Business consultancy project
Metrics Azercell
- Bu səhifədəki naviqasiya:
- By 4 Operators Annual
- Market Penetration 2017 2018 2019
- 1stQ 2ndQ 3rdQ 4thQ 1stQ 2ndQ 3rdQ 4thQ 1stQ 2ndQ 3rdQ
- Deseasoalizing
- Revenue Period SMS
| Metrics
|
|
Azercell |
|
|
By 4 Operators | |||
|
| ||||||||
|
Annual Growth Rate |
2017 |
2018 |
2019 |
AAGR |
2017 |
2018 |
2019 |
AAGR |
|
GSM Connection |
-11% |
-18% |
-24% |
-18% |
2% |
-13% |
-17% |
-9% |
|
Mobile Broadband |
30% |
27% |
21% |
26% |
23% |
23% |
18% |
22% |
|
3 G Connection |
6% |
6% |
5% |
6% |
11% |
7% |
4% |
7% |
|
4 G Connection |
67% |
66% |
41% |
58% |
95% |
82% |
48% |
75% |
|
Net addition |
114% |
18% |
16% |
49% |
52% |
25% |
20% |
32% |
|
Market Penetration |
2017 |
2018 |
2019 |
AAGR |
2017 |
2018 |
2019 |
AAGR |
|
GSM Connection |
22% |
18% |
14% |
18% |
54% |
47% |
39% |
46% |
|
Mobile Broadband |
22% |
28% |
34% |
28% |
50% |
62% |
72% |
61% |
|
3 G Connection |
17% |
19% |
20% |
19% |
13% |
14% |
14% |
14% |
|
4 G Connection |
6% |
10% |
13% |
10% |
3% |
5% |
7% |
5% |
|
Smartphone |
45% |
52% |
56% |
51% |
27% |
35% |
45% |
36% |
|
Features phones |
55% |
48% |
44% |
49% |
67% |
61% |
53% |
60% |
Simultaneously, the mobile broadband expansion and smartphone penetrations have secure connections as a raise of one metrics positively impact the other one. At the same time, the enhanced data usage affirmatively influenced value-added service consumption as well. The data increase and VAS revenue concurrently express the ascending growth trend. Presumably, there are indirect bounds among the three variables.
Figure 39.Correlation between Smartphone users and DATA and VAS Revenue.
|
1stQ 2ndQ 3rdQ 4thQ |
1stQ 2ndQ 3rdQ 4thQ |
1stQ 2ndQ 3rdQ 4thQ |
1stQ 2ndQ |
|
2016 |
2017 |
2018 |
2019 |
Meanwhile, the juxtaposition, as mentioned above of the three indicators shows the positive correlation among them. The smartphone user raises positively influenced the Data and VAS revenue metrics, with
0.71 and 0.64 R -Squared values, respectively. Simultaneously, there is also a strong correlation between Data and VAS revenue as both revenue streams are gradually increasing. The squared value between two variables is 0.71.
Figure.40 Correlation between SMS and DATA /VAS Revenue.
|
1stQ |
2ndQ 3rdQ 4thQ |
1stQ |
2ndQ 3rdQ 4thQ |
1stQ |
2ndQ 3rdQ 4thQ |
1stQ 2ndQ |
|
|
2016 |
|
2017 |
|
2018 |
2019 |
Conversely, the comparison of SMS revenue with VAS and Date revenue expressed a strong negative correlation. The R-squared value between SMS and Data revenue is (0.92) while between SMS and VAS revenue is (0.63). Compared with VAS and Data revenue SMS revenue showed the opposite trend, and the critical change happened in the Y 2017 when the SMS revenue started the gradual descending trend,
and Data revenue kept the steady upstream propensity. Meanwhile, the considerable change in VAS revenue occurred from Y2018.
Prediction analysis.
