Empirical Evaluation of the Impact of Commercial KPIs on Financial Performance of MTN Ghana Ltd

Bismark Maka¹*, Prof. Dr N. Suresh²

¹Ph.D. Scholar, M.S. Ramaiah University of Applied Sciences, Bangalore-560 054

²Ph.D. Supervisor, Faculty of Management and Commerce, M.S. Ramaiah University of Applied Sciences, Bangalore-560 054

*Corresponding Author E-mail: bdmakaus@yahoo.com

ABSTRACT:

This paper empirically examined the impact of commercial KPIs on financial performance of MTN Ghana Ltd. Time series data on the commercial KPIs was obtained from MTN database with data range spanning from January 2009 to December 2015. The commercial KPIs were carefully selected consistent with industry benchmarks. Using the DCC GARCH (Dynamic Conditional Correlation Multivariate Generalized Autoregressive Conditional Heteroskedasticiy) model to ascertain the volatility clustering and the conditional correlation between the commercial KPIs and financial performance, this study found that a high volatility in the financial performance of MTN Ghana Ltd is associated with high volatility in the independent commercial KPIs. The correlation coefficients are moderate and positive and are statistically significant as expected with number of revenue subscribers featuring prominently in its correlation with financial performance; to some extent consistent with the findings of Vadiraj and Narahari (2014)¹. However, the data failed to reject the assumption of time invariant conditional correlation maintained in the Constant Conditional Correlation MGARCH model. This paper therefore concludes that there exist a constant conditional correlation between the commercial KPIs and financial performance overtime. For purposes of robustness, a 5-year rolling window regression was also estimated to lend support to the result showed by the DCC MGARCH model. The study finds that the true coefficients are constant, and the estimated coefficients portray a random fluctuation and noise. Interestingly, the number of revenue generating subscribers and minute of use maintained a strong positive causality with revenue performance over the entire 5-year rolling period. The result was consistent with the findings from the DCC GARCH model but there seems to be a puzzle since commercial KPIs such as Net Adds and Data volume exhibit a negative relationship with financial performance.

KEYWORDS: Commercial KPIs, Financial Performance, MTN Ghana, DCC MGARCH, Rolling Window Regression.

1. INTRODUCTION:

The Telecom industry in Ghana had been a backbone behind the country’s economic performance in terms of GDP growth. A study carried out by Price Waterhouse Coopers (PwC), for the Ghana Chamber of Telecommunications in 2015, indicates that the telecom industry is one of the heavily taxed sectors in Ghana, as mobile operators are subject to 14 different taxes and regulatory fees. Mobile operators according to the study pay US$650 million in taxes each year, representing about 40% of total revenues in the sector. The report indicated further that, mobile operators’ contribution to total taxes in 2014 amounted to GHS 1.05 billion. MTN Ghana’s contribution to total taxes amounted to GHS 605 million and GHS 675.6 million in 2014 and 2015 respectively. MTN Ghana Ltd paid a whooping GHS 43.7 million as regulatory fees to the National Communication Authority in 2015. The 2005 baseline survey conducted by AICD (Africa Infrastructure Country Diagnostic) indicates that, Ghana`s ICT sector is a major performer by regional standard.

It was recorded that about 60% of the population have access to GSM voice mail signal with about 32% of the population having subscribed to mobile telephone service. The situation had witnessed a significant improvement in recent times. Ghana has access to the SAT3 submarine cable through competing international gateways and as a result, mobile footprint was assessed to have expanded dramatically to about 82%. (World Bank Policy paper 2011) The competitive environment has also resulted in high mobile penetration rate from about 32% in 2005 to 67% in 2009 (World Bank Policy paper 2011). Telecom voice penetration as at March 2016 have hit 130.97 % (NCA, 2016). AICD simulations further show that about 99% of Ghana’s population could be covered with GSM signal on a commercially viable basis, ranking Ghana as one of the most attractive markets in Africa. The assumption made was that about 4% of local income in each area could be ascertained as revenue for mobile telephony services. It was further assessed that even should the assumption be reduced to 1 % of local income, it would still be possible to cover about 95% of the population and be commercially viable.

