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We are statisticians, economists and data junkies who love to get their hands dirty integrating, cleaning, modeling and visualizing data. Our primary sandbox is B2B database marketing analytics, but we have been known to stray a bit! Come on in and see if we can help you with your analytical needs.
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www.kddanalytics.com
v1.2
� Click to edit Master subtitle styleAn introduction to our capabilities�
www.kddanalytics.com
Who are we?
� A team of statisticians, economists, business and industry subject matter experts;
� Specialists in database marketing analytics (market sizing; market simulation; segmentation; predictive prospect, campaign and churn scoring; etc), with extensive experience in the B2B space;
� Data junkies who love to get their hands dirty integrating, cleaning, modeling and visualizing client and 3rd party databases;
� Experienced professionals with particularly deep experience in the telecom, IT and energy industries.
www.kddanalytics.com
Who do we serve?
� Our clients range from large providers of B2B marketing data to small consulting groups;
� We typically wholesale our services but can and have worked directly with end users.
www.kddanalytics.com
What do we do?
� Simply put, we help you help your clients make more informed decisions via data analytics:
Statistical Modeling Data Modeling
•Prospect/Campaign Scoring
•Customer Churn Scoring
•Forecasting
•Segmentation
•Survey Sample Design
•Demand/Price Elasticity Estimation
•Market Sizing
•Market Simulation
•Market Opportunity Mapping
•Data Integration
•Customer Profiling
•Data Visualization
Cross pollination
What do we know?
How can weorganize it?
Can we predictwhat will happen?
www.kddanalytics.com
Statistical Modeling
� Scoring/
What you want to
find
Attributes
Modeling
Target Population
Using model identifies more
prospects than using no model = “lift”
Target list scored from most likely prospect to
least.
ID PERCENTILE
105343236 6
138021163 8
147116482 16
201002390 17
101047263 19
202075210 19
123136008 19
105639354 21
106080974 24
111180060 24
134079517 28
144439822 29
207068360 36
114185643 37
124073515 37
143104099 40
138019692 40
134110988 46
144390826 49
132020465 57
134107700 62
120017332 71
141080328 73
133209000 74
136196993 75
144430916 77
118052109 81
110296359 84
207093595 86
207057547 97
Best customers
Churners
Buyers
Responders
Competitors
Predictive modeling is all about finding more of those you wish to find.
Target Population
www.kddanalytics.com
Statistical Modeling
� Customer Churn/
Internal Validation - Lift Analysis
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
1 2 3 4 5 6 7 8 9 10
Decile
Lift
Cum Lift Lift Baseline
Top decile 2.3
times average
predicted churn
potential
External Validation - Lift Analysis
0.00
0.50
1.00
1.50
2.00
2.50
3.00
1 2 3 4 5 6 7 8 9 10
Decile
Lift
Cum Lift Lift Baseline
Top decile 1.6
times average
predicted churn
potential
Models should be validated on data external to the modeling sample; such as the ~1,400 additional accounts which churned in the month
following the model build.
Churn Likelihood
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0 1000 2000 3000 4000 5000 6000
Tenure (days)
Churn Probability
FULL CARE BDM PAM
Mean Tenure
Median Tenure
www.kddanalytics.com
Statistical Modeling
� Segmentation/
Segment
Customers
(sites) Percent
Market
(CiTDB) Percent Penetration Segment
