Event Analysis Template
With FootPath data it is possible to take a very quantitative
approach to analyzing the success of your in-mall events. With
our easy-to-use template you can estimate the 'Return on
Investment' in the event. Our view is that the purpose of the inmall event is to increase visitor traffic and lengthen shopper
dwell time. If the event has achieved those two objectives then
the opportunities the retail tenants have to sell their products to
shoppers has increased.
In all, we utilize a five-step process for analyzing events. These
steps are:
1. Analyse traffic to the mall during the week of the event;
2. Analyse dwell time to the mall during the week of the event;
3. Estimate the potential sales uplift at tenant retailers that has
been created through holding the event
4. Calculate the return on investment in the event using cost
data provided; and
5. If required, conduct additional analyses to give colour and
additional insight into the event in terms of:
a. Analyzing the impact on specific retailers
b. Identifying 'tweets' commenting on the event
c. Identifying the makeup of the visitor traffic to the event in terms
of regular visitors to the mall, irregular visitors to the mall or
indeed first-timers (and at a later date looking at how many of
the 'first-timers' to the mall returned to shop in the coming
months).
The following takes you through each of these steps with
reference to the downloadable Excel template entitled
'Blank_Event_Analysis_Template' below.
Download Blank_Event_Analysis_Template
STEP 1: ANALYSING TRAFFIC TO THE MALL DURING
THE EVENT
One of the complications of analyzing the effectiveness of
events within malls is to isolate the effect of the event itself as
compared to seasonality effects on the variables. For example,
lets suppose that a mall attracted 20,000 visitors when a 'craft
fair' event was in progress. Is that a good level of visitors to the
mall or not? We cannot simply compare to traffic levels the week
before as the previous week may have been a school holiday
week, or a retail sales week. Or perhaps there was poor weather
the week before which kept visitors out of the mall and at home
indoors. For similar reasons we cannot compare to the same
week the year before.
For this reason Path Intelligence has developed its own
statistical model that is capable of predicting the daily traffic to
malls. The model is called FuturePath and includes variables
that are local to the mall itself, such as the daily weather in that
locality, local events and local holiday dates. By comparing the
traffic to the event with traffic that was predicted to the mall
under FuturePath we can isolate the effect of the event itself
(and take out the influences of variables such as the weather
and the time of year).
To analyse traffic to the mall therefore our template requires that
the user inputs information on:
 Traffic levels during the event (row 5)
 Traffic levels during the week prior to the event (row 6)
 Traffic levels during the week following the event (row 7)
 Traffic levels during the same week as the event the
previous year (row 8)
FuturePath traffic predictions for the week of the event (row
9)
As you fill in data into the specified rows noted above, the
template will automatically calculate the percentage change in
those variables in rows 12-15. In row 16 the template will
calculate an average percentage change for the event across all
of the various comparators noted above.

STEP 2: ANALYSING DWELL TIME TO THE MALL
DURING THE EVENT
In addition to monitoring the increase in traffic the event creates
we also look at the uplift in average dwell time in the centre.
(With FootPath data it is possible to calculate the dwell time of
each visitor to the mall and thereby obtain an average dwell time
across all shoppers).
Once again we compare this in a number of different ways. As
before we compare the dwell time on the days/week of the event
(row 4 on the template spreadsheet) with:
 Dwell times during the week prior to the event (row 5)
 Dwell times during the week following the event (row 6)
 Dwell times during the same week as the event the previous
year (row 7)
STEP 3: ESTIMATING THE CHANGE IN SALES TO
THE MALL AS A RESULT OF THE EVENT
If you have access to sales data from each of your retail tenants
then we will incorporate your data at this step, and analyse the
sales data in a similar manner to that in steps 1 and 2 where we
have analysed traffic and dwell time information.
However, many of our clients do not have access to all of the
sales data from their tenants and it is necessary therefore to
'estimate' what this might be on the basis of the information that
you do have. Many of our clients will have a good understanding
of what the average shopper spend within the centre is. This
information is therefore inputted into rows 17 and 18.
Using what we know about visitor traffic levels and dwell times
we then calculate what the average amount spent per shopper
per minute is within the mall, and this is output in rows 22 and
23.
Once we have the amount spent per shopper per minute we can
back calculate what the overall sales might have been by
multiplying this figure by the total visitor traffic during the event
and the average dwell time (and this is output in rows 27-30).
For example, let us suppose that the average shopper spend in
the mall was £15.00 and that the average dwell time was 100
minutes. In this case the average shopper spends 15p each
minute. Perhaps we also know that the attendance at the mall
was 20,000 on the day of the event and that dwell time went up
to 100 minutes. In total shoppers spent 2.2 million minutes in the
mall on the day of the event (20,000 x 110 minutes), spending
on average 15p each minute which equates to total sales that
day of £330,000 (2.2m x 15p).
STEP 4: CALCULATING THE RETURN ON
INVESTMENT
Once we have calculated the estimated sales to retail tenants
during the event we can calculate the ROI on the event.
To begin the estimated uplift in sales generated by the event is
automatically calculated in row 4. We then include the total of
any costs associated with the event (in terms of advertising,
paying for event coordinators or event materials or indeed
paying for the event itself depending on the nature of the event).
Some events are themselves revenue generating and so any
revenue brought in by the event can be included in row 8.
With all of this data it is now possible to calculate a return on
investment in the usual way looking at the uplift in sales divided
by the total costs of the event.