Indeed, the industry’s performance in terms of profitability over the years has been benign. However, the concomitant ramifications of an expanding and a competitive sector vis a vis a weak economic fundamentals cannot be overemphasized. The Telecom sector has been bedeviled by challenges in recent times. The recent power crisis and utility tariffs hikes are reported to have scraped off a chunk of the revenues of Telecom operators in Ghana. MTN recently revealed its plan to spend at least GHS 48 million in 2016 due to the utility hikes. SIM box fraud, theft of batteries at cell sites, low penetration of data, economic challenges including cedi depreciation, forex volatilities have hard hit the telecom operators, while overcrowding of the industry has also affected the returns of the telecom industry players. MTN Ghana’s CEO delivering a speech on March 10, 2016 on the company’s performance for 2015 noted that “some of the telecom companies are not making money, so they are not paying corporate tax”. In fact, a number of the Telecom operators in Ghana have had their balance sheets under water. A significant number of the operators cannot even break even let alone make profit. Actors in the industry noted that the sector is struggling to stay afloat as profits plummeted and competition increases. The industry in the fourth quarter of 2015 and the first quarter of 2016 has witnessed an unprecedented layoffs and resignations as the Telecom companies restructured to stay in business. While some of the companies in the industry have been forced to let go of staff, others are struggling to pay staff.

In fact, majority of the Telecom companies had their balance sheet come crushing down and on the verge of falling into a financial precipice. The only way of escape is for telecom companies to explore multiple revenue generation channels and to formulate formidable key performance indicators to constantly measure their financial performance to ensure sustainability and growth. The need to understand the extent of relationships among commercial Key Performance Indicators (KPIs) and revenue has never been more critical than now in the Ghanaian Telecom industry. These relationships are pivotal to tracking and justifying the firms’ marketing priorities, which have come under growing pressure due to the magnitude of competition in the Ghanaian Telecom Sector. The objective of this study is to evaluate and assess the impact of commercial KPIs on the financial performance of a telecommunication company in Ghana. The key question to be explored by this study is to what extent does the commercial KPIs influences financial performance of MTN Ghana Ltd. As a means of contributing to the discussion of financial performance of telecom companies in Ghana, this study will bring on board, research outputs that would make a significant and original contribution to knowledge by the discovery of new facts, and or the innovative reinterpretation of known data and established ideas. This research, when completed would also contribute to the body of academic literature on the topic. This research would specifically produce body knowledge on how the commercial KPIs impact on MTN’s ability to generate enough revenue for sustainability and growth. The findings may have practical and policy implications for the management of various marketing and sales programs geared toward a synergic mix of the commercial KPIs.

2. REVIEW OF RELATED LITERATURE:

A healthy-dose of empirical research has been carried out to ascertain the financial performance of some selected firms. See Ahmadu et al (2005)², Trivedi (2010)³, Sumninde and Amiya (2013)⁴ and Gowri (2015)⁵. Two categories of literature exist in this regard. The first category uses firm level studies focusing primarily on key financial statement and non-financial indicators to measure the financial performance. Ananthi and Sriram (2012)⁶ conducted a financial performance evaluation of selected Telecom companies in India using the numerical taxonomy approach. The study deployed ratios analysis as indices to measure the financial performance and concluded that some telecom companies performed well in some ratios relative to others. Gupta (2015)⁷ also evaluated the financial performance of the Telecom companies in India with specific reference to BSNL. The study deployed trend analysis, comparative statement (balance sheet) and ratio analysis as suitable financial performance evaluation methods for the telecom industry.

Alkhatib (2012)⁸, empirically examined the financial performance of five Palestinian commercial banks listed on Palestine securities exchange (PEX) using annual time series data from 2005-2010. He deployed the correlation and multiple regression analysis to capture the impact of bank size, credit risk, operational efficiency and asset management on financial performance. He used Internal–based performance measured by Return on Assets, Market-based performance measured by Tobin’s Q model (Price / Book value of Equity) and Economic–based performance measured by Economic Value add as benchmark indicators to extensively examine the research interest. Pratheepkanth (2011)⁹ examined the impact of the capital structure on the financial performance of companies listed on the Colombo Stock exchange in Sri Lanka using data from 2005 to 2009. Using regression analysis, he finds that there exist a negative relationship between the capital structure and financial performance with a coefficient of -0.114.