Total IT
Spend ($M):
Customers
Average IT
Spend per
Employee:
Customers
Total IT
Spend ($M):
Non-
Customers
Average IT
Spend per
Employee:
Non-
Customers
1 404 4.1% 33,362 0.9% 1.2% 1 18$ 1,367$ 836$ 1,331$
2 381 3.9% 92,016 2.6% 0.4% 2 16$ 1,535$ 2,379$ 1,652$
3 374 3.8% 40,582 1.1% 0.9% 3 58$ 5,293$ 3,825$ 5,271$
4 327 3.3% 52,166 1.5% 0.6% 4 51$ 4,658$ 4,065$ 4,164$
5 259 2.6% 55,559 1.5% 0.5% 5 32$ 4,582$ 4,112$ 4,334$
6 608 6.2% 119,341 3.3% 0.5% 6 62$ 3,304$ 7,716$ 3,228$
7 444 4.5% 104,702 2.9% 0.4% 7 59$ 4,583$ 6,880$ 4,151$
8 404 4.1% 96,113 2.7% 0.4% 8 7$ 571$ 1,391$ 776$
9 378 3.9% 135,668 3.8% 0.3% 9 18$ 1,539$ 3,853$ 1,538$
10 367 3.8% 11,703 0.3% 3.1% 10 232$ 3,492$ 6,787$ 3,413$
11 270 2.8% 49,856 1.4% 0.5% 11 97$ 13,180$ 9,447$ 11,408$
12 227 2.3% 119,048 3.3% 0.2% 12 179$ 27,050$ 35,355$ 21,043$
13 454 4.6% 143,270 4.0% 0.3% 13 104$ 6,168$ 13,294$ 4,044$
14 400 4.1% 190,813 5.3% 0.2% 14 13$ 938$ 3,453$ 855$
15 366 3.7% 232,119 6.5% 0.2% 15 13$ 1,158$ 5,600$ 1,249$
16 285 2.9% 16,929 0.5% 1.7% 16 351$ 6,207$ 11,710$ 3,730$
17 285 2.9% 88,629 2.5% 0.3% 17 145$ 15,373$ 23,923$ 14,720$
18 235 2.4% 83,420 2.3% 0.3% 18 77$ 8,721$ 12,574$ 6,504$
19 197 2.0% 24,242 0.7% 0.8% 19 41$ 1,044$ 3,986$ 919$
20 195 2.0% 27,189 0.8% 0.7% 20 124$ 24,045$ 9,523$ 19,618$
21 173 1.8% 59,592 1.7% 0.3% 21 19$ 3,298$ 4,056$ 3,176$
22 169 1.7% 6,974 0.2% 2.4% 22 806$ 24,558$ 24,195$ 19,278$
23 151 1.5% 79,154 2.2% 0.2% 23 14$ 2,831$ 3,864$ 2,355$
Total 9,780 100.0% 3,593,931 100.0% 0.3% Total 5,147$ 4,647$ 507,689$ 4,110$
Segments 7,353 1,862,447 0.4% Segments 2,536$ 6,460$ 202,824$ 4,501$
Customer segmentation enhanced with
opportunity mapping…
Customer segmentation can be made actionable by enhancing with opportunity mapping.
12
3
45
6
7
89
10
11
12
13
1415
16
17
18
19
20
21
22
23
Market Penetration
Average IT
Spend (Gap)
Average
Average
High
Spend/High
Penetration
Low
Spend/High
Penetration
High
Spend/Low
Penetration
Low
Spend/Low
Penetration
www.kddanalytics.com
Statistical Modeling
� Forecasting/Non-Dynamic Simulation: 3 AR Model Average
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
11/6
/200
52/
6/200
65/
6/200
68/
6/200
611
/6/2
006
2/6/
2007
5/6/
2007
8/6/
2007
11/6
/200
72/
6/200
85/
6/200
88/
6/200
811
/6/2
008
2/6/
2009
5/6/
2009
8/6/
2009
11/6
/200
92/
6/201
05/
6/201
08/
6/201
011
/6/2
010
2/6/
2011
Actual Average Predicted MIN LC MAX UC
37,000,000
38,000,000
39,000,000
40,000,000
41,000,000
42,000,000
43,000,000
44,000,000
2014Q1 2014Q3 2015Q1 2015Q3 2016Q1 2016Q3
CRAF_FC ± 2 S.E.
Hold Out Test (18 Months)
25,000,000
26,000,000
27,000,000
28,000,000
29,000,000
30,000,000
31,000,000
32,000,000
2009
Q1
2009
Q2
2009
Q3
2009
Q4
2010
Q1
2010
Q2
2010
Q3
2010
Q4
2011
Q1
2011
Q2
2011
Q3
Actual
18_F_ARIMA(114)
18_F_LOG_ARIMA(112)
18_F_LOG_ARIMA(214)
Time series and econometric forecast
modeling.
Risk Signature
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2011
Q4
2012
Q2
2012
Q4
2013
Q2
2013
Q4
2014
Q2
2014
Q4
2015
Q2
2015
Q4
2016
Q2
2016
Q4
2017
Q2
2017
Q4
2018
Q2
2018
Q4
2019
Q2
2019
Q4
2020
Q2
2020
Q4
2021
Q2
2021
Q4
2022
Q2
2022
Q4
18_F_ARIMA(114)
18_F_LOG_ARIMA(112)
18_F_LOG_ARIMA(214)
12_F_ARIMA(214)12_F_LOG_ARIMA(110)
6_F_LOG_ARIMA(211)6_F_LOG_ARIMA(114)_GARCH(01)
www.kddanalytics.com
Statistical Modeling
� Survey sample design/ 280 cell design to yield representative sample of US business sites with overall
1.3% sampling error.
www.kddanalytics.com
Data Modeling
� Data Integration/ CensusDept.
