2b:null 2c:["$","title",null,{"children":"Advertise your Jewelry products on Thursdays to increase your chances to boost sales | Akhila Sulgante | Observable"}] 31:T45d,## Data Abstraction In this section, I have described the attributes used in the above visualizations **Dataset Availability:** Static **Data Type:** Table, Temporal **Dataset Type:** Temporal
**Attributes** **`Orderdatetime`** (_Ordinal_): This attribute includes the year, Mont, date, hour, minute, and seconds at which the purchase was made **`QuantityofSKUintheorder`** (_Quantitative, Sequential_): These attribute indicates number of items sold **`Maingem`** (_Categorical_): This attribute indicates the gem in the good that was purchased **`Categoryalias`** (_Categorical_): This attribute indicates the type of jewelry that was purchased **`Mainmetal`** (_Categorical_): This attribute indicates the metal jewelry was made from **`PriceinUSD`** (_Quantitative, Sequential_): This attribute indicates the price of the jewelry that was sold **Derived Attribute** All the date and time units were derived to use in visualization.
**` Week Number`** (_Ordinal_): This attribute indicates the week Number of a particular year **`Week Day`** (_Ordinal_): This attribute indicates the week Day32:T457,## Task Abstraction This notebook can serve the purpose of consuming and producing insights by discovering and deriving respectively Abstracting at the Task level 1. **T1:**
(_Insight_): People tend to shop mostly on weekends and in the evening. This extra time and money may allow them to browse and compare products more thoroughly, leading to higher sales
Identify the weekday where most purchases were made
Sub-task 1: (_Explore_) the weekday with the most sales(_Extereme_)
Sub-task 2: (_Explore_) the time of the day with the most sales(_Extremes_)
Sub-task 3: (_Compare_) the selected time period with weekends and the evening time 1. **T2:**
(_Insight_): People buy more rings than any other jewelry as it makes a good gift and is very popularly used for weddings and proposals
Identify the category with the most purchases made
Sub-task 1: (_Locate_) the category with most goods sold(_Extereme_)
Sub-task 2: (_Identify_) the name of the category that has most sales(_Extremes_)
Sub-task 3: (_Compare_) the identified category to check if it is rings33:T807,## Idiom Abstraction * **Marks:** Area, Line * **Channels for HeatMap:** 1. **Color Saturation:** To show the intensity of goods sold over a day of the month and weekday * Attribute: QuantityofSKUintheorder, Orderdatetime * Task: T1 * Using **express** * **Expressiveness principle** - QuantityofSKUintheorder is a quantitative attribute so it is represented with color saturation * **Effectiveness principle** - The difference in color saturation effectively conveys which days of the week have a high sale 2. **Horizontal Position:** To show days of the week. * Attribute: Weekday * Task: T1 * Using **Separate, Order, Align** * **Expressiveness & Effectiveness** - Since it is ordered and aligned, it makes it easier for users to summarize the content at a glance 3. **Vertical Position:** To show the time of the day. * Attribute: hour * Task: T1 * Using **Separate, Order, Align** * **Expressiveness & Effectiveness principle** - Since it is ordered and aligned, it makes it easier for users to summarize the content and look for peak hours * **Channels for Line Chart:** This chart is obtained by filtering data from the heatmap 1. **Color Hue:** To show the different categories of jewelry purchased * Attribute: Categoryalias * Task: T2 * Using **express** * **Expressiveness principle** - Using different colors for different categories makes it easier to distinguish * **Effectiveness principle** - This channel is effective as the attribute is Categorical 2. **Horizontal Position:** Shows years * Attribute: year * Task: T2 * Using **Separate, Order, Align** * **Expressiveness & Effectiveness** - Having years ordered makes it easier to see the trend 3. **Vertical Position:** Shows the amount of jewelry sold * Attribute: QuantityofSKUintheorder * Task: T2 * Using **Separate, Order, Align** * **Expressiveness & Effectiveness** - Since it is sequential, it makes it easier to view the data34:T771,dashboard = html`

