at!-wand presentation farcon v1 8-24-16 v2
TRANSCRIPT
AT!-WAND Challenge 1
Leveraging Predictive Analytics toIncrease Fast-food Sales
WAND Corporation Analytics Challenge Jan-Apr, 2016
Kevin Church, Co-founder Chuck Gehman, VP of Product Management
AT!-WAND Challenge 2
Topics
• Introduction to Analyze This!• Introduction to WAND Corporation
and the Analytics Challenge• Analytic Approach• Model Results for 1 Restaurant• Actionable Intelligence• WAND Learnings & Operational Plans
AT!-WAND Challenge 3
• Twin Cities based Meetup group founded in 2015. Now have 550 members
• Mission: Raise the level/awareness of local Data Science expertise
• Monthly meetings to share best practices in Data Science
• Ongoing quarterly data challenges– Winter challenge with WAND Corporation– Summer challenge with Science Museum of
Minnesota. Grand Finale this afternoon at FARCON!– Fall challenge with _____ kickoff Sept. 28th
AT!-WAND Challenge 4
Introduction to WAND
• “Highly evolved” 100 person tech company in Eden Prairie
• Building state of the art Point of Sale and Digital Menu Board solutions
• Started working on Big Data platform last year
• Jumped at the opportunity to work with AT on the challenge
AT!-WAND Challenge 5
AT!-WAND Analytics Challenge• January 27 – April 27, 2016• Predict Daily Net Sale for 50 fast-food
restaurants based on ZIP Code, Hours of Operation– Training dataset 2013-15– Test dataset January 2016
• Teams of up to 5 members, tool agnostic• Six teams competed, three crossed finish line• Panel of judges rates accuracy, creativity,
business intelligence and storytelling
AT!-WAND Challenge 6
Training Team* Approach• Data cleaning• Cluster analysis• Brainstorm 3rd party data• Assemble analytics dataset• Generate models• Sanity Check• Actionable intelligence
* Scott Sutherland, Will Craft, Jeffrey Van Voorst, Linda Ruetz, Patrick Mobley
AT!-WAND Challenge 7
Data-Cleaning: Missing Data
Jump to Low Outliers
AT!-WAND Challenge 8
Cluster Analysis: Closely Correlated Stores
533
507
525
519
506
524
522
504
470
486
483
482
485
480
473
477
479
471
476
468
474
467
518
581
513
809
210
527
464
462
2318528
516
1871
1863
1866512
531
515
1872
1869510
509
461
1868
1865459
458
201
135
32.88
55.26
77.63
100.00
Variables
Simila
rity
Dendrogram, 7 ClustersComplete Linkage, Correlation Coefficient Distance
Jump to PCA Jump to Matrix Plot
AT!-WAND Challenge 9
Brainstorm 3rd party dataWhich factors are likely to impact Net Sales within a Store?
• Business date extraction• Holidays• Weather (temp, humid, severe)• Local Events (sports, celebrations)• Consumer/Business prices• Little league season
Jump to Dataset Details
AT!-WAND Challenge 10
Analytic Approach
• Forward Selection• Test residuals for curvature• Plot model residuals vs. time to identify
“shifts”• Research shifts & outliers on Google• Test new features, add to Master dataset• Reduce model via backwards elimination• Delete/Investigate outliers dates
AT!-WAND Challenge 11
Model Results: Cluster 7, Store 506Johns Creek, GA
Jump to Review Data
Model Summary (44 terms)Sy.x R-sq R-sq(adj)
357.62 90.0% 89.56%