ClientCustomer orMarketingDatabase
CommerceDept.
10K Reports
IndustryAnalystReports
Gov’tAgencyBudgetReports
Private 3rd
Party Data
…to eliminate database whitespace or append a new field, such as sales revenue or IT spend, to a particular business site or
customer.
BureauLaborStats
Factors(e.g. $/employee)
www.kddanalytics.com
2008
Total Spend (m) Accounts Spend per Account
Agriculture 9,744$ 703,477 13,852$
Education 9,993$ 135,989 73,485$
Education Other 21,469$ 175,066 122,636$
F-I-RE 68,695$ 1,527,733 44,966$
Health Services 21,173$ 932,780 22,698$
Health Services Other 7,429$ 26,686 278,398$
Manufacturing 31,836$ 803,147 39,639$
Manufacturing Other 3,391$ 34,903 97,154$
Mining/Construction 23,622$ 1,509,277 15,651$
Public Administration 24,263$ 293,066 82,790$
Retail 58,709$ 2,965,485 19,797$
Services Other 129,777$ 1,480,184 87,676$
Services-Personal 78,797$ 4,366,790 18,045$
Transportation/Telecom 25,721$ 737,019 34,899$
Wholesale 32,207$ 866,932 37,151$
Total 546,828$ 16,558,534 33,024$
Market Potential
2008
Market Size (m)
Client Bookings
(m) Client Share
Agriculture 9,744$ 75$ 0.8%
Education 9,993$ 250$ 2.5%
Education Other 21,469$ 200$ 0.9%
F-I-RE 68,695$ 6,800$ 9.9%
Health Services 21,173$ 3,000$ 14.2%
Health Services Other 7,429$ 575$ 7.7%
Manufacturing 31,836$ 1,200$ 3.8%
Manufacturing Other 3,391$ 250$ 7.4%
Mining/Construction 23,622$ 2,900$ 12.3%
Public Administration 24,263$ 12,000$ 49.5%
Retail 58,709$ 5,000$ 8.5%
Services Other 129,777$ 10,000$ 7.7%
Services-Personal 78,797$ 8,000$ 10.2%
Transportation/Telecom 25,721$ 11,500$ 44.7%
Wholesale 32,207$ 2,750$ 8.5%
Total 546,828$ 64,500$ 11.8%
Data Modeling
� Market Sizing/
Accounts Value (m) Value per Account
F-I-RE 3,636 23,503$ 6,464,684$
Transportation/Telecom 2,360 10,375$ 4,395,409$
Services Other 2,679 3,435$ 1,282,042$
Education 5,605 6,388$ 1,139,616$
Health Services Other 6,042 6,500$ 1,075,787$
Manufacturing Other 2,173 1,746$ 803,341$
Public Administration 5,956 3,879$ 651,282$
Manufacturing 36,961 13,051$ 353,097$
Health Services 10,735 2,742$ 255,387$
Wholesale 6,654 1,546$ 232,377$
Mining/Construction 6,714 1,362$ 202,822$
Education Other 16,407 2,713$ 165,362$
Services-Personal 19,466 2,569$ 131,958$
Agriculture 1,571 153$ 97,141$
Retail 33,408 627$ 18,758$
Total 160,369 80,588$ 502,515$
Gap
Market size + Customer Sales => Market Gap (how many $ left on the table)
www.kddanalytics.com
Data Modeling
� Market Simulation/ Excel based models allowing user to conduct “what if”analyses by changing values
of model parameters.
www.kddanalytics.com
Data Modeling
� Data Visualization/Interactive Tableau dashboards…see
www.kddanalytics.com
www.kddanalytics.com
Data ModelingSegmentation and prioritization solution driven by:
* Clients’ customer data* 3rd party enrichment
data* KDD Analytics
Three components:* MarketPoint Profile =>
“What”?* MarketPoint Opportunity =>
“Where”?* MarketPoint Prospect =>
“Who”?
Deliverables* Static and interactive
dashboards/Excel workbooks* Scored and ranked prospecting
lists
� KDD MarketPoint/
Segmentation and prioritization solution driven by:
* Clients’ customer data* 3rd party enrichment data* KDD Analytics
Three components:* MP Profile => “What”?* MP Opportunity => “Where”?* MP Prospect => “Who”?
Deliverables* Static and interactive
dashboards/Excel workbooks* Scored and ranked prospecting
lists
See www.kddanalytics.com.
www.kddanalytics.com
Contact Info
� Let us know how we can help you:
www.kddanalytics.com