Ndzi, David

Powell, Henry

Ramlall, Shalini

Sanders, David

English

Portsmouth University Research Portal (Pure)

Source Code - GO, Country Code 21.                                                                                   Shelmark 4964.150000  
 
Journal of computing in systems and engineering, 2008, (9).                                                                                      PCR   ISSN  1472-9083 
 
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IMPROVING CUSTOMER GENERATION BY INCREASING 
WEBSITE PERFORMANCE AND INTEGRATING IT SYSTEMS 
 
S Ramlall*, DA Sanders**, H Powell* and D Ndzi ** 
 
 
* Motiontouch Ltd, Dunsfold Park, Cranleigh, Surrey GU6 8TB UK. 
**Systems Engineering Research Group, University of Portsmouth, Portsmouth, PO1 3DJ UK. 
 
 
Email: shalini@motiontouch.com 
 
 
 
Abstract 
 
This paper describes the research into the design of 
commercial websites.  The research will lead to the 
identification of design elements that may have 
significant impact on user behaviour.  The research 
will also investigate how these elements could be 
altered to provide a better browsing experience to 
users and ultimately increase online conversion and 
profit.  The research focuses on the design of 
commercial websites.  Their goal is to generate sales by 
selling products and services online or by promoting 
their products and services online and turning 
enquiries into later offline sales.  The research is using 
data collected at a SME to determine how changes to 
website design affect online conversion rate and impact 
upon Key Performance Indicators (KPIs) of online 
business models.  In order to track customer behaviour 
online, a custom online tracking system was created to 
monitor how users behave while browsing a website.  
Google Analytics is also used to collect behavioural 
data and other statistics.  The rest of the sales cycle is 
tracked through a Customer Relationship Management 
(CRM) system, which was customised by implementing 
extra modules in order to record information at various 
stages of the sales cycle.  The online tracking system is 
not fully integrated with the CRM system at present. 
However, it is still possible to build customer profiles 
across the two systems manually.  
 
Keywords: web, WWW, browser, filter, Internet. 
 
Introduction 
 
The aim of the research is to investigate the design of 
commercial websites with a view to identifying web 
design elements that are key to the success of online 
business models that rely on Pay Per Click advertising 
campaigns to attract customers. 
 
Research Objectives:  The objectives of the research 
are: 
 
1. To build customer profiles that cover the various 
stages of an online business model which relies mainly 
on PPC to attract customers – starting from the initial 
visit of the customer to a website to the delivery of the 
customer’s order. In order to do this: 
 
a. Different IT systems were integrated together and 
customised to collect data that is required to generate 
customer profiles for the sales cycle. Most of the 
integration work focused on data and this was 
accomplished through the introduction of Microsoft 
CRM (MSCRM), which is used as a central data 
repository. In order to achieve this, the MSCRM 
system was extended through the development of a 
number of custom modules using .NET. 
 
b. A user tracking system was developed for the 
website. The system records very detailed browsing 
history of users as well as information regarding where 
users came from e.g. which search engine or which 
PPC campaign and search keywords was used. This 
data provides a customer profile for the initial stage of 
the business model. 
 
2. To identify website design elements which can 
increase the performance of a website and subsequently 
a business. By experimenting with the design and 
navigation of a website and then analysing customer 
profiles, the impact of the changes on a business can be 
understood. 
 
3. To identify metrics for measuring the impact of 
changes to web design on various stages of an online 
business model. So far some of the measures identified 
include: 
 
Source Code - GO, Country Code 21.                                                                                   Shelmark 4964.150000  
 
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 performance of the site as measured by conversion 
rate. 
 quality of enquiries. 
 number of enquiries that turn into opportunities. 
 number of opportunities that turn into orders. 
  type of customers who enquire e.g. individuals, 
small companies or corporate. 
 profit per enquiry. 
 the potential of an enquiry to generate repeat 
business. 
 
4. To demonstrate that by using a system such as the 
one described in 1(a) and 1(b) to build complete 
customer profiles that are used to measure the impact 
of changes to a website and to identify key web design 
elements, the following can be improved: 
 
 Customer generation. 
 Sales. 
 Marketing. 
 
5. To integrate a Customer Relationship Management 
(CRM) system with other applications including 
websites to create an integrated IT system that can 
deliver benefits such as improved: 
• Customer generation. 
• Sales. 
• Marketing. 
• Information flow. 
 
Research Outline 
 
The research was split into 2 stages. 
 
1. The first one consisted of implementing and 
integrating IT systems. This was vital for creating 
complete customer profiles that spanned the various 
stages of the business model. These systems are now 
collecting data that will be analysed to test hypotheses 
in the later stages of the research.  
 
2. The second stage of the research is focusing 
on building a dynamic website which can collect data 
about user behaviour on websites. Layout and 
navigation are also being experimented with to achieve 
high conversion rates. 
 
