Beyond Mapping III
|
Map
Analysis book with companion CD-ROM for hands-on exercises and further reading |
GIS Analyzes In-Store
Movement and Sales Patterns — describes
a procedure using accumulation surface analysis to infer shopper movement from
cash register data
Further Analyzing In-Store Movement and Sales
Patterns — discusses
how map analysis is used to investigate the relationship between shopper
movement and sales
Continued Analysis of In-Store Movement and
Sales Patterns — describes
the use of temporal analysis and coincidence mapping to enhance shopping
patterns
Author’s Notes: The figures
in this topic use MapCalcTM
software. An educational CD with online
text, exercises and databases for “hands-on” experience in these and other
grid-based analysis procedures is available for US$21.95 plus shipping and
handling (www.farmgis.com/products/software/mapcalc/
).
<Click
here> right-click to download a printer-friendly version of this topic
(.pdf).
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_____________________________
(GeoWorld, February 1998, pg. 30-32)
There are two fundamental types of
people in the world: shoppers and non-shoppers.
Of course, this distinction is a relative one, as all of us are shoppers
to at least some degree. How we perceive
stores and what prompts us to frequent them form a large part of retail
marketing’s
Figure 26.1. Establishing Shopper Paths. Stepped accumulation surface analysis is used to model shopper movement based on the items in a shopping cart.
The floor plan of a store is a continuous surface with a complex of
array of barriers strewn throughout. The
main aisles are analogous to mainline streets in a city, the congested areas
are like secondary streets, while the fixtures form absolute barriers (can’t
climb over or push aside while maintaining decorum). Added to this mix are the entry doors,
shelves containing the elusive items, cash registers, and finally the exit
doors. Like an obstacle race, your
challenge is to survive the course and get out without forgetting too
much. The challenge to the retailer is
to get as much information as possible about your visit.
For years, the product flow through the cash registers have been analyzed to
determine what sells and what doesn’t. Data analysis originally focused on
reordering schedules, then extended to descriptive statistics and insight into
which products tend to be purchased together (product affinities). However, mining the data for spatial
relationships, such as shopper movement and sales activity within a store, is
relatively new. The left portion of
Figure 1 shows a map of a retail superstore with fixtures (green) and shelving
nodes (red). The floor plan was
digitized and the fixtures and shelving spaces were encoded to form map
features similar to buildings and addresses in a city. These data were gridded at a 1-foot
resolution to form a continuous analysis space.
The right portion of Figure 26.1 shows the plausible path a shopper took to
collect the five items in a shopping cart.
It was derived through stepped accumulation surface analysis described
in last month’s column. Recall that this
technique constructs an effective proximity surface from a starting location
(entry door) by spreading out (increasing distance waves) until it encounters
the closest visitation point (one of the items in the shopping cart). The first leg of the shopper’s plausible path
is identified by streaming down the truncated proximity surface (steepest
downhill path). The process is repeated
to the establish the next tier of the surface by spreading from the current
item’s location until another item is encountered, then streaming over that
portion of the surface for the next leg of the path. The spread/stream procedure is continued
until all of the items in the cart have been evaluated. The final leg is delineated by moving to the
checkout and exit doors.
Similar paths are derived for additional shopping carts that pass through the
cash registers. The paths for all of
carts during a specified time period are aggregated and smoothed to generate an
accumulated shopper movement surface.
Although it is difficult to argue that each path faithfully tracks
actual movement, the aggregate surface tends to identify relative traffic
patterns throughout the store. Shoppers
adhering to “random walk” or “methodical serpentine” modes of movement confound
the process, but their presence near their purchase points are captured.
Figure 26.2. Shopper Movement Patterns. The paths for a set of shoppers are aggregated and smoothed to characterize levels of traffic throughout the store.
The left portion of Figure 26.2 shows an aggregated movement surface
for 163 shopping carts during a morning period; the right portion shows the
surface for 94 carts during an evening period of the same day. The cooler colors (blues) indicate lower
levels of traffic, while the warmer colors (yellow and red) indicate higher
levels. Note the similar patterns of
movement with the most traffic occurring in the left-center portion of the
store during both periods. Note the
dramatic falloff in traffic in the top portion.
The levels for two areas are particularly curious. Note the total lack of activity in the
Women’s Wear during both periods. As
suspected, this condition was the result of erroneous codes linking the
shelving nodes to the products.