CoefficientsTerm Coef SECoef T-Value P-Val VIFConstant 3358 488 6.88 0DayNo -23.16 5.39 -4.30 0.000 18.4DayNo^2 0.80 0.17 4.73 0.000 19.1DOW_Tue 202.10 41.70 4.85 0.000 1.8DOW_Wed 414.50 41.90 9.88 0.000 1.8DOW_Thu 683.20 42.30 16.14 0.000 1.8DOW_Fri 1011.0 42.20 23.95 0.000 1.8DOW_Sat -1336.8 42.10 -31.74 0.000 1.8DOW_Sun -1666.2 43.50 -38.29 0.000 1.9Month_Jan -512.50 50.30 -10.19 0.000 1.7Month_Feb -138.70 49.80 -2.78 0.005 1.5Month_Mar -107.30 49.20 -2.18 0.030 1.5Month_Apr -166.50 53.80 -3.10 0.002 1.5Month_May 245.80 50.30 4.89 0.000 1.7Month_Jul -377.70 57.00 -6.63 0.000 2.1Month_Aug -323.60 47.60 -6.80 0.000 1.5Month_Sep -347.80 48.40 -7.18 0.000 1.5Month_Oct -138.60 48.40 -2.87 0.004 1.6Month_Nov -223.30 52.40 -4.26 0.000 1.7Year_2014 -75.10 25.50 -2.95 0.003 1.2
CoefficientsTerm Coef SECoef T-Value P-Val VIFNewYr -3244.0 213.00 -15.22 0.000 1.15D_B4PalmFri 267.00 119.00 2.25 0.025 1.1Easter -679.00 259.00 -2.62 0.009 1.1MomDay -1010.00 213.00 -4.74 0.000 1.1DadDay -975.00 212.00 -4.61 0.000 1.1MemDay_SaSu -524.0 153.00 -3.41 0.001 1.1MemDay -3259.0 213.00 -15.31 0.000 1.1Ramadan 213.40 55.70 3.83 0.000 1.84Jul_13-14 -3752.0 258.00 -14.56 0.000 1.04JulWkEnd -1363.0 185.00 -7.36 0.000 1.1LaborDay -2744.0 213.00 -12.88 0.000 1.1Halloween -543.00 213.00 -2.55 0.011 1.1MTW_B4Thx 380.00 129.00 2.95 0.003 1.2Blk_F -3291.0 214.00 -15.34 0.000 1.1Blk_Sa -1022.00 215.00 -4.75 0.000 1.1XmasEve -2821.0 210.00 -13.44 0.000 1.0Dec26 -1885.0 210.00 -8.97 0.000 1.0Dec27-31 -846.00 112.00 -7.59 0.000 1.2MeanVisMiles 20.20 5.48 3.68 0.000 1.1Event2_Snow -810.00 187.00 -4.33 0.000 1.1Event2_ThndrStrm -239.00 101.00 -2.36 0.019 1.0AvgTemp<32 -200.80 94.80 -2.12 0.034 1.2Petrol$ 1190.00 332.00 3.58 0.000 263.8Petrol$^2 -207.80 54.90 -3.78 0.000 263.15Sun -411.00 118.00 -3.49 0.000 1.2
AT!-WAND Challenge 12
Predict Net Sale for Store
506: SanityCheck
Jump to Date Effects
Jump to Petrol$ Effect
AT!-WAND Challenge 13
Predict Net Sale for Store 506: - Factors NOT in Model
- Deleted Rows- Is the Model Statistically Valid?
Factors tested but NOT significant …1. Holidays (MLK, Valentines, Presidents, Lent, Columbus, Veterans) 2. CPI$ (Beef, Chicken, Bread, Coffee, Nat.Gas, Electric)3. Weather (Temp, Press, Humid, DewPt, WindSpeed, Precipitation)
No Dates were deleted in creating this model
Residuals are …* Not normally distributed* Not constant w.r.t. time* Constant with respect to X* Constant w.r.t. Ypredict
Jump to Residuals Charts
AT!-WAND Challenge 14
Analytic Conclusions• Learn a lot from very little by leveraging dates and
ZIP code• Cluster analysis helpful to develop features• Study model residuals to identify previously
unknown influences• R-sqrd not always helpful when comparing models• Linear regression superior to time series analysis if
you want BI• Leverage power of technology to simplify analytics• SME critical to achieving a valid model
AT!-WAND Challenge 15
Actionable Intelligence1. Southerners lose their appetites when it snows!2. Speaking of snow, take a few more holidays.3. Do Holiday dips suggest competitive sales (e.g.,
Mother’s Day specials/discount)?4. Temp/Humid effects suggest an A/C upgrade is
called for (Store 482 losing $60/d for each 10% increase in humidity)?
