Analytics and the Finance Team

A typical Monday morning in many companies involves a panic stricken Finance team rushing around at some ungodly hour desperately trying to provide explanations as to why sales are down 1% on the budget and showing 3% growth on the previous year. Spreadsheets of increasing complexity are being pummelled into submission, powerpoints are being generated and words are being crafted in ways that do not offend delicate egos nor upset the political apple cart. A final check to ensure all of the action points from last week have been closed down – and then it is done! Only the world has moved on – the critical questions from last week have been superseded by other questions. Somebody had spotted a rounding error. Nobody could remember how much buffer was built into the forecast. What impact will the unseasonally bad weather forecast have on sales next week? And so the chaos starts again with only a few days to turn it round in time. And in some companies this maelstrom of chaos happens daily.

When sales and profits are growing there is little need for complex analysis – the urgency to understand the drivers of performance is more muted where the return on investment is 7% compared to the target 10%. One of the consequences of this is that the analytical role became the preserve of finance.

Growth and profit have until recently been driven by an unrelenting focus on operational efficiency. For this the classic Finance approach is perfect – systematic variance analysis that deconstructs performance variances and delivers clear accountability.

Most of the companies I have worked for have at various points had teams of specialist analysts, typically having maths, science and economist backgrounds. But the number of people employed in these roles has significantly reduced in recent years. After all, the numbers are easier to access. IT are claiming one version of the truth and Finance can do anything nowadays in their Access databases. So why have two teams doing what is seen as the same thing, ie the numbers? An easy call to drive operational efficiency and reduce unnecessary overheads is to reduce the analyst pool.

We have lived through an era where progress has been made through driving operational efficiency, compliance and standardisation and customer demand has been relatively strong. Is this sufficient for companies to thrive in the future?

Finance rarely look at the world from a customer perspective – it tends to be transaction based and ignores customer circumstances and attitudes. However it is now relatively easy to identify at an individual level motivations and preferences and hence empower communication and engagement at an individual level.

The one size fits all approach to marketing is no longer relevant. We are not simply looking at another level of sophistication with CRM – we are now at a point where a product can be tailored specifically for an individual, where a promotion is tailored based on individual preference.

So What are the Consequences?

There are a number of companies who have used analyst capability alongside classic Finance with continued success. These companies will continue to exploit this competitive advantage.
For companies that have reduced their analytical talent or simply have never had this capability – with the tools that are available today it is relatively easy to play catch up. The risk is in getting the right data from your system (which may require significant IT investment) and can find the right resource.
For many smaller companies this type of customer centric analysis was simply not affordable – it is now. A good example is with regard to market research – in the past this required dedicated resource (bought in or in house) to ask customers questions. The ability to generate online surveys at nominal cost, combined with the ability to handle large volumes of data without expensive tools such as SAS means the playing field has at least partially levelled with the blue chip beasts.
The world of retail is already seeing niche operators springing up specialising in customer and/or product niches. This is clearly happening in the analysis world as well – and this again comes back to the tool capability and cost – you no longer need excessive server and application running costs to produce the analysis and insight that you did 10 years ago.
Hence companies no longer need to get support from the big blue chip consultancies in these areas and can have confidence in the ability of smaller specialist operators. It is these operators that are also working with some of the best analysts who are attracted by the flexibility of the working relationship and avoiding the red tape and bureaucracy that goes with working for a “normal” company.
Of these new analytical companies the ones that thrive will be the ones that can communicate clearly and effectively to ensure clients are making decisions on the best information available. Those that fall into the old analytical trap of poor communication and analysis paralysis will simply not survive.