Haverland Electric Heater Market Analysis

This assignment requires an analysis of Haverland's French subsidiary's strategies in the electric heating market. It involves examining sales data to identify trends, assessing their market position, and evaluating their communication tactics, including online presence and social media usage.

Running head: STATISTICS
Statistics
Name of the Student:
Name of the University:
Author’s note:

1STATISTICS
Table of Contents
Question and Answers of “Haverland” Case Study.....................................................................................................................................................2
Answer No. 1:...........................................................................................................................................................................................................2
Answer No. 2:...........................................................................................................................................................................................................2
Answer No. 3:...........................................................................................................................................................................................................3
Answer No. 4:...........................................................................................................................................................................................................4
Answer No. 5:...........................................................................................................................................................................................................5
References:...................................................................................................................................................................................................................7

2STATISTICS
Question and Answers of “Haverland” Case Study
Answer No. 1:
Current Situation and SWOT Analysis:
In spite of the maturity, the electric heating market in France is still rising. The establishment is driven by new machinery and technology
that carries out pioneering energy-efficient products. In recent days, Housing construction is an additional factor that is increasing the growth. It
is believed that the market would prolong to enhance in coming twenty years. The challenges regarding heating represent an individual
household along with businesses. This apprehension is linked to the challenges of circumstances and exigency to develop energy-efficient
solutions.
SWOT analysis mainly focuses to highlight strength, weakness, opportunity and threat of any incident or any subject. In accordance to
the SWOT analysis of heating market,
Strength: The strength of “Haverland” Electric-heater Company in France is that the company is lucrative and specifically renovated. The
quality of insulation is very important for maintaining energy consumption and cost down. The directive of electric heating installation has
granted conformity with new rules that requires the use of extremely proficient heating tools. More of it, a full range of electric inertia heaters is
available to cover up all the sectors of market. The availability of different types of radiators is the strength of electric heating systems along
with affordability, efficiency and reliability.
Weakness: Staffs must know the affects of performance of company. Otherwise, new obstacles for electric heating would gradually become
accountable. The deficient of innovation might be a great matter of question.
Opportunity: A wide range of electric inertia radiators, electric towel rails, electric radiant heating panel and natural convection heaters make
their major launch in France. A wide range of electric inertia heaters is ready to cover all the market segments. Selling market of gas heating
systems has a vast prospect of huge improvement. The opportunity is the increasing trend value of heating projects. The weather condition is
significantly influencing the acquisition of equipment. “Haverland France” has structured brand awareness through their dealers. Haverland’s
current communicating strategy has an objective to be cheap and efficient. Hence, the online communication is an excellent choice of media for
the heating company.
Threat: The requirement for steady renovation creates a modern business circumstances. However, “Haverland” has no privacy or discretion
clauses about the environmental facets. The strategic move of Spanish heating companies of the market is a threat for this French company
(Persson and Werner 2011).
Answer No. 2:
Haverland’s Customer Profile:
The customers of “Haverland” customer profile depends upon various types of heating markets that are Heating system, Gas, electricity,
Electricity and wood as supplement, renewable energy or hybrid, renewable energy combined with other energies and other heating systems.
Commitment of the company is to enlarge international markets led to strong enlargement of network of commercial agents and distributors.
Sustainable consumption for insulation work or equipment choice is synonymous with the opulence of consumers to select high-end
technology for their heating systems. Household consumption of energy for heating depends on housing types and socio-demographic factors
such as level of income and different structural processes. The substitution of equipments eventually depends upon available new products.
Customers have a propensity to link up the performance and cost-effective attributes of electric heating with the most-possible comfort or
expediency afforded by other systems. In the ground of reality, a large majority of people selects the system that is least costly for installation

