Difference between revisions of "ANLY482 AY2016-17 T2 Group7: Marketline"

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| style="vertical-align:top;width:14%;" | <div style="padding: 3px; text-align:center; line-height: wrap_content; font-size:15px; border-bottom:2px solid #0163bd; font-family:Century Gothic"> [[ANLY482_AY2016-17_T2_Group7: Exploratory Data Analysis| <b>Exploratory Data Analysis</b>]]
 
| style="vertical-align:top;width:14%;" | <div style="padding: 3px; text-align:center; line-height: wrap_content; font-size:15px; border-bottom:2px solid #0163bd; font-family:Century Gothic"> [[ANLY482_AY2016-17_T2_Group7: Exploratory Data Analysis| <b>Exploratory Data Analysis</b>]]
  
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| style="vertical-align:top;width:14%;" | <div style="padding: 3px; text-align:center; line-height: wrap_content; font-size:15px; border-bottom:5px solid #0163bd; font-family:Century Gothic"> [[ANLY482_AY2016-17_T2_Group7: Text Mining| <b>Text Analytics</b>]]
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| style="vertical-align:top;width:14%;" | <div style="padding: 3px; text-align:center; line-height: wrap_content; font-size:15px; border-bottom:5px solid #0163bd; font-family:Century Gothic"> [[ANLY482_AY2016-17_T2_Group7: Euromonitor| <b>Text Analytics</b>]]
  
 
| style="vertical-align:top;width:14%;" | <div style="padding: 3px; text-align:center; line-height: wrap_content; font-size:15px; border-bottom:2px solid #0163bd; font-family:Century Gothic"> [[ANLY482_AY2016-17_T2_Group7: Gap Analysis| <b>Gap Analysis</b>]]
 
| style="vertical-align:top;width:14%;" | <div style="padding: 3px; text-align:center; line-height: wrap_content; font-size:15px; border-bottom:2px solid #0163bd; font-family:Century Gothic"> [[ANLY482_AY2016-17_T2_Group7: Gap Analysis| <b>Gap Analysis</b>]]
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With reference to Chart 11 in the Exploratory Data Analysis, we have selected 2 databases, Lawnet and Euromonitor to focus on. This is due to the fact that these 2 databases are the most commonly used amongst the Law and Business students respectively, as these 2 schools are the 2 biggest contributors to the searches during the Term. In addition, Marketline has been chosen for further analyses requested by the sponsor.
  
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<br>
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[[File:Text2.png|thumb|left|500px|alt=Alt Text|''Chart 13: Text mining process'' First, we use SAS Enterprise Miner 14.1 to carry out text analytics. We import ‘euromonitor_text_data’ and ‘lawnet_text_data’ respectively by using the File Import function and running though the text mining process in Chart 13.]].
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With reference to Chart 11 in the Exploratory Data Analysis, we have selected 2 databases, Lawnet and Euromonitor, to focus on for this interim phase. This is due to the fact that these 2 databases are the most commonly used amongst the Law and Business students respectively, as these 2 schools are the 2 biggest contributors to the searches during the Term.
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[[File:Text3.png|thumb|right|500x300px|alt=Alt text|''Chart 14: Text Parsing Configuration''
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We configure text parsing so that Parts of Speech such as ‘Aux’, ‘Conj’, ‘Det’, ‘Interj’, ‘Part’, ‘Prep’, ‘Pron’ and Types of Attributes including ‘Num’ and ‘Punct’ are all ignored.]].
  
