Analytics Transition Year Module 2

Transcription

1 Analytics Transition Year Module 2

2 Overview of Project Module 1 Module 2 Module 3 Module 4 Overview Introduction to the challenge Using Analytics to understand the problem Interpretation of Analysis New Company Strategy and Business Report Learning Outcomes 1. Background of the company 2. The Business Plan 1. Understanding Data 2. Applying Analytics 1. Statistical Techniques 2. Market Segmentation 1. Forming a Strategy 2. Completing a business report

3 Objective 1: Understanding Data

4 Data Types: Data Data can be classified into two different types. Categorical Values or observations that can be sorted into groups or categories: Bar charts and pie graphs are used to graph categorical data Numerical Values or observations that can be measured. And these numbers can be placed in ascending or descending order: Scatter plots and line graphs are used to graph numerical data

5 Data Types: Categorical Categorical Nominal Values or observations can be assigned a code in the form of a number where the numbers are simply labels. You can count but not order or measure nominal data Examples: Gender Eye colour Ordinal Values or observations can be ranked (put in order) or have a rating scale attached. You can count and order, but not measure, ordinal data Examples: House numbers Swimming level

6 Data Types: Numerical Numerical Discrete There are only a finite number of values possible or if there is a space on the number line between each 2 possible values Examples: Number of questions you get right in a test Number of people in your class Continuous You can measure continuous data. Values or observations may take on any value within a finite or infinite interval Examples: Height Time Temperature

7 Our Data: Variable Data Type Customer No: A specific code for each client Order No: The number of the invoice for the order Product Code: A six digit code made up of text and numbers which relates to a specific product of a specific size Product Name: Name of the product purchased Product Size: Size of the product sold Seller: Who sold the bars on behalf of the company Consumer: Description of individual buying the product Location: County in which the purchase took place Date: Date of purchase Time: Time of purchase No. Bars: Number of bars purchased Customer No. Order No. Product Code Product Name Product Size Seller Consumer Location Date Time No. of Bars

8 Our Data: Variable Data Type Customer No: A specific code for each client Order No: The number of the invoice for the order Product Code: A six digit code made up of text and numbers which relates to a specific product of a specific size Product Name: Name of the product purchased Product Size: Size of the product sold Seller: Who sold the bars on behalf of the company Consumer: Description of individual buying the product Location: County in which the purchase took place Date: Date of purchase Time: Time of purchase No. Bars: Number of bars purchased Customer No. Order No. Product Code Product Name Product Size Seller Consumer Location Date Time No. of Bars Categorical Numerical Categorical Categorical Categorical Categorical Categorical Categorical Categorical Numerical Numerical

9 Connections within the Data As you can see from the data not all of the variables are related To start our analysis we need to explore which variables are related What variables do you think relate to Corner Shops? Consumer Type Location Date Time No. Boxes Corner Shop

10 Connections within the Data As you can see from the data not all of the variables are related To start our analysis we need to explore which variables are related What variables do you think relate to Corner Shops? Consumer Type Location Date Time No. Boxes Corner Shop

11 Connections within the Data As you can see from the data not all of the variables are related To start our analysis we need to explore which variables are related What variables do you think relate to Vending machines? Consumer Type Location Date Time No. Bars Vending Machine

12 Connections within the Data As you can see from the data not all of the variables are related To start our analysis we need to explore which variables are related What variables do you think relate to Vending machines? Consumer Type Location Date Time No. Bars Vending Machine

13 Connections within the Data As you can see from the data not all of the variables are related To start our analysis we need to explore which variables are related What variables do you think relate to Mace Shops? Consumer Type Location Date Time No. Bars Mace

14 Connections within the Data As you can see from the data not all of the variables are related To start our analysis we need to explore which variables are related What variables do you think relate to Mace Shops? Consumer Type Location Date Time No. Bars Mace

15 Connections within the Data Product Code Customer No. Product Name Product Size Seller Type Example: Product Code: CDS146 CD Choco Dark S - Small Example: Customer No: MC205 MC Mace 205 Particular Store

16 Sorting the Data The first problem with our data is that the variable number of bars sold relates to number of boxes sold for the Corner Shops For this we need to Segment the data into two specific sets: 1. Sales data for Corner Shops 2. Sales data for Mace and Vending machines Our Data Corner Shops Data Other Data

17 Important Questions for Companies A company has three very important questions to investigate if they wish to maximise profits. 1. When are they selling most, this needs to be looked at for: i. Dates ii. Times 2. Where are they selling most, this needs to be looked at for: i. Counties ii. Vendor Type 3. What are they selling most, this needs to be looked at for: i. Type of product ii. Size of product It must be noted that we will have to examine the two sets of data separately since we cannot compare number of bars with number of boxes

18 Short Analytics Puzzle Dave in the marketing department for Seacláid Ltd. mentioned an idea to sell some of the medium sized bars on Aerlingus flights. By making some assumptions, calculate how much this might be worth to Seacláid Ltd.