5. Could coupons be cannibalizing Net Sale?6. Not all Store Re-Openings are the same!
AT!-WAND Challenge 16
Suggested WAND Next Steps1. Partner with Store Managers to improve models.
a. Add features to test impact of National sporting events & Fantasy sports,b. Test interaction of wind speed x direction
2. Learn how to monitor model residuals to detects changes in Net Sales.
3. Use models for Fraud/Error detection. Errors? Who can void/comp meals? Free drinks? Make-up orders? Theft when Manager offsite? X-check against employee work schedules? X-check against inventory errors.
4. Offer a consultancy to improve restaurant performance (e.g., dynamic scheduling, dynamic pricing, fact-based purchasing, Holiday “gimmicks”). Target stores with flat or decreasing Net Sales or in States with increasing minimum wages.
5. Run an experiment to see if there is value in restaurants capturing local weather data.
6. Study sales within a day for dynamic staffing.
AT!-WAND Challenge 17
Q&A
AT!-WAND Challenge 18
Support Slides
AT!-WAND Challenge 19
Data-Cleaning: Low Outliers
Jump Back
AT!-WAND Challenge 20
Principle Components AnalysisInforms the Cluster Analysis
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
% o
f Tot
al V
aria
nce
in N
et S
ales
Number of Principle Components
WAND Stores Net SalesPrinciple Components vs. %Variance
Jump Back
AT!-WAND Challenge 21
Cluster Analysis: Correlation Distance?
5000
2500
0
4000
2000
0
1000050000
4000
2000
0500025000 400020000
459
522
458
524
459 522
Stores 458, 459 Similarity = 88.58
Stores 522, 524 Similarity = 95.18
WAND Data Challenge, Daily Net Sales from 1/1/13-12/30/14Matrix Plot of Stores 458, 459, 522, 524
Jump Back
AT!-WAND Challenge 22
Store & Cluster Count Map
Jump Back
AT!-WAND Challenge 23
Analytics Dataset• Dependent Variable: Daily Net Sales for a Store• Features
– Date related (Day of Week, Day of Week No., Day of Month, Month, Year)
– Holidays (NewYr, MLK_SaSu, MLK, Feb1-7, ValDay, ValDay_WkEnd, PresDay_SaSu, PresDay, Lent, 5D_B4PalmFri , PalmFri, Easter, Passover, MomDay, MemDay_SaSu, MemDay, DadDay, Ramadan, WkEnd>Jul4, Jul4, 4JulWkEnd, LtJlyErlyAug,LbrDay_SaSu, LaborDay, ColumDay, Halloween, VetDay, MTW_B4Thx, Blk_F, Blk_Sa, 10daysB4Xmas, XmasEve, Dec27-31
– Weather (Temp, Humid, DewPt, Wind Speed, Pressure, Precipitation, Visibility, Cloud Cover, Weather Events)
– Events (Local Sports, Homecoming, National Sports)– Consumer Price Index (Beef, Chicken, Bread, Coffee, Petrol,
Natural Gas, Electricity)Jump Back
AT!-WAND Challenge 24
Predict Net Sale for Store 506: Date Effects
Jump Back
AT!-WAND Challenge 25
Predict Net Sale for Store 506: Petro Dollars
Jump Back
AT!-WAND Challenge 26
Predict Net Sale for Store 506: Residuals 4-in-1 Plot
43210-1-2-3-4
99.99
9990
50
10
1
0.01
N 1051AD 4.158P-Value <0.005
Standardized Residual
Perc
ent
60005000400030002000
2.5
0.0
-2.5
-5.0
Fitted Value
Stan
dard
ized
Resid
ual
3.752.501.250.00-1.25-2.50-3.75
150
100
50
0
Standardized Residual
Freq
uenc
y
1093
100291
182
072
963
854
745
636
527
418
3921
2.5