Through integration of websites with systems 
introduced in stage 1, it will be possible to build user 
profiles that span the business process. Implementation 
of stage 1 and stage 2 will provide the ability to collect 
data and measure performance at various points in a 
business model and potentially deliver increased 
information flow, increased productivity and better 
decision making. 
 
In the first year, the research integrated a MSCRM 
system with other applications to create new electronic 
systems. The features of the MSCRM system were 
extended by building custom modules using .NET. The 
MSCRM system became the central repository for most 
of the data collected. The systems were designed to 
integrate with websites to allow for the automatic 
handling of enquiries from web sites as well as 
recording browsing data for users in order to build 
accurate customer profiles. 
 
The MSCRM system enabled the implementation of 
business logic through automated processes. Most of 
the initial research focused on: data migration, data 
integration, and ways to use .NET to extend existing 
functionalities and how to build integrated systems. 
Ways of measuring the benefits of the new system are 
being investigated and will be measured on the target 
system implemented at the collaborating company 
against the “automatic” winning of more business, 
improvement in sales processes and the faster and more 
efficient servicing of customers. 
 
Some of the deliverables so far have been: 
 
• A new integrated MSCRM system which has been 
customised to meet business requirements and 
deliver benefits in terms of sales management, 
productivity, information flow and reporting on key 
performance indicators 
• New automated business and engineering processes. 
• New custom MSCRM modules that improve project 
management, quality control management and 
measure customer satisfaction. 
• The ability to generate and view critical engineering 
and business data and statistics in real time though 
dashboards and custom reports. 
 
The research is now focusing on the development and 
integration of websites with the MSCRM system. 
Websites at the collaborating company were static at 
the beginning of the research. Little was known about 
customer behavior on websites or sources of traffic, 
etc. At the time the use of web analytics was just 
starting to become the norm for any good online 
strategy. The launch of Google Analytics which is a 
free analytics tools made the use of analytics more 
widespread. Small companies which had previously not 
been able to afford expensive web analytics packages 
such as WebTrend now had access to the same 
technology as bigger companies. Web analytics 
allowed websites to be designed to meet the specific 
needs of customers. 
 
The backend of a website at the collaborating company 
was changed in order to make the website dynamic. As 
part of this a custom tracking module was created to 
track users and build a history of their activity on the 
website. Ways of integrating the website with the 
MSCRM systems are now being investigated. This will 
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Journal of computing in systems and engineering, 2008, (9).                                                                                      PCR   ISSN  1472-9083 
 
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allow emails from the website to be automatically 
converted into lead records and assigned to a sales 
person. Also user activity will be recorded in the 
MSCRM system to provide a complete profile of each 
customer. 
 
As well as changing the backend of the website, the 
look, layout and content of the website have also been 
updated. This required an understanding of who the site 
visitors are and what they are looking for. The data 
collected by the backend was used to answer these 
questions, in order to work out where redesign was 
required and what the best design would be. The data 
collected by the tracking module has so far been 
successfully used to identify browsing patterns and 
build customer profiles. This has helped identify 
customer segments, which have been targeted via 
segment specific online marketing campaigns and web 
pages. So far, results have shown an increase in 
conversion rate on the website.  
 
Some of the deliverables from this work have been: 
 
• New dynamic website which can track user activity 
and customise website content based on the user’s 
browsing history and search keyword. 
• New improved layout, look, navigation and content 
for the website. 
• Improved conversion rate on the website due to 
changes made to landing pages based on data 
analysis carried out using the customer profile built 
using data from the online tracking module and 
MSCRM. 
 
The work carried out so far with the integration of 
MSCRM and the development of the website has 
enabled the collection of a large amount of data that 
can now be used to: 
 
• Build customer profiles which can be used to drive 
marketing efforts by 
• Better targeting advertising by identifying market 
segments and niches 
• Identifying and understanding the requirement and 
expectations of web site users. This knowledge can 
used to improve the website and increase conversion 
rates 
• Measure business performance at different stages in 
business processes 
 
Make informed decisions regarding the management of 
a number of business processes. 
 
The research is starting to look at ways of using 
customer profiles to drive marketing initiatives. This 
part of the research is still in its early stages. The goal 
of commercial websites is to generate sales by selling 
products and services online or by promoting their 
products and services online and then turning enquiries 
into later offline sales.  The design of a site and its 
navigation can have a significant impact on the site’s 
usability, which can in turn affect the users’ experience 
with the site. 
 
The research is identifying design elements that may 
have an impact on user behaviour and is considering 
how these elements can be altered to increase online 
conversion and profit. 
 
The literature review for this part of the work has 
identified key design elements as well as human 
factors, which are affected by these design elements.  A 
new basic model has been developed and data is being 
collected through a series of online experiments.  These 
experiments have investigated (and are investigating) 
how users react to changes made to specific design 
elements on web pages.  The experiments are being 
carried out on dummy web sites and on company 
websites at the collaborating company.  Some 
preliminary analysis has already shown that small 
changes to a page’s design can have a big impact on 
lead generation and cost per lead. 
 
