US2011046991A1PendingUtilityA1
Systems and methods for comparing and improving sales performance over heterogeneous geographical sales regions
Est. expiryMar 17, 2023(expired)· nominal 20-yr term from priority
Inventors:Roger L. Tobin
G06Q 30/0205G06Q 30/02G06Q 10/063
53
PatentIndex Score
0
Cited by
0
References
0
Claims
Abstract
A method of improving sales performance over multiple geographical sales regions includes determining ( 305 ) characteristics of each of the geographical sales regions and determining sales parameters, over which control can be exercised, for each of the sales regions. The method further includes modeling ( 310, 315, 320, 405 ) each of the sales regions based on the determined characteristics and sales parameters to create a model and determining ( 410 ), using the model, scores for sales parameters and revenue for each of the sales regions that allow direct comparison between each of the sales regions. The method also includes comparing ( 415 ) the scores for each of the sales regions to determine how to adjust the sales parameters to increase revenue for each of the sales regions.
Claims
exact text as granted — not AI-modified1 . A method of improving sales performance over a plurality of geographical sales regions, comprising:
determining characteristics of each of the geographical sales regions; determining sales parameters, over which control can be exercised, for each of the sales regions; modeling each of the sales regions based on the determined characteristics and sales parameters to create a model; determining, using the model, scores for sales parameters and revenue for each of the sales regions that allow direct comparison between each of the sales regions; and comparing the scores for each of the sales regions to determine how to adjust the sales parameters to increase revenue for each of the sales regions.
2 . The method of claim 1 , wherein the characteristics comprise at least one of population, number of households, geographic size, number of retail businesses, average income of residents, cost of living, number of competitors, and size of competitors.
3 . The method of claim 1 , wherein the sales parameters comprise at least one of price and sales force size.
4 . The method of claim 1 , wherein modeling each of the sales regions comprises:
creating a logged model for revenue (R) that comprises:
ln
(
R
)
=
β
R
+
∑
n
=
1
N
β
n
R
ln
(
F
n
)
+
∑
i
=
1
N
-
1
∑
j
=
i
+
1
N
β
ij
R
ln
(
F
i
)
ln
(
F
j
)
where F x represents one of the determined characteristic for an associated sales region, and
N is a number of the determined characteristics of the associated sales region.
5 . The method of claim 4 , wherein modeling each of the sales regions further comprises:
performing linear regression analysis on the logged revenue model to determine the parameters β R ,β n R and β ij R .
6 . The method of claim 5 , wherein modeling each of the sales regions comprises:
creating a logged model for sales parameters (C m ) that comprises:
ln
(
C
m
)
=
β
m
+
∑
n
=
1
N
β
n
m
ln
(
F
n
)
+
∑
i
=
1
N
-
1
∑
j
=
i
+
1
N
β
ij
m
ln
(
F
i
)
ln
(
F
j
)
.
7 . The method of claim 6 , wherein modeling each of the sales regions further comprises:
performing linear regression analysis on the logged sales parameter model to determine the parameters β m ,β n m and β ij m .
8 . The method of claim 7 , wherein modeling each of the sales regions further comprises:
determining a revenue norm ( R ) for each of the sales regions according to:
R
_
=
exp
(
β
R
)
∏
n
=
1
N
F
n
α
n
R
,
where
α
n
R
=
β
n
R
+
∑
k
=
n
+
1
N
β
nk
R
.
9 . The method of claim 8 , wherein modeling each of the sales regions further comprises:
determining sales parameter norms ( C m ) for each of the sales regions according to:
C
_
m
=
exp
(
β
m
)
∏
n
=
1
N
F
n
α
n
m
where
α
n
m
=
β
n
m
+
∑
k
=
n
+
1
N
β
nk
m
.
10 . The method of claim 9 , wherein determining scores comprises:
determining a revenue score (S R k ) for each region k according to:
S
R
k
=
R
k
R
_
k
where R k represents an actual revenue for region k.
11 . The method of claim 10 , wherein determining scores further comprises:
determining sales parameter scores (S m k ) for each region k according to:
S
m
k
=
C
m
k
C
_
m
k
where C m k represents an actual sales parameter value for region k.
