Weather Impact Assessment

WEATHER IMPACT ASSESSMENT National Online Food Ordering Company

EXECUTIVE SUMMARY

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WeatherAlpha | Proprietary & Confidential

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*

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* 4% established based on a 2009 interview with Client CEO.


IMPACT OF WEATHER ON FOOD DELIVERY COMPANY

» To complete this task, WeatherAlpha used a three-phased approach to conduct a comprehensive weather impact assessment

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Develop Baseline Metrics

Conduct Order Analysis

Define Weather Based Marketing

Opportunities

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Key Deliverables

None

Preliminary Observations Weather-Driven Business Analysis

Rationale 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

Phases


FIRST PHASE: IDENTIFICATION OF BASELINE METRICS

» Baseline Metric 1 - Orders by Month

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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).



» Baseline Metric 2 - Orders by Day of Week

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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).

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» Baseline Metric 3: Orders by Hour of Day

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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.

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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.

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Weather Conditions

Daily Rainfall

Hourly Rainfall Intensity

Daily Snowfall

Hourly Snow/Ice Intensity

Temperature

Temperature Anomaly

Dewpoint

Humidity

Wind

Cloud Cover

Forecast/Prior Effects

Past/Lagged Effects

Snow Cover

Precipitation effects

Temperature/humidity effects

Other notable weather effects


THIRD PHASE: MARKETING STRATEGIES

» In the third phase, WeatherAlpha developed weather-based marketing strategies for Client to create new lines of revenue

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Removed due to client confidentiality

WEATHER IMPACT ASSESSMENT

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OF THE 14 WEATHER CONDITIONS WEATHERALPHA ANALYZED, THE FOLLOWING 8 WERE SELECTED AS HAVING THE GREATEST IMPACT ON CLIENT REVENUE

» Weather Impact Report Card

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Note: For nearly every weather condition analyzed, Madison was significantly more weather sensitive than Philadelphia.

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% )


CONDITION 1: HOURLY RAINFALL INTENSITY

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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

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Hourly rainfall (hundreths of inches)

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CONDITION 2: HOURLY SNOW/ICE INTENSITY

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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.


CONDITION 3A: TEMPERATURE ANOMALY WARM DAYS

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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.

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Degrees above normal (F)

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CONDITION 3B: TEMPERATURE ANOMALY COLD DAYS

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Degrees Below Normal (F)

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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.


CONDITION 4: HIGH DEWPOINT

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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.


CONDITION 5: WIND & RAIN

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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.

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CONDITION 6: CLOUD COVER

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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.

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CONDITION 7: FORECAST/PAST EFFECTS

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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.

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CONDITION 8: SNOW COVER

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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.

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HOW CAN CLIENT LEVERAGE WEATHER?

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CLIENT CAN LEVERAGE THE UPCOMING WEATHER TO CREATE NEW REVENUE STREAMS VIA WEATHER-BASED MARKETING CAMPAIGNS

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Removed due to client confidentiality

CONTACT US.

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WEATHERALPHA TEAM

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Daniel Alexander

Co-Founder, Chief Meteorologist + Data Scientist

[email protected]

Mobile: (203) 241-4253

Jason Chen

Co-Founder, Chief Strategy + Operations Officer

[email protected]

Mobile: (617) 955-0759

Brooke Cunningham

Chief Alliances Officer

[email protected]

Mobile: (347) 556-9613

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.

THANK YOU.

Date post:	18-Nov-2014
Category:	Self Improvement
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Weather Impact Assessment

Self Improvement