Data analytics based content

1. Assessing the
Long Term Impact
of Promotions
Looking back and forward:
combining 2 different research methodologies

2. 3
In the past Request now
Conjoint Analysis Studies
for Pepsico Promotions
insights

3. But...
But...

4. 5
Long term impact? Create a model based
on historical sales
But what about…

5. Proposed solution
Short term Long term

6. Selection of
category & country
Requirements
EDLP

7. Business
Questions
“Identify the immediate and long term
impact of specific existing and new
promotion types on SKU, brand and
market size”
There’s an issue…

8. Looking at history Looking at the future
Looking at the future

9. 3 years of detailed sales data by
retailer (SKU volumes, prices,
distribution, etc) and promotion
executions in the same period
Modeling of impact of promotion
types in the period of promotion,
the post-promotion dip and the
long term impact on the brand
and the market
Creation of simulation tool that
predicts outcomes for different
promotion scenarios
Looking at history:
Long term model

10. Week: 0 3 6 9 12 15
Brand 1 (12 SKUs)
Week: 0 3 6 9 12 15
Brand 2 (9 SKUs)
Week: 0 3 6 9 12 15
Brand 4 (4 SKUs)
Week: 0 3 6 9 12 15
Brand 3 (20 SKUs)
Model
Market data
Weekly
volume
Weekly
volume
Weekly
volume
Weekly
volume
Validation – Overall correlation: 0.949

11. Respondents completed 12
virtual shopping strips
(conjoint based)
We assess sensitivities
to attributes
(brand, price, promotions)
Create a simulation tool that
predicts outcomes for different
scenarios (different promotion
types on different brands)
Looking at future:
Short term model

12. 93%
74%
134%
108%
125%
65%
74%
70%
66%
63%
Y% discount
Y% discount on accompanying product
2 for €X
2 for €XX
Now for €Z
Revenue % growth Volume Share % growth
Immediate impact of promotion
on SKU’s volume and revenue
Also, identification of:
- Impact of promotion on 1 or several
SKUs on total brand volume and revenue
- Source of volume (i.e. where does the
increased volume come from)

13. Week:
Promo-week
Post-promo
‘dip’
Stabilization
Volume impact on SKU X – Promotion type “Multi-buy”
0%
56%
-2% -1% -1%
1 1.5 2 2.5 3 3.5 4 4.5 5

14. Key insights
•In general, the volume impact
measured in the Short term (future)
model tends to be overestimated if
compared to the (initial) volume
impact in the Long term (historical)
model.
•Deeper promotions (like “2nd for
free”) do not always have more
impact than other promotions:
initially they get a higher volume
due to higher volume per purchase
but in the long run the SKU / brand
volume may stabilize to a lower
volume level than for other
promotions
1
Promotion: “2 for X€” for SKU A
Looking at future (Short term model): volume +134%
Looking at history (Long term model) : volume +56%
Promo type Price cut (e.g. Y% off)
Multi-buy
(e.g. “2nd half price”
or “2 for X€”)
SKU initial volume impact +38% +56%
SKU LT impact +1% -1%
Brand LT impact +0% -1%

15. Key insights
•The best promotion type for one
channel is not necessarily also the
best promotion type in the other
channel.
•Often, the promotion that has the
highest impact is determined by the
type of channel, the type of
promotions that consumers are
used to in that channel, and the type
of consumers that go to that
channel.
2
Promotion “2 for X€” Promotion “Now Y€”
High-Low channel +171% +150%
EDLP channel +58% +246%
SKU B volume uplift, based on Short term (future)
model:

16. Key insights
•Different promotions have a different
impact on short term and long term
(high initial impact followed by a low
or even negative long term impact
or vice versa)
•Therefore, it is important to combine
different promotion types in order to
have a total positive result on both
short term and long term.
•In any case, promotions with
negative long term impact should be
avoided at all times
3
Promotion “2 for X€” Promotion “Now Y€”
High-Low channel +1% +6%
EDLP channel -1% +1%
SKU B Long term volume impact, based on Long term
(historical) model:
Promotion “2 for X€” Promotion “Now Y€”
High-Low channel +171% +150%
EDLP channel +58% +246%
SKU B volume uplift, based on Short term (future)
model:

17. BOY Constraints
Applicability
Frequency