1. Segmentation Strategies: An Application to Nanostores in Bogota
By: Xiaodan Pan
Advisor: Dr. Anthony Craig
Sponsor: MIT megacity logistics lab

2. Segmentation Strategies: An Application to Nanostores in Bogota
Agenda
Project introduction
Key concepts
Segmentation strategies
Market diffusion
Assortment planning
Research insights

3. Segmentation strategies
Agenda
Project introduction
Key
concepts
Segmentation strategies
Market
diffusion
Assortment
planning
Research
insights 1 3 5 2 4 6

4. 1. Project Introduction Nanostore features Small volume High frequency
Low value
Market challenges
Assortment planning
Urban logistics
Source:

5. Geographic distribution of nanostore clients
1. Project Introduction
Geographic distribution of nanostore clients
Bogota city
7.4 M inhabitants
4,690 inhabitants/km2
Pilot company
30,148 nanostores
550 products
400 quadrants

6. Segmentation strategies
Agenda
Project introduction
Key
concepts
Segmentation strategies
Market
diffusion
Assortment
planning
Research
insights 1 3 5 2 4 6

7. 2. Key concepts Product market diffusion
In a social system, customers tend to be influenced significantly by the number of adopters; for example, the imitators need to observe the performance of a product before purchasing.
Principal component analysis
PCA provides a roadmap to convert a high-dimensional dataset into a significantly low- dimensional picture, while revealing the hidden structure and information underlying the dataset.

8. Segmentation strategies
Agenda
Project introduction
Key
concepts
Segmentation strategies
Market
diffusion
Assortment
planning
Research
insights 1 3 5 2 4 6

9. 3. Segmentation strategies
3.1 Sketch segmentation: this study developed a sketch segmentation strategy based on a basic ordering pattern analysis in conjunction with an extended value matrix.
Ordering pattern
analysis
Extended value matrix
Sketch segmentation

10. Ordering pattern analysis for product category
3.1 Sketch Segmentation
Ordering pattern analysis for product category
Sales for each product ranged from $1 to $731K with 1 to 27K orderlines.

11. Ordering pattern analysis for product category
3.1 Sketch Segmentation
Ordering pattern analysis for product category
Each product was ordered by 1 to 18K clients from 1 to 396 quadrants.

12. Extended value matrix for sketch segmentation
Segment I
Frequent order-High unit value
Segment II
Infrequent order-High unit value
Segment III
Infrequent order-Low unit value
Segment IV
Frequent order-Low unit value
Number of orderlines
Sales per orderline
Frequency of customers order the product.
Value of product delivered when ordered.

13. Summary information of sketch segmentation for the product category
Segment summary
All
Products
Frequent order
High unit value
Low unit value
Infrequent order
1. Total for each segment
1.1 Number of products
550 23
108
153
266
1.2 Percent of total products
100% 4%
20%
28%
48%
1.3 Sales
$6,207,283
$2,685,841
$2,534,484
$545,015
$441,942
1.4 Percent of total sales
43%
41% 9% 7%
1.5 Number of orderlines
960,899
155,585
643,235
49,807
112,272
1.6 Percent of total orderlines
16%
67% 5%
12%
2. Average for each segment
1.1 Sales per product
$ 11,286
$116,776
$23,467
$3,562
$1,661
1.2 Index vs. all product
1.00
10.35
2.08
0.32
0.15
1.3 Orderlines per product
1,747
6,765
5,956
326
422
1.4 Index vs. all product
3.87
3.41
0.19
0.24
1.5 Sales per orderline
$6.46
$17.26
$3.94
$10.94
1.6 Index vs. all product
2.67
0.61
1.69

14. 3. Segmentation strategies
3.2 Precision segmentation: this study developed a precision segmentation strategy built on a diffusion mapping analysis in alignment with a factor value matrix.
Diffusion mapping
analysis
Factor value
matrix
Precision segmentation

15. 3.2 precision Segmentation
Diffusion mapping analysis for the product category
Sales
Top products
Orderlines
Clients
Quadrants
Amount
Percent
Number
$1,830,332
30% 5 1%
65,836 7%
22,125
73%
397
99%
$3,119,121
50% 20 4%
293,907
31%
29,526
98%
400
100%
$4,340,805
70% 55
10%
555,557
58%
29,986
$5,589,258
90%
159
29%
807,230
84%
30,107
The top 20 products represented 50% of the total sales with 31% of the total orderlines; ordered by 98% of the total clients from all 400 quadrants.

16. 3.2 Precision Segmentation
Framework of attribute parameters for the product category
1. Number of orderlines
2. Number of quadrants
3. Number of clients
4. Sales per orderline
Sales performance
Built on the diffusion mapping analysis, an attribute parameter framework was designed to describe the sales performance for each product.

17. 3.2 Precision Segmentation
By applying the principal component analysis, two significant segment factors were extracted: 1) a broad product factor, F1 and 2) a value product factor, F2.
Factor loadings of principal component analysis
Scree plot of principal component analysis
Factor loadings  F1 F2 F3 F4
1. Number of orderlines
0.963
0.080
-0.251
-0.065
2. Number of quadrants
0.826
-0.070
0.560
-0.003
3. Number of clients
0.971
0.071
-0.220
0.067
4. Sales per orderline
-0.089
0.993
0.076
0.000

18. 3.2 precision Segmentation
Factor value matrix for precision segmentation
Segment I
Broad sale-High unit value
Segment II
Narrow sale-High unit value
Segment III
Narrow sale-Low unit value
Segment IV
Broad sale-Low unit value
Broad product factor
Value product factor
Breadth of customers order the product.
Value of product delivered when ordered.

