Customer profitability – an essential question that often remains unanswered
We were taught in our first consulting role that strategy is all about making choices – deciding when, where and how to compete. To choose or make decisions, you need data and most importantly, data around customer profitability.
Companies, however, struggle with reporting information around a customer’s growth and profitability especially if they have multiple customers with numerous services or product lines but this information is essential when successfully re-allocating resources (staff, investments, promotional spend).
This can matter a great deal as relying on annualised averages for example simply won’t cut it. In a recent example, a client identified a customer as being their most profitable but having reviewed the correct data, realised it was actually loss-making. Something gets lost between the never-ending road of system upgrades, complex customer and product hierarchies and systems simply not talking to each other.
Given this increasing amount of data from multiple sources, many finance departments give up. At best, it becomes an annual exercise that provides a snapshot for senior management but lacks the granularity to deliver on-going monthly reporting. It doesn’t have to be that way.
There is a path between building another module onto your ERP system and living in “Excel (macro) hell”. And the benefits are almost always more than worth it as it provides crucial data for day-to-day decision making. In the words of W. Edwards Deming: “In God we trust. All others must bring data.”
Why is customer profitability so hard in the first place?
Let us begin by stating that customer profitability is by no means obvious or easy to define as it is not an exact science. The principle is straight forward, ie take the customer’s revenue, subtract any directly attributable cost and then add indirect cost using the best available allocation method.
Accumulating this data in a way that is can be correctly interpreted can be challenging and as a result doesn’t happen with any degree of regularity. The depletions by customer, the direct attributable product or service cost and the indirect cost by relevant metric (ie sales person or campaign) often need to be sourced from different systems and some are not captured in any system at all.
In addition to this pure data and system component, it also requires a cross-functional effort to agree on how to allocate and report on this. Finally, the CFO / FD will normally demand that the totals reconcile with the overall business results which adds a final layer of complexity / required diligence.
Most often the biggest issue however is the quality of the available data. Often, we find that our clients use multiple customer and product hierarchies throughout the different departments of sales, finance and marketing. Second, the main ERP system cannot deliver the data in the granularity required, and systems, offline and online data sources need to be combined.
These are by no means insurmountable issues but given the cross-functional effort required, the cemetery of other similarly failed initiatives and the day-to-day drumbeat of ‘urgent’ tasks, customer profitability often lands in the “too hard” basket.
ERP or Microsoft Excel?
Finally, there is the issue of where to fit it in the overall IT and reporting “ecosystem”. The main issue with the traditional approach to building a customer profitability report is that it often mixes financial accounting functionality with management accounting requirements.
Financial accounting is locked in major ERP and related systems that are all integrated around the same data set and work in lock step. This provides the insurance to the accountants “that it all adds up”.
It also opens up a Pandora’s box the moment you try to add new functionality such as customer profitability reporting to it.
Timeline, cost and overall disruption often result in quick abandonment in the planning phase already. The management accounting part of the business for these type of analyses lives in the wonderful world of Microsoft Excel. Now to be clear, we love Excel, but for this level of reporting and analyses it is often insufficient. For example, a finance team has a bright analyst that takes it upon him (or her) self to build the mother of all Excel workbooks to deliver a dynamic customer profitability cube.
The downside of this being that it often lives only as long as the analyst stays in role and over time corrupts as the maintenance of the tool is abandoned. Neither of these two solutions is sustainable. The first one, ie a full system integrated solution, is too costly and painful and not flexible enough should any changes (as is often required) be needed. The second one is too flimsy and dependent on one or two people.
The cost of not knowing
Given that most companies are in the business of selling something (product or service or both) to someone (customer or consumer) in order to make a profit, the knowledge to know when and to what extent this transaction is successful is elementary. And … not knowing does indeed come at a cost. This cost comes generally in three elements:
1. Direct financial cost of making suboptimal decisions or not knowing when to step in. How to decide if a discount should be given, a project should be stopped, or a bid should be pulled if the people in charge don’t know what financial benefit they are delivering.
In the case of one client our profitability cube showed that one specific customer (out of 6,000+) was significantly loss-making in what normally is a highly profitable industry (alcohol). Upon investigation it was found that a discount was loaded per bottle rather than per case (12 bottles) which meant this customer had received 12 times the normal discount. Needless to say, they never complained about this generosity.
2. The cost of operating in a “directional haze”. How to choose between customers and how to decide how to price the project or service is difficult if we don’t know the profitability. This then results in a mixture of gut instinct and relying on that what worked in the past, will work again. This is hardly a proven recipe for success of course.
With one industrial client’s projects, it was a well-known adagio that one bad project could wipe out the profits of ten good ones. However, how profitable individual projects were, what the exact drivers were that drove a project from the black to the red and (most importantly) to know when this was happening was more a matter of opinion than of fact.
3. The organisational cost of “not keeping score”. There is something neat and tidy and incontrovertible about knowing “the number”. For customer managers, project leaders or product managers to understand in the form of a simple number what they contribute and how this is changing year-on-year is enlightening.
With many a client, we see a shift in behaviours of Key Account Managers once they are confronted with how their actions don’t just move sales but also affect profitability (NB: too often in opposite directions!) In short, not knowing is costly, both in direct money terms but also in terms of organisational clarity and simply knowing how well you did. The benefit of lifting the haze and putting customer profitability front-and-centre of the monthly reporting suite is paramount.
Getting it right
So customer profitability is very important but its natural “habitat” is difficult to locate within the IT ecosystem of a business. The best alternative is often to set up a lightweight bespoke solution that sources the data from all relevant systems/persons.
We find that the most important part of setting up such a system is choosing how to source data. Ideally you want to have a first version up and running and productive in a few months. This avoids the pitfall of a major system integration and still provides a system solution.
Once a proof of value has been created, selected data sources can possibly be automated, but those decisions then become simple cost benefit exercises. The next step is selecting from all available product and customer hierarchies the granularity level at which the ultimate customer profitability data cube will be built. Here we tend to choose the lowest level possible.
If you have 900,000 outlets supplied via 300 distributors, the lowest level should be the outlet level. This will allow the user to slice and dice your profitability data now, but also in the future when customer groups and segments might change.
Subsequently the data outputs of the system need to be thought through, we always ensure relatively simple data files (e.g. Excel files, or flat data files for more voluminous data) can be downloaded from the system for quick analysis and/or customer reporting jobs. Second if a data warehouse is already present for the organisation, the customer profitability cube could be interfaced there as well to make the results available to the entire organisation in a unified way.
The initial setup of such a system should take three months or less. This can be done by choosing the right level of granularity accuracy and relevancy for each step in the process. This keeps initial investment low and allows the business to make smart decisions into which part of the system it would need or want to expand at a later date.
Customer profitability is crucial if a company wants to compete successfully. The cost of not having a granular grasp on this affects both direct cost and organisational effectiveness. However, too often the need for change is realised but gets “stuck” in too ambitious and integrated a system overhaul. This is the type of overkill that mixes financial accounting functionality with management accounting purposes and often means the effort is abandoned. Taking a more fit-for-purpose approach where the customer profitability report is built separately whilst pulling data from the relevant systems is often a much easier and pragmatic approach.
The light that shines in a business that knows its profitability by account, service and product line and more importantly, the intersection of the these, is simply put enlightening. Its people can take decisions knowing how it will impact the bottom-line.
An outcome so simple yet so powerful!
