CONCLUSIONS (Student 1) This section should include a clear, concise statement of the significant findings of the work, generally in order of importance. The conclusions are taken from the major points of the discussion. Conclusions are frequently followed by recommendations for improving the experimental procedure or for future work, building on the results of the study. Recommendations should be specific and justified technically by the results and discussion of the experiment. Results Experimental Data Location Volume Collected [L] Time Elapsed [s] Flow Rate [m^3/s] Distance [m] Diameter [mm] Area of Pipe [m^2] Total Head [m] Static Head [m] Dynamic Head [m] Velocity [m/s] Constant Coefficient Total Pressure Trial 1 1 20.0 272 0.000074 0.0000 25 0.0004909 0.185 0.172 0.013267 0.5102 0.2955 0.175 2 20.0 272 0.000074 0.0603 13.9 0.0001517 -0.068 -0.096 0.027047 0.7284 0.6694 0.175 3 20.0 272 0.000074 0.0687 11.8 0.0001094 -0.581 -0.621 0.040135 0.8874 0.7625 0.175 4 20.0 272 0.000074 0.0732 10.7 0.0000899 -1.390 -1.445 0.054828 1.0372 0.7935 0.175 5 20.0 272 0.000074 0.0811 10 0.0000785 -1.81 -1.871 0.052339 1.0134 0.9297 0.175 6 20.0 272 0.000074 0.1415 25 0.0004909 0.153 0.137 0.016420 0.5676 0.2656 0.154 Trial 2 1 20.0 153 0.000130 0.0000 25 0.0004909 0.043120 0.9198 0.2895 0.225 2 20.0 153 0.000130 0.0603 13.9 0.0001517 0.034723 0.8253 1.0437 0.223 3 20.0 153 0.000130 0.0687 11.8 0.0001094 0.039319 0.8783 1.3609 0.225 4 20.0 153 0.000130 0.0732 10.7 0.0000899 0.148809 1.7087 0.8507 0.223 5 20.0 153 0.000130 0.0811 10 0.0000785 0.093390 1.3536 1.2296 0.221 6 20.0 153 0.000130 0.1415 25 0.0004909 0.054 1.0293 0.2587 0.15 Analysis of Experimental Data As shown in the figure below, it is apparent that the right side and left side of the variation share an inverse correlation. We calculated the variation using equation 12 and compared it to the theoretical variation. We were given both A1 and A5 to calculate the theoretical variation.  We found that the theoretical variation gave a gave a negative results. Figure 1 - Variation Comparison From the figure shown below, we can see that the plot is quite steady for the low discharge pressure head compared to the high discharge plot. For the low discharge plot, the measured and calculated pressure head are relatively horizontal until about point 5 where it starts to drop. As for the high discharge plot, the values for calculated and measured are not consistent. We used equation 3 to calculate the pressure head. Figure 2a – Pressure head Figure 2b – Pressure head Using the measured and theoretical flow rates, the discharge was determined using a linear regression analysis. The correlation are positive for both the low and high discharge plot. Equation 6 and 7 were used to calculate the flow rate. Figure 3a – Flow rate Figure 3b – Flow rate Discussion For the divergent flow, it is not valid along the 14 degree tapered section upstream because there.

1. CONCLUSIONS (Student 1)
This section should include a clear, concise statement of the
significant findings of the work, generally in order of
importance. The conclusions are taken from the major points of
the discussion. Conclusions are frequently followed by
recommendations for improving the experimental procedure or
for future work, building on the results of the study.
Recommendations should be specific and justified technically
by the results and discussion of the experiment.
Results
Experimental Data
Location
Volume Collected [L]
Time Elapsed [s]
Flow Rate [m^3/s]
Distance [m]
Diameter [mm]
Area of Pipe [m^2]
Total Head [m]
Static Head [m]
Dynamic Head [m]
Velocity [m/s]
Constant
Coefficient
Total Pressure
Trial 1

2. 1
20.0
272
0.000074
0.0000
25
0.0004909
0.185
0.172
0.013267
0.5102
0.2955
0.175
2
20.0
272
0.000074
0.0603
13.9
0.0001517
-0.068
-0.096
0.027047
0.7284
0.6694
0.175
3
20.0
272
0.000074
0.0687
11.8
0.0001094

