By: Omkar Nitin Sadekar
Project Report
INTRODUCTION
The goal of lead generation is to
attract prospects by capturing and stoking interest in a company’s
services and products within the targeted demographic. Building a sales
pipeline and nurturing leads until they become clients are the main
goals of the process. All businesses—small, medium- sized, and large—as
well as B2C and B2B organizations across industries—rely on lead
generation to grow their businesses. Even 60% of marketers think that
lead generation is an important tactic. The company X Education, which
offers consumers online education services, is represented in the
dataset used in this project. Lead generation analytics is essential for
businesses to analyze and monitor the Lead generation funnel as the
ED-tech market is growing daily. It provides the sales and marketing
teams with practical information that enable them to take calculated
risks when investing in brand development and advertising.
Big Data Analytics for Customer-Centered Processes
Companies
such as P&G pioneered the Marketing-Mixed Analytics discipline in
order to communicate with individual customers while putting a
competitive product on the market. They began by gathering data from
vendors to better understand how different channels influenced their
customers (grocers) and consumers (Product Users). Companies can harness
the power of Big Data and utilize these tools to test marketing
campaigns for various groups of society. It is now necessary to stay up
with the pace of Analytics 4.0, which is the introduction of AI in
analytics and automated data channeling systems. Many organizations have
yet to implement technologies and methods in the recent period of
Analytics 2.0 and Analytics 3.0 when Big data hit the market and
executives recognized its powers to provide high-quality actionable
insights from a large pool of data. Making an entire firm data literate
and expecting employees and senior management to interact fluidly in
order to exploit data to achieve corporate goals is not as simple as it
seems.[1]
Business Objectives
Plan of Action
The plan involves cleansing and
analyzing the dataset to understand the factors affecting customer
retention. Through exploratory data analysis and segmentation, key
drivers influencing conversion rates, such as website engagement and
lead origin, will be identified. Actionable insights will be derived to
optimize these touchpoints and improve overall customer experience.
Strategies will be implemented, monitored, and evaluated for their
effectiveness, with documentation and knowledge sharing ensuring
continuous improvement in retention efforts.
