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An AI project : The AIM of the project is to come out with Business Insights on the data provided and Train a Machine Learning model which can predict the success of campaign with highest accuracy percentage.
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The low product line has the highest demand as 80 customers selected, followed by middle product line with 61 customers and finally upper product line. The following extracts shows the demand of the three bicycles on the basis of number of customers, sex, and education.
Analyzing the popularity and demand for three bicycles using sex showed that males have a higher proportion of using bicycles than females. This is show in the following extract and chart.
Also, the level of education determines the use of bicycles. The demand for bicycles varies across the different levels of education. The analysis revealed that non-college high school diploma do not use bicycles. The following pie chart shows the proportion of each education level with respect to the use of bicycles.
Education
Number of Customers
Percentage
Non-High School Diploma
0
0%
High School Diploma
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1%
Some-College -level work
67
37%
College Degree
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54%
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However, the use of the three products line varied greatly with the age of customers. The following frequency distribution table shows the age group of customers and the frequency of using the three products line.
Bin
Frequency
Cumulative %
Bin
Frequency
Cumulative %
20
10
5.56%
25
62
34.44%
25
62
40.00%
30
45
59.44%
30
45
65.00%
35
32
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32
82.78%
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16
86.11%
40
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20
10
91.67%
45
8
96.11%
45
8
96.11%
50
7
100.00%
50
7
100.00%
More
0
100.00%
More
0
100.00%
As it can be seen from the histogram, the distribution of age of customers and the frequency on uses of bikes is negatively skewed. That is, at early ages, customers use bicycles more than old ages. At age group 20-25, the demand of bicycles is high and it decreases as age increases. The mean age, median age, mode of an average customer is showed in the following table. The table also shows the average income that most customers receive,
Mean Age
28.98889
Mode Age
25
Median Age
27
Average Income
35672.22
Median Income
34000
More analysis have been done on individual products lines in order to determine the mean age of a customer at a given product line; average salary, average miles/ week, average times/ week among other analysis. The following discussion focuses on each of the three product lines.
a) Lower Product Line.
The following analysis shows the profile of an average customer who chooses to by Low Product Line.
Mean Age
28.6
Sex
Males
55%
Females
45%
Status
Single:
36%
Married.
64%
Mean Salary
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The analysis of the data has been done using excel statistical sof.docx
The analysis of the data has been done using excel statistical software. First, the demand and popularity of each product has been analyzed using pie charts. The extracts from excel shows the distributions of the three product lines across age, sex and education. The three types of bicycles have analyzed in terms of the number of customers using them, sex, and education levels.
The low product line has the highest demand as 80 customers selected, followed by middle product line with 61 customers and finally upper product line. The following extracts shows the demand of the three bicycles on the basis of number of customers, sex, and education.
Analyzing the popularity and demand for three bicycles using sex showed that males have a higher proportion of using bicycles than females. This is show in the following extract and chart.
Also, the level of education determines the use of bicycles. The demand for bicycles varies across the different levels of education. The analysis revealed that non-college high school diploma do not use bicycles. The following pie chart shows the proportion of each education level with respect to the use of bicycles.
Education
Number of Customers
Percentage
Non-High School Diploma
0
0%
High School Diploma
2
1%
Some-College -level work
67
37%
College Degree
97
54%
Graduate Degree at work
14
8%
However, the use of the three products line varied greatly with the age of customers. The following frequency distribution table shows the age group of customers and the frequency of using the three products line.
Bin
Frequency
Cumulative %
Bin
Frequency
Cumulative %
20
10
5.56%
25
62
34.44%
25
62
40.00%
30
45
59.44%
30
45
65.00%
35
32
77.22%
35
32
82.78%
40
16
86.11%
40
16
91.67%
20
10
91.67%
45
8
96.11%
45
8
96.11%
50
7
100.00%
50
7
100.00%
More
0
100.00%
More
0
100.00%
As it can be seen from the histogram, the distribution of age of customers and the frequency on uses of bikes is negatively skewed. That is, at early ages, customers use bicycles more than old ages. At age group 20-25, the demand of bicycles is high and it decreases as age increases. The mean age, median age, mode of an average customer is showed in the following table. The table also shows the average income that most customers receive,
Mean Age
28.98889
Mode Age
25
Median Age
27
Average Income
35672.22
Median Income
34000
More analysis have been done on individual products lines in order to determine the mean age of a customer at a given product line; average salary, average miles/ week, average times/ week among other analysis. The following discussion focuses on each of the three product lines.
a) Lower Product Line.
The following analysis shows the profile of an average customer who chooses to by Low Product Line.
Mean Age
28.6
Sex
Males
55%
Females
45%
Status
Single:
36%
Married.
64%
Mean Salary
30700
Average Miles
88
Average Time/week
3.01 ...
Analyzing Customer Journey And Data From 360 Degree PowerPoint Presentation S...