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From the following actions applied to these 2 databases, we could then repeat these steps for the rest of the databases.
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<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
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<div style="background:#307FBB; line-height:0.3em; font-family:sans-serif; font-size:120%; border-left:#bbdefb solid 15px;"><div style="border-left:#fff solid 5px; padding:15px;"><font color="#fff"><strong>Databases</strong></font></div></div>
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<div style="background:#307FBB; line-height:0.3em; font-family:sans-serif; font-size:120%; border-left:#bbdefb solid 15px;"><div style="border-left:#fff solid 5px; padding:15px;"><font color="#fff"><strong>Text Analytics</strong></font></div></div>
 
  
 
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| style="vertical-align:top;width:14%;" | <div style="padding: 3px; text-align:center; line-height: wrap_content; font-size:15px; font-family:Century Gothic"> [[ANLY482_AY2016-17_T2_Group7: Lawnet| <b>Lawnet</b>]]
 
| style="vertical-align:top;width:14%;" | <div style="padding: 3px; text-align:center; line-height: wrap_content; font-size:15px; font-family:Century Gothic"> [[ANLY482_AY2016-17_T2_Group7: Lawnet| <b>Lawnet</b>]]
  
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| style="vertical-align:top;width:14%;" | <div style="padding: 3px; text-align:center; line-height: wrap_content; font-size:15px; border-bottom:3px solid #0163bd; font-family:Century Gothic"> [[ANLY482_AY2016-17_T2_Group7: Marketline| <b>Marketline</b>]]
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| style="vertical-align:top;width:14%;" | <div style="padding: 3px; text-align:center; line-height: wrap_content; font-size:20px; border-bottom:3px solid #0163bd; font-family:Century Gothic"> [[ANLY482_AY2016-17_T2_Group7: Marketline| <b>Marketline</b>]]
  
 
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<!-- Start Information -->
 
<!-- Start Information -->
  
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<big>'''Marketline'''</big>
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<br>
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[[File:NumSearches Programme Education Admission T1.png|thumb|left|500px|alt=Alt Text|''Chart 16: No. of Searches on Marketline by Programme, Education and Admission in Term 1'']].
  
−
<br/>
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[[File:NumSearches Programme Education Admission T2.png|thumb|right|500px|alt=Alt text|''Chart 17: No. of Searches on Marketline by Programme, Education and Admission in Term 2'']].
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[[File:Text1.png|500px]]<br/>
 
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''Chart 12: Text analytics data preparation''<br/>
 
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Firstly, we need to format the search queries to lowercase form for standardization purposes. We do that by using Tableau’s ‘LOWER()’ function, filtering out two data sets: euromonitor’s data being “euromonitor_text_data” and lawnet’s data being “lawnet_text_data”.
 
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<br/>
 
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[[File:Text2.png|500px]]<br/>
 
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''Chart 13: Text mining process''<br/>
 
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After which we use SAS Enterprise Miner 14.1 to carry out text analytics. We import ‘euromonitor_text_data’ and ‘lawnet_text_data’ respectively by using the File Import function and running though the text mining process in Chart 13: Text mining process.
 
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<br/>
 
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[[File:Text3.png|500px]]<br/>
 
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''Chart 14: Text Parsing Configuration''<br/>
 
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We configure text parsing so that Parts of Speech such as ‘Aux’, ‘Conj’, ‘Det’, ‘Interj’, ‘Part’, ‘Prep’, ‘Pron’ and Types of Attributes including ‘Num’ and ‘Punct’ are all ignored.  
 
−
 
 
−
<big>'''Euromonitor'''</big>
 
  
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<br/>
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<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
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[[File:Chart17.png|900px]]<br/>
 
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''Chart 15: Search Count in Euromonitor by Schools & Admission Years''<br/>
 
  
 
{| class="wikitable"
 
{| class="wikitable"
 
|-
 
|-
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! Subject Matter: !! Contrasting BBM against Bsc(IS) users across Admission Years
+
! Subject Matter: !! Understanding who uses Marketline Advantage
 
|-
 
|-
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| Thought Process: || As our team consists of BBM and Bsc(IS) students, we discussed among ourselves and then with our peers of our faculties about how often we use Euromonitor in our research. Amidst our sharings, we found out that more often than not, BSc(IS) students do not use Euromonitor as much as their BBM counterparts. However, some of the BSc(IS) students shared that they have used Euromonitor rather intensively in their 1st - 2nd years, mainly for researching on the University Core modules which they have to take (eg. TWC, BGS).
+
| Thought Process: || Our Project Sponsor requested for Marketline Advantage to be included in our Textual Analytics after our Interim presentation. We generated these 2 charts as we want to find out who are the users of Marketline before diving deeper into the analysis.
−
Thus, we attempted to verify this discussion through the analysis of the data.  
 
