The following is an example summary report we produced for a client in May,
2006
To help protect the identity of this client, the product they sell has been replaced
by *****
Analysis of sales figures you provided (for
January 12th through May 15th, 2006)
revealed several important factors that may have influenced daily sales in Seattle
and Tacoma stores:
1a. Day-of-Week by Temperature (Interaction)
- Weekend sales tended to increase
as daytime high temperatures increased.
- In contrast,
mid-week sales tended to increase
as low temperatures became less extreme.
- The
difference is subtle but important: Extremely cold morning temperatures
on the weekend were not associated
with fewer sales (as long as it eventually turned out to be a
warm day). However, when daily low temperatures
dropped during the week, mid-week sales remained
low even if the daily high temperatures
were relatively warm.
- In summary, warm weather
may have encouraged weekend shoppers to buy *****,
whereas mid-week shoppers appeared to be more concerned
about avoiding extremely low temperatures. Further
investigation confirmed that mid-week shoppers bought
far fewer ***** when low temperatures approached or dropped below
freezing.
- This likely indicates market segmentation;
your weekend consumer audience likely differs from your weekday consumer audience
in both preference and behavior (see footnote *1).
1b. Practical Application of this Knowledge
- You may already be aware of this market segmentation. If not, you may be able to
use this information to increase sales by:
- Planning different promotions on
weekends and weekdays that specifically focus on the preferences of each consumer
audience.
- Modifying retail displays based on the day of week, [material omitted
to protect client].
- varying content of advertisements based on whether the
content is likely to be viewed during the week or over the weekend. [further discussion
omitted].
2a. Seasonal trend (progression of time)
- The number of weekday sales increased
steadily with each passing week.
- In contrast, weekend
sales also increased over time, but the
amount of increase in weekend sales grew with
each passing weekend.
- We found no indication
that the rates of increase in weekday
or weekend sales were beginning to slow by May 15th,
despite your indication that demand for your product usually peaks near the end
of June. However, we noticed a large effect around May 14th, likely due to
Mother's Day.
- Even after investigating several
models excluding sales after May 10th, no indication of slowing sales were noted.
2b. Addressing your question and comment
- You requested that we address the question of when your
sales season really begins.
- Using consistent,
predictable demand for your product to define the beginning of the
sales season, your suspicion was confirmed; buying patterns were fairly
inconsistent throughout January and became more predictable
during the second week of February despite the
overall low number of sales that month (see footnote *2).
3. Precipitation / Day-of-Week / Temperature (Interaction)
- Another important interaction involved precipitation and can explained as follows:
- Regardless of the day-of-week, sales tended to be lower when it
rained.
- Sales tended to be dramatically lower on rainy weekends.
- Warmer average daily temperatures tended to offset this negative effect on sales when
it rained on weekdays.
- However, warmer temperatures
did not help offset the negative effect of rain
on sales when it rained on a Saturday
or Sunday.
- This interaction accounted for the apparently paradoxical reversals in Saturday/Sunday
buying patterns in February and March, including the extremely
low sales on a rainy Saturday,
March 18th followed by higher sales on a dry Sunday
the 19th. The following weekend, sales resumed a more common pattern where Saturday
sales exceeded Sunday sales.
- This is another clear
indication that you have two different consumer audiences: weekday and weekend shoppers.
4. Daily low temperature.
- In general, cold temperatures were associated with
low sales.
5. Average wind-speed.
- Higher wind-speeds were strongly associated with fewer sales.
- This could be an indication of general
consumer behavior, the necessity to move your product indoors on
windy days, or a combination of the two. In addition,
wind speed is often a general indication of poor weather.
6. Stability of the temperature throughout the day.
- Greater fluctuation of temperature throughout the day
was associated with fewer sales.
- Note that stability of temperature could be an indicator of general
weather conditions, such as the motion of storm fronts,
or unpredictable and constantly-changing weather.
In summary, using these four simple effects and two
interactions, we were able to develop models from a limited set of sales
data that predicted both future and past sales in the SeaTac area with impressive
accuracy (see online figures 1, 2, and 3). Additional information, such as the accuracy
of currently-used sales predictions, is needed in order to assess the potential
benefit of predictive modeling services. It is also important to note that our source
of recent historic weather data only included daily ground-related weather data
from SeaTac airport. We were unable to use SeaTac weather variables to predict
sales in your retail location in Mount Vernon. When we attempted a brief analysis combining sales
from geographically distant regions, more general
weather indicators such as barometric pressure and wind direction began to dominate
the model, indicating that the weather differs significantly between the regions.
(*1) We were unable to more thoroughly explore
weekend
vs.
weekday shopper behavior because of limited data;
only 18 weekends occurred between January 12th and May 15th. Using historic sales
figures (or this year's complete sales figures after the season ends), we would
be able to provide a more thorough analysis and comparison between
weekend and
weekday shopper preferences and
behaviors.
(*2) Stating that your
sales season begins in early
February would be supported by your sales figures. However, to accurately estimate
the beginning of
next year's sales season
would require analysis of sales figures during the same months from previous years.