your overall sales are growing at a good clip and, within this, your share of repeat sales is also rising quite strongly then you’re probably doing a good job of generating sales from both repeat and new customers. Keep it up!
If your sales are growing strongly but your share of repeat sales is stuck at a low level it implies that you’re winning lots of new customers but the ones you’ve sold to before aren’t returning to buy again. If you were doing a better job of keeping customers happy after they’ve bought you could be growing even faster than you are. You need to develop better ideas and processes for earning loyalty and stimulating repeat sales.
If your sales aren’t growing and your repeat sales ratio is very high, it probably points to a lack of success in winning new customers. In which case, new lead generation and conversion is the area you need to focus on.
If your sales are stuck but your repeat sales ratio is quite low, it means you’re winning new customers but churning through existing ones at a high rate. If you were to do a better job of keeping hold of your existing customers, your sales should start growing again.
It all depends on the context. But by digging into your financials and measuring ratios like these you can uncover ways to optimise your tactics and your execution.
Customer retention rate vs customer churn rate
Your customer churn rate (sometimes referred to as customer turnover rate) is the rate at which you are losing existing customers.
You calculate the metric using the following formula:
100 x customers who became inactive during the period/ total active customers at the start of the period.
An inactive customer is one who had purchased from you at some time previously but didn’t do so during the period in question. A customer who becomes inactive in the period is one who bought from you in the previous period but didn’t buy from you in the current period.
With sales to returning customers usually your most profitable ones, keeping hold of those existing customers and encouraging them to keep buying is crucial. That doesn’t happen by itself. By tracking your rate of customer churn you’re taking your first step to minimising it.
What is a good level of customer churn?
In practice different levels of churn are normal in different industries, depending on the nature of the product or service and the closeness of the relationship between buyers and sellers. You should usually be able to find some market data on normal levels of churn in your industry.
Compare your own level of churn with your industry average — does it seem higher or lower?
If your churn rate is below the average for your industry — congratulations! Keep doing what you’re doing.
Or is your company winning new customers and making first sales but then suffering customer churn that’s clearly worse than your industry norm? If so it’s time to investigate the causes of the churn and implement some remedies.
Here are two places to look for the causes of high customer churn:
Your product or service. Customers might have been disappointed with the performance of the product or service you’ve supplied.
Your after-sales service. Customers might be dissatisfied with the level of support you’ve provided to them after their first purchase. In some industries after-sales service isn’t relevant but in others it’s very important.
If you’ve fallen down on either of these counts, you ought to have some evidence already in the form of complaints or poor customer feedback. Even if that’s not the case, reach out to some first time customers that haven’t returned to buy again. Explain why you’re contacting them and ask for their honest feedback.
Most customers want their suppliers to care about them. If they’re unhappy, they would much prefer you contact them than ignore them. If you approach them in the right way you could even win them back.
To do that though, you’ll likely need to make substantive changes to what you’re offering. That could include product changes to improve performance or reliability as well as bolstering your after-sales customer care.
If your investigations don’t turn up any problems with your product or your after sales service, that leaves one final place to look to explain your high churn. It may simply be that your marketing is ignoring customers once they’ve bought from you.
This is a common mistake. Too many companies assume that once a customer has taken the plunge and made a first purchase they will come back to buy again and again without any more encouragement.
If your marketing doesn’t include tactics to show your customers you care about them after they’ve bought, you should work on that as a priority. Reach out to them and tell them you’d value their feedback. What do they like about what you do? What could you differently or better?
Add customers to your e-mail distribution list and keep sharing valuable content with them. From time to time, send them targeted promotions tailored to their profile. Keep working to earn their trust and confidence, just as you did before they made their first purchase.
What other techniques can you use to gather valuable client data?
So far I’ve focused on gathering financial data on customers based on their purchasing patterns. But don’t forget you can also build up data on customers by simply reaching out and asking them questions. You can cover what they like and don’t like, the factors that lie behind their preferences, how they weigh up buying decisions and so on.