Customer 360 is a tool used by marketers to understand their customers end to end journey starting from the initial stage of awareness till the end stage of loyalty. A customer 360 program collects customer data form multiple sources and analysis different touch points of the customer. An organization performs a customer 360 check to understand their customers and make changes in their marketing strategies in order to increases the overall customer engagement and experience. This presentation is helpful for marketing managers and organizations with an objective to understand their customer by collecting data from multiple sources, analyze the data and develop multiple marketing strategies in order to increase their customer delight and improve the customer engagement with an objective of boosting the organization revenues. Initially this presentation highlights the need of customer 360 program within the organization by understanding multiple problems such as low conversion rate, low retention rate and poor sales of the organization. Once the problem is identified the current situation of the organization is analyzed, this is done by understanding the customer interaction history with the organization, the customer acquisition cost, the flow of organizations products and services, the marketing campaigns carried out by the company, the data collected through the CRM systems etc. Once all the data is collected it is analyzed in comparison with the customer journey of the organization. The multiple steps of the customer journey can be awareness, acquisition, conversion, retention and loyalty. After carefully studying the customer data multiple strategies to improve the customer experience are analyzed, these can be identifying the customer demographic, Defining the ideal customer profile, analyzing he customer psycho graphic data and understanding the customer behavioral data. After the initial analyses multiple new marketing strategies are devised to improve the customer experience in general. These strategies can be new segmentation strategy, the positioning strategy of the organization, deciding upon the communication channels, designing a new brand launch process and developing a strategic messaging map. Once all the strategies are developed, sale forecasting is done in order to understand the impact of these strategies on the organization. The customer value life cycle is also forecasting in comparison to the cost of customer acquisition. A training plan for the sales and marketing team is developed and multiple risk associated to eh entire process is analyzed, and a mitigation plan is developed for the same. In the end multiple customer cases are studied to understand the true impact of customer 360 and the performance of the entire is tracked with the help if KPIs or key performance indicators. https://bit.ly/2YQDfUK
Funnels Workshop Web Summit 2014 @geckoboard @GA
The document discusses customer funnels and analytics. It defines a funnel as visualizing a customer's journey from stranger to follower through various stages like acquisition, activation, retention, revenue, and referral. It emphasizes measuring key metrics at each stage of the customer journey. It distinguishes between vanity metrics that don't change behavior and actionable metrics that can help improve the funnel. The document provides resources for testing and optimizing the funnel through techniques like A/B testing and cohort analysis.
MidTerm memo
The document describes building a model to predict customer responses to a home equity line of credit mailing. Reducing the mailing by 25% captures 77.77% of responses. The most impactful variables are customer segments - prospects who are high status seniors have a 10.12% higher response probability, and prospects who are Dinki's (double income, no kids) have a 12.45% higher probability. An alternative 36% mailing reduction captures 44% of responses based on lift analysis.
Prediction of customer propensity to churn - Telecom Industry
- A logistic regression model was found to best predict customer churn with the highest AUC and accuracy.
- The top variables increasing churn risk were credit class, handset price, average monthly calls, billing adjustments, household subscribers, call waiting ranges, and dropped/blocked calls.
- Cost and billing variables like charges and usage were significant, validating an independent survey.
- A lift chart showed targeting the highest risk 30% of customers could identify 33% of potential churners. The model allows prioritizing retention efforts on the 20% riskiest customers.
Lottery marketing effectiveness case study
Applications of predictive analytics and econometrics for measuring the effectiveness of Lottery Corp. marketing and media. This study is able to differentiate marketing effectiveness across different types of lottery games, including instant (scratch) games, draw games and high jackpot intererstate games like Powerball and Mega Millions.
Defining Target Market for Telemarketing Campaigns
IE Business School MBD Program
Retail Analytics Project O1 Group C:
Annie Pi – Anchal Jaiswal – Cedric Viret – Melody Ucros – Miguel Martin Romero – Pablo Dosal - Victor Kausch
An introduction to data mining and its techniques
Data mining is the process of extracting patterns and relations so that profit of an organisation gets enhanced.
Consumer Insights PowerPoint Presentation Slides
Gather relevant data about consumers and potential customers using content-ready Consumer Insights PowerPoint Presentation Slides. Go through the human behaviour trends and understand your clients better with the help of ready-made consumer insights PPT presentation templates. Get consumer insights from data, surveys, sales, focus groups, direct interviews, online data and more. Make informed decisions for your business using consumer insights PowerPoint presentation slideshow. This deck comprises of templates such as research methodology, consumer insight assumptions, key stats, data collection and processing, consumer insight capabilities, consumer insight components, tools for consumer insights, YouTube analytics, google trends, google analytics, and more. These templates are completely customizable. Edit colors, text, icon and font size as per your need. Add or remove content, if needed. Download consumer insights PPT templates to understand your customers better. Our Consumer Insights Powerpoint Presentation Slides team are a cooperative lot. They will respond to all your specifications.
Personal Loan Risk Assessment
To identify the segment of customers, who have a higher tendency to default, if they are offered a Personal Loan
To leverage the existing Two-Wheeler Loan (TW) customer base to cross-sell the Personal Loan product
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The analysis of the data has been done using excel statistical sof.docx
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Analyzing Customer Journey And Data From 360 Degree PowerPoint Presentation S...