 
|-
 
|-
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| Analysis: || From Chart 15, we observe that the number of searches performed by BSc(IS) users across all admission years are significantly lower than their BBM counterparts. Thus, this could possibly verify our thoughts that BSc(IS) users indeed use Euromonitor for research lesser than their BBM counterparts.
+
| Analysis: || From Charts 16 and 17, we can see that Marketline is used mainly by Undergraduate Students from Bachelor of Business Management. Diving deeper, we observe that Business Students from AY_2013 are contributing the most to the number of searches in Marketline. This is an interesting point and we would like to find out more about it.
 +
|}
  
−
Most interestingly, BSc(IS) users in AY_2016 have performed the most number of searches as compared to their faculty users from the other admission years. The first year of the SMU BSc(IS) curriculum usually consists of University Core Modules such as BGS (Business, Government & Society) and TWC (Technology and World Change) which are by nature, research-intensive modules. Thus, it would be more probable that BSc(IS) users in AY_2016, meaning they are in their first year in 2016, are performing such high number of searches because they are enrolled in such research-intensive modules. The number of research-intensive modules in the curriculum decreases significantly as the typical BSc(IS) user moves into his/her 2nd year and thereafter. This could be shown by the low number of searches performed by BSc(IS) users in AY_2015 (1st/2nd Year in 2016), AY_2014 (2nd/3rd Year in 2016) and AY_2013 (3rd/4th Year in 2016.  
+
[[File:Searches Business AY2013 T2 2016.png|700px]]<br/>
 +
The chart generated above showcases the searches performed by Business students from AY_2013 in Term 2 of 2016.  
  
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Contrasting with BBM users, the number of searches across all academic years remains high. This could be due to the nature of the BBM curriculum which consists of research-intensive modules throughout.  
+
As mentioned in analysis of Charts 16 and 17, we found out that the most popular search terms conducted by Business students from AY_2013 are ‘singapore’, ‘industry’ and ‘india’ respectively.  
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|}
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In the Concept Linking map, we can see that ‘singapore’ is closely linked to words such as:
 +
‘departmental’, ‘isetan’, ‘retail’, ‘rate’, ‘store’, ‘usage’, ‘fitness’, ‘employment’.  
 +
<br/>
  
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[[File:plots (30720).png|500px]]<br/>
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[[File:2nd most popular search term.png|700px]]<br/>
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Among 30720 cases, 8257 (26.88%) are dropped after parsing the data. <br/>
+
For the 2nd most popular search term, ‘industry’, we can see from the Concept Linking map that it is closely linked to words such as ‘legal’, ‘apparel’, ‘guide’, ‘headphone’, ‘emerge’, ‘retail’, ‘stationery’, ‘top’. <br/>
  
−
[[File:plots (frequency w singapore).png|500px]]<br/>
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[[File:3rd most popular search term.png|700px]]<br/>
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In addition to parsing of the data, we noticed that the Term “singapore” has the greatest frequency of 2175, followed by the Terms “consumer” and “tourism”. <br/>
+
For the 3rd most popular search term, ‘india’, we can see from the Concept Linking map that it is closely linked to ‘itc’ which is the name of an Indian conglomerate specializing in 5 business segments including Fast-Moving Consumer Goods, Hotels, Paperboards & Packaging, Agri Business & Information Technology, ‘business’, ‘agriculture’, ‘fmcg’, ‘tobacco’, ‘agri’, ‘hotel’, ‘moving’ which probably comes from the term ‘Fast-Moving Consumer Goods’. <br/>
  
−
[[File:plots (concept linking Singapore).png|500px]]<br/>
+
[[File:Dashboard various linking words.png|700px]]<br/>
−
From the graph above we noticed that ‘singapore’ is linked to ‘hot drinks’, ‘hot’, ‘drink’, ‘singapore travel’, ‘consumer lifestyle’, ‘lifestyle’, ‘singapore consumer’ and ‘singapore airline’ <br/>
+
This is the dashboard which showcases the various linking words at a glance. <br/>
  