Initially, the consistently high traffic in the Cards & Candy
department was thought to be a data error as well. But the data links held up. It wasn’t until the client explained that the
sample data was for a period just before Valentine’s Day that the results made
sense. Next month we will explore
extending the analysis to include sales activity surfaces and their link to
shopper movement.
__________________________
Author’s
Note: the analysis
reported is part of a pilot project lead by HyperParallel, Inc.,
Further Analyzing In-Store Movement and Sales
Patterns
(GeoWorld, March 1998, pg. 28-30)
Last month’s column described a procedure for deriving maps of shopper
movement within a store by analyzing the items a shopper purchased. An analogy was drawn between the study of
in-store traffic patterns and those used to connect shoppers from their homes
to a store’s parking lot… aisles are like streets and shelving locations are
like street addresses. The objective of
a shopper is to get from the entry door to the items they want, then through
the cash registers and out the exit. The
objective of the retailer is to present the items shoppers want (and those they
didn’t even know they wanted) in a convenient and logical pattern that insures
sales.
Figure 26-3. Establishing a shoppers route as the steepest downhill path over a proximity surface.
Though conceptually similar, modeling traffic within a store versus
within a town has some substantial differences.
First the vertical component of the shelving addresses is important as
it affects product presentation. Also,
the movement options in and around store fixtures (verging on whimsy) is
extremely complex, as is the characterization of relative sales activity. These factors suggest that surface analysis
(raster) is more appropriate than the traditional network analysis (vector) for
modeling in-store movement and coincidence among maps.
Path density analysis
develops a “stepped accumulation surface” from the entry door to each of the
items in a shopper’s cart and then establishes the plausible route used to
collect them by connecting the steepest downhill paths along each of the
“facets” of the proximity surface. The
Figure 26.3 illustrates a single path superimposed on 2-D and 3-D plots of the
proximity surface for an item at the far end of the store. The surface acts like mini-staircase guiding
the movement from the door to the item.
Figure 26-4. Analyzing coincidence between shopper movement and sales activity surfaces.
The procedure continues from item to item, and finally to the checkout
and exit. Summing and smoothing the
plausible paths for a group of shoppers (e.g., morning period) generates a
continuous surface of shopper movement throughout the store— a space/time
glimpse of in-store traffic. The upper
left inset of Figure 26.4 shows the path density for the morning period
described last time.
OK, so much for review. The lower left
inset identifies sales activity for the same period. It was generated by linking the items in all
of the shopping carts to their appropriate shelving addresses and keeping a
running count of the number of items sold at each location. This map summarizing sales points was
smoothed into a continuous surface by moving a “roving window” around the map
and averaging the number of sales within a ten-foot radius of each analysis
grid cell (1 square foot). The resulting
surface provides another view of the items passing through the checkouts— a
space/time glimpse of in-store sales action.
The maps in the center identify locations of high path density and high sales
activity by isolating areas exceeding the average for each mapped
variable. As you view the maps note
their similarities and differences. Both
seem to be concentrated along the left and center portions of the store,
however, some “outliers” are apparent, such as the pocket of high sales along
the right edge and the strip of high traffic along the top aisle. However, a detailed comparison is difficult
by simply glancing back and forth. The
human brain is good at a lot of things, but summarizing the coincidence of
spatially specific data isn’t one of them.
The enlarged inset on the right is an overlay of the two maps identifying all
combinations. The dark tones shows where
the action isn’t (low traffic and low sales).
The orange pattern identifies areas of high path density and high sales
activity— what you would expect (and retailer hopes for). The green areas are a bit more baffling. High sales, but low traffic means only
shoppers with a mission frequent these locations— a bit inconvenient, but sales
are still high.
The real opportunity lies in the light blue areas indicating high shopper
traffic but low sales. The high/low area
in the upper left can be explained… entry doors and women’s apparel with the
data error discussed last time. But the
strip in the lower center of the store seems to be an “expressway” simply
connecting the high/high areas above and below it. The retailer might consider placing some
end-cap displays for impulse or sale items along the route.
Or maybe not. It would be silly to make
a major decision from analyzing just a few thousand shopping carts over a
couple of days. Daily, weekly and
seasonal influences should be investigated.
That’s the beauty of in-store analysis— its based on data that flows
through the checkouts every day. It
allows retailers to gain insight into the unique space/time patterns of their
shoppers without being obtrusive or incurring large data collection expenses.
The raster data structure of the approach facilitates investigation of the
relationships within and among mapped data.
