1. Market basket analysis
Team members: Mai Owies.
Nahla Qendeel.
Sana Al Barri.
Submitted to: Dr. Kamal Irshid.
       Dr. Mohammed Dweikat.

2. Outline About the company Project objetives. Market basket analysis.
Sales analysis
Demand forecast
Recommendation

3. About the company
Super Store Palestine was established in 2001 and was the first of its kind in the city of Nablus
And with a view to continuing work on the reward of our customers, Super Store operates Palestine to provide regular monthly offers aimed at savings on customers and include offers great discounts on purchases or instant customer gifts or otherwise.
Super Store Palestine succeeded in attracting and operation of labor trained and experienced in this field and also works on the implementation of specialized training courses aimed at providing services and means of high - quality connection..

4. The objective According to section 1.
Leverage customer transaction data for right product bundling and promotions.
product placement in the store.
visualizing the degree of attraction or repellence between items. .

5. According to section 2.
Find the number of sales per week during the month.
Find the number of products sold every day of each week.
Find a product name that is sold every day

6. According to section 3
Explain what is the demand forecasting
Important of demand forecasting.
Characteristics of forecasting.
Explain exponential smoothing technique.

7. Market basket analysis
section1
Market basket analysis

8. Over View Data mining
Data mining is the computing process of discovering patterns in large data set.
it enables these companies to determine relationships among many factors.
And enables them to determine the impact on sales, customer satisfaction, and corporate profits

9. Type of data mining
Classification: Stored data is used to locate data in predetermined groups.
Clusters: Data items are grouped according to logical relationships or consumer preferences.
Associations: find the relationships between items in transactions.
Sequential patterns: Data is mined to anticipate behavior patterns and trends

10. Market basket analysis
Market Basket Analysis (MBA) is a data mining technique that helps a retailer in several ways; many business enterprises accumulate large quantities of data from their day-to-day operations.

11. Example MBA

12. Benefit of MBA Product Promotions and Placement .
Market Basket Analysis empowers marketing and sales organizations to make better, informed decisions about how and where to deploy their efforts and resources.

13. Data preparation
Main data source used for a Market Basket Analysis is customer purchase transaction data. And this data may be noisy, there for we can’t use it in the original form in market basket analysis, so we prepared and cleaned data. As appeared in the next slide.

14. Data preparation

15. Data preparation

16. Terminology For each transaction, we have an item set.
Rules are statements of the form

17. The output of a market basket analysis is generally a set of rules that we can then exploit to make business decisions (related to marketing or product placement).

18. Measures of rules Support: how many instances satisfy the rule.
Confidence: measure how often the right side of the rule hold, given the left side.
Left: measure how much better the rule is for prediction than a random guess value greater than one indenticate useful rule.

21. Section 2
Sales analysis

22. Introduction
Sales analysis examines sales reports to see what goods and services have and have not sold well, how to measure the effectiveness of a sales force.

23. Find the number of sales per week during the month:

27. We conclude from the charts that the greater sales are on the first week of the month and then decrease during the middle of the month for many reasons.
Employees receive salaries at the beginning of the month.
Some customers prefer to buy all their household needs at the beginning of the month.

28. Find the number of products sold every day of each week
January

30.  Week 3 (January)

31. Week 4 (January)

32. February

36. Find the most products are sold daily
January Week 1(January)

37. Week 2(January)

38. Week 3(January)

39. Week 4(January)

40. February
Week 1(February)

41. Week 2(February)

42. Week 3(February)

43. Week 4(February)

44. Section 3
Demand forecasting

45. What is a demand forecasting
Demand forecasting is predicting the future demand for the firm’s product

46. The importance of demand forecasting
Planning and schedule production.
Make provisions for finances.
Planning advertisement.

47. Characteristics of forecasts
Forecasts are always wrong. Should include expected value and measure of error.
Long term forecasts are less accurate than short term forecasts.

48. Methods of demand forecasting
There are several methods of demand forecasting applied in terms of the purpose of forecasting, data required, data availability and the time frame within which the demand is to be forecasted. Each method varies from one another and hence the forecaster must select that method which best suits the requirement.

49. Simple exponential smoothing:
The simplest of the exponentially smoothing methods is suitable for forecasting data with no trend or seasonal pattern. For example, the data in Figure 3.1do not display any clear trending behavior or any seasonality.

50. In our project we implement exponential smoothing forecasting technique according to this available historical demand:

51. We want to predict the demand for week 9 using this formula:
Ft=Ft-1 + ᾳ (At-1-Ft-1)
Where Ft =new forecast.
Ft-1 =previous forecast.
ᾳ =smoothing constant (0≤ᾳ≤1).
A= actual demand for previous period.

52. Forecasting for product 1
  نخلة عدس مجروش 1كغم
Actual demand

54. The difference between actual demand and forecast and the impact of smoothing constant shown in the figure below:

55. As a result of the previous we extract the predicted demand and mean squared error for week 9 according to different alpha.

56. Forecasting for product 2
سنكرز شوكولاتة 50غم نوجا كراميل فول سوداني Actual demand

58. The difference between actual demand and forecast and the impact of smoothing constant for the product2 shown in the figure below:

59. As a result of the previous we extract the predicted demand and mean squared error for week 9 according to different alpha.

60. Forecasting for product 3
كادبوري شوكولاتة 28غم سادة بوبلي Actual demand

62. The difference between actual demand and forecast and the impact of smoothing constant for the product3 shown in the figure below

63. As a result of the previous we extract the predicted demand and mean squared error for week 9 according to different alpha.

64. Forecasting for product 4
بينار بودينج فراولة&شوكولاتة&موز
Actual demand

66. The difference between actual demand and forecast and the impact of smoothing constant for the product3 shown in the figure below

67. As a result of the previous we extract the predicted demand and mean squared error for week 9 according to different alpha.

68. Recommendation
The main objective of market basket analysis and sales increased profit by utilization these results
Discount of sale
When the relationship is strong between two products we can put another product with a weak relationship with one of these two products.
When the product has little of demand ,we advise you to make discount encourage customer to buy.

69. Shelves arrangement
When the relationship is strong between products we have two strategies:
put each product away from the other in order that the customer see all the products in the supermarket.
Put the products beside other to facilitate the customer so we have customer satisfaction

70. We can reduce number of employee in week where the total amount of sales is few in order to decrease cost and we can make promotion in this week to increase amount of sales .

71. Thank You