Transcript Forecasting Basics:

YOUR HOST FOR TODAYS WORKSHOP
Kevin Anderson, Kevin R Anderson Consulting
 29 year veteran of gravity models. Industry experience includes analyst at Howard L Green
and Manager of Location Research BI-LO Supermarkets. Familiarity with Power:Site (DOS
version of SItesPlus), SitesPlus, Model II, Business Analyst Huff Model, and SAM (Site check
Research Groups proprietary C-Store, Fuel gravity/forecasting model).
 Kevin has an undergraduate degree in Business Administration, and a MBA from Northern
Michigan University.
 Email: [email protected]
Cell: 864-884-3745
John Hefner III, Director of Facilities Research for Schnuck Markets, Inc
 48-year grocery veteran in conjunction with 32 years in site research methodologies. He
presently manages a three-person department at Schnuck’s Markets . John started out using
the Locus model in 1981, followed by Model I & II, Power:Site and eventually Site Pro Plus. His
expertise includes site location studies, metro markets analysis, acquisition strategies and due
diligence for legal projects.
 John has an undergraduate degree in Accounting/Finance from the University of Missouri–St.
Louis and a M.B.A. from Webster University.
 Email: [email protected]
WHAT WILL WE LEARN TODAY:
What are the basic types of Forecasting?
How do other forms of forecasting come into play
during the Forecasting Phase?
Model definition and purpose?
What inputs need to be set?
What is the process?
When is it a good forecast? How do I know?
Some caveats when forecasting with gravity modeling.
BEFORE WE START FORECASTING
 Make sure the model is balanced!
 Unbalanced models produce results that are suspect. Before starting the
forecasting process look one more time at the balanced model.
 Collect all competitive changes and have them ready to input into the
model.
 You can add them later, but it is always best to have your changes laid out
before hand.
 Although it may not be possible to collect all changes, it will minimize the
duplication of work if you collect it up front
 THINK
 Think about what you are doing and make a list of tactics to explore.
 Do not dwell on what you are doing; don’t over think the problems so much
that you cannot make a decision.
HOW OTHER METHODS OF SALES FORECASTING
RELATE TO GRAVITY MODELING
 ANALOG (or Analogous Store).
 In models without sister stores or new markets - analogs can give us a basic
framework to start on our understanding of the sales forecast.
 Comparing similar competitors with subject competitors can give us an
indication on how our store should perform when setting the Power, Curve or
other model parameters.
 REGRESSION (or Multiple Regression)
 Helps us explore correlations on what factors that make a site successful.
 We can take these factors and compare them to new locations to determine
would be the sites with the highest probability of success, this
benchmarking can be used in other areas of the organization.
 Information derived from these alternative ways of forecasting can be
used to help the modeler increase the accuracy of the sales forecast
within the Gravity Modeling environment.
TYPES OF SALES FORECASTING IN GRAVITY
MODELS
Subject Stores and Competitive Stores.
 Subject Stores – Our or clients stores that are the target of the
sales forecast
 These are the stores for which the model was built
 Formats and Sizes can be part of the investigation
 Competitive Stores – What is happening and when will happen
 New, remodels, expansions, closing and relocations that will
happen to competitor stores (and sister stores not part of the
analysis).
 Format and Sizes also can be explored.
 Alternative formats can also be explored.
THE ORDER OF FORECASTING IN GRAVITY
MODELING – SITESPLUS
• First we start with a Balanced Model in the SitesPlus Project File.
• This is a fully calibrated model for the Study Area and competitors.
• Once model is fully calibrated, make sure to save an backup copy.
• Second we open a new model for the competitive impacts.
• Enter all competition that is projected for the model.
• Enter the existing stores Loyalty Factors.
• Test to see the relevance of the forecast.
• Finally, open a new model for the subject site (s)
• Enter the subject stores, and open all relevant competition.
• Make sure all Loyalty Factors are still relevant from the previous model.
• Double check the effects of the manual overrides.
SITES PLUS FORM: ADDING MODELS
 Go and start another model within the project file’
 Separate models for Competitive Changes and Subject Site Forecasting.
