This document discusses how semantic analytics can help food and beverage brands make better decisions. It defines semantic analytics as extracting meaningful insights from large amounts of unstructured data like social media posts and reviews. The benefits mentioned include understanding consumer sentiment, finding opportunities for growth, and learning what consumers want. It provides examples of how semantic analysis could help industries like alcohol brands identify popular outlets or coffee houses target places needing a new supplier. The methodology section outlines the process of querying data sources, extracting relevant data, analyzing and segmenting it, then generating actionable insights and leads for brands.

1. Semantic
Analytics
Helps Food and Bev.
Brands Make Better
Decisions
Oct. 2018
1

2. 2
Semantic search is changing
the way digital marketing
should be performed.

3. 3
Semantic
Analytics
Defined
Table of Contents
3
Benefits How it Works :
Methodology

4. Semantic
Analytics
Defined
4

5. 5
Semantic search
is changing the way
digital marketing
should be
performed
As social media and user-
generated content took over the
web, many companies realized
they could find ways to mine this
massive data set for meaningingful
insights. After this, many people
found out they were focusing on
the wrong part of sentiment
analyzation. Just knowing if
someone is talking about a given
topic or brand is less important than
knowing how they are feeling.

6. Semantic Analysis
Became Smarter
6
Many research areas have tried to gain
valuable insights from these large
volumes of freely available user
generated content.
However, extracting meaningful and
actionable knowledge from user
generated content is a complex
endeavor.
Why Is It Hard to Glean Information ?
1. Each social media service has its own
data collection specificities and
constraints.
2. The volume of messages/posts
produced can be overwhelming for
automatic processing and mining
3. Social media texts are usually short,
informal, with a lot of abbreviations,
jargon, slang and idioms
Semantic Analysis is Dependent Upon Machine Learning

7. 7
Summary : Quick benefits
Extract relevant and useful information from large bodies of
unstructured data.
Find an answer to a question without having to ask
a human.
Discover the meaning of colloquial speech
in online posts.
Uncover specific meanings of
words used in foreign languages
mixed with our own.
1
2
3
4

8. Benefits
Specifically
for the
Foodservice
Industry
8

9. 9
reviewIQ : Benefits of Semantic Analytics for F&B Industry
Locate popular and
unpopular operators and
understand why
Receive new opportunities
for growth
Target operators based on
their own clients’ needs and
satisfaction
Learn what consumers
really want
Update your offer based
on consumer sentiment
Monitor satisfaction in all
your outlets and get alerts
SUPPLIERS CHAIN OPERATORS

10. Qualification
on guided
themes
1
10
Compute compatibility scores with silos of lexical fields
Ambiance
Party style
Food
Healthy style
Identify operators of which consumers
are qualifying them as :
Beer temples
Great wine selection
Ethnic ambiance
Specific food type
Cocktail Terrasse
Happy hour Dancefloor
ConcertSports
Bio
Vegan
Gluten free Wifi
Fresh products
Quinoa
Alcohol brands often want to identify outlets with great night activity.

11. 11
Unguided Keywords Extraction
Know more about outlets your are visiting.
Extract most recurring keywords with
positive and negative sentiment.
Delicious seafood Wonderful view
Noisy place Long service
Let your CRM know how consumers define the place.

12. 12
Popularity Score
Differentiate Lighthouse places vs
Mainstream outlets
Popularity become a smart segmentation
criteria when brands want to push their
premium products to iconic places.
Build a custom-made model based on :
Number of reviews
Fréquency of reviews
Evolution of rating
Recurring dithyrambic keywords

13. 13
Extract food & beverage
keywords fro menus
GOAL

14. Methodology
How We Help
You Grow
In Detail !
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15. Semantic Analytics
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16. 16
Step 1 : Query Based on Goals
GOAL QUERY
“I want to get my
coffee served in
more outlets in a new
area. How do I target
places with the
highest potential for
to purchase from a
new supplier?”
Which coffee houses
have negative
connotations on
social networks?”

17. Step 2 : Data Mining
“Social media sentiment is the perceived positive or negative
mood being portrayed in a social media post or engagement,”
says Nick Martin, Social Engagement Coordinator at Hootsuite.
Aggregated from
top social sites

18. 18
Step 3 : Data Extraction
Text is analyzed from billions of surveys, social media
reactions, reviews, emails, and more into actionable insights.

19. 19
Step 4 : Data is Layered with Operator Info
Data is analyzed, segmented,
then attached to your prospect list.

20. 20
Step 5 :
Extract Locations
with Bad Coffee
Leads = Operators with Bad Coffee

21. 21
Step 6 :
Qualification
Leads = Operators with Bad Coffee

22. 22
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