Weather Impact Assessment
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Weather Impact Assessment

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This is an example of a Weather Impact Assessment performed for a national food ordering company by WeatherAlpha, the leading provider of business weather evaluations, which turn your data into ...

This is an example of a Weather Impact Assessment performed for a national food ordering company by WeatherAlpha, the leading provider of business weather evaluations, which turn your data into instantly actionable intelligence.

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Weather Impact Assessment Weather Impact Assessment Presentation Transcript

  • WEATHER IMPACT ASSESSMENT National Online Food Ordering Company
  • EXECUTIVE SUMMARY 2
  • IMPACT OF WEATHER ON FOOD DELIVERY COMPANY » The client tasked WeatherAlpha with evaluating the influence of weather on orders and revenue in Madison, WI and Philadelphia, PA » Project Objectives: • • • Complete a statistical analysis of sampled data Provide Client with a full understanding of which weather conditions increase or decrease online orders / revenue in Madison, WI and Philadelphia, PA Explore weather-based digital advertising opportunities » Data: • • • Sample size includes dates between 01/01/2005 – 12/31/2010 Hourly order data was compared with corresponding hourly weather data For some weather conditions, a comparison of aggregate daily order data with aggregate daily weather data was preferred » Assumptions: • • Weather at nearest weather station is representative of the weather at each city Client revenue was calculated using 4% of the order total* * 4% established based on a 2009 interview with Client CEO. WeatherAlpha | Proprietary & Confidential 3
  • IMPACT OF WEATHER ON FOOD DELIVERY COMPANY » To complete this task, WeatherAlpha used a three-phased approach to conduct a comprehensive weather impact assessment 1 Develop Baseline Metrics Phases Rationale Key Deliverables 2 3 Conduct Order Analysis Define Weather Based Marketing Opportunities  Develop understanding of order and sales trends to determine seasonal, weekly, and diurnal patterns in data  Identify weather conditions that increase/decrease online orders  Determine if there are other variables that work with weather  Identify when and how Client can boost orders  None  Preliminary Observations  Weather-Driven Business Analysis WeatherAlpha | Proprietary & Confidential 4
  • FIRST PHASE: IDENTIFICATION OF BASELINE METRICS » Baseline Metric 1 - Orders by Month 16 Philadelphia Madison 4 14 3.5 12 3 10 2.5 8 2 6 1.5 4 1 2 0.5 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec KEY OBSERVATIONS  Both locations exhibit similar monthly fluctuations in order volume  Two distinct periods of greater sales, Feb-Apr and Oct-Dec, coincide with the heart of the spring and fall semesters.  Jun-Aug is relatively quiet at both locations as many students leave for the summer.  Madison shows greater annual variability, with its biggest sales month (Dec) being 151% more than its smallest sales month (Aug). Conversely, the biggest sales month in Philadelphia (Feb) is 68% greater than its smallest sales month (Jul). WeatherAlpha | Proprietary & Confidential 5
  • FIRST PHASE: IDENTIFICATION OF BASELINE METRICS » Baseline Metric 2 - Orders by Day of Week Philadelphia Madison 3 10 2.5 Hourly Orders 3.5 12 Hourly Orders 14 8 6 2 1.5 4 1 2 0.5 0 0 Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat KEY OBSERVATIONS  At both locations, there is little variability in order volume based on day of the week.  Madison’s biggest sales day (Wed) is 6% greater than its smallest sales day (Sat).  Philadelphia’s biggest sales day (Sun) is 5% greater than its smallest sales day (Thurs). WeatherAlpha | Proprietary & Confidential 6 Sun