 By adding in the changes in separate models we can more effectively
manage what our impacts are to sister stores and competitors
Pressing this
button will add
a tab (to the
left)
Once tab appears
fill out the form
Filling out this
field names the
model
This sets the
opening date for
the forecast
Use these fields
to add subtitles
to the heading
ADDING MODELS TO THE PROJECT FILE
• ALWAYS start with a BALANCED model before Adding a New Model.
• Press the Calculate and Balance Icons on the Menu Bar before
adding a new model to make sure it is truly balanced.
• ALWAYS start with the Competitive Changes Model. You can add
all changes to this model, but only open the competitive changes.
• Add all models to the Project File from the previous model.
Otherwise the changes made will not be carried forward.
Balanced
Model
Competitive
Changes
Model
Forecast
Model 1
THE STEPS INVOLVED FOR FORECASTING
WITHIN SITES PLUS – LOYALTY FACTOR
Once all changes
are made and we
are satisfied with
the effects, click ok
and you will exit the
dialog
Once al changes
are made press
calculate to see
the effects
Buds is a specialty
market needs
special treatment
Epicurean
Delight is a
specialty
store
SOME NOTES ON LOYALTY FACTORS
Add the Loyalty Factors If Needed
 Accessed through the Data ribbon at the top of the model.
 Determine which stores need loyalty curves. For rule of thumb; all
Limited Assortment/Gourmet, Supercenters, Upscale stores should be
reviewed for Loyalty curves, not conventional supermarkets.
 Some conventional supermarkets, however, may need a Loyalty Factor,
after the first volume check, you may want to add them into the model.
An example is a store that is getting impacted rather too severely, this
may warrant a Loyalty Factor for that store
 Stores that typically do not get impacted by conventional stores opening
should also get loyalty factors.
 Should be done immediately before Adding Stores. AND NOT IN THE
BALANCED MODEL
THE STEPS INVOLVED FOR FORECASTING
WITHIN SITES PLUS – ADDING STORES
 First within the competitive changes model - enter the competitive
changes within the model.
 New Stores. These are added via the Add New button on the Store Data Form.
 Remodels. These are typically added via the Rebuild function on Store Data
Form but can also be added via the Add New button
 Expansions. Like Remodels use the Rebuild button on the Store Data Sheet,
but can use the Add New button also.
 Conversions.
 With the Rebuild button we keep the existing data for the store before the
proposed changes. All curve, manual overrides will be added to the ‘new’
record
 Add New command will not carry over all the curve and manual overrides.
It maybe more appropriate for a new chain acquiring a store from a
weaker competitor.
STORES DATA ENTRY SHEET - SITESPLUS
Insert New
Stores
Close
Existing
Stores
Without
Removing it
from the
model
Remove
the stores
Make
changes
Remodels
Relocations,
& Expansions
INPUTS NEEDED FOR THE SALES FORECAST.
Typical store inputs needed for sales forecast: Location, size, curve, PWTA and
Power. Plus any overrides also can be added to a stores. Later we will go over
tips on how and what to put in the necessary field, but it is much the same for
either type of forecast that you do – Subject Stores or Competitor Stores,
Sales: $0
or $XXX
Dependent
on size and
location
Set by
neighboring
Stores;
Sister
Stores
Chain Average
or Completive
Comparisons
SPECIAL CONSIDERATIONS FOR EXISTING
VOLUMES
When making changes to stores that are in the Balanced Model and will
receive a Remodel, Conversion or Expansion the analyst needs to
decide whether to keep the existing (or from the Balanced Model)
volume or leave the field null in the record .
• Adding the existing volume to the store in the volume field
• If store is on the periphery of the Study Area this option should be looked at for
viability. It will keep huge swings in volume to a minimum.
• Leaving volume field blank will allow more volume to be ‘captured’ from
beyond.
• Could inflate the sales forecast by assuming more volume could come from
outside the Study Area.