Bhunia (2011)¹⁰ studied the financial performance of two public sector drug and pharmaceutical companies(KAPL and RDPL) listed on the BSE in India with a twelve-year data spanning from 1997-2009. The researcher deployed the accounting ratio analysis to analyze the financial performance in terms of liquidity, solvency, profitability and financial efficiency alongside regression analysis. From the study of the financial performance of the selected pharmaceuticals, the study disclosed that the liquidity position was strong in case of KAPL and RDPL thereby reflecting the ability of the companies to pay short term obligations on due dates. Ntow–Gyamfi and Afoley (2012)¹¹ examined the performance of foreign and local banks in Ghana along the following dimensions; Return on Assets, Return on Equity, Asset Quality, Capital Adequacy, Management Efficiency, Earning Performance, Liquidity and Bank size using data time series from 2005-2010.They deployed ratio analysis of the various financial metrics and compared the outcomes of the local and foreign banks. They find various differences in ratios for the two types. Their study employed the use of two profitability ratios. Vincent et al (2012)¹² investigated the impact of banks risk management structure on their performance during the crisis. Using a sample of 372 banks in 2007/2008, the study found that banks with Chief Risk Officer that reports directly to the board performed better relatively during the crisis period. They noted however that standard corporate governance did not improve performance during the crisis period. Marcia et al (2016)13 examined the impact of corporate social responsibility and financial performance of U.S commercial banks. The study found that financial performance has a positive relationship with corporate social responsibility. The relationship was found to be statistically significant. Allen et al (2006)¹⁴ examined the agency cost theory by investigating the relationship between the capital structure and performance of selected US commercial banks. Using a simultaneous equation model, the study found a causality running from capital structure and performance. Margaritis and Pssillaki (2010)15 examined the relationship between capital and ownership structures on firm performance of French manufacturing firms. Using a non-parametric data envelopment analysis, the study used performance measures to examine if more efficient firms uses more or less debt in their capital structure.

The second strand of literature attempts to examine the financial performance of Telecom companies using selected industry indicators. Anlesinya et al (2014)16 examined whether corporate social responsibility has significant positive effect on the financial performance of MTN Ghana Limited. The study administered questionnaire on 35 management staff of MTN Ghana Limited, employed standard multiple regression and hierarchical multiple regression for the analysis. The research results showed that CSR at the aggregate level did not have significant positive effect on financial performance but community CSR has a positive while environmental CSR has negative effect on financial performance of MTN Ghana Limited. The study however has left key performance indicators of MTN Ghana unexplored lending support for a more comprehensive study in that regard. Vadiraj and Narahari (2014)1 attempted to develop a model that could predict the future trends of average revenue per user (ARPU) so that telecom service providers could formulate a strategy to increase their ARPU. The study using a multiple linear regression has been able to explain that subscriber base; number of operators and percentage of new users added periodically are the main determinants of average revenue per user (ARPU). Rahul and Xue (2012)17 attempted to examine the relationship between some selected factors and their contribution to the revenue of the Telecom industries in China and India. Using time series data collected from secondary sources from 2000 to 2010 on number of subscribers, technology innovation, and government regulation and policies, their granger causality test found no causality running from number of subscribers to the revenue of the telecom Industry in both China and India. They however found a causality running from technological innovation to the revenue of the Telecom Industries in both countries. Shmelev (2013)18 developed a model for calculating Telecom Company’s revenue and margin indicators. The study crafted the model for calculating the revenue of Telecom companies based on the Business Metric Framework (BMF) developed by the TeleManagement Forum, a global non-profit association for service providers in the Telecommunication sector. Examining the relationship between the two categories of KPIs in the BMF, the study concluded that it is possible to create a function depending on the target KPIs lower levels, to calculate the final financial indicator at given rates and obtain a performance management tool based on key performance indicators.

To this end, it has been ascertained that the literature that explored the industry’s financial performance using commercial KPIs are very few if not rare. Unlike the previous literatures that explored financial performance of Telecom companies using financial statement analysis as well as selective indicators, this study follows the model developed by Shmelev (2013)18 to empirically ascertain the impact of identified commercial KPIs on financial performance of MTN Ghana. This study would fill the research gap by deploying an eclectic redefinition of the strategic and tactical KPIs developed in the Business Metric Framework and a careful selection of variables consistent with industry benchmark and the commercial activities undertaken by MTN Ghana to drive its revenue.