  • FIRST PHASE: IDENTIFICATION OF BASELINE METRICS » Baseline Metric 3: Orders by Hour of Day Philadelphia Madison 40 12 10 Hourly Orders 30 25 20 15 10 8 6 4 2 0 0 12:00 AM 1:00 AM 2:00 AM 3:00 AM 4:00 AM 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM 9:00 PM 10:00 PM 11:00 PM 5 12:00 AM 1:00 AM 2:00 AM 3:00 AM 4:00 AM 5:00 AM 6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM 11:00 AM 12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM 9:00 PM 10:00 PM 11:00 PM Hourly Orders 35 KEY OBSERVATIONS  Both locations exhibit similar diurnal fluctuations in order volume  At both locations, orders peak around the dinnertime hours of 6-8 p.m.  At both locations, there is a secondary lunchtime spike in orders between 12-2 p.m.  At both locations, orders are negligible during the early morning hours of 4-9 a.m.  Madison has a larger late night customer base than Philadelphia. WeatherAlpha | Proprietary & Confidential 7
  • SECOND PHASE: WEATHER IMPACT REPORT CARD » In the second phase, WeatherAlpha completed analysis of 14 weather conditions in both geographies over a 6 year period to develop a Weather Impact Report Card. Weather Conditions Daily Rainfall Precipitation effects Hourly Rainfall Intensity Daily Snowfall Hourly Snow/Ice Intensity Temperature Temperature/humidity effects Temperature Anomaly Dewpoint Humidity Wind Cloud Cover Other notable weather effects Forecast/Prior Effects Past/Lagged Effects Snow Cover WeatherAlpha | Proprietary & Confidential 8
  • THIRD PHASE: MARKETING STRATEGIES » In the third phase, WeatherAlpha developed weather-based marketing strategies for Client to create new lines of revenue Removed due to client confidentiality WeatherAlpha | Proprietary & Confidential 9
  • WEATHER IMPACT ASSESSMENT 10
  • OF THE 14 WEATHER CONDITIONS WEATHERALPHA ANALYZED, THE FOLLOWING 8 WERE SELECTED AS HAVING THE GREATEST IMPACT ON CLIENT REVENUE » Weather Impact Report Card Weather Condition Madison, WI Philadelphia, PA Hourly Rainfall Intensity Hourly Snow/Ice Intensity Temperature Anomaly Dew Point Wind Cloud Cover Forecast/Past Effects Snow Cover High Impact ( >25% ) Moderate Impact ( 10-25% ) Low Impact ( <10% ) Note: For nearly every weather condition analyzed, Madison was significantly more weather sensitive than Philadelphia. WeatherAlpha | Proprietary & Confidential 11
  • CONDITION 1: HOURLY RAINFALL INTENSITY Philadelphia Madison 270 Hourly spend ($) Hourly spend ($) 250 230 210 190 170 150 None 5 10 15 20 Hourly rainfall (hundreths of inches) 25 80 75 70 65 60 55 50 45 40 35 30 None 5 10 15 20 25 Hourly rainfall (hundreths of inches) KEY OBSERVATIONS  Rainfall is associated with increased order volume and hourly spend on both locations.  In Madison, hours with rain have 25% more hourly order spend than dry hours. Increasing intensity generally magnifies this effect, up to a 35% increase.  In a given year, rainfall in Madison results in $2000-$2500 additional revenue for Client over dry hours*.  In Philadelphia, rainfall results in a 8-20% increase in hourly spend, determined by intensity. This results in an extra yearly revenue of $400-$500 over dry hours*. * Revenue estimates based on sales increase, frequency of rainfall, and 4% figure cited previously WeatherAlpha | Proprietary & Confidential 12
  • CONDITION 2: HOURLY SNOW/ICE INTENSITY Madison 6 18 5.5 16 5 Hourly Orders Hourly Orders Philadelphia 14 12 10 4.5 4 3.5 3 8 2.5 6 Light Moderate Heavy 2 Light Moderate Heavy KEY OBSERVATIONS  Frozen precipitation is associated with increased order volume and hourly spend for both locations.  In Madison, frozen precipitation results in a 25-38% increase in order volume, though the trend of increasing intensity is inconclusive.  For Madison, falling snow/ice is estimated to account for $600-1000 in extra yearly revenue over dry hours.  In Philadelphia, frozen precipitation results in a 28-50% increase in order volume. Generally speaking, order volume increases with greater precipitation intensity.  For Philadelphia, falling snow/ice is estimated to account for $200-300 in extra yearly revenue over dry hours. WeatherAlpha | Proprietary & Confidential 13
