Discovering Insights in Revenue Data

Discovering Insights in Revenue Data

Are you sitting on a goldmine?

Buried treasure is the stuff of pirate folklore. An old wooden chest, filled with loot, would be secreted on some uninhabited island, its subterranean location revealed only by a single X on an old, weathered map. In reality, the whole notion of pirates burying treasure is a historical nonsense but let’s not let the facts get in the way of using it as a good analogy for this discussion.

Your customer revenue data may well contain hidden insights that, once identified, can be worth their weight in gold.  The process and accompanying spreadsheet called ‘Customer & Revenue Dynamics’ (that you can download for free in the tools section of my website here) will create a sort of ‘treasure map’ of your customer data and shows you a few X’s where you might want to start digging.

 

 

Customer and Revenue Dynamics

The simple idea underpinning the tool is that the amount your customers spend with you changes over time, some will spend more, some will spend less, and others will stay pretty constant. Some new customers will come along, some existing customers will leave.

The drivers of these changes will be many and varied, but if you can identify them, recognize patterns or detect common causes, you might just uncover some significant insights. Which, in turn, can help you take effective action.

A couple of examples will make the point; say you identify the top 25% of all your current customers by revenue, you then isolate the top ten by their growth % and discover most of them are from a particular market sector or customer segment. Probably a good idea to divert resources to explore this opportunity a little deeper.

Or maybe you look at the top ten customers with the biggest absolute drop in revenue. You find that in the majority of cases you are losing out to the same competitor. Time to benchmark that competitor’s offering and develop a defensive response.

Of course, all this is nothing more than common sense.  Real customer data has always been a valuable source of insights.  What limits some businesses in preforming this type of analysis is getting at the data efficiently.  The Customer and Revenue Dynamics tool is designed to do just that.

 

The Process

You start by identifying every customer who has transacted with you over a specific period. Say we want to look at year on year changes, we select the 12-month period for which we have the most current data, which we designate as ‘current year’, and the corresponding data from the previous 12 months, designated ‘prior year’. So the current year might be Jan to Dec 2018 and the prior year would be Jan to Dec 2017.

 

 

These data are then put into a three-column spreadsheet. Column one contains the customer name, column two the prior year revenue and column three, the current year revenue. This is then simply copied and pasted into the tool.

Given these data the tool tags the customer as either a grower – if their revenue is increasing year on year, a faller, if their revenue is decreasing, lost – if they had zero revenue in current year, or new – if they had zero revenue in prior year. Once it has tagged every customer it sorts them into four equal sized groups based on the quartiles of current revenue.  The 25% of customers who spent the least through to the top 25% of customers, who spent the most.  These groups are named micro, small, medium and large. It will then use the same quartiles to group customers based on the prior year revenue.

 

 

So let’s take a minute to review how each customer is tagged.  They are either a grower, faller, new or lost and they are currently a micro, small, medium or large customers and we also see what size quartile they were in the previous year.  It’s this rich data set that means we can start looking for insights.

 

 

The tool will automatically show you a revenue walk – from prior revenue to current revenue – by showing how much growth came from the growers, how much was lost by the fallers, how much growth came from new customers and how much was lost from ex customers. You can also see this walk based on the quartiles – how much growth did last year’s micro customers provide and so on.

 

 

You can also isolate customer groups based on their migration between the quartiles.  Say you notice that seven customers went from ‘small’ to ‘large’, you can quickly identify them and again sort them with different criteria.

The tool isn’t useful for every business. If you have a small population of customers, you probably know enough without further analysis. If you have a multi thousand customer business with good data discipline, you will probably be doing something similar already. But if you are a business unit with several hundred customers (the tool can handle up to 1500) it may well be worth exploring.

Once you have pulled together a list of all your customers, with their prior year and current year revenue it will take only a couple of seconds to create your own treasure map.  It might be worth the spade work – so get digging!

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