3. -0.581
-0.621
0.040135
0.8874
0.7625
0.175
4
20.0
272
0.000074
0.0732
10.7
0.0000899
-1.390
-1.445
0.054828
1.0372
0.7935
0.175
5
20.0
272
0.000074
0.0811
10
0.0000785
-1.81
-1.871
0.052339
1.0134
0.9297
0.175
6

4. 20.0
272
0.000074
0.1415
25
0.0004909
0.153
0.137
0.016420
0.5676
0.2656
0.154
Trial 2
1
20.0
153
0.000130
0.0000
25
0.0004909
0.043120
0.9198
0.2895
0.225
2
20.0
153
0.000130
0.0603
13.9
0.0001517

5. 0.034723
0.8253
1.0437
0.223
3
20.0
153
0.000130
0.0687
11.8
0.0001094
0.039319
0.8783
1.3609
0.225
4
20.0
153
0.000130
0.0732
10.7
0.0000899
0.148809
1.7087
0.8507
0.223
5

6. 20.0
153
0.000130
0.0811
10
0.0000785
0.093390
1.3536
1.2296
0.221
6
20.0
153
0.000130
0.1415
25
0.0004909
0.054
1.0293
0.2587
0.15
Analysis of Experimental Data
As shown in the figure below, it is apparent that the right side
and left side of the variation share an inverse correlation. We
calculated the variation using equation 12 and compared it to

7. the theoretical variation. We were given both A1 and A5 to
calculate the theoretical variation. We found that the
theoretical variation gave a
gave a negative results.
Figure 1 - Variation Comparison
From the figure shown below, we can see that the plot is quite
steady for the low discharge pressure head compared to the high
discharge plot. For the low discharge plot, the measured and
calculated pressure head are relatively horizontal until about
point 5 where it starts to drop. As for the high discharge plot,
the values for calculated and measured are not consistent. We
used equation 3 to calculate the pressure head.
Figure 2a – Pressure head
Figure 2b – Pressure head
Using the measured and theoretical flow rates, the discharge
was determined using a linear regression analysis. The
correlation are positive for both the low and high discharge
plot. Equation 6 and 7 were used to calculate the flow rate.
Figure 3a – Flow rate
Figure 3b – Flow rate
Discussion
For the divergent flow, it is not valid along the 14 degree
tapered section upstream because there is a large fluctuation
between point 5 and 6 and also the static and total pressure must

8. maintain its accuracy.. However, for the converging flow it is
valid at 21 degree tapered section upstream because the
difference between the values on point 1 and 2 are minimal. The
assumptions made in deriving the Bernoulli equation are
incompressible, frictionless and steady flow. As shown in the
table, the static head and dynamic head both fluctuated greatly
from one point to another. Also, the velocities increases
steadily from point 1 to point 5 but drops significantly at point
6.
The difference between the measured and the predicted total
pressure head loss could be due to human error alone or a
combination with inconsistent flow. We noticed that during the
experiment the water pump did not run properly. Calculation
wise, the resolution of the manometers was five millimeters, so
this is subject to human error. Also, the values differ because of
the the variation in the viscosity. From this, the data was
enough to be compromised.
From figure 2a & 2b, it is safe to say that figure 2a is a laminar,
converging flow. From figure 2a, the values are parallel to each
other where it fluctuates minimally from one point to another
which follows Bernoulli’s principle. However, values for the
measured and calculated values for pressure head fluctuates
largely and doesn’t obey they Bernoulli’s principle.
To use Bernoulli’s equation to predict pressure in the
converging section of the tube, the static and total pressure
must be known for at least one point. With only the flow rate
and geometry, our calculation will be limited. To use the
Bernoulli’s equation properly, there must be at least 3 known
values of pressures at a given point.
In summary, the team believes that this experiment was a
success overall. The pressure decrease at the converged section
of the tube due to the increase in velocity. As shown in figure

9. 2a, the total pressure head is relatively similar throughout the
tube. Also from figure 2a, the predicted values of total pressure
is larger than the measured values.
State of the Industry Research Series :
Customer Loyalty in Retail
www.eknresearch.com
’12 FALL
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Table of Contents
Executive Summary 3
Customer Loyalty in Retail 6