3. MTN GHANA LTD IN PERSPECTIVE:

3.1. Business Performance:

MTN Ghana is considered the Telecom giant in the Ghanaian economy controlling close to over an average of 51 % of market share (MTN Database). The company’s robust business model innovations over the decades have translated into a financial gain driving its sustainable growth among industry players relative to its competitors as depicted in the figure below.

Table 1

Promotional Strategy	Activity	Associated Commercial KPI
MTN Pulse and Chill Promo	Build Points every month on MTN Pulse and Chill with Beats by Dre Headsets, Movie Tickets, Shopping Vouchers and Airtime. Send CHECK to 567 to check your promo Points	Data usage
MTN Pulse Mash-up Bundles	Combo bundles	MOU and data usage
MTN Dream Number	Subscribe and win	Net adds, MOU
+MTN Special Sunday	Pay 50p/Ghc1 and Talk and Text all day Sunday	MOU
MTN Nkomode Onnet/Offnet)	Make calls and pay for only the first minute	MOU
Akwaaba Relaod	Gives bonus to new acquisitions	Net adds
Just For You offer	Stretched Usage Targets	Usage(Revenue)

3.3 MTN Business Analytics Roadmap

	KEY INITIATIVES	Q1	Q2		Q3	Q4
CUSTOMER ANALYTICS AND TARGETING	Deep Dive Customer Analysis	Data usage behaviour, Predictive Churn, Propensity to adopt Products and Services (MoMo & Data)
	Segmentation and Targeting	• Clustering & profiling and insight shared with commercial KPIs • With special focus on HV segment monitoring eg Top 20% subs based on CVS
	Embedding Analytics in the Business	Define mechanisms to drive analytical operating model (forums, reporting, etc.) Define Training plan for key managers	Key managers training in embedding analytics	Develop an Analytical operating model to be embedded within business

Source: MTN Ghana

The customer analytics is aimed at enhancing locational performance analysis to monitor and evaluate all commercial KPIs at all geographical areas. The embedded analytics across the MTN Business will also optimize resources utilization to drive sustainable revenue growth.

4. EMPIRICAL MODEL AND DATA:

4.1. Data:

The main motivation of the research is to extract the behavioral patterns of the commercial KPIs in the Telecom Industry to understand their dynamic impact on revenue generation over time. This paper uses various commercial KPIs from MTN data warehouse to investigate the hypothesis. Time series data on five commercial KPIs namely Monthly Revenue Generating Subscribers(RGS30), Minute of use(MOU), SMS Count, Net Adds and Data Volume were used. Service revenue, which is an aggregation of Voice, Data, SMS and Value Added Services (VAS), is defined by this study as a proxy variable to measure financial performance in the Telecom industry. The study used a sample period from January 2012 (2012m01) to December 2015 (2015m12) on a monthly data frequency. The variables in their raw state are not seasonally adjusted.

4.2. The Econometric Model:

Time series variables are characterized by non-stationarity which presents serious challenges for most dynamic econometric models. Stationarity causes the variables to be mean-reverting but non-stationary causes temporal shocks to persist into the long run (Dimitrios and Stephen, 2011)¹⁹. The Augmented Dickey-Fuller (ADF) test of unit root was carried out and the result indicates that the variables are not stationary in their levels but in their first difference. The variables therefore follow an I(1) process.

