05 Dec 2011
By Jim Manzi, Applied Predictive Technologies
FOR FINANCE directors of large companies, rolling out major initiatives company-wide always comes with high costs and risks attached. Never has this been more relevant than now, with the eurozone crisis and renewed speculation about a double-dip recession heaping further pressure on British companies.
Despite this, many directors plough ahead with big investments based on manual financial analysis. But there are many problems with this approach. Much more can be gained through rigorous business testing using advanced analytical technology, with the potential to increase profits by as much as 5%.
If yours is a retail chain, refurbishing all your outlets could cost millions. The same applies if you're a hotel group considering opening in a string of new locations.
While you're hoping for increased profits and customer satisfaction, the gains could fall short of the implementation costs. Worse still, you could find disastrous, unexpected losses on your hands. So how can FDs accurately predict the outcome of proposed investments?
Most adopt a fairly methodical manual approach to exploring a hypothesis. A retail group might try a promotion in a small number of stores. A bank might change the layout of a few branches. Analysts will study spreadsheets of resulting data and produce a recommendation for or against rollout for the FD to review with the rest of the board.
The fundamental problem is that such trials are difficult to run in a statistically reliable manner. The findings are too often distorted by all the surrounding 'noise' factors, such as weather, transport issues and competitor activity. Misleading findings can give false confidence to a company to implement a harmful change, or to withhold a beneficial one.
The good news is technology is now available to cut through this noise and enable fully meaningful trials, even when working with small samples. The latest analytical systems will help ensure trials are scientifically set up, with carefully chosen test and control groups, and will adjust for the many misleading factors. Not only will this provide an accurate 'yes/no' answer to whether an initiative is worthwhile, it will also reveal how it can be tailored for optimal effect.
A restaurant chain, for example, may learn that expanding its serving staff will increase profits in city centres, but not in suburban areas. A bank may find that upgrading branches for business customers will increase profits, while upgrading consumer-focused branches will only increase costs. Hence, companies can adapt initiatives for optimum gain based on certain products, locations or customer segments.
Never before has this approach been more relevant than in the current climate. Shoppers are filling their baskets with promotional products, but which offers will make the smallest dent in retailers' profits? Companies are working hard to minimise expenditure, but which costs can be safely trimmed and which will harm the business?
Testing can provide the answers and bring huge benefits. Our experience shows that a company-wide 'test and learn' approach can boost profit margins by as much as 5%.
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