10. Research Findings 12
Recommendations 25
The Retail Honor Board: Best Practices from Leading Retailers
27
Exhibit 1: Features and Benefits Comparison of Loyalty
Programs of Top Retailers 29
Exhibit 2: EKN Loyalty Program Assessment Framework 33
Exhibit 3: Customer Segmentation 39
Exhibit 4: Loyalty
Solution
Vendor Landscape 42
Executive Summary
Edgell Knowledge Network 4
Executive Summary

11. Over the years, as consumers realized the economic benefit of
participating in a loyalty program, they signed
up with more and more programs. Today, an average American
household holds membership with 18 loy-
alty programs1. They are loyal to everyone, and by being so
they are loyal to no one.
On the other hand, Apple is a prime example of a retailer who
has built a strong emotional connection with
its large base of loyal customers through product leadership, a
unique retail experience and a consistent
cross-channel delivery of the Apple brand promise. Apple does
not offer a loyalty program.
Agreed, not every retailer enjoys Apple’s product leadership,
and certain formats are built to compete on
price. Yet, customer loyalty programs require close inspection -
why they exist, what value they deliver to the
consumers and retailers, and how.
Edgell Knowledge Network conducted an industry survey of
60+ retailers to benchmark the current state of
loyalty programs in the retail industry. This report presents the
survey findings, highlights the key takeaways
and offers prescriptive insights including a new framework for

12. customer loyalty in the retail industry. Key
findings include:
• There is a disconnect between retailers’ perception of the top
drivers of customer loyalty and their
top strategies to improve their loyalty programs
• Customers of retailers who offer a loyalty program are no
more loyal than those of retailers who
do not offer one
• Retailers have multiple systems for customer engagement,
regardless of whether they offer a loy-
alty program or not
• Retailers expect there to be a significant increase in revenue
contribution from members enrolled
in their loyalty program over the next 3 years. They are also
gearing up for the next wave of technol-
ogy platform upgrades.
The template that most of today’s customer loyalty programs
follow was designed in the early 1980s. It was
a time when the consumers’ spending power was increasing but
they had limited choices. It was a simpler

13. time focused on rewarding repeat transactions with an economic
reward – discounts.
Since then, the framework has evolved in response to changing
consumer behavior and retailers’ expansion
into new channels. Consumers now have the ability to earn,
track and redeem rewards across a variety of
channels, and retailers are able to better utilize the data they
collect through their program, as well as other
sources, to deliver targeted offers based on consumer
preferences and behavior.
However, the loyalty program has remained one-dimen-
sional, still focused on delivering transactional economic
value to the consumer. For retailers, growth into new
channels has been accompanied by new technology sys-
tems, often of varying size, scope and integration with ex-
isting systems. Look under the hood of a retail loyalty or
customer engagement program and you’re likely to find
a patchwork of systems cobbled together to get the job
done.
Loyalty is an emotional
concept, while
loyalty programs are

14. transactional in nature.
1 The 2011 COLLOQUY Loyalty Census
State of the Industry Research Series: Customer Loyalty in
Retail5
Executive Summary
• Activity and frequency continue to be the measures of
program effectiveness further highlighting
their transactional nature. Strategic influence or emotion
measures such as Net Promoter Score
(NPS) see low adoption.
A new vision for loyalty programs emerges from the findings
and EKN’s interviews with retail executives -
the loyalty program as a customer-facing version of the
retailer’s cross-channel engagement strategy. EKN’s
Loyalty Program Assessment Framework, included in this
report, provides a template for a holistic customer
engagement and loyalty program.
Loyalty is ultimately an emotional concept. To truly foster

15. customer loyalty, loyalty programs will need to
expand in their scope and ambition from rewarding frequent
activity with a monetary reward, to being the
vehicle through which the retailer can build a rewarding
personal relationship with the customer.
The similarity of the benefits
offered, program structure, and
how and where customers can
redeem “points” is indicative of
the commoditization of the retail
customer loyalty program.
Now, to measure and reward loyalty as a product of a
customer’s frequency, quality, context, emotion and
profitability of engagement with them across channels,
retailers need new metrics, and the ability to segment
customers based on the above parameters. EKN’s Cus-
tomer Segmentation Framework illustrates a segmenta-
tion approach and offers a starting off point to retailers.
The report also includes a detailed Loyalty