0.0
-2.5
-5.0
Observation Order
Stan
dard
ized
Resid
ual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for NetSales_506P17c, Final Model
Jump Back
AT!-WAND Challenge 27
Predict Net Sale for Store 506: Residuals Control Chart
Jump Back
AT!-WAND Challenge 28
Predict Net Sale: Date Extraction DetailsCluster 1 2 3 4 5 6 7Store 459 1863 2318 462 479 482 506Avg Net Sale 4,144$ 3,048$ 5,867$ 4,661$ 3,859$ 4,451$ 4,669$ R-Sqrd 81.1% 70.0% 87.5% 84.7% 69.8% 65.6% 89.6%Sy.x 278$ 255$ 520$ 402$ 307$ 437$ 358$
Day of Month
Day of Week (M-Su)Mon $0 $494 $0 $0 $550 $0 $0Tue $82 $360 $94 $220 $463 $0 $202Wed $297 $277 $332 $0 $309 $0 $415Thu $610 $120 $851 $496 $145 $355 $683Fri $1,205 $191 $1,597 $1,981 $293 $1,011 $1,011Sat $579 $496 $1,084 $1,119 $0 $0 $1,337Sun $213 $0 $0 $1,046 $905 $394 $1,666
Jan $568 $401 $726 $732 $653 $563 $513Feb $230 $0 $0 $0 $0 $258 $139Mar $0 $0 $0 $346 $112 $256 $107Apr $164 $0 $0 $0 $0 $0 $167May $140 $166 $307 $0 $0 $173 $246Jun $0 $106 $245 $480 $0 $191 $0Jul $99 $0 $179 $400 $0 $469 $378Aug $543 $142 $614 $509 $268 $0 $324Sep $417 $250 $753 $674 $364 $0 $348Oct $296 $0 $347 $130 $91 $350 $139Nov $228 $0 $496 $0 $182 $0 $223Dec $0 $206 $0 $0 $0 $192 $0
2013 $0 $197 $1,685 $151 $0 $0 $02014 $122 $0 $0 $387 $95 $0 $752015 $309 $261 $246 $0 $0 $0 $0
Jump Back
AT!-WAND Challenge 29
Predict Net Sale: Jan-Jun EffectsCluster 1 2 3 4 5 6 7Store 459 1863 2318 462 479 482 506Avg Net Sale 4,144$ 3,048$ 5,867$ 4,661$ 3,859$ 4,451$ 4,669$ R-Sqrd 81.1% 70.0% 87.5% 84.7% 69.8% 65.6% 89.6%Sy.x 278$ 255$ 520$ 402$ 307$ 437$ 358$
NewYr $1,876 $929 $1,404 $655 $1,988 $1,473 $3,244Jan1-7 $696MLK_SaSu $404MLK $1,014Feb1-7 $381 $425 $397 $390 $4031stSuFeb $958ValDayValDay_WkEndPresDay_SaSu $685PresDay $1,066Lent $105 $374 $1965D_B4PalmFri $419 $267PalmFri $830 $637 $707Easter $912 $1,129 $1,042 $1,112 $1,981 $679PassoverApr11-13_2015 $1,061SprngSlmp $238MomDay $735 $930 $672 $806 $1,010MemDay_SaSu $454 $827 $418 $524MemDay $942 $1,077 $838 $1,462 $763 $3,259DadDay $739 $946 $975Ramadan $202 $213
AT!-WAND Challenge 30
Predict Net Sale: July-Dec EffectsCluster 1 2 3 4 5 6 7Store 459 1863 2318 462 479 482 506Avg Net Sale 4,144$ 3,048$ 5,867$ 4,661$ 3,859$ 4,451$ 4,669$ R-Sqrd 81.1% 70.0% 87.5% 84.7% 69.8% 65.6% 89.6%Sy.x 278$ 255$ 520$ 402$ 307$ 437$ 358$
4dB4Jul4 $6583rdJuly $2,1074thJuly $820 $1,225 $2,531 $1,311 $1,4634Jul_13-14 $3,7525thJuly $1,487WkEnd>Jul4 $751 $742 $1,251 $546 $1,363LtJlyErlyAug $663 $421 $741 $398LbrDay_SaSu $372 $675 $410LaborDay $572 $670 $1,520 $333 $892 $2,744ColumDay $490Halloween $625 $543VetDayMT_B4Thx $570TuB4Thx $502WB4Thx $1,407 $1,446MTW_B4Thx $672 $609 $880 $380TuWThxSaSu $2,513Blk_F $3,291Blk_FSa $923 $916 $389 $1,414Blk_Sa $1,022Dec14-23 $463Dec18-24 $433 $511 $1,162Dec23 $1,298Dec24 $4,340 $2,274 $2,821Dec26 $1,885Dec26-29 $246Dec26-31 $614 $434 $1,474 $364 $472Dec27-31 $846
Jump Back
AT!-WAND Challenge 31
Predict Net Sale: CPI$ & Weather
Jump Back
AT!-WAND Challenge 32
Predict Net Sale: Miscellaneous
AT!-WAND Challenge 33
Coupons Cannibalize Net Sale?