19 Objective 2: Applying Analytics

20 Introducing the Data On the next slide we will be introduced to some of the data relevant to the sales of the products of Seacláid Ltd. Notable Information: The data shown represents the sales over five years between 08 and 12 Seacláid Ltd. started by selling to corner shops and through vending machines. From January 12 they began a promotional offer with Mace shops. The bars are sold to corner shops in boxes of 20. The vending machines are stocked bar by bar. The promotion with Mace involves Mace shops advertising Seacláid Ltd. products in conjunction with a raffle that is held bi-monthly by Seacláid Ltd. Buying a bar in a Mace shop makes you eligible to win the next raffle. The prize is 100.

21 Fill in these tables using the graphs in the next slides! Vending machines and Mace Corner Shops Question Answer Question Answer Best Months Best Month Best Times Best Times Best Location Best Vendor Best Product Best Product Size Best Location Best Vendor Best Product Best Product Size

22 Identifying when Seaclaid Ltd. sells most Total Sales by Month for VM & Mace For vending machines and Mace, which months had the highest sales for Seacláid Ltd.? Can you explain why these months might have high sales?

23 Identifying when Seaclaid Ltd. sells most Total Sales by Month for Corner Shops For corner shops, which months had the highest sales for Seacláid Ltd.? Can you explain why these months might have high sales?

24 Identifying when Seaclaid Ltd. sells most Total Sales by Hour for VM & Mace For vending machines and Mace, which time periods did Seacláid Ltd. sell the most in? Can you explain why these time periods might have high sales?

25 Identifying where Seaclaid Ltd. sells most Total Sales by County for VM & Mace For vending machine and Mace, which counties did Seacláid Ltd. sell the most in? Can you explain why these counties might have high sales?

26 Identifying where Seaclaid Ltd. sells most Total Sales by County for Corner Shops For corner shops, which counties did Seacláid Ltd. sell the most in? Can you explain why these counties might have high sales?

27 Identifying where Seaclaid Ltd. sells most Total Sales by Vendor Can you explain why this graph is incorrect? If we knew the number of bars in a box would this make a difference?

28 Identifying what Seaclaid Ltd. sells most of Total Sales by Product Type for VM & Mace For vending machine and Mace, which products had the highest sales for Seacláid Ltd.? Do these sales figures make sense?

29 Identifying what Seaclaid Ltd. sells most of Total Sales by Product Type for Corner Shops For corner shops, which products had the highest sales for Seacláid Ltd.? Do these sales figures make sense?

30 Identifying what Seaclaid Ltd. sells most of Total Sales by Product Size for VM & Mace For vending machine and Mace, which product size had the highest sales for Seacláid Ltd.? Can you explain why these sizes might have high sales?

31 Identifying what Seaclaid Ltd. sells most of Total Sales by Product Size for Corner Shops For corner shops, which product size had the highest sales for Seacláid Ltd.? Can you explain why these sizes might have high sales?

32 This is what your tables should look like! Vending machines and Mace Corner Shops Question Best Months Answer March September December Best Times 12:00 13:00 19:00 22:00 Best Location Best Vendor Best Product Best Product Size Clare Vending Machine Choco Milk Small Question Best Month Best Times Best Location Best Vendor Best Product Best Product Size Answer March April December N/A Clare Corner Shop Choco Milk Small

33 Vending Machine Game On the next slide there are three graphs superimposed on each other showing number of bars sold per hour. Each graph relates to a single vending machine placed in one of the following three places: Football Pitch Bus Station School You are tasked with deciding where each vending machine is located based on the peek times for sales. Hint: Start to think about what times would most bars be sold in each of the three venues.

34 Vending Machine Game Vending Machine Sales per hour School Bus Station Football Pitch

35 Vending Machine Game Vending Machine Sales per hour School Bus Station Football Pitch

36 Short Analytics Puzzle Assume that you have just come up with a great new joke and you can pass it on to four people in 30 minutes. Each of these four in turn pass it on to four other people in the next 30 minutes and so on. Each person will tell the joke only once. How long will it take for everybody in the world to get to know the joke? Assume that nobody hears it more than once and the population of the world approximately 7.1 billion.

37 Puzzle Solution 30min 4 people 2(30 min) 4*4= 4^2= 16 3(30 min) -> 4*4*4 = 4^3 = 64 X(30 min) -> 4^x = 7.1 billion Xln(4) = ln(7.1 billion) X= ln(7.1 billion)/ln(4) =16.36 Time = min segments = 8Hours 11 Minutes

38 Short Puzzle Is this equation correct? Obviously not. But can you make this expression correct by drawing just one straight line? -Try it! Using only the 4 numbers and the 2 symbols shown can you make a true expression? -Try it!

39 Puzzle Solution

40 Next Week: Interpretation of Analysis Use Statistical Techniques Group all your findings so far Market Segmentation Identify methods of Improvement