• If the store is in the middle of the trade area it make sense to do this because it
keeps the sales from beyond (outside the study area) will not vary greatly (no
more than 10% in total given the PWTA).
THE EFFECTS OF PWTA AND OLD VOLUME ON
FORECAST FOR RELO/EXPNS/REMODS/CONVS
Scenario
Keep existing Volume
Leave Volume Field Blank
Scenario
Keep existing Volume
Leave Volume Field Blank
PWTA
85%
85%
PWTA
35%
35%
Old
Volume
Prior
Outside
Sales
$200,000
$200,000
$30,000
$30,000
Old
Volume
Prior
Outside
Sales
$200,000
$200,000
$130,000
$130,000
Projected
Volume
$250,000
$258,824
Variation
Projected
Volume
$250,000
$628,571
Variation
Post
Outside
Sales
$30,000
$38,824
29%
Post
Outside
Sales
$30,000
$408,571
1260%
Beware: If you keep the existing volume on the store it will increase the
totals by that amount on the Projected Volume Tables
THE STEPS INVOLVED FOR FORECASTING
WITHIN SITES PLUS – SETTING THE CURVE
Store Size
(Total Area)
2,500
5,000
10,000
15,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
Store Size
(Sales Area)
1,900
3,750
7,500
11,250
15,000
22,500
30,000
37,500
45,000
59,500
60,000
Suggested store curves based on store sizes.
Source: SitesPlus v.2010,4 help file
Consider a
Curve of:
99.9
99
95
91
88
80
73
65
58
50
49
Curve relates to size, but also relates to
location and the type of store. In models ALDI
and Save-A-Lot typically have lower curves
than the default.
 If a remodel: Keep the Curve the same and
change it only if there are extenuating
circumstances such as a total change in
format.
 If expansion/relocation: Change the Curve to
reflect the change in size.
 In new stores: Start with the Curve at default
and adjust if there are sister stores nearby or
the format demands it
 You can change (and probably should) the
Curve if the physical nature of the roadways
near the store changes (i.e. from a 5 lane
roadway to a 4 lane median divided).
THE STEPS INVOLVED FOR FORECASTING
WITHIN SITES PLUS – SETTING THE CURVE
(CONT)
In large models or models were the Curve was adjusted for a number of
stores beyond the default you will need to look at the stores that are
going to be forecasted (whether Subject Stores or Competitors).
• In cases of large models look at the like chains in the study area to see
what the average Curve is by size. This will give you a good idea what the
Curve should be in the forecast.
• Some chains will be well represented in an area so their Curve’s
maybe higher than defaults.
• Likewise there may be few in the area and may necessitate a lower
Curve.
• Alternative Formats in the balanced model will have consistently lower
curves than the default.
• This should hold true in the forecasting models – Look at the average
Curve for format/chain and apply in the forecast models.
THE STEPS INVOLVED FOR FORECASTING
WITHIN SITES PLUS – SETTING THE CURVE
(CONT)
• Special cases may exist with a Conventional store who’s image is
discount and is one of only a few in an area.
• The Curve may be lower than the calculated default.
• If a new sister store comes to the area you may have to raise the Curve
on both stores (to show realistic impacts).
• Pay attention to the market share draws past sister stores or
stronger competitors.
• You maybe justified in having a higher (or lower) Curve than what is
calculated based upon size.
• Pay attention to any deviation beyond the default – are you reasons still
valid for the deviation from default? Or should it be changed?
THE STEPS INVOLVED FOR FORECASTING
WITHIN SITES PLUS – SETTING THE PWTA
Setting the PWTA is as critical as the other factors, but we just
have more “indicators” as to the value.
• For New and Relocated/Replacement and some Conversions stores –
Look at the surrounding model stores to see what are the values of
PWTA.
• Exceptions arise with Alternative Formats, (i.e. limited assortment, supercenters,
and upscale/gourmet).
• Pay attention to the strength and placement of sister stores outside the Study
Areas (if there are considerable sister stores outside the Study Area – a higher
PWTA is warranted).
• For Relocated/Remodels/Expansions – use the existing value from
the balanced model.