12 . The method of claim 11 , wherein comparing the scores for each of the sales regions comprises:
investigating relationships between sales parameter norms and revenue norms according to:
S R =δ 1 S m +δ 2 S m 2 +δ 3 S m 3 + . . . +δ p S m p
for p sales regions and one sales parameter (C m ).
13 . The method of claim 11 , wherein comparing the scores for each of the sales regions comprises:
investigating relationships between sales parameter norms and revenue norms according to:
S R =δ 11 S i S j +δ 12 S i S j 2 +δ 13 S i S j 3 + . . . +δ 1p S i S j p +δ 21 S i 2 S j +δ 22 S i 2 S j 2 +δ 23 S i 2 S j 3 + . . . +δ 2p S i 2 S j p +δ 31 S i 3 S j +δ 32 S i 3 S j 2 +δ 33 S i 3 S j 3 + . . . +δ 3p S i 3 S j p + . . . +δ p1 S i p S j +δ p2 S i p S j 2 +δ p3 S i p S j 3 + . . . +δ pp S i p S j p
for p sales regions and two sales parameters {C i , C j }.
14 . A computer-readable medium containing instructions for controlling at least one processor to perform a method of improving sales performance over a plurality of geographical sales regions, the method comprising:
determining sales parameters, over which control can be exercised, for each of the sales regions; modeling each of the sales regions based on known characteristics associated with each of the sales regions, and determined sales parameters to create a model; determining, using the model, scores for sales parameters and revenue, for each of the sales regions that allow direct comparison between each of the sales regions; comparing the scores for each of the sales regions; and adjusting the sales parameters to increase revenue for at least one of the sales regions based on the comparing.
15 . A system for improving sales performance over a plurality of geographical sales regions, comprising:
a memory configured to store instructions; and a processor configured to execute the instructions in the memory to:
determine one or more characteristics of each of the geographical sales regions, wherein the one or more characteristics comprise at least one of population, number of households, geographic size, number of retail businesses, average income of residents, cost of living, number of competitors, and size of competitors,
determine one or more sales parameters, over which control can be exercised, for each of the sales regions, wherein the one or more sales parameters comprise at least one of price and sales force size, model each of the sales regions based on the determined one or more characteristics and the one or more sales parameters to create a model, determine, using the model, scores for sales parameters and revenue, for each of the sales regions that allow direct comparison between each of the sales regions, and compare the scores for each of the sales regions to determine how to adjust the sales parameters to increase revenue for at least one of the sales regions.
16 . A method for increasing sales revenue in each sales region of a plurality of sales regions, comprising:
modeling sales parameters and revenue for each of the sales regions of the plurality of sales regions, based on one or more characteristics associated with each of the sales regions, to produce sales parameter and revenue norms; and comparing the sales parameter and revenue model norms to actual sales parameter and revenue values to determine future targets for the actual sales parameters to increase future revenues in each of the sales regions.
17 . The method of claim 16 , wherein the sales parameters comprise at least one of price and sales force size.
18 . The method of claim 16 , wherein the one or more characteristics comprise at least one of population, number of households, geographic size, number of retail businesses, average income of residents, cost of living, number of competitors, and size of competitors.
19 . The method of claim 16 , wherein the sales parameter and revenue model norms represent theoretical averages adjusted for the one or more characteristics.
20 . The method of claim 16 , wherein modeling sales parameters and revenue for each of the sales regions comprises:
creating a logged model for revenue (R) that comprises:
ln
(
R
)
=
β
R
+
∑
n
=
1
N
β
n
R
ln
(
F
n
)
+
∑
i
=
1
N
-
1
∑
j
=
i
+
1
N
β
ij
R
ln
(
F
i
)
ln
(
F
j
)
where F x represents a characteristic of N characteristics for an associated sales region.
21 . The method of claim 20 , wherein modeling sales parameters and revenue for each of the sales regions further comprises:
performing linear regression analysis on the logged revenue model to determine the parameters β R ,β n R and β ij R .
22 . The method of claim 21 , wherein modeling sales parameters and revenue for each of the sales regions further comprises:
creating a logged model for sales parameters (C m ) that comprises:
ln
(
C
m
)
=
β
m
+
∑
n
=
1
N
β
n
m
ln
(
F
n
)
+
∑
i
=
1
N
-
1
∑
j
=
i
+
1
N
β
ij
m
ln
(
F
i
)
ln
(
F
j
)
.