−
[[File:plots (concept linking consumer).png|500px]]<br/>
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[[File:Searches Business AY2013 T1 2016.png|700px]]<br/>
−
From the graph above we noticed that ‘consumer’ is linked to ‘consumer health’, ‘consumer foodservice’, ‘electronics’, ‘’, ‘singapore consumer’, ‘trend’, ‘consumer electronics’ and ‘global’. <br/>
+
The chart generated above showcases the searches performed by Business students from AY_2013 in Term 1 of 2016.
 +
The most popular search terms are ‘singapore’, ‘hot’ and ‘beverage’.
 +
For the most popular search term, ‘singapore’, we can see from the Concept Linking map that it is closely linked to words such as ‘hotel’, ‘sport’, ‘fast’ which probably comes from the term ‘Fast-Moving Consumer Goods’, ‘drink’, ‘toothpaste’, ‘milk’, ‘machinery’, ‘snack’. <br/>
  
−
[[File:plots (concept linking tourism).png|500px]]<br/>
+
[[File:2nd most popular search term drink.png|700px]]<br/>
−
From the graph above, we noticed ‘tourism’ is linked to ‘medical’, ‘sport’, ‘cultural tourism’, ‘wellness’, ‘medical tourism’, ‘cultural’, ‘wellness tourism’ and ‘travel’. <br/>
+
We have grouped the following terms ‘hot’, ‘beverage’ and ‘hot beverage’ under the term ‘drink’ in order to have a clearer Concept Linking map.
 +
For the 2nd most popular search term,’drink’, we can see from the Concept Linking map that it is closely linked to ‘bon cafe’ which is the name of a retail coffee establishment in Singapore, ‘massimo’ which probably refers to the ‘Massimo Zanetti Beverage Group’ which specializes in coffee, ‘bon’ which probably refers to the brand ‘bon cafe’, ‘hot drink’, ‘japan’, ‘costa’ which probably refers to the brand ‘Costa Coffee’, ‘korea’ and ‘energy’. <br/>
  
−
[[File:plots (text topic function).png|500px]]<br/>
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[[File:3rd most popular search term group.png|700px]]<br/>
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This is the result shown by function Text Topic.<br/>
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For the 3rd most popular search term ‘group, we can see from the Concept Linking map that it is closely linked to words such as ‘airport’, ‘ltd’ as in ‘pte ltd’, ‘super group’ which probably refers to the coffee company of the same name, ‘super’, ‘massimo’ which probably refers to the coffee company ‘Massimo Zanetti Beverage Group’, ‘changi’ which goes with ‘airport’. <br/>
  
−
[[File:plots (text topic function enlarged).png|500px]]<br/>
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[[File:NumSearches Month Day Hours.png|thumb|left|500px|alt=Alt Text|''Chart 18: No. of Searches on Marketline in terms of Month, Day and Hours in Term 2'']].  
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From the graph above, results from Text Topic function shows that “singapore”, “retail”, “beer”, “milk” and “juice” are of the same topic, “medical”, “tourism”, “technology” and “health” are of the same topic, and “lifestyle”, “consumer”, “singapore”, “japan” are of the same topic.<br/>
 
  
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<big>'''Lawnet'''</big>
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<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>
  
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[[File:plots (lawnet dashboard).png|500px]]<br/>
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{| class="wikitable"
−
Among 172363 cases, 36066 are dropped (20.92%). As compared to euromonitor, lawnet has a larger amount of searches.<br/>
+
|-
−
 