`35:T734,dateparts = html`

`36:T6ef,Trunctableau = html`

`37:T76a,calender = html`

`38:[{"id":"e518fc053f96c29ff40f10a257fc78a284ec2d2bf99e18613933964e16b8219dd94c75a2f970506590712630e90b94f03af7a456178fb39ed1ff85d1748aaded","href":"https://static.observableusercontent.com/files/e518fc053f96c29ff40f10a257fc78a284ec2d2bf99e18613933964e16b8219dd94c75a2f970506590712630e90b94f03af7a456178fb39ed1ff85d1748aaded","name":"cleaned_jewellerybuying.csv","type":"text/csv","lastModified":1678771038160.526,"size":14884543}] 2f:{"id":"abb58ec377d75593","slug":null,"title":"Advertise your Jewelry products on Thursdays to increase your chances to boost sales","thumbnail":"b4eeddfb87d024d0e5638741340a831f331e7615c3f811d86c1f5e2316a0fdc9","access_level":"public","create_time":"$D2023-03-14T03:12:36.714Z","update_time":"$D2024-01-26T23:23:09.801Z","view_time":null,"first_public_time":"$D2023-03-14T03:12:36.714Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"e76678b312b62ab3","login":"akhila","name":"Akhila Sulgante","avatar_url":"https://avatars-next.observableusercontent.com/2e264e285632a44937edf6eb6b531fffbd152d07233e70f4c70c2cd9a5cd9e09","tier":"starter_2024"},"version":514,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"667daa7e86be0e8e","login":"akhila","name":"Akhila Sulgante","avatar_url":"https://avatars-next.observableusercontent.com/2e264e285632a44937edf6eb6b531fffbd152d07233e70f4c70c2cd9a5cd9e09","tier":null}],"members":[{"type":"individual","id":"667daa7e86be0e8e","login":"akhila","name":"Akhila Sulgante","avatar_url":"https://avatars-next.observableusercontent.com/2e264e285632a44937edf6eb6b531fffbd152d07233e70f4c70c2cd9a5cd9e09","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Advertise your Jewelry products on Thursdays to increase your chances to boost sales","cells":[{"id":0,"value":"# Advertise your Jewelry products on Thursdays to increase your chances to boost sales\n_A notebook understanding Human Behavior when buying jewelry online_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":362,"value":"This Visualization is created by [Akhila Sulgante](https://akhilasulgante.github.io/portfolio) as a part of [CS 7295 class](https://johnguerra.co/classes/data_visualization_spring_2023/) at Northeastern under the guidance of [prof. John Alexis Guerra Gómez](https://johnguerra.co/)\n\n[Walkthrough video](https://youtu.be/C3slmXdoPK4)
\n[Tweet](https://twitter.com/akhila_sulgante/status/1636084514048061440?t=rvIREzEXLWQ8bw9clz3j2Q&s=08)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":487,"value":"## A dashboard to understand trends in buying jewelry","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":491,"value":"Click on areas in the below map to see the corresponding month, Jewelry category and gem sold on the particular time of the weekday","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":284,"value":"htmlElement4 = html`