3STATISTICS
and maintain. Financial considerations supersede the demands of customers for producing environment friendly choices. There are also other
labels of safety and performance standards. Consumers must ensure electric heater and radiators as safe and reliable.
The percentages of electric heating in electric consumption is 28% for Buildings, 30% for Houses, 10% for Council houses, 50% for
second homes and 35% for Empty property in France. A large proportion of energy consumption could be regulated when consumers started
thinking about heating requirements of future with the promising period of heating installations. Households began to anticipate their future
heating requirements with according to the seasonality of market.
Answer No. 3:
Month Wise multiplicative decomposition
method
Column
1
Colum
n 2
Column 3 Colum
n 4
Column
5
Column
6
Column 7 Column 8 Column 9
Year Month Time Cases MA CMA Sn*e Sn final Sn
2012 1 1
49451
1.5374816
6
2 2
60045
2.2635498
9
3 3
10584
35493.7
5
31772.87
5
0.3331143
3
0.4645596
5
0.7329619
4 4
21895
28052 25696.62
5
0.8520574
2
0.8750541
2
1.3806221
1
5 5
19684
23341.2
5
27426.5 0.7177000
3
0.7634629
7
1.2045584
8
6 6
41202
31511.7
5
36291.12
5
1.1353189 1.2001841
7
1.8935980
9
7 7
43266
41070.5 50158.87
5
0.8625791
5
0.8321703
1
1.3129619
1
8 8
60130
59247.2
5
65362.5 0.9199464
5
0.8515812
6
1.3435876
6
9 9
92391
71477.7
5
78616 1.1752187
8
1.2376060
9
1.9526407
7
10 10
90124
85754.2
5
90492.37
5
0.9959292
2
1.0109541
4
1.5950392
2
11 11 10037
2
95230.5 95136.5 1.0550314
5
1.0550314
5
1.6645824
9
12 12
98035
95042.5 98255 0.9977609
3
0.9977609
3
1.5742235
5
2013 1 13
91639
101467.
5
94039.5 0.9744734
9
0.9744734
9
1.5374816
6
2 14 11582
4
86611.5 80732.5 1.4346638
6 1.4346638
6
2.2635498
9
3 15
40948
74853.5 68704.12
5
0.5960049
7
0.4645596
5 0.7329619
4 16
51003
62554.7
5
56793 0.8980508
2
0.8750541
2
1.3806221
1
5 17
42444
51031.2
5
52450.12
5
0.8092259
1
0.7634629
7
1.2045584
8
6 18
69730
53869 55120.37
5
1.2650494
5
1.2001841
7
1.8935980
9
7 19
52299
56371.7
5
65230.12
5
0.8017614
6
0.8321703
1
1.3129619
1
8 20
61014
74088.5 77901.87
5
0.7832160
7
0.8515812
6
1.3435876
6
9 21 11331
1
81715.2
5
87162.75 1.2999934 1.2376060
9
1.9526407
7
10 22 10023
7
92610.2
5
97698.87
5
1.0259790
6 1.0109541
4
1.5950392
2
11 23
95879
102787.
5
1.6645824
9
12 24 10172
3
1.5742235
5
Averag
e
18.933075
2
Note: MA= moving average CMA= centred moving average Sn = seasonal estimate e = error
Computation of the normalization factor
L = number of months in the year = 12
Normalization factor = L/(sum of average monthly estimates)
L/12 = 1.577756
Monthly multiplicative indices of 2014 are-
January 1.537482
February 2.26355
March 0.732962
April 1.380622
May 1.204558

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June 1.893598
July 1.312962
August 1.343588
September 1.952641
October 1.595039
November 1.664582
December 1.574224
(Box et al. 2015)
Answer No. 4:
Month Wise Sales
data of “Haverland”
Year
Janua
ry
Febru
ary March April May June July
Augu
st
Septe
mber
Octob
er
Nove
mber
Dece
mber
Annual
Total
2012 49451 60045 10584 21895 19684 41202 43266 60130 92391 90124
10037
2 98035 687179
2013 91639
11582
4 40948 51003 42444 69730 52299 61014
11331
1
10023
7 95879
10172
3 936051
Totals
14109
0
17586
9 51532 72898 62128 110932 95565
12114
4
20570
2
19036
1
19625
1
19975
8
162323
0
Monthly
Averages 70545
87934
.5 25766 36449 31064 55466
47782.
5 60572
10285
1
95180.
5
98125
.5 99879
67634.
58333
Monthly
Indices
1.043
03149
1.300
14108
0.3809
58952
0.538
91069
0.459
29166 0.820083414
0.7064
80289
0.895
57734
1.5206
86532
1.4072
75617
1.450
81843
1.476
74452
Deseasonalization of the monthly sales using seasonal
indices
Yea
r Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec
201
2
47410.84
1
46183.45
0 27782.521
40628.25
3
42857.29
9
50241.23
1
61241.62
4
67141.04
7
60756.11
1
64041.47
1
69183.02
0
66385.89
1
201
3
87858.32
6
89085.71
7
107486.64
6
94640.91
3
92411.86
8
85027.93
6
74027.54
3
68128.12
0
74513.05
6
71227.69
6
66086.14
7
68883.27
6
(Verbesselt et al. 2010)
By linear regression equations: b a
7845.288244 -1011.705
Sales=b(Season)+a
The linear regression equation for model trend in this case
is- Sales = 7845.288244 *(t) - 1011.705
We may need to check r2 to decide on the goodness of the
fit.
Generation of the deseasoned monthly forecasts for 2014
Year January February March April May June July August
Septemb
er October
Novemb
er
Decemb
er
2014
195120.
501
202965.
789
210811.
078
218656.
366
226501.
654
234346.
942
242192.
231
250037.
519
257882.
807
265728.
095
273573.
383
281418.
672
Seasonality
indices 1.043 1.300 0.381 0.539 0.459 0.820 0.706 0.896 1.521 1.407 1.451 1.477
The monthly forecasts for
2014
Yea
r January February March April May June July August
Septemb
er October
Novemb
er
Decemb
er
201
4 203516.826 263884.160
80310.3
67
117836.2
53
104030.3
20
192184.0
40
171104.0
37
223927.9
35
392158.9
12
373952.6
69
396905.3
07
415583.4
80
201
2 49451 60045 10584 21895 19684 41202 43266 60130 92391 90124 100372 98035
201
3 91639 115824 40948 51003 42444 69730 52299 61014 113311 100237 95879 101723