ANALYSIS
## 'data.frame': 9240 obs. of 20 variables:
## $ Lead.Origin : chr "API" "API" "Landing Page Submission" "Landing Page Submission" ...
## $ Lead.Source : chr "Olark Chat" "Organic Search" "Direct Traffic" "Direct Traffic" ...
## $ Do.Not.Email : chr "No" "No" "No" "No" ...
## $ Converted : num 0 0 1 0 1 0 1 0 0 0 ...
## $ TotalVisits : num 0 5 2 1 2 0 2 0 2 4 ...
## $ Total.Time.Spent.on.Website : num 0 674 1532 305 1428 ...
## $ Page.Views.Per.Visit : num 0 2.5 2 1 1 0 2 0 2 4 ...
## $ Last.Activity : chr "Page Visited on Website" "Email Opened" "Email Opened" "Unreachable" ...
## $ Specialization : chr "Select" "Select" "Business Administration" "Media and Advertising" ...
## $ How.did.you.hear.about.X.Education : chr "Select" "Select" "Select" "Word Of Mouth" ...
## $ What.is.your.current.occupation : chr "Unemployed" "Unemployed" "Student" "Unemployed" ...
## $ Tags : chr "Interested in other courses" "Ringing" "Will revert after reading the email" "Ringing" ...
## $ Lead.Quality : chr "Low in Relevance" NA "Might be" "Not Sure" ...
## $ Lead.Profile : chr "Select" "Select" "Potential Lead" "Select" ...
## $ Asymmetrique.Activity.Index : chr "02.Medium" "02.Medium" "02.Medium" "02.Medium" ...
## $ Asymmetrique.Profile.Index : chr "02.Medium" "02.Medium" "01.High" "01.High" ...
## $ Asymmetrique.Activity.Score : num 15 15 14 13 15 17 14 15 14 13 ...
## $ Asymmetrique.Profile.Score : num 15 15 20 17 18 15 20 15 14 16 ...
## $ A.free.copy.of.Mastering.The.Interview: chr "No" "No" "Yes" "No" ...
## $ Last.Notable.Activity : chr "Modified" "Email Opened" "Email Opened" "Modified" ...## [1] 9240 20## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
## always returns an ungrouped data frame and adjust accordingly.
## ℹ The deprecated feature was likely used in the dplyr package.
## Please report the issue at <https://github.com/tidyverse/dplyr/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
| x | |
|---|---|
| Lead.Origin | 0.0000000 |
| Lead.Source | 0.3896104 |
| Do.Not.Email | 0.0000000 |
| Converted | 0.0000000 |
| TotalVisits | 1.4826840 |
| Total.Time.Spent.on.Website | 0.0000000 |
| Page.Views.Per.Visit | 1.4826840 |
| Last.Activity | 1.1147186 |
| Specialization | 15.5627706 |
| How.did.you.hear.about.X.Education | 23.8852814 |
| What.is.your.current.occupation | 29.1125541 |
| Tags | 36.2878788 |
| Lead.Quality | 51.5909091 |
| Lead.Profile | 29.3181818 |
| Asymmetrique.Activity.Index | 45.6493506 |
| Asymmetrique.Profile.Index | 45.6493506 |
| Asymmetrique.Activity.Score | 45.6493506 |
| Asymmetrique.Profile.Score | 45.6493506 |
| A.free.copy.of.Mastering.The.Interview | 0.0000000 |
| Last.Notable.Activity | 0.0000000 |
## [1] 6372 14## Rows: 6,372
## Columns: 14
## $ Lead.Origin <chr> "API", "API", "Landing Page Sub…
## $ Lead.Source <chr> "Olark Chat", "Organic Search",…
## $ Do.Not.Email <chr> "No", "No", "No", "No", "No", "…
## $ Converted <fct> No, No, Yes, No, Yes, Yes, Yes,…
## $ TotalVisits <dbl> 0, 5, 2, 1, 2, 2, 8, 8, 11, 5, …
## $ Total.Time.Spent.on.Website <dbl> 0, 674, 1532, 305, 1428, 1640, …
## $ Page.Views.Per.Visit <dbl> 0.00, 2.50, 2.00, 1.00, 1.00, 2…
## $ Last.Activity <chr> "Page Visited on Website", "Ema…
## $ Specialization <chr> "Select", "Select", "Business A…
## $ How.did.you.hear.about.X.Education <chr> "Select", "Select", "Select", "…
## $ What.is.your.current.occupation <chr> "Unemployed", "Unemployed", "St…
## $ Lead.Profile <chr> "Select", "Select", "Potential …
## $ A.free.copy.of.Mastering.The.Interview <chr> "No", "No", "Yes", "No", "No", …
## $ Last.Notable.Activity <chr> "Modified", "Email Opened", "Em…## [1] 6098 14| Index | Variable_Name | Variable_Type | Sample_n | Missing_Count | Per_of_Missing | No_of_distinct_values | mean | median | var |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Lead.Origin | character | 6098 | 0 | 0 | 4 | NA | NA | NA |
| 2 | Lead.Source | character | 6098 | 0 | 0 | 16 | NA | NA | NA |
| 3 | Do.Not.Email | character | 6098 | 0 | 0 | 2 | NA | NA | NA |
| 4 | Converted | factor | 6098 | 0 | 0 | 2 | NA | NA | NA |
| 5 | TotalVisits | numeric | 6098 | 0 | 0 | 11 | 3.04 | 3 | 5.99 |
| 6 | Total.Time.Spent.on.Website | numeric | 6098 | 0 | 0 | 1584 | 524.87 | 275 | 316517.80 |
| 7 | Page.Views.Per.Visit | numeric | 6098 | 0 | 0 | 30 | 2.36 | 2 | 3.68 |
| 8 | Last.Activity | character | 6098 | 0 | 0 | 16 | NA | NA | NA |
| 9 | Specialization | character | 6098 | 0 | 0 | 19 | NA | NA | NA |
| 10 | How.did.you.hear.about.X.Education | character | 6098 | 0 | 0 | 10 | NA | NA | NA |
| 11 | What.is.your.current.occupation | character | 6098 | 0 | 0 | 6 | NA | NA | NA |
| 12 | Lead.Profile | character | 6098 | 0 | 0 | 6 | NA | NA | NA |
| 13 | A.free.copy.of.Mastering.The.Interview | character | 6098 | 0 | 0 | 2 | NA | NA | NA |
| 14 | Last.Notable.Activity | character | 6098 | 0 | 0 | 14 | NA | NA | NA |
| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| Lead.Origin | 6098 | ||||||
| … API | 2071 | 34% | |||||
| … Landing Page Submission | 3423 | 56% | |||||
| … Lead Add Form | 577 | 9% | |||||
| … Lead Import | 27 | 0% | |||||
| Do.Not.Email | 6098 | ||||||
| … No | 5688 | 93% | |||||
| … Yes | 410 | 7% | |||||
| Converted | 6098 | ||||||
| … No | 3184 | 52% | |||||
| … Yes | 2914 | 48% | |||||
| TotalVisits | 6098 | 3 | 2.4 | 0 | 1 | 4 | 10 |
| Total.Time.Spent.on.Website | 6098 | 525 | 563 | 0 | 27 | 1013 | 2272 |
| Page.Views.Per.Visit | 6098 | 2.4 | 1.9 | 0 | 1 | 3 | 10 |
| What.is.your.current.occupation | 6098 | ||||||
| … Businessman | 5 | 0% | |||||
| … Housewife | 9 | 0% | |||||
| … Other | 13 | 0% | |||||
| … Student | 189 | 3% | |||||
| … Unemployed | 5238 | 86% | |||||
| … Working Professional | 644 | 11% | |||||
| Lead.Profile | 6098 | ||||||
| … Dual Specialization Student | 19 | 0% | |||||
| … Lateral Student | 19 | 0% | |||||
| … Other Leads | 458 | 8% | |||||
| … Potential Lead | 1489 | 24% | |||||
| … Select | 3879 | 64% | |||||
| … Student of SomeSchool | 234 | 4% | |||||
| A.free.copy.of.Mastering.The.Interview | 6098 | ||||||
| … No | 4087 | 67% | |||||
| … Yes | 2011 | 33% |
| Vname | Group | TN | nNeg | nZero | nPos | NegInf | PosInf | NA_Value | Per_of_Missing | sum | min | max | mean | median | SD | CV | IQR | Skewness | Kurtosis | X0. | X10. | X20. | X30. | X40. | X50. | X60. | X70. | X80. | X90. | X100. | LB.25. | UB.75. | nOutliers | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | Page.Views.Per.Visit | All | 6098 | 0 | 1347 | 4751 | 0 | 0 | 0 | 0 | 14415.54 | 0 | 10 | 2.36 | 2 | 1.92 | 0.81 | 2.00 | 0.87 | 0.95 | 0 | 0 | 0 | 1.33 | 2.0 | 2 | 2.5 | 3 | 4.0 | 5 | 10 | -2.00 | 6.00 | 207 |