  • CONDITION 3A: TEMPERATURE ANOMALY WARM DAYS Philadelphia Madison 72 220 71 Daily Orders 73 230 Daily Orders 240 210 200 190 180 70 69 68 67 170 66 160 65 150 5 6 7 8 9 10 11 12 13 Degrees above normal (F) 14 15 5 6 7 8 9 10 11 12 13 14 Degrees Above Normal (F) KEY OBSERVATIONS  Frozen precipitation is associated with increased order volume and hourly spend for both locations.  In Madison, frozen precipitation results in a 25-38% increase in order volume, though the trend of increasing intensity is inconclusive.  For Madison, falling snow/ice is estimated to account for $600-1000 in extra yearly revenue over dry hours.  In Philadelphia, frozen precipitation results in a 28-50% increase in order volume. Generally speaking, order volume increases with greater precipitation intensity.  For Philadelphia, falling snow/ice is estimated to account for $200-300 in extra yearly revenue over dry hours. WeatherAlpha | Proprietary & Confidential 14 15
  • CONDITION 3B: TEMPERATURE ANOMALY COLD DAYS Philadelphia Madison 84 390 82 Daily Orders Daily Orders 360 330 80 78 76 300 74 72 270 -5 -6 -7 -8 -9 -10 -11 -12 Degrees Below Normal (F) -13 -14 -15 -5 -6 -7 -8 -9 -10 -11 -12 -13 -14 -15 Degrees Below Normal (F) KEY OBSERVATIONS  Abnormally cold weather is associated with greater daily order volume on both locations.  In Madison, increasing the magnitude of the cold relative to normal results in incremental increases in daily order volume. A very cold day may have up to 20% more orders vs. a typically cold day and up to 30-40% more than a day with seasonable temperatures.  A very cold day in Madison may see ~ $75 more revenue compared to a normal day.  In Philadelphia, cold days see greater order volume than warm days, though the trend of increasing cold anomaly is inconclusive. WeatherAlpha | Proprietary & Confidential 15
  • CONDITION 4: HIGH DEWPOINT 8.5 Philadelphia Madison 2.4 7.5 2.2 Hourly Orders Hourly Orders 8 7 6.5 6 5.5 2 1.8 1.6 1.4 1.2 5 1 40 45 50 55 60 65 45 50 55 60 65 70 Dewpoint (F) KEY OBSERVATIONS  High dewpoints in Madison are associated with increases in hourly order volume, especially above 60 ºF. At very high dewpoints, hourly orders are as much as 30% greater than drier hours. A very moist day may be responsible for up to a $50 increase in daily revenue.  In Philadelphia, high dewpoints cause a decrease in hourly order volume, the opposite effect of that in Madison. WeatherAlpha | Proprietary & Confidential 16
  • CONDITION 5: WIND & RAIN 18 Madison 4 Philadelphia 16 Hourly Orders Hourly Orders 3.5 14 12 3 2.5 10 8 5 10 15 20 2 5 10 15 20 KEY OBSERVATIONS  At both locations, increasing wind speed during wet hours results in a corresponding increase in order volume. This effect (though not shown here) is also observed when the weather is dry.  The positive effect of rain on Madison and Philadelphia order volume are accentuated by increasing wind. For instance, a rainy hour with strong winds in Madison will have 20-25% more revenue than a rainy hour with no wind and a 50% increase over a dry hour. WeatherAlpha | Proprietary & Confidential 17