• May have to make adjustments in special circumstances, but those are few and far between.
THE STEPS INVOLVED FOR FORECASTING WITHIN
SITES PLUS – SETTING POWER
 One important thing to remember about Power is that it is based upon the
relative position of stores within the model: all markets average Power (in the
balanced model) will be 100 – this does not matter if it is an upscale or lower
income area. So comparing models across markets the analyst needs to look at
RELATIVE position of sister stores compared to competitors NOT ACTUAL!
 Look at sister stores within the model – what is the Power? Are they lower than
they should be because of customer appeal? Higher?
 What is the size and the format of the stores? Usually Power is reduced for a
larger store size especially in the replacement/expansion scenarios this is due
to the fact that more of the sales for that store will be explained by the size.
 If the remodel expands product offering and increase or decrease of Power
maybe warranted (i.e. adding more extensive perishable dept. may warrant
higher Power; likewise going to a limited assortment should yield a lower
Power).
SETTING POWER – AN EXAMPLE
Here is the average Power by chain in our sample model What should be the Power?
Upscale/Service
Supermarket
Conventional
EDLP
Supermarkets
Note: our chain is a EDLP operator and we put the site in at 120 – Where we
right or wrong? And Why?
THE STEPS INVOLVED FOR FORECASTING
WITHIN SITES PLUS –MANUAL OVERRIDES
 With both Subject Stores and Competitive stores Manual Overrides
should be used as sparingly as possible.
 Usually these overrides (Manual Power, Manual Curves , Sister
Store, and Directional Curves) are used to explain something
unusual about the store with relationship to the sectors or other
stores within the model.
 When using the REBUILD button when adding a Expansion /
Remodel or other capital expenditures be very careful about the
manual overrides that automatically carry over to the ‘new’ record.
 You may want to change the overrides or delete them all
together in order to more accurately explain the changes.
 Is the spotting you used to calibrate the balanced model still
viable?
MANUAL OVERRIDES - MANUAL POWER
Store you
want to
affect
change
The Sector
you want to
manipulate
The
override
Power
 Manual Power is more effective on the
‘close-in’ sectors (less than radius distance).
 They can go from 1-9999, but try to limit this
value to a multiple of 4 from the default
Power otherwise you may get some
‘different’ results.
 Higher than default, the more volume from
the sector, likewise lower power than default
lower amount of volume.
 When to Use? - When you want to explain
more volume from a sector due to
demographics or fit.
MANUAL OVERRIDES - MANUAL CURVE
Stores
you want
to affect a
change
The Sector
you want to
manipulate
The
override
Curve
 Manual Curves work better beyond Radius
Distance (or the ‘Outboard’ sectors).
 Values are 1-99.
 Lower than the balance model Curve for
the store will increase volume, higher than
default will decrease volume from a
specific sector.
 Demographic fit is the best reason to use
this similar to with Manual Power.
 When more than a few Manual Curves are
used the analyst may want to consider a
Directional Curve instead.
MANUAL OVERRIDES - DIRECTIONAL CURVE
The
affected
Store
Compass Direction
for the Curve
The
override for
the store
When
to start
the
Curve
When to
end the
Curve
 Directional
Curves are similar to
manual curves but will apply the same
curve to all sectors impacted based
upon compass directional and
distance values.
 It is an easy way to apply Curves to a
large number of sectors in a model to
limit or expand the stores market
share along a roadway or some other
physical object.
 Sometimes they are used to explain
Sister Store effect.
 Best Use – To “force’ volume
directionally to explain access,
demographics, and sister store effects
MANUAL OVERRIDES - SISTER STORES
Affected
Stores
Adjustment
for strength
of effect
How the
effect is
calculated
 Sister Store Pairs
command will
reduce the outlets volume in sectors
near sister stores from the same
chain.
 Although the sales will ‘pull’ past each
other, volume will decline at a faster
rate than without the Sister Store
command implemented.
 This effect can also be mimicked with
the Directional Curve and increasing
the default curve, but will not have the
effect localized to the chain.