23 . The method of claim 22 , wherein modeling sales parameters and revenue for each of the sales regions further comprises:
performing linear regression analysis on the logged sales parameter model to determine the parameters β m ,β n m and β ij m .
24 . The method of claim 23 , wherein the revenue norm ( R ) for each sales region comprises:
R
_
=
exp
(
β
R
)
∏
n
=
1
N
F
n
α
n
R
where
α
n
R
=
β
n
R
+
∑
k
=
n
+
1
N
β
nk
R
.
25 . The method of claim 24 , wherein the sales parameter norms ( C m ) for each sales region comprise:
C
_
m
=
exp
(
β
m
)
∏
n
=
1
N
F
n
α
n
m
where
α
n
m
=
β
n
m
+
∑
k
=
n
+
1
N
β
nk
m
.
26 . The method of claim 25 , wherein modeling sales parameters and revenue for each of the sales regions further comprises:
determining a revenue score (S R k ) for each region k according to:
S
R
k
=
R
k
R
_
k
where R k represents an actual revenue for region k.
27 . The method of claim 26 , wherein modeling sales parameters and revenue for each of the sales regions further comprises:
determining sales parameter scores (S m k ) for each region k according to:
S
m
k
=
C
m
k
C
_
m
k
where C m k represents an actual sales parameter value for region k.
28 . The method of claim 27 , wherein comparing the sales parameter and revenue norms to actual sales parameter and revenue values comprises:
investigating relationships between sales parameter norms and revenue norms according to:
S R =δ 1 S m +δ 2 S m 2 +δ 3 S m 3 + . . . +δ p S m p
for p regions and one sales parameter (C m ), wherein the parameters {δ 1 , δ 2 , . . . , δ p } are determined using linear regression analysis.
29 . The method of claim 27 , wherein comparing the sales parameter and revenue norms to actual sales parameter and revenue values comprises:
investigating relationships between sales parameter norms and revenue norms according to:
S R =δ 11 S i S j +δ 12 S i S j 2 +δ 13 S i S j 3 + . . . δ 1p S i S j p +δ 21 S i 2 S j +δ 22 S i 2 S j 2 +δ 23 S i 2 S j 3 + . . . δ 2p S i 2 S j p +δ 31 S i 3 S j +δ 32 S i 3 S j 2 +δ 33 S i 3 S j 3 + . . . δ 3p S i 3 S j p + . . . +δ p1 S i p S j +δ p2 S i p S j 2 +δ p3 S i p S j 3 + . . . δ pp S i p S j p
for p regions and two sales parameters {C i , C j }, wherein the parameters {δ 11 , δ 12 , . . . , δ pp } are determined using linear regression analysis.
30 . A computer-readable medium containing instructions for controlling at least one processor to perform a method for increasing sales revenue in each sales region of a plurality of sales regions, the method comprising:
modeling sales parameters and revenue for each of the sales regions of the plurality of sales regions, based on one or more characteristics associated with each of the sales regions, to produce sales parameter and revenue norms, wherein the one or more characteristics comprise at least one of population, number of households, geographic size, number of retail businesses, average income of residents, cost of living, number of competitors, and size of competitors, and wherein the sales parameters comprise at least one of price and sales force size; and comparing the sales parameter and revenue model norms to actual sales parameter and revenue values to determine future targets for the actual sales parameters to increase future revenues in each of the sales regions.
31 . A system for increasing sales revenue in each sales region of a plurality of sales regions, comprising:
a memory configured to store instructions; and a processor configured to execute the instructions in the memory to:
create logged sales parameters and revenue models for each of the sales regions of the plurality of sales regions to produce sales parameter and revenue norms, and
compare the sales parameter and revenue model norms to actual sales parameter and revenue values to determine future targets for the actual sales parameters to increase future revenues in each of the sales regions.
32 . A system for increasing sales revenue in each sales region of a plurality of sales regions, comprising:
means for creating logged sales parameters and revenue models for each of the sales regions of the plurality of sales regions to produce sales parameter and revenue norms; and means for comparing the sales parameter and revenue model norms to actual sales parameter and revenue values to determine future targets for the actual sales parameters to increase future revenues in each of the sales regions.Join the waitlist — get patent alerts
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