  • CONDITION 6: CLOUD COVER Philadelphia Madison 76 290 74 270 72 Daily Orders 78 310 Daily Orders 330 250 230 210 70 68 66 190 64 170 62 150 60 1 2 3 4 5 6 7 8 Fractional Sky Cover (tenths) 9 10 1 2 3 4 5 6 7 8 9 10 Fractional Sky Cover (tenths) KEY OBSERVATIONS  Increases in average daily cloud cover have a generally positive impact on daily order volume for both locations, though the trend is not entirely definitive.  At both Madison and Philadelphia, the highest amount of daily orders occurs on days with nearly maximum cloud cover.  Interestingly, at both locations, days that are nearly free of cloud cover have greater daily orders than partly cloudy days  It is difficult to attribute any revenue gain or loss to cloud cover alone, as this variable is directly related to likelihood of precipitation. WeatherAlpha | Proprietary & Confidential 18
  • CONDITION 7: FORECAST/PAST EFFECTS Philadelphia 120 100 90 80 70 60 50 40 30 20 10 0 100 No Future Precip Future Precip Hourly Spend ($) Hourly spend ($) Madison 80 No Past Precip 60 Light Past Precip 40 Moderate Past Precip 20 1 2 3 4 5 6 Hours prior to event 0 1 2 3 4 5 6 Hours after event KEY OBSERVATIONS  There were increases in hourly spend prior to the onset of precipitation as well as after it had departed. Though Philadelphia is displayed here, a similar impact was observed in Madison.  For 3-4 hours leading up to a precipitation event, there is a spike in hourly order volume. This is likely the result of decisions based on weather forecast information.  For 3 hours after a precipitation event had ended, hourly order volume remains elevated, likely the effect of damp or snowy ground and continued cloud cover. Moreover, greater precipitation intensity is associated with a more pronounced uptick.  This observation - of forecast and past effects on sales beyond the time frame of the event itself - is important for a few reasons. Firstly, it extends the period of revenue gain for Client. Also, it argues for the use of forecast and past weather triggers for marketing optimization. WeatherAlpha | Proprietary & Confidential 19
  • CONDITION 8: SNOW COVER Philadelphia Madison 450 100 95 400 Daily Orders Daily Orders 90 350 300 250 85 80 75 70 65 200 60 150 None 1" + 3" + 6" + 10" + None 1" + 3" + 6" + 10" + KEY OBSERVATIONS  The existence of snow cover at both locations results in increased daily order volume.  In Madison, increasing snow cover is associated with incremental increases in daily orders. Days with 6” or more of snow see a 57% uptick in orders, while 10”+ of snow cover results in an 84% gain.  This means that in Madison, having a foot of snow on the ground will translate into an additional $200/day in revenue over a winter day with no snow cover!  In Philadelphia, snow cover is associated with increases in daily orders, though the trend is not as pronounced as in Madison. Here, 10” or more of snow will result in an extra $30/day of revenue. WeatherAlpha | Proprietary & Confidential 20
  • HOW CAN CLIENT LEVERAGE WEATHER? 21
  • CLIENT CAN LEVERAGE THE UPCOMING WEATHER TO CREATE NEW REVENUE STREAMS VIA WEATHER-BASED MARKETING CAMPAIGNS Removed due to client confidentiality WeatherAlpha | Proprietary & Confidential 22
  • CONTACT US. 23
  • WEATHERALPHA TEAM Daniel Alexander Co-Founder, Chief Meteorologist + Data Scientist dan@weatheralpha.com Mobile: (203) 241-4253 Jason Chen Co-Founder, Chief Strategy + Operations Officer jason@weatheralpha.com Mobile: (617) 955-0759 Our Social Responsibility. We believe that giving back to the community should be a real priority. Each year, up to 5% of WeatherAlpha’s profit will be donated to weather disaster and relief funds around the world. We value our planet and respect Mother Nature. We don’t wish to use Mother Nature for our sole benefit – we wish to help our clients better understand and value weather, and in doing so we will also help communities that have been shattered by weather events. Brooke Cunningham Chief Alliances Officer brooke@weatheralpha.com Mobile: (347) 556-9613 WeatherAlpha | Proprietary & Confidential 24
  • THANK YOU.