The outcome of descriptive and diagnostic analyses justifies the assumption of revenue stream alteration. The evaluation of historical and current data interpreted Azercell` s revenue generators existing position, and the comparison of SMS and airtime metrics with Data and Value-Added services outlines the traditional revenue streams subdue with the new ones. The digressive trend of SMS traffic is endorsed by Data traffic upward trend. In comparison with Data and SMS traffic, Airtime displayed the fluctuation trend by ascending and descending disposition by the influence of the various factors.
Meanwhile, for the explicit comprehension of future trends, the historical data assessment is not enough. Based on the historical data trendline, the future prediction of key revenue generators should be executed. Although the modern mobile telecom industry is very fragile to the market dynamics, the appropriate prediction of revenue generators can assist the specifying the future position of the company in terms of revenue streams.
The prediction analyses of the remaining semiannual and the whole Y 2020 were executed according to the evaluation of 4 years data for the period January 2016-June 2019 through the Time series forecasting model components. (Lind, Marchal, & Wathen, 2012) Time series forecasting model assists in forecasting the future data by application the relevant patterns. As the first step, the historical data was calculated by defining the Moving average pattern for each quarter and year correspondingly. The moving average assisted in defining the trend line by eliminating seasonality impact and deviation of quarterly data. The next implemented pattern was Seasonality component for defining the seasonality index by the original data and the central moving average. The seasonality index emphasized the seasonality fluctuations over the one year for the adjusting seasonality effect through Deseasoalizing component. The linear trend regression was used by specifying the trend line through trend equation Y=a+bt in the Forecast pattern. Y was the projected trend, and a was the intercept of Y, b was the slope of the line, the average change and t was the value of selected time. The deseasonalized traffic was considered as dependent variable Y, whereas the period was defined as the independent X variable, and the future trend line calculated according to the Y=a+bt equation. As the last pattern, the forecast of the Y 2019 semiannual and Y 2020, the total projection was done by multiplying the trend to the seasonality index. The revenue of all metrics, SMS, Airtime, Data, and VAS were calculated according to the time series forecast components.
Table.13 Actual/ Forecast Figures (2017-2020)
|
Revenue |
Period |
SMS |
Voice |
Data |
VAS |
|
Actual |
2017 |
-16% |
-2% |
38% |
4.4% |
|
Actual |
2018 |
-20% |
0% |
34% |
3.6% |
|
Actual/Forecast |
2019 |
-24% |
-1% |
17% |
7.3% |
|
Actual/Forecast |
2020 |
-33% |
-1% |
17% |
9.0% |
According to the time series forecast prediction, the forecasted data is close to the original data. The projection confirms the SMS revenue ongoing decrease in the next 18 months periods. Together with SMS, the Voice revenue will also diminish but compared to SMS, the downward trend will not be so aggressive, and the marginal decrease will be 1%. The Data and VAS revenue will keep the steady ascending trend in further periods. Even though the forecast trendline coincides with actual data, the explicit prediction for the 18 months cannot be achieved. The Mobile Telecom Market is very sensitive to any external factors and can be exacerbated with any economic variations.
Figure 41.Data revenue Time Series Forecast.
Figure.42 VAS revenue Time Series Forecast.
In addition to the internal data projection, the overall Azerbaijan Mobile Telecom Industry and peculiar Azercell market forecast for the next two years were assessed based on GSMA Intelligence prediction data. According to the GSMA data, GSM (2G) growth and market penetration will express the downward trend (GSMA Intelligence Data, 2019). The AAGR rate will decrease by 30% and 23% in Azercell and overall country outcomes, respectively. Concurrently, the market share of 2G will also diminish and consist of 8% of the market share. Meanwhile, the Mobile Broadband the annual growth rate and market penetration outcomes by Azercell and by country will express the upwards tendency and also, the 4G connection will predominant the 3G connection. Simultaneously, with the extension of mobile broadband, the smartphone will strengthen the position in the market by mitigating the features phones position.
Table.14 Predicted Growth and Market Penetration Metrics
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