For example, differences in shopper movement between two time periods
simply involve subtracting two maps. If
a percent change map is needed, the difference map is divided by the first map
and then multiplied by 100. If average
sales for areas exceeding 50% increase in activity are desired, the percent
change map is used to isolate these areas, then the values for the
corresponding grid cells on the sales activity map are averaged. From this perspective, each map is viewed as
a spatially defined variable, each grid cell is analogous to a sample plot, and
each value at a cell is a measurement—all just waiting to unlock their
secrets. Next time we will investigate
more “map-ematical” analyses of these data.
__________________________
Author’s
Note: the analysis reported is part of a
pilot project lead by HyperParallel, Inc.,
Continued Analysis of In-Store Movement and Sales
Patterns
(GeoWorld, April 1998, pg. 26-28)
The first part of this series described a procedure for estimating
shopper movement within a store, based on the items found in their shopping
carts. The second part extended the
discussion to mapping sales activity from the same checkout data and introduced
some analysis procedures for investigating spatial relationships between sales
and movement. Recall that the raster
data structure (1-foot grids) facilitated the analysis as it forms a consistent
“parceling” of geographic space. Within
a “map-ematical” context, each value at a grid cell is a measurement, each cell
itself is analogous to a sample plot, and each gridded map forms a spatially
defined variable.
From this perspective, the vast majority of statistical and mathematical
techniques become part of the
The
The recognition that maps are data as well as pictures fuels this “data
mining” perspective. Cognitive
abstractions of data coupled with physical features for geographic reference
form new and useful views of the spatial relationships within a data set. For example, Figure 26.5 shows three
“snapshots” of an animated sequence of the surfaces depicting shopper movement
(left side) and sales activity (right side).
The checkout data for a twenty-four hour period was divided into hourly
segments and the movement and sales surfaces generated were normalized, then
assigned a consistent color ramp for display.
\
Figure
26.5. Snapshots from a movie of hourly
maps of shopper movement and sales activity.
When viewed in motion the warmer tones (reds) of higher activity appear
to roll in and out like wisps of fog under the
Although the human brain is good at many things, detailed analysis of mapped
data is not one of them. Visualizing the
hourly changes provides a general impression of the timing and patterns in
shopper movement and sales activity.
However, additional insight results from further map-ematics identifying
locations of “significant” difference at each time step. This is accomplished by subtracting two
surfaces (e.g., movement at
Segmentation of a data set forms the basis of many of the extended data mining
procedures. In addition to time (e.g.,
hourly time steps) the data can be grouped through spatial partitioning. For example, each department’s “footprint”
can be summarized into an index of shopper “yield” as a ratio of its average
sales to its average movement—calculated hourly shows which departments are
performing best at each time step.
A third way to segment a data set is by data characteristics. For example, traditional product “affinity”
analysis that notes which items tended to be purchased together can be extended
to its spatial implications. Common
sense suggests that items with a high product affinity, such as shampoo and
conditioner, should have a high spatial affinity (shelved close together). Proximity analysis is used to determine
effective distance between items, normalized to an affinity index, then compared
to the pair’s product affinity index.
Miss-matches identify inconveniently shelved items—similar products
shelved far apart, or dissimilar products close together. The affinity information also assists in
optimizing the shelving of impulse and sales items for frequently changed
action aisle and end-cap displays.
Figure
26.6. Departmental comparison of shopper
movement patterns.
Figure 26.6 shows another data characteristics segmentation
analysis. The top left map summarizes
all of the shopper paths that contained items from Department 5 (Electronics
delineated by the dotted rectangle).
Note the concentration of paths within the vicinity of the Department
indicating that purchasers of these items tended not to venture into other
departments. The bottom left inset is a
similar map for Department 3 (Card & Candy). Note the larger number and greater dispersion
of paths compared to Department 5. The
large map on the right shows areas of large differences in path density between
shopping carts containing items from Departments 3 (orange) and 5 (blue). It is expected that the areas within the
departments (dotted rectangles) show large differences. The blue areas at the top, however, show more
shoppers purchasing electronics traveled to men’s wear that those purchasing
cards & candy… a bit of common sense verified by empirical data. It leads one to wonder what insights might be
gained from analysis of the orange area (more cards & candy traffic) or
other departmental comparisons.
__________________________
Author’s
Note: the analysis
reported is part of a pilot project lead by HyperParallel, Inc.,
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