${dashboard}

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":500,"value":"##### Please rerun the above cell to correct alignment","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":369,"value":"## Introduction\n\nWhen it comes to buying jewelry, human behavior patterns can be quite varied, particularly when making purchases online. People may approach this type of transaction with different levels of trust, emotional attachment to the product, and decision-making styles. Some may be more rational and analytical, while others may be more impulsive and emotionally driven. Additionally, factors such as the price, quality, and design of the jewelry can also influence buying behavior. Understanding these human behavior patterns is crucial for online jewelry retailers to cater to their customer's needs and preferences, enhance their shopping experiences, and ultimately increase sales.\n\nThis notebook provides a comprehensive analytic dashboard to learn about user buying trends focusing on the date Unit parameters/\n. The data used in this notebook is obtained from an open source which is available on [Kaggel](https://www.kaggle.com/datasets/mkechinov/ecommerce-purchase-history-from-jewelry-store) ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":376,"value":"## Actual insights\n1. People tend to buy jewelry during evening time and on Thursdays or Fridays\n2. Since jewelry is a seasonal item, the sales is not distributed uniformly across months and few months like November, August see a peak\n3. Ear ring is the most sold items in the eCommerce space in the provided data\n4. No matter what the season or time, diamond is the most popular choice of jewelry to buy online ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":378,"value":"## Made-up insights(Hypothesis)\n1. People tend to shop mostly on weekends and in the evening. This extra time and money may allow them to browse and compare products more thoroughly, leading to higher sales[**Selected**]\n * **Considerations**\n * I will need enough data on sales to comprehend by the weekday\n * I need to iterate all the records and sum all the Goods sold by weekday\n * In terms of the idiom (visualization), I would need to display a heatmap that aggregates data by Weekday and days of the month\n \n* People buy more rings than any other jewelry as it makes a good gift and is very popularly used for weddings and proposals [**Selected**]\n * **Considerations**\n * I will need enough data on sales to the jewelry of all categories\n * I need to iterate all the records and sum all the Goods sold by Category\n * In terms of the idiom (visualization), I would need to display a line graph that compares data on category level\n ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":380,"value":"## Target Audience\n1. Jewelry retailers and wholesalers could use the analytics notebook to monitor their inventory levels, track the popularity of certain products, and adjust their pricing strategies based on demand. They could also use the notebook to identify trends in customer behavior and tailor their marketing efforts accordingly.\n\n2. Online marketplaces that sell jewelry could use the notebook to monitor their sellers' sales performance, identify top-selling products, and optimize their platform to improve user experience.\n\n3. Individual jewelry designers could use the notebook to identify which gem are most popular, and adjust their product offerings accordingly.\n\n4. Users who want to gain insights into their sales and marketing strategies in order to optimize their business performance.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":382,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":392,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":399,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":319,"value":"## Data Exploration","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":437,"value":"### Exploring data through different parts of the time unit\nChange the parts of the dates for row and column to see the trend accordingly","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":265,"value":"htmlElement3 = html`

${dateparts}

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":440,"value":"This visualization is helpful when users wants to understand the under the amount of sales made per different date aspect","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":445,"value":"### Exploring data through different time units\nSelect the time unit you want to view","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":243,"value":"htmlElement2 = html`

${Trunctableau}

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":449,"value":"This visualization lets users explore trend over different time unit period","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":451,"value":"### Month Calender view\nSelect different month and year option provided in the dropdown","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":333,"value":"htmlElement5 = html`