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! Subject Matter: !! Understanding the sudden increase in number of searches on Marketline in Term 2
−
[[File:plots (concept linking slr).png|500px]]<br/>
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|-
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The most popular search Terms are ‘slr’, ‘ltd’ followed by ‘pte’. These stands for Singapore Law Review, Ltd as in Pte Ltd and Pte as in Pte Ltd respectively. This means that students search for Singapore Law Review a lot. <br/>
+
| Thought Process: || We wanted to understand the usage distribution across the Term 2 period better.
 +
|-
 +
| Analysis: || We noticed that there are 2 significant spikes within days of each other. These spikes are on 14 and 18 Jan respectively. Thus, we conducted a deep-dive into finding out what are the terms searched for during these 2 days.  
 +
|}
  
−
[[File:plots (concept linking slr enlarged).png|500px]]<br/>
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[[File:14 jan most popular search terms.png|700px]]<br/>
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From the graph above, we noticed that ‘slr’ which stands for Singapore Law Review, is linked to words which are presumably names such as ‘chum tat’, ‘ngiam’, ‘chiew’, ‘chiew hock’, ‘chum’ and the time period ‘1974-1976’. These could possibly tell us the popular cases associated with the Singapore Law Review and the time period for which cases took place in. <br/>
+
For 14 Jan, we can see that the most popular search terms are ‘hot’, ‘singapore’ and ‘drink’ respectively. This is similar to that of the searches performed by Business students from AY_2013 in Term 1 of 2016, which remains the same for ‘hot’ and ‘singapore’ but replaces the ‘drink’ with ‘beverage’. <br/>
  
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[[File:plots (concept linking singapore lawnet).png|500px]]<br/>
+
[[File:Topics 14 jan.png|700px]]<br/>
−
From the graph above, we noticed that ‘singapore’ is linked to words such as ‘overseas enterprise’, ‘pte’ (presumably pte in Pte Ltd, the short form for Private Limited), ‘global singapore’, ‘southeast’, ‘finance’, ‘institutional’, ‘law’, ‘ltd’ (presumably Ltd in Pte Ltd) and ‘development bank’. <br/>
+
These are the topics for 14 Jan. <br/>
  
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[[File:plots (text topic function lawnet).png|500px]]<br/>
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[[File:18 jan most popular search terms.png|700px]]<br/>
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This is the result shown by function Text Topic.<br/>
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For 18 Jan, the most popular search terms are ‘india’, ‘perfetti’ which probably refers to ‘Perfetti Van Melle’ which is one of the largest manufacturers and distributors of confectionery, and ‘confectionery’. <br/>
  
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[[File:plots (text topic function lawnet enlarged).png|500px]]<br/>
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[[File:18 jan 2nd most popular search term.png|700px]]<br/>
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From the table above, results from Text Topic function shows that “slr”, “wlr”, “teck”, “attorney-general” are of the same topic, this is possibly because people who searched for singapore law review (slr), also searched for world law review (wlr) while the attorney-general is the legal advisor to the government and “teck” could be someone’s name.  “sghc”, “bin”, “rahmart” and “iskandar” are of the same topic as “sghc” stands for singapore high court.  
+
For the 2nd most popular search term ‘perfetti’, we can see from the Concept Linking map that it is closely linked to words such as ‘report’, ‘melle’, ‘van’, ‘perfetti van’ and ‘company’. This tells us that the company name ‘Perfetti Van Melle’ is being searched instead of a broad research topic. <br/>
  
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The name ‘Rahmart’, ‘bin’ and ‘Iskandar’ is an interesting search Term whereby it features a former policeman of the name ‘Iskandar bin Rahmat’ who was charged for committing double murder at Kovan MRT in 2013. This is a widely known local criminal case which most probably is being used as a prime example of criminal cases in the SMU Bachelor of Laws Curriculum, thereby explaining the popularity of these keywords.
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[[File:Confectionery confectionary.png|700px]]<br/>
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More interestingly, the ‘Rahmart’ is in fact a misspelling of the name ‘Rahmat’. This could possibly indicate that majority of the searches for ‘Rahmart’ were performed by users who are not of the Malay descent. Or this could possibly be due to a misspelling from the course material that was provided to the users, presumably Law students.<br/>
+
For ‘confectionery’ and its misspelling ‘confectionary’, we grouped them together in order to understand the linked words as one entity: Confectionery, instead of trying to analyze both the original term and misspelled term separately. The high number of searches for the misspelled term ‘confectionary’ may show how students, despite at the university level, still make the confusion between ‘confectionary’ and ‘confectionery’.  
 +
For the term ‘confectionery’, we can see from the Concept Linking map that it is closely linked to the words ‘singapore’, ‘india’ and ‘china’. <br/>
  