4.2.1. DCC GARCH:

Engle (2002)²⁰ in his study noted that correlations are crucial for most of the common tasks of financial management. Asset allocations and risk assessment also depends on correlations. The need for a reliable estimate of correlation between financial variables had been a motivation for researchers in both academia and industry. But it turns out that the assumption of constant conditional correlation over time seems too restrictive for practical purposes like the study under examination. Consequently, many researchers and authors have advocated for models with time varying conditional correlation; a result of which the DCC-GARCH by Engle (2002)²⁰ has become the most widely used model with time varying conditional correlation. The study pointed the DCC-GARCH as a new class of multivariate model which has the flexibility of a univariate GARCH model along with a parsimonious parametric model. Technically, the multivariate GARCH model generalizes the univariate GARCH model and allow for a relationship between the volatility processes of multiple series. Thus knowing how changes in the volatility of one commercial KPI affect the volatility in financial performance measured by the revenue generation requires such parsimonious model. Engle (2002)20 concluded that the model performs well in different situations and also provides a sensible empirical result. The paper would deploy this model for the analysis and determine how the volatilities in the commercial KPI are correlated with the volatilities in revenue. The DCC-GARCH model is deemed suitable for this study since it is of utmost interest to examine the uncertain movement in revenue on account of the movements in the selected commercial KPIs. To circumvent the problem of unit root in time series data, the log difference of the variables was taken as a precursor to the estimation. Besides, the chow break point test was carried out and the result indicated that the dataset does exhibit structural changes as seen in fig 2 in the appendix.

4.2.2. Multiple Regressions (Rolling Window):

For purposes of financial decision making and forecasting the behavior of commercial KPIs on revenue generation, a multiple regression model is deployed. The rolling window regression is deemed appropriate in this regard to account for changes in promotional programs and business model dynamics that are likely to drive the commercial KPIs across time.

The asymptotic normality test was carried out and the result indicates that the data follows a normal distribution pattern. The nonlinear model defined above was informed by the relationship exhibited by the commercial KPIs with revenue in the scatter plot shown in the appendix.

5. EMPIRICAL RESULT AND ANALYSIS:

5.1. Estimation from DCC- GARCH:

The DCC-GARCH model can be useful in carrying out various empirical exercises in relation financial variables particularly from volatility clustering, correlations to in and out-of-sample forecasting. For the purposes of this study, the volatility and correlation components would be exclusively utilized. The log-likelihood function of the model is therefore decomposed into two parts, consisting of the volatility component and the correlation component. The result from the estimation indicates that the parameters in the volatility component for almost all the independent variables are statistically significant at about 5% significant levels. It can therefore be concluded that the selected commercial KPIs exhibits a substantial amount of volatility and that periods of high volatility can be followed by periods of low volatility.

Though the variables exhibit much volatility, the trend seems to cluster around the mean with net adds portraying high volatilities relative to the other commercial KPIs. The volatility became more pronounced and featured prominently 2011 m06 to 2014 m03. The clustering of the volatiles around the mean could cause a constant conditional trend among the commercial KPIs. The assumption could be ascertained by examining the correlation trend between the dependent KPI variable and the independent KPIs.

The second and most important part for this study is the correlation component. The conditional quasi correlation between the standardized residuals of the MTNs financial performance measured by revenue generation and the selected commercial KPIs is presented in table 2.

The quasi correlation between the commercial KPIs and revenue is aptly demonstrated on fig 3. RGS30 is highly correlated with revenue with coefficient of 0.58 followed by Minute of Use with a correlation of 0.43. SMS count and data volume have moderate correlations with revenue with coefficients of 0.35 each. The correlation of Net Adds with revenue is relatively low and this is explained by the saturated market. Current market penetration is 119% (NCA, Sept 2016) hence new acquisitions, which is the basis of Net Adds, are mostly secondary users.

The correlation coefficients are positive and statistically significant at 1% level except for net adds that is significant at 10% level, The estimated conditional correlation between the volatilities in revenue and the other commercial KPIs is moderately high on average and positive. This indicates that a high volatility in the financial performance of MTN Ghana Ltd is associated with high volatility in the independent commercial KPIs.

Table 3 shows the output of the DCC GARCH I(1) process.

Correlation Relationship	Coeffici-ent	Z-statistcis	Standard Error
(Revenue and RGS30)	0.584	7.85**	0.744
Revenue and SMS Count	0.355	3.58**	0.099
Revenue and data volume	0.347	3.50**	0.099
Revenue and Minute of Use	0.430	4.70**	0.091
Revenue and Net adds	0.210	1.91*	0.110

**Significant at 1% level. * Significant at 10% level

Surprisingly, the adjustment parameters from the output table are not statistically significant. The estimated Wald test cannot reject the null hypothesis that lamda1 and lamda2 equals zero. This indicates that the data cannot reject the assumption of time invariant conditional correlation maintained in the Constant Conditional Correlation MGRACH model. The DCC MGARCH model therefore reduces to CCC MGARCH Model. A valid conclusion can be drawn from the model that, the correlation between the commercial KPIs and financial performance of MTN Ghana Ltd is constant overtime.