8/26/158/19/158/12/158/5/157/29/157/22/157/15/157/8/157/1/15
1500
1000
500
0
-500
-1000
-1500
BusinessDate
Indi
vidua
l Valu
e
_X=0
UCL=1314
LCL=-1314
+2SL=876
-2SL=-876
+1SL=438
-1SL=-438
1
Results include specified rows: 912:973
I Chart of Store 482 Net Sale RESI29
Jump Back
AT!-WAND Challenge 34
Monitor Model Residuals: Early Warning System
1/29/1
5
1/22/1
5
1/15/1
51/8
/151/1
/15
1/29/1
5
1/22/1
5
1/15/1
51/8
/151/1
/15
2000
1000
0
-1000
-2000
BusinessDate
Indi
vidua
l Valu
e
_X=0
_X=0
UCL=1581
UCL=574
LCL=-1581
LCL=-574
+2SL=1054
+2SL=383
-2SL=-1054
-2SL=-383
+1SL=527+1SL=191
-1SL=-527-1SL=-191
Net Sale (Actual - Average) Net Sale (Actual - Predicted)I Chart of Store 509 Net Sale (Data vs. Model Variation)
Jump Back
AT!-WAND Challenge 35
Error/Fraud Detection:
Model Residuals
OK 420-2-4
99.999
90
50
10
10.1
N 91AD 0.153P-Value 0.957
Standardized ResidualPe
rcen
t
50004500400035003000
3.0
1.5
0.0
-1.5
-3.0
Fitted Value
Stan
dard
ized
Resid
ual
210-1-2-3
16
12
8
4
0
Standardized Residual
Freq
uenc
y
1201101009080706050403020101
3.0
1.5
0.0
-1.5
-3.0
Observation Order
Stan
dard
ized
Resid
ual
Normal Probability Plot Versus Fits
Histogram Versus Order
Residual Plots for NetSales_509P3
1/31/131/28/131/25/131/22/131/19/131/16/131/13/131/10/131/7/131/4/131/1/13
1000
0
-1000
1/31/141/28/141/25/141/22/141/19/141/16/141/13/141/10/141/7/141/4/141/1/14
1000
0
-1000
1/31/151/28/151/25/151/22/151/19/151/16/151/13/151/10/151/7/151/4/151/1/15
1000
0
-1000
BusinessDate
Indi
vidua
l Valu
e
_X=0
UCL=789
LCL=-789
+2SL=526
-2SL=-526
+1SL=263-1SL=-263
2013
_X=0
UCL=789
LCL=-789
+2SL=526
-2SL=-526
+1SL=263-1SL=-263
2014
_X=0
UCL=789
LCL=-789
+2SL=526
-2SL=-526
+1SL=263-1SL=-263
2015
Results exclude specified rows: 94:124
I Chart of Store 509 Net Sale RESI3 by Year
AT!-WAND Challenge 36
Error Detection: Correction?