| 2 | Total.Time.Spent.on.Website | All | 6098 | 0 | 1351 | 4747 | 0 | 0 | 0 | 0 | 3200676.00 | 0 | 2272 | 524.87 | 275 | 562.60 | 1.07 | 985.75 | 0.82 | -0.69 | 0 | 0 | 0 | 70.00 | 165.8 | 275 | 426.2 | 861 | 1138.6 | 1412 | 2272 | -1451.38 | 2491.62 | 0 |
| 1 | TotalVisits | All | 6098 | 0 | 1347 | 4751 | 0 | 0 | 0 | 0 | 18508.00 | 0 | 10 | 3.04 | 3 | 2.45 | 0.81 | 3.00 | 0.66 | -0.03 | 0 | 0 | 0 | 2.00 | 2.0 | 3 | 3.0 | 4 | 5.0 | 6 | 10 | -3.50 | 8.50 | 204 |
## $`0`
## $`0`
## $`0`
## $`0`
| Lead.Origin | Lead.Source | Attribute | Count | sum | mean | median |
|---|---|---|---|---|---|---|
| API | Olark Chat | TotalVisits | 881 | 332 | 0.3768445 | 0 |
| API | Organic Search | TotalVisits | 310 | 1296 | 4.1806452 | 4 |
| Landing Page Submission | Direct Traffic | TotalVisits | 1726 | 5964 | 3.4553882 | 3 |
| Landing Page Submission | TotalVisits | 1218 | 5357 | 4.3981938 | 4 | |
| Landing Page Submission | Organic Search | TotalVisits | 458 | 2451 | 5.3515284 | 5 |
| API | Referral Sites | TotalVisits | 51 | 232 | 4.5490196 | 4 |
| API | TotalVisits | 762 | 2365 | 3.1036745 | 3 | |
| API | Direct Traffic | TotalVisits | 65 | 294 | 4.5230769 | 4 |
| Landing Page Submission | Referral Sites | TotalVisits | 13 | 71 | 5.4615385 | 5 |
| Lead Add Form | Reference | TotalVisits | 441 | 86 | 0.1950113 | 0 |
| Lead Add Form | Welingak Website | TotalVisits | 128 | 15 | 0.1171875 | 0 |
| Lead Add Form | TotalVisits | 1 | 0 | 0.0000000 | 0 | |
| Lead Import | TotalVisits | 27 | 8 | 0.2962963 | 0 | |
| Lead Add Form | Olark Chat | TotalVisits | 2 | 2 | 1.0000000 | 1 |
| Landing Page Submission | Pay per Click Ads | TotalVisits | 1 | 3 | 3.0000000 | 3 |
| Landing Page Submission | bing | TotalVisits | 2 | 6 | 3.0000000 | 3 |
| API | Social Media | TotalVisits | 1 | 2 | 2.0000000 | 2 |
| Landing Page Submission | WeLearn | TotalVisits | 1 | 2 | 2.0000000 | 2 |
| Lead Add Form | Live Chat | TotalVisits | 2 | 0 | 0.0000000 | 0 |
| API | bing | TotalVisits | 1 | 2 | 2.0000000 | 2 |
| Lead Add Form | Click2call | TotalVisits | 3 | 2 | 0.6666667 | 0 |
| Landing Page Submission | testone | TotalVisits | 1 | 5 | 5.0000000 | 5 |
| Landing Page Submission | TotalVisits | 1 | 4 | 4.0000000 | 4 | |
| Landing Page Submission | Press_Release | TotalVisits | 1 | 6 | 6.0000000 | 6 |
| Landing Page Submission | Social Media | TotalVisits | 1 | 3 | 3.0000000 | 3 |
| VARIABLE | CATEGORY | Converted:No | Converted:Yes | TOTAL |
|---|---|---|---|---|
| Lead.Origin | API | 1159 | 912 | 2071 |
| Lead.Origin | Landing Page Submission | 1970 | 1453 | 3423 |
| Lead.Origin | Lead Add Form | 37 | 540 | 577 |
| Lead.Origin | Lead Import | 18 | 9 | 27 |
| Lead.Origin | TOTAL | 3184 | 2914 | 6098 |
| Do.Not.Email | No | 2859 | 2829 | 5688 |
| Do.Not.Email | Yes | 325 | 85 | 410 |
| Do.Not.Email | TOTAL | 3184 | 2914 | 6098 |
| How.did.you.hear.about.X.Education | Advertisements | 21 | 26 | 47 |
| How.did.you.hear.about.X.Education | 9 | 12 | 21 | |
| How.did.you.hear.about.X.Education | Multiple Sources | 63 | 48 | 111 |
| How.did.you.hear.about.X.Education | Online Search | 272 | 292 | 564 |
| How.did.you.hear.about.X.Education | Other | 73 | 66 | 139 |
| How.did.you.hear.about.X.Education | Select | 2484 | 2207 | 4691 |
| How.did.you.hear.about.X.Education | SMS | 7 | 5 | 12 |
| How.did.you.hear.about.X.Education | Social Media | 26 | 23 | 49 |
| How.did.you.hear.about.X.Education | Student of SomeSchool | 113 | 117 | 230 |
| How.did.you.hear.about.X.Education | Word Of Mouth | 116 | 118 | 234 |