5STATISTICS
January February March April May June July August September October November December
0.000
50000.000
100000.000
150000.000
200000.000
250000.000
300000.000
350000.000
400000.000
450000.000
203516.826
263884.160
80310.367
117836.253104030.320
192184.040
171104.037
223927.935
392158.912373952.669
396905.307
415583.480
Month wise estimated sales of 2014
Year 2014
Monthly predicted sales of 2014
January February March April May June July August Septembe
r October November December
0.000
50000.000
100000.000
150000.000
200000.000
250000.000
300000.000
350000.000
400000.000
450000.000
Scatterplot of sales of three years
2014
2012
2013
Months
Monthly Predicted Sales
The calculations and results infer that the month wise sale of electrical products would grow in 2014 rather than 2012 and 2013.
However, in March 2014, the sale is going to be less than March 2013. The sale of September to December in 2014 is going to be significantly
greater than sales of September to December 2013. The heating market is found to be seasonal from November to March in both 2012 and 2013.
Sales of all types of heating system are peak in February, October and November. Households focus on their heating investment projects from
May to August.
Answer No. 5:
The loyalty strategy is utilised for dealers and fitters of electrical suppliers who are wholesalers and electricians. Almost 35% of
households of France use electricity as its power source. The utilization of electricity in domestic purpose and primary power resources have
grew in recent days. It is helpful to distinguish between individual private housing where individuals choose their own heating system and
collective housing or shared building. Consumers would be perfectly suggested for considering their preference of heating system with minute
care (Dietz et al. 2009). This occurred because new suburban or commercial buildings would take at least 20 to 25 years for it.

6STATISTICS
As Haverland is one of the leading companies in the manufacturing sector and sale of electric heaters, the French subsidiary receives
advantages from the expertise and information of the Spanish company. The cost strategy of “Haverland” is helping customer needs, as they
believe that the customer association is supreme. Although the company has an international perspective, the ability is to meet local necessities is
a foremost achievement factor for their strategy.
Electric heating suppliers mainly communicate via internet and on television. “Haverland” like other French companies have their own
website for delivering constant and nonstop communication to their customers. In the modern world, social networks are the key communicating
media to incorporate business. “Haverland” accordingly represent themselves on Facebook, Twitter and Youtube. The use of social networks
allows brands to uplift their vision, promote loyalty and better understand their consumers.

7STATISTICS
References:
Box, G.E., Jenkins, G.M., Reinsel, G.C. and Ljung, G.M., 2015. Time series analysis: forecasting and control. John Wiley & Sons.
Dietz, T., Gardner, G.T., Gilligan, J., Stern, P.C. and Vandenbergh, M.P., 2009. Household actions can provide a behavioral wedge to rapidly
reduce US carbon emissions. Proceedings of the National Academy of Sciences, 106(44), pp.18452-18456.
Persson, U. and Werner, S., 2011. Heat distribution and the future competitiveness of district heating. Applied Energy, 88(3), pp.568-576.
Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D. (2010). Detecting trend and seasonal changes in satellite image time series. Remote
sensing of Environment, 114(1), 106-115.