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Traditional Accounting Measures

Traditional accounting measures of firms performance developed by managers within firms of the first three industrial revolutions may be less relevant for the management of tech companies. This is as a result of their rear looking nature and monetary focus, when tech companies have large initial costs and lower income when starting out. The investments made by these companies are likely to have large future economic benefits (which may not be initially measurable in monetary terms), suggesting that alternative performance measurement techniques may be more appropriate for these alternative firms.

The first alternative measure to be discussed is Economic Value Added (EVA). The initial creation of EVA was in part to determine the difference between “growth for its own sake” (Stern, 2001) and the creation of value. Proponents of EVA claim “it is the best proxy for value creation” (Hopper, 2007), a measure of the value managers are creating in a tech firm is important to measure the long term potential success of the firm.

The calculation of EVA takes into account the cost of capital invested, as well as the returns in the year, this makes it a more realistic measure of the economic benefit to shareholders as a result of managements actions. This is a large benefit of using EVA instead of traditional measures such as residual income (RI).

As EVA allows for more capitalisation of research and development (R&D), managers are not disincentivised from investing in the technology required to maintain the firms competitive advantage, and ultimately lead to the ability to make profits in the future (even if it requires loss making periods in accounting terms whilst large investments are made). There are however disincentives for managers to invest around year end, so projects may not be started / funded at the most appropriate time. This is relative to the managers time horizon, if they intend to remain in the firm for a longer period of time (five or more years) there is likely to be little overall benefit to them in manipulating the timing of investment for slight improvements in EVA in a single year.

EVA still has an underlying focus on the operating profit of the firm (even after adjustments), so if a firm is in it’s early stages the initial measurement of EVA is likely to be negative. It may be possible to mitigate this by “Evaluating performance on the basis of improvements in EVA” (Bhinmani, 2015; Bhimani, 2017; Bhimani, 2017), the result being that managers can show they are improving although EVA suggests that value is decreasing.

It is claimed that EVA reduces the bias introduced by accounting standards, (ACCA, 2011) however the adjustments made to NOPAT as part of the EVA calculation are subject to management bias in the same way that GAAP adjustments may be subject to management bias. This is alluded to by Stern in 2001 where he caveats EVA’s ability to align the goals of shareholders and managers with the need for proper implementation (Stern, 2001). For instance, if a manager has authorised expenditure on marketing for a new product, they determine the length of time the expenditure will benefit the firm, meaning that they can extend the period over which the expense affects adjusted NOPAT reducing current year expenses and deferring expenses into later periods which they may not relate to. If the manager in this context was considering leaving in 2 years’ time but was able to spread the cost of marketing (that relates to say 3 years) over 5 years, EVA will have been higher in their final 2 years than it should have been.

Hopper argues that the use of EVA as a measurement can “induce parochial behaviour” (Hopper, 2007). Conflicts between functions within a business, or a lack of understanding of the overall direction of the firm by elements within it, can be problematic for any company, however in a rapidly developing industry like technology this issue is exasperated by factors such as the rate of change required to maintain competitive advantage, the constraints on new firms resources (given the high levels of investment required in tech firms). Comparatively the metric of LTV considers the profitability of customers, and rejects customers that would make negative contributions to the firm. This measure is unlikely to cause conflicts between functions within the firm as it is in each functions best interest to have customers which are making negative contributions.

Measures of performance for Tech firms should have consideration of the future potential of the firm, and not predominantly backwards looking (as EVA is). As many Tech companies are in their early stages, previous performance may not be the best indicator of what is truly happening within the firm, and where the actions of managers are taking the firm. A more appropriate measure in the early stages for a tech firms lifecycle may be the use of relevant key performance indicators (KPI’s).

Customer Lifetime Value (LTV) is “the present value of the future cash flows attributed to the customer relationship” (Farris, 2006). The forward looking nature of LTV alongside customer numbers / customer growth can be a useful indicator for investors of the future performance of a company.

LTV can be calculated by multiplying “contribution margin per customer” by “expected average lifespan” (Bhimani, 2017). To measure the profitability of customers firms can take LTV/CAC*, provided this is >1, on average the customers are profitable. If this were <1 the firm would be losing money on each customer it acquired, this may be the case in the early stages as a customer base is built, and the product/service range is tailored to meet their needs, which in turn can drive additional revenues increasing the LTV. LTV can be closely linked to the firms strategies (Bhimani, 2017), and regular monitoring of this by management can show how their innovations are adding to the potential future value of the firm, in contrast to EVA which only shows value as being added once revenues are earnt.

*Customer Acquisition Costs (CAC)

The calculation of LTV can be complicated and costly (Bhimani, 2017), this may pose a problem for start-up tech firms with scarce resources. However, the firm will need to perform some form of analysis to aid the decision making of managers, and it is likely that implementing Stern Stewart’s EVA will incur large fees, in addition to the cost of having an employee to maintain the information feeding the metrics and produce and summarise the metrics for managers.

The use of alternative KPI’s by management may be seen by sceptical investors as a way of window dressing poor results. This also opens up a potential risk that managers pick the KPI’s that show the best results and can lead to failure to recognise potential performance issues. LTV can be calculated in multiple ways, which can lead to significantly different results (Schoder, 2007), this could have a similar impact to picking KPI’s with the best results. EVA has a more fixed method of calculation, which may make it a more comparable metric for measuring the value added.

The lack of grounding in financial information may reduce the confidence managers put on some LTV, and some may question the accuracy of it’s calculation (especially in a start-up where there is less internal data to base the calculations on). If managers are

Overall a forward looking measure such as LTV appears to be more appropriate then EVA for tech firms in their early stages, as a result of the reduced reliance on current profitability, and considering the potential for management bias to enter either of these metrics.