| How.did.you.hear.about.X.Education | TOTAL | 3184 | 2914 | 6098 |
| What.is.your.current.occupation | Businessman | 1 | 4 | 5 |
| What.is.your.current.occupation | Housewife | 0 | 9 | 9 |
| What.is.your.current.occupation | Other | 6 | 7 | 13 |
| What.is.your.current.occupation | Student | 118 | 71 | 189 |
| What.is.your.current.occupation | Unemployed | 3008 | 2230 | 5238 |
| What.is.your.current.occupation | Working Professional | 51 | 593 | 644 |
| What.is.your.current.occupation | TOTAL | 3184 | 2914 | 6098 |
| Lead.Profile | Dual Specialization Student | 0 | 19 | 19 |
| Lead.Profile | Lateral Student | 0 | 19 | 19 |
| Lead.Profile | Other Leads | 291 | 167 | 458 |
| Lead.Profile | Potential Lead | 326 | 1163 | 1489 |
| Lead.Profile | Select | 2341 | 1538 | 3879 |
| Lead.Profile | Student of SomeSchool | 226 | 8 | 234 |
| Lead.Profile | TOTAL | 3184 | 2914 | 6098 |
| A.free.copy.of.Mastering.The.Interview | No | 1995 | 2092 | 4087 |
| A.free.copy.of.Mastering.The.Interview | Yes | 1189 | 822 | 2011 |
| A.free.copy.of.Mastering.The.Interview | TOTAL | 3184 | 2914 | 6098 |
## Warning in FUN(X[[i]], ...): NAs introduced by coercion| Variable | Target | Unique | Chi-squared | p-value | df | IV Value | Cramers V | Degree of Association | Predictive Power |
|---|---|---|---|---|---|---|---|---|---|
| Lead.Origin | Converted | 4 | 538.136 | 0.000 | NA | 0.57 | 0.30 | Strong | Highly Predictive |
| Do.Not.Email | Converted | 2 | 128.944 | 0.000 | NA | 0.09 | 0.15 | Weak | Somewhat Predictive |
| How.did.you.hear.about.X.Education | Converted | 10 | 9.073 | 0.423 | NA | 0.00 | 0.04 | Very Weak | Not Predictive |
| What.is.your.current.occupation | Converted | 6 | 583.465 | 0.000 | NA | 0.45 | 0.31 | Strong | Highly Predictive |
| Lead.Profile | Converted | 6 | 901.205 | 0.000 | NA | 0.50 | 0.38 | Strong | Highly Predictive |
| A.free.copy.of.Mastering.The.Interview | Converted | 2 | 57.436 | 0.000 | NA | 0.03 | 0.10 | Weak | Not Predictive |
| TotalVisits | Converted | 6 | 11.992 | 0.032 | NA | 0.00 | 0.04 | Very Weak | Not Predictive |
| Total.Time.Spent.on.Website | Converted | 8 | 1166.617 | 0.000 | NA | 0.86 | 0.44 | Strong | Highly Predictive |
| Page.Views.Per.Visit | Converted | 7 | 78.101 | 0.000 | NA | 0.05 | 0.11 | Weak | Not Predictive |
## Warning in FUN(X[[i]], ...): NAs introduced by coercion
## Warning in FUN(X[[i]], ...): NAs introduced by coercion| Variable | Target | Unique | Chi-squared | p-value | df | IV Value | Cramers V | Degree of Association | Predictive Power |
|---|---|---|---|---|---|---|---|---|---|
| Lead.Origin | Converted | 4 | 538.136 | 0.000 | NA | 0.57 | 0.30 | Strong | Highly Predictive |
| Do.Not.Email | Converted | 2 | 128.944 | 0.000 | NA | 0.09 | 0.15 | Weak | Somewhat Predictive |
| How.did.you.hear.about.X.Education | Converted | 10 | 9.073 | 0.422 | NA | 0.00 | 0.04 | Very Weak | Not Predictive |
| What.is.your.current.occupation | Converted | 6 | 583.465 | 0.000 | NA | 0.45 | 0.31 | Strong | Highly Predictive |
| Lead.Profile | Converted | 6 | 901.205 | 0.000 | NA | 0.50 | 0.38 | Strong | Highly Predictive |
| A.free.copy.of.Mastering.The.Interview | Converted | 2 | 57.436 | 0.000 | NA | 0.03 | 0.10 | Weak | Not Predictive |
| TotalVisits | Converted | 6 | 11.992 | 0.038 | NA | 0.00 | 0.04 | Very Weak | Not Predictive |