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[[File:lawnet iskandar.png|500px]] [[File:lawnet rahmart.png|500px]]<br/>
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[[File:Topics 18 jan.png|700px]]<br/>
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[[File:lawnet iskandar rahmart.png|500px]]<br/>
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These are the topics for the 18 Jan analysis. <br/>
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When we searched “rahmart” or “iskandar bin rahmart”in lawnet we could not find anything as the correct name of the case should be “rahmat”, but SAS Text Miner grouped “rahmart” and “iskandar”, “bin” together so we speculate that many students searched for “iskandar bin rahmart” and found nothing. A recommendation system which will automatically link “rahmart” and the “iskandar bin rahmat” case would be welcomed.<br/>
 
  
  

Latest revision as of 08:40, 16 April 2017

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With reference to Chart 11 in the Exploratory Data Analysis, we have selected 2 databases, Lawnet and Euromonitor to focus on. This is due to the fact that these 2 databases are the most commonly used amongst the Law and Business students respectively, as these 2 schools are the 2 biggest contributors to the searches during the Term. In addition, Marketline has been chosen for further analyses requested by the sponsor.

Alt Text
Chart 13: Text mining process First, we use SAS Enterprise Miner 14.1 to carry out text analytics. We import ‘euromonitor_text_data’ and ‘lawnet_text_data’ respectively by using the File Import function and running though the text mining process in Chart 13.

.

Alt text
Chart 14: Text Parsing Configuration We configure text parsing so that Parts of Speech such as ‘Aux’, ‘Conj’, ‘Det’, ‘Interj’, ‘Part’, ‘Prep’, ‘Pron’ and Types of Attributes including ‘Num’ and ‘Punct’ are all ignored.

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Databases


Marketline

Alt Text
Chart 16: No. of Searches on Marketline by Programme, Education and Admission in Term 1

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Alt text
Chart 17: No. of Searches on Marketline by Programme, Education and Admission in Term 2

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Subject Matter: Understanding who uses Marketline Advantage
Thought Process: Our Project Sponsor requested for Marketline Advantage to be included in our Textual Analytics after our Interim presentation. We generated these 2 charts as we want to find out who are the users of Marketline before diving deeper into the analysis.
Analysis: From Charts 16 and 17, we can see that Marketline is used mainly by Undergraduate Students from Bachelor of Business Management. Diving deeper, we observe that Business Students from AY_2013 are contributing the most to the number of searches in Marketline. This is an interesting point and we would like to find out more about it.

Searches Business AY2013 T2 2016.png
The chart generated above showcases the searches performed by Business students from AY_2013 in Term 2 of 2016.

As mentioned in analysis of Charts 16 and 17, we found out that the most popular search terms conducted by Business students from AY_2013 are ‘singapore’, ‘industry’ and ‘india’ respectively. In the Concept Linking map, we can see that ‘singapore’ is closely linked to words such as: ‘departmental’, ‘isetan’, ‘retail’, ‘rate’, ‘store’, ‘usage’, ‘fitness’, ‘employment’.

2nd most popular search term.png
For the 2nd most popular search term, ‘industry’, we can see from the Concept Linking map that it is closely linked to words such as ‘legal’, ‘apparel’, ‘guide’, ‘headphone’, ‘emerge’, ‘retail’, ‘stationery’, ‘top’.

3rd most popular search term.png
For the 3rd most popular search term, ‘india’, we can see from the Concept Linking map that it is closely linked to ‘itc’ which is the name of an Indian conglomerate specializing in 5 business segments including Fast-Moving Consumer Goods, Hotels, Paperboards & Packaging, Agri Business & Information Technology, ‘business’, ‘agriculture’, ‘fmcg’, ‘tobacco’, ‘agri’, ‘hotel’, ‘moving’ which probably comes from the term ‘Fast-Moving Consumer Goods’.