${calender}

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":457,"value":"This month view and week number along with weekday lets users to explore trend for a particular month","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":459,"value":"### Exploring categories of jewelry that were sold","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":115,"value":"vl.markBar(vl.markBar({tooltip : true}))\n .encode(vl.y().fieldN(\"Categoryalias\").sort(\"-x\"), vl.x().count(\"QuantityofSKUintheorder\"), vl.color({scheme: \"pastel\"}))\n .data(final_data)\n.render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":461,"value":"This visualization helps users explore what category of jewelry was sold the most","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":463,"value":"### Viewing the sales over period of the time","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":323,"value":"vl.markBar(vl.markBar({tooltip : true}))\n .encode(vl.y().sum(\"PriceinUSD\").sort(\"-x\"), vl.x().fieldT(\"Orderdatetime\"))\n .data(final_data)\n.render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":465,"value":"The above visualization gives a quick glance at the peak sale period","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":494,"value":"## 5 exploration idioms\n1. Date parts vizualization\n2. Date Truncation visualization\n3. Month-wise calender view\n4. Category-wise product sold\n5. Sales pattern of the company\n\nReasons I chose Analytic dashboard as my main visualization\n1. Comprehensive\n2. Using heatmap makes it easier to show trends in a week and by hour\n3. Responsive feature lets sellers dive deep in understanding trend by month, popular gem and category","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":356,"value":"navio(final_data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"cleaned_jewellerybuying = await FileAttachment(\"cleaned_jewellerybuying.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"cleaned_jewellerybuying","database":"$undefined"},{"id":13,"value":"### Data processing","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":503,"value":"### Steps for Processing data\n1. Loading the file in the notebook\n2. Removing all the blank values from the category and order Date columns(Please refer above table for exact column names)\n3. Processing categories to remove \"jewelry.\" from the string\n4. Converted date from string format to date","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":16,"value":"raw_data = FileAttachment(\"cleaned_jewellerybuying.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":178,"value":"raw_data.map(d=>{\n d[\"Orderdatetime\"] = date_formatter(d[\"Orderdatetime\"]?.slice(0,-4))\n return d\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":153,"value":"date_formatter = d3.timeParse(\"%Y-%m-%d %H:%M:%S\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"removed_empty = raw_data.filter((d) => d.Categoryalias != \"\" && d.Orderdatetime !== \"\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":26,"value":"function groupDataByDate(removed_empty) {\n return removed_empty.reduce((groups, item) => {\n \n const [date, time] = item.Orderdatetime.toString().split(\" \");\n if (!groups[date]) {\n groups[date] = [];\n }\n groups[date].push(item);\n return groups;\n }, {});\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":67,"value":"function category(data){\n data = data.filter(function(row) {\n return isNaN(row.Categoryalias);\n});\n for(let i = 0; i < data.length; i++) {\n data[i][\"Categoryalias\"] = data[i][\"Categoryalias\"].replace(\"jewelry.\", \"\")\n \n}\n return data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"category_clean = category(removed_empty)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":187,"value":"final_data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":140,"value":"final_data = category_clean.map((d) => {\n delete d.OrderID\n delete d.PurchasedproductID\n delete d.CategoryID\n delete d.BrandID\n delete d.UserID\n return d;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":354,"value":"## Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":117,"value":"import { vl } from \"@vega/vega-lite-api\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":350,"value":"import {navio} from \"@john-guerra/navio\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":306,"value":"## Tableau rendering","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":282,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":263,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":292,"value":"divElement4 = {\n return htmlElement4.querySelector(\"#viz1678872278473\");\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":296,"value":"{\n let vizElement = divElement4?.getElementsByTagName(\"object\")[0];\n vizElement.style.width = \"1400px\";\n vizElement.style.height = \"1000px\";\n let scriptElement = document.createElement(\"script\");\n scriptElement.src = \"https://public.tableau.com/javascripts/api/viz_v1.js\";\n vizElement.parentNode.insertBefore(scriptElement, vizElement);\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":267,"value":"divElement3 = {\n return htmlElement3.querySelector(\"#viz1678864890209\");\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":270,"value":"{\n let vizElement = divElement3?.getElementsByTagName(\"object\")[0];\n vizElement.style.width = \"1400px\";\n vizElement.style.height = \"800px\";\n let scriptElement = document.createElement(\"script\");\n scriptElement.src = \"https://public.tableau.com/javascripts/api/viz_v1.js\";\n vizElement.parentNode.insertBefore(scriptElement, vizElement);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":232,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":249,"value":"divElement2 = {\n return htmlElement2.querySelector(\"#viz1678861829309\");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":247,"value":"{\n let vizElement = divElement2?.getElementsByTagName(\"object\")[0];\n vizElement.style.width = \"1400px\";\n vizElement.style.height = \"500px\";\n let scriptElement = document.createElement(\"script\");\n scriptElement.src = \"https://public.tableau.com/javascripts/api/viz_v1.js\";\n vizElement.parentNode.insertBefore(scriptElement, vizElement);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":328,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":330,"value":"divElement5 = {\n return htmlElement5.querySelector(\"#viz1678904161978\");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":337,"value":"{\n let vizElement = divElement5?.getElementsByTagName(\"object\")[0];\n vizElement.style.width = \"1400px\";\n vizElement.style.height = \"900px\";\n let scriptElement = document.createElement(\"script\");\n scriptElement.src = \"https://public.tableau.com/javascripts/api/viz_v1.js\";\n vizElement.parentNode.insertBefore(scriptElement, vizElement);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W38","stdlib":"1","titling":"auto"}} 30:"$2f:body"