| Total.Time.Spent.on.Website | Converted | 8 | 1166.617 | 0.000 | NA | 0.86 | 0.44 | Strong | Highly Predictive |
| Page.Views.Per.Visit | Converted | 7 | 78.101 | 0.000 | NA | 0.05 | 0.11 | Weak | Not Predictive |
## [1] "Lead.Origin"
## [2] "Lead.Source"
## [3] "Do.Not.Email"
## [4] "Converted"
## [5] "TotalVisits"
## [6] "Total.Time.Spent.on.Website"
## [7] "Page.Views.Per.Visit"
## [8] "Last.Activity"
## [9] "Specialization"
## [10] "How.did.you.hear.about.X.Education"
## [11] "What.is.your.current.occupation"
## [12] "Lead.Profile"
## [13] "A.free.copy.of.Mastering.The.Interview"
## [14] "Last.Notable.Activity"
##
## One Sample t-test
##
## data: Leads_df$Total.Time.Spent.on.Website
## t = 38.153, df = 6097, p-value = 1
## alternative hypothesis: true mean is less than 250
## 95 percent confidence interval:
## -Inf 536.7253
## sample estimates:
## mean of x
## 524.8731##
## One Sample t-test
##
## data: Leads_df$Total.Time.Spent.on.Website
## t = 38.153, df = 6097, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 250
## 95 percent confidence interval:
## 510.7497 538.9965
## sample estimates:
## mean of x
## 524.8731
##
## Two Sample t-test
##
## data: Converted$Total.Time.Spent.on.Website and NotConverted$Total.Time.Spent.on.Website
## t = 13.716, df = 1998, p-value = 1
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf 373.9537
## sample estimates:
## mean of x mean of y
## 701.317 367.424##
## Two Sample t-test
##
## data: Converted$Total.Time.Spent.on.Website and NotConverted$Total.Time.Spent.on.Website
## t = 13.716, df = 1998, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 286.1509 381.6351
## sample estimates:
## mean of x mean of y
## 701.317 367.424##
## Pearson's Chi-squared test
##
## data: Leads_df$Lead.Origin and Leads_df$Converted
## X-squared = 538.14, df = 3, p-value < 2.2e-16## Leads_df$Converted
## Leads_df$Lead.Origin No Yes
## API 1081.34864 989.65136
## Landing Page Submission 1787.27976 1635.72024
## Lead Add Form 301.27386 275.72614
## Lead Import 14.09774 12.90226##
## API Landing Page Submission Lead Add Form
## 2071 3423 577
## Lead Import
## 27##
## Chi-squared test for given probabilities
##
## data: deg_count
## X-squared = 267.74, df = 3, p-value < 2.2e-16## API Landing Page Submission Lead Add Form
## 2012.34 3231.94 548.82
## Lead Import
## 304.90## Lead.Origin Lead.Source Do.Not.Email Converted TotalVisits
## <char> <char> <char> <fctr> <num>
## 1: API Olark Chat No No 0
## 2: API Organic Search No No 5
## 3: Landing Page Submission Direct Traffic No Yes 2
## 4: Landing Page Submission Direct Traffic No No 1
## 5: Landing Page Submission Google No Yes 2
## ---
## 6094: Landing Page Submission Direct Traffic No Yes 5
## 6095: Landing Page Submission Direct Traffic Yes Yes 8
## 6096: Landing Page Submission Direct Traffic No No 2
## 6097: Landing Page Submission Direct Traffic Yes No 2
## 6098: Landing Page Submission Direct Traffic No Yes 6
## Total.Time.Spent.on.Website Page.Views.Per.Visit Last.Activity
## <num> <num> <char>
## 1: 0 0.00 Page Visited on Website
## 2: 674 2.50 Email Opened
## 3: 1532 2.00 Email Opened
## 4: 305 1.00 Unreachable
## 5: 1428 1.00 Converted to Lead
## ---
## 6094: 210 2.50 SMS Sent
## 6095: 1845 2.67 Email Marked Spam
## 6096: 238 2.00 SMS Sent
## 6097: 199 2.00 SMS Sent
## 6098: 1279 3.00 SMS Sent
## Specialization How.did.you.hear.about.X.Education