 With the levers and button selectors
we can cut volume coming from
sectors that are near the sister stores
almost to nothing or have it pulling
past theses stores with relative
strength.
MANUAL OVERRIDES - SOME RULES OF THUMB
 Use Directional Curves to limit or expand sales in a particular compass
direction.
 Use if the site is located on a particularly strong roadway. You can stretch the
market shares along the road so that you can more adequately explain what
sectors are giving their volume to the site.
 Use default value to (i.e. what was used in the competition model) as your
baseline. Below or above this baseline will increase or decrease volumes.
 Manual Powers and Curves should be used only to explain demographic
fit and only if it is VERY necessary in the forecasted model.
 Sister Stores should be used for stores within the same chain. Some
experimentation with the parameters should be done to get the desired
effect.
LARGE (AND METRO ) MODELS VS SMALLER
MODELS
•
Large and Metro Models typically encompass multiple sites and a county
or larger governmental unit.
• Can do multiple what if scenarios to see the impacts to the store network.
• Give us quick answers to questions such as;
• What are the impacts from chain A coming to the area?
• What are the effects of a alternative format upon our stores? What are
the optimal storing strategy for an area .
• Some compromises must be made when balancing (or calibrating) these
models.
• Will have to change default Curve for stores beyond what you would
normally do; or add more manual overrides to get the model to balance.
• It adds complexity to the models (i.e. Power of 100 on the north side of
the model could be equivalent to a 90 on the south side of the model).
• Take time to balance the model –on larger models it may take over a
week to balance.
LARGE MODELS VS SMALLER MODELS
• Small Models are typically single or two site models
• With a smaller model we have some advantages:
• Able to keep the stores to default values within the model.
• Designed only for one or two sites so lower number of sectors and
stores to evaluate.
• Easier and quicker to calibrate talking hours instead of days.
• USUALLY the sales forecast is more accurate.
• But we have some disadvantages:
• If area is on the border or outside the model area we have to do a
new model
• If doing multiple models in an area it can be as time consuming as
building a larger model.
LARGE MODELS VS SMALLER MODELS
• So which is better?
• If you plan to review a large number of sites in and area or are trying
to get and idea how the competitors market shares look over
distance – Use the large model.
• If you are going to be looking at a large number of sites over a given
time frame (say 24 months) then use a large model.
• If you only have a site or two in the area, use a smaller model
(based upon the estimated Primary/Secondary trade areas).
• Even if you expect to have a large number of sites in an area but will
be spread out for the next 36 months – break them up to smaller
models.
• REALLY fragmented markets (Geographically or Demographically)
are also candidates for smaller models.
SALES FORECAST EVALUATION -VOLUME
Volume of the subject site or competitive sites should be looked at with
regards to the following:
 Does the volume make sense?
 Is it higher or lower than expected?
 What does our intuition say?
 What does the analog say?
 Is the store/format a match to the surrounding demographics?
 Is it the only store in the segment or are there others. How do they
compare? How does the volume compare to the analog?
 An abnormally high volume could be an indicator of erroneous
inputs into the model. Do these make sense?
 Is this a better site than what was anticipated by the analyst?
SALES FORECAST EVALUATION – COMPETITIVE
IMPACTS
Another critical area to look at in evaluating a model is the competitive
impacts. This is a check on the viability of the of the sales forecast
and the effectiveness of the Loyalty Factors.
 Second look at the competitive impacts:
 Do the competitive impacts make sense?
 Are they to high – May have to Add/Adjust the store loyalty factor, curves or
sister store effect.
 Are you impacting the supercenters too much? Are you having only limited
impact upon a sister store?
 If they are too low – look at the other parameters in the model are they set
right?
 The stores curve may need to be adjusted because of market changes.
 Sister store closing.
 Sister store opening
 Roadway change
SALES EVALUATION – MARKET SHARES
As with a balanced model; Market Shares should be evaluated with
regard to Distance, Demographics , Natural barriers, Competitive
Impacts and Sister store locations.