Analyzing Customer Journey And Data From 360 Degree PowerPoint Presentation S...
Prediction of customer propensity to churn - Telecom Industry
Prediction of customer propensity to churn - Telecom Industry
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Pro_Squad.pptx
- 1. Hackathon Machine Learning By Pro Squad Apoorva, Deepak, Kunal & Yogesh
- 2. Problem Statement Problem: A mall is doing a coupon campaign and wants to ensure the success of campaign using a Robust prediction model built with Machine Learning techniques. Context: Mall has provided historical data which comprises of recommended coupons, customer details and coupon consumption details of previous years. Relevance: Mall is going to run the campaign again and based on the historical data of coupons effectiveness they want to increase the footfalls in the Mall which will help the mall to increase business for the shops in the mall. Aims and Objectives: The AIM of the project is to come out with Business Insights on the data provided and Train a Machine Learning model which can predict the success of campaign with highest accuracy percentage.
- 3. Challenges in Historical Data • 26 features – 9 Numerical and 17 Categorical • Missing values in 5 Columns • Categorical Columns have Multiple labels, going to maximum 25 labels in 1 column. • Categorical Data has outliers and skewness • Most of the features are correlated
- 4. Missing Value Treatment Missing Values • Car – There are 84 values only out of 10147 in this column which is less then 1% hence we removed this column as it has no impact. • Bar, CoffeeHouse, CarryAway, RestaurantLessThan20, Restaurant20To50 – These have missing values around 2% hence we have used the Feature engineering technique to fill the most commonly occurring value out of the total values available in these columns.
- 5. Binning Occupation column has 25 labels and the data frequency variation is very high creating outliers and skewness, so we used the Binning technique to reduce the number of labels hence removed the outliers and skewness
- 6. Binning contd.. Outliers: on the Left side image we can see two dots, these are outliers which we tackled with binning and hence the Right side image is the result of binning on the categorical column Skewness: on the Left side image we can see the curve is skewed on the right, which we have tackled with binning and post processing the Right side image is the result of binning on the categorical column
- 7. Data Analysis Success of Coupons (Historical Data) 28% 27% 25% 11% 9% Coffee House Restaurant(<20) Carry out & Take away Bar Restaurant(20-50) Coffee House, Carry out and Restaurant(<20) were the most successful coupons Age Vs Coupons (Historical Data) 164 862 817 751 495 363 235 692 268 1271 1216 885 570 516 303 739 <21 21 26 31 36 41 46 50+ N Y Age group from 21 to 31 and 50+, the coupon usage is very high. Below 21 years the coupon distribution is low and hence the usage.
- 8. Data Analysis contd.. Occupation Vs Coupon Success (Historical Data) Student, Unemployed, computer professionals and Retired categories the success rate is high. Marital Status (Historical Data) Age group from 21 to 31 and 50+, the coupon usage is very high. Below 21 years the coupon distribution is low and hence the usage. N, 860 Y, 1262 0 200 400 600 800 1000 1200 1400 40% 38% 17% 4% 1% Single Married partner Unmarried partner Divorced Widowed
- 9. Data Analysis contd.. Multicollinearity Chart Colour Legend • Yellow shade – Correlation is 0 • Red and Dark Green is -1 and +1 Business Understanding • Customer ID, Temperature, Time, Weather, Direction, Passenger and Driving Distance impact is very low • Age, Has Children, Marital status, Gender, Occupation the impact is intermediate. • Restaurant type visit rating has the highest impact
- 10. Machine Learning Model ML Model 1: Logistic Regression Logistic Regression Cross Validation Accuracy 68.97% ML Model 2: Decision Tree Hyper Tuning Cross Validation Accuracy 70.95% Decision Tree Hyper Tuning Cross Validation Accuracy 76.46% Random Forest ML Model 3: Random Forest ML Models with their accuracy scores
- 11. Machine Learning Model Random Forest – Hyper Tuning to get accuracy No of Estimators: We used Randomize Search and Grid Search to find the optimum number of Estimators (Trees) which can give the highest accuracy score and then used the same in our Machine Learning Model. No of Folds: We used 5 folds to create random test and train split within the model to generate 5 accuracy scores and based on which the average score got select as the most optimum score. Random State: We have tuned the Random state to 80 which is giving the maximum accuracy score in our model.
- 12. Business Insights Advantages to Business 1. Coffee, Restaurant (<20) and Take away coupons are more successful. 2. Coupons are mostly used by age group 21 to 31 and 50+ 3. Computer Workers, Retired, students and Unemployed are mostly using the coupons. 4. Customers tend to use the coupons if Driving Distance is between 5 to 15 minutes. 5. Customers tend to use the coupons mostly when the weather is sunny. 6. Carry away coupons utilization is most for customers using it 1~3 times in a month. 7. Most footfalls are at 7:00 AM and 6:00 PM, probably to pick a snack.
- 13. ThankYou