## <char> <char>
## 1: Select Select
## 2: Select Select
## 3: Business Administration Select
## 4: Media and Advertising Word Of Mouth
## 5: Select Other
## ---
## 6094: Business Administration Select
## 6095: IT Projects Management Select
## 6096: Media and Advertising Select
## 6097: Business Administration Select
## 6098: Supply Chain Management Select
## What.is.your.current.occupation Lead.Profile
## <char> <char>
## 1: Unemployed Select
## 2: Unemployed Select
## 3: Student Potential Lead
## 4: Unemployed Select
## 5: Unemployed Select
## ---
## 6094: Unemployed Potential Lead
## 6095: Unemployed Potential Lead
## 6096: Unemployed Potential Lead
## 6097: Unemployed Potential Lead
## 6098: Unemployed Potential Lead
## A.free.copy.of.Mastering.The.Interview Last.Notable.Activity Converted_No
## <char> <char> <int>
## 1: No Modified 1
## 2: No Email Opened 1
## 3: Yes Email Opened 0
## 4: No Modified 1
## 5: No Modified 0
## ---
## 6094: No Modified 0
## 6095: No Email Marked Spam 0
## 6096: Yes SMS Sent 1
## 6097: Yes SMS Sent 1
## 6098: Yes Modified 0
## Converted_Yes
## <int>
## 1: 0
## 2: 0
## 3: 1
## 4: 0
## 5: 1
## ---
## 6094: 1
## 6095: 1
## 6096: 0
## 6097: 0
## 6098: 1## TotalVisits Total.Time.Spent.on.Website Page.Views.Per.Visit
## Min. : 0.000 Min. : 0.0 Min. : 0.000
## 1st Qu.: 2.000 1st Qu.: 50.0 1st Qu.: 1.250
## Median : 3.000 Median : 206.0 Median : 2.000
## Mean : 3.062 Mean : 361.6 Mean : 2.483
## 3rd Qu.: 4.000 3rd Qu.: 418.2 3rd Qu.: 3.000
## Max. :10.000 Max. :2272.0 Max. :10.000## TotalVisits Total.Time.Spent.on.Website Page.Views.Per.Visit
## Min. : 0.000 Min. : 0.0 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 0.0 1st Qu.: 0.000
## Median : 3.000 Median : 766.0 Median : 2.000
## Mean : 3.006 Mean : 703.3 Mean : 2.234
## 3rd Qu.: 5.000 3rd Qu.:1250.0 3rd Qu.: 3.000
## Max. :10.000 Max. :2253.0 Max. :10.000## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##
## Call:
## lm(formula = TotalVisits ~ Total.Time.Spent.on.Website + Page.Views.Per.Visit,
## data = S_no)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1079 -0.5669 -0.5279 -0.4605 7.6913
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.279e-01 4.230e-02 12.48 < 2e-16 ***
## Total.Time.Spent.on.Website 2.720e-04 5.485e-05 4.96 7.43e-07 ***
## Page.Views.Per.Visit 9.810e-01 1.326e-02 73.97 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 3181 degrees of freedom
## Multiple R-squared: 0.6492, Adjusted R-squared: 0.6489
## F-statistic: 2943 on 2 and 3181 DF, p-value: < 2.2e-16## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
##
## Call:
## lm(formula = TotalVisits ~ Total.Time.Spent.on.Website + Page.Views.Per.Visit,
## data = S_yes)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6996 -0.8847 -0.4719 -0.2560 8.2222
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.719e-01 4.768e-02 9.897 <2e-16 ***
## Total.Time.Spent.on.Website 6.305e-04 5.512e-05 11.438 <2e-16 ***
## Page.Views.Per.Visit 9.357e-01 1.719e-02 54.442 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.572 on 2911 degrees of freedom
## Multiple R-squared: 0.6384, Adjusted R-squared: 0.6382
## F-statistic: 2570 on 2 and 2911 DF, p-value: < 2.2e-16## `geom_smooth()` using formula = 'y ~ x'## Warning: The following aesthetics were dropped during statistical transformation:
## colour.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?