5.2. Estimation from Rolling Window Regression:

To lend support to the output presented by the DCC GARCH model in the previous section, a 5-year rolling window regression was carried out to determine the causal trend (true coefficient) between the commercial KPIs and financial performance overtime. The paper attempts to estimate the coefficient for time period t by estimating the regression using observations [t-w/2….t+w/2] where w is defined as the window width. For a given window width, we roll through the sample space using w observations for estimation. The rolling estimates become the true regression coefficient plus sampling error. Fluctuations in the estimates could be just a mere manifestation of error if the sampling error if large. However, if the true coefficients are trending, then we expect the estimated coefficients to exhibit trend plus noise. That notwithstanding, if the true coefficients are constant, then we expect the estimated coefficients to portray a random fluctuation and noise as found in the data for this empirical exercise. The output result from the rolling regression model is depicted in fig 4 below. The result is also consistent with the estimation from the DCC GRACH model above.

The purpose of this analysis is to ascertain the degree of causality or interrelation between the selected commercial KPIs and financial performance proxy by service revenue. The result shows that MTN’s revenue performance is associated with the number of revenue generating subscribers and Minutes of Use more than SMS count, Data Volume and Net Adds. Interestingly, RGS30 and MOU maintained a strong positive causality with revenue performance over the entire 5-year rolling period. SMS count also maintained similar positive position on average during the period. The other commercial KPIs exhibited a negative causality over the sample period. This presents a puzzle which would be ascertained in subsequent empirical exercise.

6. CONCLUSION AND RECOMMENDATION:

An assessment of the financial performance of Telecom Companies in Ghana has become imperative. The challenges bedeviling the industry, including market saturation and high competition along with internal structural problems call for a holistic assessment of the industry’s financial performance. Rethinking into a more robust and a cascading method of financial performance analysis has become necessary. The recent crisis of most telecom companies in Ghana has proven that the traditional and conventional approaches of mainstream finance theories are ill-equipped to support corporate management decision making capabilities in the industry. The study hypothesized an unconventional method of using selected commercial KPIs as a threshold for financial performance measurement within the industry. The hypothesis was examined through an empirical exercise to ascertain the degree of interrelatedness and causality that runs from the selected commercial KPIs to revenue performance of the Telcos. The approach itself cannot replace the conventional approach used by the mainstream financial management, but rather as a supporting technique. The study deems the approach a potent technique in that, it would provide corporate management within the industry a decision tool to juxtapose the right mix of revenue and expenditure patterns.

To ensure robustness of the technique, two financial econometric models were deployed. MTN Ghana, the leading Telecom Company which has had a remarkable financial performance over the period was used in the study. First the DCC GARCH model was used to examine the volatility clustering and the correlation pattern that runs from the commercial KPI to the revenue performance. The result indicates that the estimated conditional correlation between the volatilities in revenue and the other commercial KPIs is moderately high on average and positive. Thus the study found a strong association of financial performance with the industry’s commercial KPIs. Second, a 5-year rolling window regression was carried out to ascertain the true coefficient of adjustment between the commercial KPIs and financial performance. The result was consistent with the findings from the DCC GARCH model but with a puzzle. Against this background a valid conclusion could be drawn that the commercial KPIs are suitable estimates for financial performance.

Premised on the conditional relationship between the commercial KPIs and financial performance, this study recommends that MTN’s corporate management segregate the commercial KPIs into strategic KPIs and operational KPIs. Commercial KPIs with high predictive capability to revenue must be classified under the strategic domain while those with moderate and low conditional correlation are classified under the operational KPI. Financial decision on strategic innovative business models can be anchored to the strategic and operational KPIs in suitable proportions. This system when properly implemented, will contribute to the delivery of the institution’s commitment to improving value for money and its financial forecasting capacity. Financial forecasting and cash-flow management processes will be further developed in order to improve longer-term planning.

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