Dashboard various linking words.png
This is the dashboard which showcases the various linking words at a glance.

Searches Business AY2013 T1 2016.png
The chart generated above showcases the searches performed by Business students from AY_2013 in Term 1 of 2016. The most popular search terms are ‘singapore’, ‘hot’ and ‘beverage’. For the most popular search term, ‘singapore’, we can see from the Concept Linking map that it is closely linked to words such as ‘hotel’, ‘sport’, ‘fast’ which probably comes from the term ‘Fast-Moving Consumer Goods’, ‘drink’, ‘toothpaste’, ‘milk’, ‘machinery’, ‘snack’.

2nd most popular search term drink.png
We have grouped the following terms ‘hot’, ‘beverage’ and ‘hot beverage’ under the term ‘drink’ in order to have a clearer Concept Linking map. For the 2nd most popular search term,’drink’, we can see from the Concept Linking map that it is closely linked to ‘bon cafe’ which is the name of a retail coffee establishment in Singapore, ‘massimo’ which probably refers to the ‘Massimo Zanetti Beverage Group’ which specializes in coffee, ‘bon’ which probably refers to the brand ‘bon cafe’, ‘hot drink’, ‘japan’, ‘costa’ which probably refers to the brand ‘Costa Coffee’, ‘korea’ and ‘energy’.

3rd most popular search term group.png
For the 3rd most popular search term ‘group, we can see from the Concept Linking map that it is closely linked to words such as ‘airport’, ‘ltd’ as in ‘pte ltd’, ‘super group’ which probably refers to the coffee company of the same name, ‘super’, ‘massimo’ which probably refers to the coffee company ‘Massimo Zanetti Beverage Group’, ‘changi’ which goes with ‘airport’.

Alt Text
Chart 18: No. of Searches on Marketline in terms of Month, Day and Hours in Term 2

.
















Subject Matter: Understanding the sudden increase in number of searches on Marketline in Term 2
Thought Process: We wanted to understand the usage distribution across the Term 2 period better.
Analysis: We noticed that there are 2 significant spikes within days of each other. These spikes are on 14 and 18 Jan respectively. Thus, we conducted a deep-dive into finding out what are the terms searched for during these 2 days.

14 jan most popular search terms.png
For 14 Jan, we can see that the most popular search terms are ‘hot’, ‘singapore’ and ‘drink’ respectively. This is similar to that of the searches performed by Business students from AY_2013 in Term 1 of 2016, which remains the same for ‘hot’ and ‘singapore’ but replaces the ‘drink’ with ‘beverage’.

Topics 14 jan.png
These are the topics for 14 Jan.

18 jan most popular search terms.png
For 18 Jan, the most popular search terms are ‘india’, ‘perfetti’ which probably refers to ‘Perfetti Van Melle’ which is one of the largest manufacturers and distributors of confectionery, and ‘confectionery’.

18 jan 2nd most popular search term.png
For the 2nd most popular search term ‘perfetti’, we can see from the Concept Linking map that it is closely linked to words such as ‘report’, ‘melle’, ‘van’, ‘perfetti van’ and ‘company’. This tells us that the company name ‘Perfetti Van Melle’ is being searched instead of a broad research topic.

Confectionery confectionary.png
For ‘confectionery’ and its misspelling ‘confectionary’, we grouped them together in order to understand the linked words as one entity: Confectionery, instead of trying to analyze both the original term and misspelled term separately. The high number of searches for the misspelled term ‘confectionary’ may show how students, despite at the university level, still make the confusion between ‘confectionary’ and ‘confectionery’. For the term ‘confectionery’, we can see from the Concept Linking map that it is closely linked to the words ‘singapore’, ‘india’ and ‘china’.

Topics 18 jan.png
These are the topics for the 18 Jan analysis.


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