People are often flattered to be approached and asked for their opinions or feedback, provided it’s not intrusive and doesn’t take too much of their time. So don’t be shy about doing it. If you think it’s appropriate you can offer them a small incentive (e.g. a discount off their next purchase) in exchange for taking part.
One way to engage with customers is simply to ask them questions, either over the phone or via a traditional survey. But don’t limit yourself to the traditional approach. Loyalty schemes are another common way to achieve the same result, for example.
Then you can feed all the extra data you’ve gathered into your customer analytics to make your decision making even more robust and sophisticated.
Customer analytics software and tools
From a technology point of view customer analytics involves three stages:
Collect. You collect raw customer data using multiple tools such as your CRM system, Google Analytics and any marketing solutions you use (e.g. e-mail marketing). The data you gather might include customer activity on your website and social media pages, their purchase history, any socio-demographic information you have on them and many other things besides.
Organise. Next you use a piece of software called a customer data platform (CDP) to combine all these data in a single customer database. By consolidating your data, the CDP creates a single easily-readable profile for each of your customers. The result is seamless with accurate data that your other systems can access and use.
Analyse. Finally, you can use customer analytics software to read the data from your CDP and analyse it in ways we’ve already touched on in this article. Your software can segment your customers using different criteria in order to pick out your most valuable customers. It can also highlight positive or negative trends you may not otherwise notice.
Some customer analytics software tools can be quite expensive but the payback they generate can be significant, as we’ve seen.
If you’re still running your startup business from your kitchen table using only a minimum amount of software and doing your planning on spreadsheets, analytics software is not something you need yet. Your number of customers and your volume of sales are probably still small enough that you can apply the analytics concepts in this report manually.
In this case, just download your customer and sales data into a spreadsheet, sort it according to the criteria you want (e.g. sales per customer) and scan your best customers for common characteristics.
It’s a low tech approach that may have you flipping between applications. But at this stage you can probably spot quite easily where your sales leads are mostly coming from, what types of customers buy from you most frequently and so on. Some simple manual analysis should yield the insights you need to take the right decisions on key topics.
If you’ve already proven your business concept and are making a meaningful volume of sales, customer analytics software is something to seriously consider:
As your business grows it becomes much more important to take good decisions – there’s more money at stake and you’re trying to grow a proven concept into a thriving business.
Switching to software will free up time that you can spend on vital business development work.
Conclusion: Three reasons why analysis of customer data can transform your profits
We’ve seen that customer analytics can have a big impact on profits. Why is the impact so large?
Three reasons stand out.
First, because it’s only by analysing your customer data that you ensure you’re working on the right things. Spending lots of money to win customers who only like your lowest margin products is wasteful. Focusing your marketing on Facebook is a bad idea if your most profitable customers come to you through Instagram. Working hard at the wrong things won’t bring you the success you want.
Second, the customer data from your business are gold dust. Many businesses operate on the basis of hunches and gut feelings. They ‘put themselves in the shoes of their customer’ and offer the customer what they themselves would want to buy. But until you test and validate your assumptions about your customer, that’s all they are — assumptions.
By contrast, your customer data are facts from the real world. They are evidence of what real customers will and won’t pay for. They prove what products people like and don’t like. Which benefits they value and which they don’t. How they do and don’t like being pitched to.
By proceeding on the basis of facts, you can do more of what works and stop doing what doesn’t.
Third, customer analytics are especially powerful for generating customer loyalty and repeat sales. Isn’t it much easier to pitch something to someone when you already know a lot about them and you can see how they’ve responded to offers in the past? What they’ve reacted positively to and what turned them off?
And that’s vital because it’s much more profitable selling to existing customers than spending money to win new ones. Once you’ve done the hard work and incurred the cost to win a customer it’s a terrible waste not to take full advantage of the opportunity to sell them things they want or need.
So customer analytics enable you to maximise your sales potential with your most profitable customers.