 How do the market shares over distance look? Are they falling like
expected ?
 Are you pulling from a wrong demographic segment too strongly?
 Are there any unusual blips (market shares higher/lower than what you
would expect)?
 Are you pulling strong market shares past sister stores?
 Are you pulling strong market shares past alternative formats or stronger
competitors?
 If any of the previous items do not appear to be normal review the
impacts. Could the inputs need adjustment? Might you need to add
sisters stores or directional curves to adjust market shares?
GIS – GEOGRAPHIC INFORMATION SYSTEMS
Since its common adoption in the earlier adoption in the early 80’s
it has helped sales forecaster get a handle on the demographics
and physical geography of an area (and to produce colorful
maps!)
• Thematic mapping is invaluable for reviewing market shares
on large and small models.
• Running two themes together gives you a good idea of sister
store effects.
• Your competitive database can show you were stores are
outside of the study area – allowing you to confirm or adjust
the study area.
Map showing market shares and tapestry clusters
SPREADSHEETS AND STATISTICAL PROGRAMS
Excel and SPSS help us conduct preliminary analysis on our competitive
and customer database, along with refining our presentation
materials for the Gravity Model output.
 Excel can take the sales forecast data from the gravity model and develop
our ROI and ROIC calculations.
 Excel can also be used for adding additional information to the SITESPLUS
report output.
 SPSS and other statistical programs help us determine our benchmarking
with regards to customer typology and can be used to help us to determine
what is exactly an acceptable market share from a sector for a store.
 Alteryx, Tableau and other programs help bring various data sources
together for developing actionable presentation materials for the big data
era.
THE GROWTH CURVE
Growth Curves are a good way to
“Step” sales up over a period of
time
 You can have more than one
curve to use for forecasting.
 Having a Do Nothing curve aids in
showing projected sales declining
over time with respect of a lack of
capital spend.
 New Store Curve can allow you to
ramp up sales for a particular
unit, especially if you include
PHARMACY sales in the SitesPlus
volumes.
 Above all else should use store
data vs industry standard curves.
THE CAVEATS
Although Gravity Modeling is objective it is not totally objective forecasting tool.
• Subjectivity is introduced by the analyst during balancing and forecasting phase –
these can affect the accuracy of the forecast.
• Subjectivity is also introduced during the data collection phase of the project which
will also affect the sales forecast
• Basic knowledge of stores (competitors and subject stores) is necessary.
• Intimate knowledge probably not critical, but a knowledge of format and how the
format will be accepted by customers is a necessity.
• Are you using Total Sales vs Net Sales (AWV less Pharmacy, Gas, and other non food
sales ), for the stores in the analysis? If you are using Total sales make sure the
impacts make sense and the forecasted volume makes sense because Pharmacy
sales mature at a much slower rate than grocery sales.
• Knowledge of the study area (demographics , geography, etc.)
• The macro factors within a model (sector radius, barriers, leakages and potential),
should be set during the balancing phase and should not be changed during
forecasting.
• However, new changes (i.e. adding a bridge or cross point to a river or barrier) will
change the dynamic of the market area.
•
WHEN TO CONDUCT THE POST MORTEM
• Post Mortems are necessary for the continued success of the analyst,
department, and company.
• A look at the viability of the assumptions will help in future projects.
• When reviewing the model; look at the inputs for the new stores
(both competitors and subject stores).
• Where were the errors?
• Are the sales right for the wrong reasons or wrong for the right
reasons
• Once the Post Mortems are complete; publish out to the relevant
individuals.
• Make sure there are qualitative as quantitative analysis.
• Steps for improving on the quality of the work.
• Don’t just blame operations – take some responsibility.
SUMMARY
• Establish a process for the developing sales forecast
• Look at all the data inputs for both competitor stores and subject
site.
• Make sure they are logical and can be defended to an outside
person.
• Review them with a colleague.
• Review the market shares, volume and competitive impacts for
anything out of the ordinary.
• Make sure Alternative formats in the model behave in a way that is
expected.
• Make sure you start the forecasting process with a balanced model.