Graphs 1 and 2 show how the number of leads and the 
cost of leads have varied from month to month over a 
4.5-year period at the collaborating company. The 
graphs also show some of the significant changes that 
took place over that time as well as some external 
factors that are thought to have affected lead 
generation. 
 
Graph 1 shows an upward trend between 2005 and 
2008. Overall the average lead per year has been 
increasing over time. There is a drop in the number of 
leads in February 2009 which is due to the fact that the 
advertising spend was halved. Despite this the number 
of leads produced is still high. 
 
Graph 2 shows the average cost per lead between 2005 
and 2009. From 2005 to 2006 there is a significant 
increase in cost per lead suggesting that the online 
advertising and the websites’ performance were poor. 
The cost per lead has been steadily going down since 
April 2006. There is a significant drop in cost between 
July 2007 and August 2007. This is due to changes 
made to the advertising campaigns and landing pages 
based on data analysis carried out using data gathered 
by the online tracking system that was implemented on 
the main website. By combining the data captured by 
the online tracking system with the data in MSCRM it 
was possible to identify, for the first time, the type of 
leads that each of the online advertising campaigns 
were generating. By using other data that was collected 
by the online tracking system e.g. search phrase used, 
search engine and country, basic behavioural and 
demographical profiles were built. These profiles 
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Journal of computing in systems and engineering, 2008, (9).                                                                                      PCR   ISSN  1472-9083 
 
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allowed for a better understanding of users who were 
visiting the site and provided opportunities for content 
targeting, segmentation and personalisation. 
 
In May 2008, the backend of the main website was 
updated in order to capture more behavioural data. 
Some elements of the website were also changed from 
static to dynamic e.g. the navigation was made dynamic 
and displayed different links depending on the 
advertising campaign that users came from. By the end 
of June 2008, enough data had been captured by the 
new system to carry out analysis. As a result of the 
analysis, the landing page of one of the main 
advertising campaigns was replaced by a different 
landing page. The campaign ran two adverts with 
different messages but the same landing page. Using 
the new dynamic features now available on the site, the 
title of the landing page was set to change on the fly to 
match the exact title of the advert that the user had 
clicked on. The idea was to increase relevancy between 
the advert and the landing page to promote conversion. 
The new landing page combined with the dynamic title, 
brought a big increase in performance, which 
contributed to a big increase in number of leads in July 
2008 and a lowering the cost per lead from July 2008 
onwards. 
 
In November 2008, the main website was redesigned to 
have a new look, updated navigation and updated 
content (screenshots in Appendix A). The layout of the 
site stayed the same, as did most of the content. The 
look of the site however was changed to give the site a 
more modern look. The new site was launched at the 
end of November 2008, but changes were made over 
the next two months to refine the website in order to 
bring the advertising and website performance back to 
what it was before the launch of the site. As can be 
seen from the graph, the cost per lead went up in 
December 2008 but started coming down again in 
January 2009.  
 
In February 2009, due to the economic downturn, the 
advertising budget was halved and the advertising 
campaigns were targeted to the UK only. In order to 
generate a high number of leads with half the budget, it 
was crucial to increase the performance of all landing 
pages. As the data in the MSCRM system and the 
online tracking module was getting richer, the customer 
profiles were also expanding. As a result of this better 
understanding of customers, segmentation was now an 
option. It was decided that there were two ways of 
achieving segmentation on a landing page: 
 
1. Have a number (it was decided that 4 might be the 
optimum number) of options (in graphical form) for 
users to choose from.  
2. Have a short questionnaire and allow user to 
effectively choose the services that they required. 
In March 2009 both landing pages were tried out on 
different campaigns. They both performed better than 
the “non-segmented” landing pages achieving higher 
conversion rates than the latter. This contributed in 
lowering the cost of leads between March 2009 and 
August 2009. 
 
All of the changes made to the website so far have 
relied heavily on data analysis. The data for this comes 
from the MSCRM system and the online tracking 
system. By combining data from the two systems, it is 
possible to build customer profiles which play a crucial 
role in understanding the users who visit the website. 
This in turn allows better targeting of online 
advertising and also better landing page design. 
 
Initial data analysis as shown in Graphs 1 and 2 
indicate improved customer generation both from a 
quantitative point of view and a cost point of view. It 
appears that this improved customer generation is the 
result of improved website performance and integrated 
IT systems.  
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Journal of computing in systems and engineering, 2008, (9).                                                                                      PCR   ISSN  1472-9083 
 
152
 
 
 
Graph 1: Total leads per month 
 
 
 
 
 
Graph 2: Cost per lead per month 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Source Code - GO, Country Code 21.                                                                                   Shelmark 4964.150000  
 
Journal of computing in systems and engineering, 2008, (9).                                                                                      PCR   ISSN  1472-9083 
 
153
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Source Code - GO, Country Code 21.                                                                       Shelmark 4964.150000  
 
Journal of computing in systems and engineering, 2008, (9).                                                                          PCR   ISSN  1472-9083 154
 


Improving customer generation by increasing website performance and integrating IT systems

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