Consumer Goods Technology - October 2018 - 13

F I G U R E 1:

data (from usual suspects like IRI, Nielsen, GfK and NPD) was
selected by 19%, while one-fourth of respondents expressed a
desire to move beyond more traditional streams, selecting consumer demand/shopper intelligence as most important.
Historical and POS data are also considered to be most important for optimizing ROI forecasting, with 56% of respondents
pointing to both as "most critical" to the process. But nearly onethird (31%) identify data from relevant consumer promotions
and events, and 22% call for syndicated data - both of which
again illustrate efforts to broaden the range of insights that drive
the planning process (Figure 4).
This goal is also evident in industry-wide efforts to coordinate trade promotions with other consumer marketing activity, including both corporate events and focused consumer
promotions. Results in this area were fairly positive, with all
respondents claiming at least some level of alignment, 14% even
professing to be "always aligned" and only an equal 14% saying
they "rarely" coordinate activity (Figure 5).
Respondents overwhelmingly (82%) believe that integrating
consumer promotion event data into trade planning would
improve ROI, with 24% saying it would "dramatically increase"
results and 58% projecting a more marginal impact (Figure 12).
Correspondingly, data from digital consumer promotions
(64%) and corporate advertising schedules (18%) are viewed
as the elements that would be most effective to optimize ROI.
Going beyond company-specific data, 15% of respondents also
point to social media sentiment as beneficial (Figure 13).
Alleviating the crtical issue of out-of-stock situations could be
a major benefit of a move toward daily measurement of trade
promotion performance, a capability that is becoming increasingly possible for many consumer goods companies. Nearly half
(46%) of respondents say that daily tracking could "potentially
end" out of stocks (Figure 6).
The greatest benefit, however, would be the ability to arm retailers with better intelligence about store traffic, basket makeup,
and shopping behavior that, in turn, would give CPGs better
planning intelligence, according to respondents. It also would
help CPGs better align trade and consumer activity, they say.
Better intelligence also can be influenced by retailers who
collaborate more openly in the planning process (according to
40% of respondents), improve program compliance in stores
(26%) and provide daily inventory data (20%), respondents
say (Figure 7).

Efforts to Improve Measurement
In terms of the metrics most critical to understand before executing a promotion, respondents note sell-in volume (66%) and
predicted incremental POS (51%) as the top 2 (Figure 8), with the
second coming as a surprise to CGT survey partner Capgemini
(page 14). Incremental sales is the top priority to measure after

What is the most important area of focus for improving
trade promotion performance?

67%

Promotion planning and optimization
Promotion execution at retail

17%

Data and insights

14%

Replacing technology solutions

3%

F I G U R E 2:

Which of these data types is most important for gaining
insights?

Accurate historical performance results

33%

Consumer demand and shopper intelligence

25%
19%

Syndicated research data
Retail point-of-sale data
Store inventory tracking data

17%
6%

F I G U R E 3:

Which statement best describes the current level of your
trade promotion optimization technology?
Basic: Use predictive planning on historical
data but don't use other external data

42%

Advanced: Use historical, POS and syndicated
data to deliver recommended
promotions (but no AI)

28%

Don't own or have access to a TPO

17%

Highly advanced: Can leverage multiple
sources of internal/external data via AI
to predict ROI
Don't know

11%

3%

the promotion (also 66%), followed by compliance rates and
amounts of cannibalization/halo effect (Figure 9).
Baseline measures took a hit from respondents, only 6% of
whom were "highly confident" in their accuracy for reflecting
non-promoted volume or "highly satisfied" with the frequency
in which they're received.
Attitudes toward performance measurement aren't a whole
lot better, with only 9% of respondents professing to measure

CONSUMERGOODS.COM | OCTOBER 2018 | CGT

13



Consumer Goods Technology - October 2018

Table of Contents for the Digital Edition of Consumer Goods Technology - October 2018

Contents
Consumer Goods Technology - October 2018 - Cover1
Consumer Goods Technology - October 2018 - Contents
Consumer Goods Technology - October 2018 - 3
Consumer Goods Technology - October 2018 - 4
Consumer Goods Technology - October 2018 - 5
Consumer Goods Technology - October 2018 - 6
Consumer Goods Technology - October 2018 - 7
Consumer Goods Technology - October 2018 - 8
Consumer Goods Technology - October 2018 - 9
Consumer Goods Technology - October 2018 - 10
Consumer Goods Technology - October 2018 - 11
Consumer Goods Technology - October 2018 - 12
Consumer Goods Technology - October 2018 - 13
Consumer Goods Technology - October 2018 - 14
Consumer Goods Technology - October 2018 - 15
Consumer Goods Technology - October 2018 - 16
Consumer Goods Technology - October 2018 - 17
Consumer Goods Technology - October 2018 - 18
Consumer Goods Technology - October 2018 - 19
Consumer Goods Technology - October 2018 - 20
Consumer Goods Technology - October 2018 - 21
Consumer Goods Technology - October 2018 - 22
Consumer Goods Technology - October 2018 - 23
Consumer Goods Technology - October 2018 - 24
Consumer Goods Technology - October 2018 - 25
Consumer Goods Technology - October 2018 - 26
Consumer Goods Technology - October 2018 - 27
Consumer Goods Technology - October 2018 - Cover4
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