19. 3.2 Precision Segmentation
Summary information of precision segmentation for the product category
Segment summary
All
Products
Broad sale
High unit value
Low unit value
Narrow sale
1. Total for each segment
1.1 Number of products
550 49
152
110
239
1.2 Percent of total products
100% 9%
28%
20%
43%
1.3 Sales
$6,207,283
$4,022,851
$1,690,547
$260,177
$233,708
1.4 Percent of total sales
65%
27% 4%
1.5 Number of orderlines
960,899
442,158
444,943
20,456
53,342
1.6 Percent of total orderlines
46% 2% 6%
2. Average for each segment
2.1 Sales per product
$11,286
$82,099
$11,122
$2,365
$978
2.2 Index vs. all product
1.00
7.27
0.99
0.21
0.09
2.3 Orderlines per product
1,747
9,024
2,927
186
223
2.4 Index vs. all product
5.16
1.68
0.11
0.13
2.5 Sales per orderline
$6.46
$9.10
$3.79
$12.72
$4.38
2.6 Index vs. all product
1.41
0.59
1.97
0.68

20. Segmentation strategies
Agenda
Project introduction
Key
concepts
Segmentation strategies
Market
diffusion
Assortment
planning
Research
insights 1 3 5 2 4 6

21. Framework of market diffusion for the product category
1. Client diffusion parameters
Number
of clients
Orderlines
per client
Correlation ?
On average, each product was ordered by 4% of the total clients from 43% of the total quadrants.
There was plenty of space for the pilot company to improve its product diffusion level in the nanostore market.
Number
of quadrants
Orderlines
per quadrant
Correlation ?
2. Quadrant diffusion parameters

22. 4. Market diffusion
Orders from more quadrants can positively influence market purchasing intention by individual quadrant, within the local quadrant or throughout the entire city.
Correlation analysis of quadrant diffusion parameters
Top 159 products (90% of total sales)
(Correlation coefficient = 0.737)
Bottom 391 products (10% of total sales)
(Correlation coefficient = 0.743)

23. Segmentation strategies
Agenda
Project introduction
Key
concepts
Segmentation strategies
Market
diffusion
Assortment
planning
Research
insights 1 3 5 2 4 6

24. 5. Assortment planning
All products were further classified into traditional A, B and C categories based on similar total sales to segments from the precision segmentation strategy.
Sample framework for precision assortment and traditional assortment
Percent of total sales
Precision assortment
Traditional assortment
Segment
Number of products
Category
65%
Broad sale-High unit value 49 A
Top 43 products
27%
Broad sale-Low unit value
152 B
Middle 197 products 4%
Narrow sale-High unit value
110
Narrow sale-Low unit value
239 C
Bottom 310 products
Total: 100%
Total: 550

25. 5. Assortment planning
Distribution of A products with precision segmentation
Segment I
Broad sale – High unit value
A products has shown very high market diffusion level, all dispersing in the broad sale-high unit value and broad sale low unit value segment.

26. 5. Assortment planning
Distribution of B products with precision segmentation
Segment II
Narrow sale – High unit value
Segment I
Broad sale – High unit value
Product 68 was ordered by 755 clients from 216 quadrants.
Group I: B products in the narrow sale-high unit value segment with large number of clients and quadrants.
These products might move to the broad sale-high unit value segment through assortment and promotion.

27. 5. Assortment planning
Distribution of C products with precision segmentation
Group II: C products in the narrow sale-high unit value segment with significantly higher unit value.
Some clients might have preference for these high- value products.
Segment II
Narrow sale – High unit value
Product 299 was ordered by five clients from five quadrants with 303 dollars per orderline.
Product 427 only contributed to 308 dollars of total sales; it was ordered by 6,116 clients from 370 quadrants.
Group III: C products in the broad sale-low unit value segment.
These might serve as essential-needs or popular- sale products for the core customers in the market.
Segment IV
Broad sale – Low unit value

28. 5. Assortment planning
Distribution of C products with precision segmentation
Group IV: The last group consists of the bottom products in the narrow sale-low unit value segment.
Those products are recommended as the candidates for delisting.
Segment III
Narrow sale – Low unit value

29. Segmentation strategies
Agenda
Project introduction
Key
concepts
Segmentation strategies
Market
diffusion
Assortment
planning
Research
insights 1 3 5 2 4 6

30. 6. Research insights
Product diffusion plays an important role in the nanostore market; orders from more quadrants may in turn positively influence the market purchasing intention.
The pilot company should search for more opportunities from products other than those traditional top selling products for the nanostore market.
A precision segmentation strategy could be applied to differentiate the valuable products from the delisting candidates among the traditional B and C products.
The pilot company should be cautious when delisting the low-sales products, as some might serve as the essential-needs or popular-sale products for the nanostore market.

31. 6. Research insights
Future work: Considering the city infrastructure and income distribution, a sophisticated segmentation strategy is also the key to address urban distribution challenge in the nanostore market.
Single tier logistics model
with one supplier depot
Two-tiers logistics model
with dynamic city satellites

32. Thanks for all your unwavering supports of this work.
Source:
Thanks for all your unwavering supports of this work.