AT!-WAND Challenge 37
Error/Fraud Detection: 4/5 < -$240
1/31/131/28/131/25/131/22/131/19/131/16/131/13/131/10/131/7/131/4/131/1/13
500
0
-500
1/31/141/28/141/25/141/22/141/19/141/16/141/13/141/10/141/7/141/4/141/1/14
500
0
-500
1/31/151/28/151/25/151/22/151/19/151/16/151/13/151/10/151/7/151/4/151/1/15
500
0
-500
BusinessDate
Resid
ual N
et S
ale, A
ct-P
red
$
_X=0
UCL=720
LCL=-720
+2SL=480
-2SL=-480
+1SL=240
-1SL=-240
2013
_X=0
UCL=720
LCL=-720
+2SL=480
-2SL=-480
+1SL=240
-1SL=-240
2014
_X=0
UCL=720
LCL=-720
+2SL=480
-2SL=-480
+1SL=240
-1SL=-240
2015
6
6
Results exclude specified rows: 94:124
I Chart of Store 462 Net Sale RESI7 by Year
AT!-WAND Challenge 38
Error/Fraud Detection: 8 consecutive < $0
1/31/131/28/131/25/131/22/131/19/131/16/131/13/131/10/131/7/131/4/131/1/13
500
0
-500
1/31/141/28/141/25/141/22/141/19/141/16/141/13/141/10/141/7/141/4/141/1/14
500
0
-500
1/31/151/28/151/25/151/22/151/19/151/16/151/13/151/10/151/7/151/4/151/1/15
500
0
-500
BusinessDate
Indi
vidua
l Valu
e
_X=0
UCL=729
LCL=-729
+2SL=486
-2SL=-486
+1SL=243
-1SL=-243
2013
_X=0
UCL=729
LCL=-729
+2SL=486
-2SL=-486
+1SL=243
-1SL=-243
2014
_X=0
UCL=729
LCL=-729
+2SL=486
-2SL=-486
+1SL=243
-1SL=-243
2015
222
Results exclude specified rows: 94:124
I Chart of Store 467 Net Sale RESI5 by Year
Jump Back
AT!-WAND Challenge 39
Data Review: Store 506
1st Quartile 3705.1Median 4950.33rd Quartile 5451.7Maximum 7528.6
4601.8 4735.7
4886.2 5002.5
1063.9 1158.7
A-Squared 22.45P-Value <0.005Mean 4668.8StDev 1109.3Variance 1230558.6Skewness -0.505620Kurtosis -0.611348N 1057Minimum 1526.6
Anderson-Darling Normality Test
95% Confidence Interval for Mean
95% Confidence Interval for Median
95% Confidence Interval for StDev
700060005000400030002000
Median
Mean
50004900480047004600
95% Confidence Intervals
Summary Report for NetSales_506
8000
7000
6000
5000
4000
3000
2000
1000
NetSa
les_506 5217
3185
2013201420152016
YearTime Series Plot of NetSales_506
Jump Back
AT!-WAND Challenge 40
Store Re-Openings
12/31/
13
12/27/
13
11/27/
13
10/28/
13
9/28/1
3
8/29/1
3
7/30/1
3
6/30/1
3
5/31/1
35/1
/134/1
/133/2
/13
1/31/1
31/1
/13
6000
5000
4000
3000
2000
1000
0
BusinessDate
522
Time Series Plot of Store 522 Net Sale
12/29/
159/2
9/15
6/30/1
53/3
1/15
12/30/
149/3
0/14
7/1/14
4/1/14
12/31/
1310/
1/13
7/2/13
4/2/13
1/1/13
18000
16000
14000
12000
10000
8000
6000
4000
2000
BusDate
2318
_Net
$4349
201320142015
Year
Time Series Plot of 2318_Net$
Jump Back
AT!-WAND Challenge 41
Store Location & Clusters Map
1-red, 2-blue, 3-green, 4-cyan, 5-brown, 6-orange, 7-purple
Jump to Map 2
AT!-WAND Challenge 42
Predict Net Sale: 1 Model per ClusterDate Extraction Effects
Jump to Details
AT!-WAND Challenge 43
Predict Net Sale: 1 Model per ClusterHoliday Effects
Jump to Details
AT!-WAND Challenge 44
Actionable Intelligence1. Southerners lose their appetites when it snows!2. Speaking of snow, take a few more holidays.3. Do Holiday dips suggest competitive sales (e.g.,
Mother’s Day specials/discount)?4. Temp/Humid effects suggest an A/C upgrade is
called for (Store 482 losing $60/d for each 10% increase in humidity)?
5. Could coupons be cannibalizing Net Sale?6. Not all Store Re-Openings are the same!
AT!-WAND Challenge 45
Suggested WAND Next Steps1. Partner with Store Managers to improve models.
a. Add features to test impact of National sporting events & Fantasy sports,b. Test interaction of wind speed x direction
2. Learn how to monitor model residuals to detects changes in Net Sales.
3. Use models for Fraud/Error detection. Errors? Who can void/comp meals? Free drinks? Make-up orders? Theft when Manager offsite? X-check against employee work schedules? X-check against inventory errors.
4. Offer a consultancy to improve restaurant performance (e.g., dynamic scheduling, dynamic pricing, fact-based purchasing, Holiday “gimmicks”). Target stores with flat or decreasing Net Sales or in States with increasing minimum wages.
5. Run an experiment to see if there is value in restaurants capturing local weather data.
6. Study sales within a day for dynamic staffing.