Quickly analyze product reviews and forum data to measure feedback and sentiment

1. P O W E R E D
B Y
Best Practices for Analyzing
Disparate Voices with Quid
Understanding +
Analyzing Voices

2. What are my
audiences
customers
competitors
adversaries
saying?

3. A B OU T TH IS TR A IN IN G
The goal of this session is to go
through an end-to-end workflow
to analyze alternative datasets
such as product reviews and
forums to measure feedback and
sentiment.

4. ANALYZING VOICES
Obtain the data.
Sometimes this is
straightforward, such
as utilizing social
media listening tool &
downloading tweets
in a CSV.
Sometimes this
involves using a
scraper or merging
multiple datasets.
Upload into Quid.
If you have an Opus
account, you’re good
to go. Drag, drop,
upload. If not, talk to
your Quid client
service rep and
they’ll take care of
you.
Analyze. .
Utilize Quid to
quickly make sense
& discover patterns
across thousands of
unique voices.
Scope your analysis.
What are you trying to
‘hear’? Does it come
from one source?
How the data
formatted?
1 2 3 4

5. U N D ER STA N D IN G H OW QU ID C A N H ELP
Quid can also be used to analyze any unstructured, custom dataset with long-form text. All you need
is a .csv file with up to 15,000 rows. Here are a few examples:
CUSTOMER COMPETITORS INTERNAL DATA
Product Reviews Product Review Comparison NPS Data
Online Forums Job Postings Employee Surveys
Survey Responses Professional Profiles Call Center Transcripts
Social Media Clinical Trials Project Reports
Corporate Filings Internal Documents

6. Amazon.com Product Reviews Kickstarter.com Companies
Glassdoor Employment Reviews Yelp.com Reviews
Public Facebook Data CreditKarma.com Card Reviews
Twitter social media data CruiseCritic.com Cruise Reviews
DailySrength.com Forum Posts DisneyTouristBlog.com Blog Posts
Play.Google.com Reviews for Games LinkedIn.com Profiles
TuDiabetes Forum Posts NerdWallet.com Comments
HealthBoards.com Forum Posts NYTimes.com Article Comments
TripAdvisor.com Reviews Reddit.com Forum Posts
ConsumerAffairs.com Reviews RottenTomatoes.com Movie Reviews
YouTube.com Comments TrustPilot.com reviews
GETTING THE CORRECT DATA
If there is a platform that contains content you are interested in, there may be a way to access that
data. To date, we have worked with more than 125 data sources. Below are a few examples:

7. Sample Prompt:
You are on the Market Insights Strategy Team for the skin and body care company Aveda, and you are interested in
understanding brand perception and consumer sentiment for Aveda Salons over the last 5 years. You have access to Yelp
Yelp reviews between 2012 and 2017 discussing customer experiences at the salon.
How has Aveda’s brand recognition changed over time?
Identify the volume of conversation about Aveda.
What topics are core to the primary conversation?
What peripheral topics, if any, may be of interest?
What topics represent Aveda’s largest opportunity/biggest wins?
What topics have the lowest/highest yelp star ratings?
Does the star score match the sentiment of client comments?
How well correlated for the Yelp ratings and the Quid Sentiment score?
What salon locations have the most/least reviews?
What topics do consumers care about the most at each location?
FR A MIN G TH E QU ESTION
A successful Quid analysis depends on the framing the question properly. Try to articulate the scenario and
especially “the why” so that you are able to map the intent behind doing this analysis in Quid.

8. To upload into Quid, we need to get it into .CSV format. There must be one column of
free-text for the Quid algorithms to analyze.
The more meta-data associated the free-text, the better. Star ratings, demographic
information about the commenter, and dates can all be useful data to pivot on within Quid.
UPLOADING DATA IN QUID

9. FORMATTING THE
.CSV
Depending on the service providing the social media content, there may need to be a few
adjustments to the .CSV
Like all Quid analyses, you will need one column of free text for Quid’s NLP algorithms to
analyze.
All other columns will become pieces of meta-data that you can pivot on once the data is
visualized. Make sure the headers of your columns make sense and you will be able to identify
what each column represents.

10. Time: To access the granular time data that social media data often contains, the .CSV file might
need to be slightly altered.
Quid’s timeline field requires time to be formatted in one of the below ways:
• MM/DD/YY HH:MM A/M (2 or 4 digit year)
• DD/MM/YY HH:MM A/M (2 or 4 digit year)
• MM/DD/YY HH:MM (2 or 4 digit year)
• DD/MM/YY HH:MM (2 or 4 digit year)
To get this data in the right format, go to Format Cells, select “Custom” category, and type in:
mm/dd/yy hh:mm:ss
FORMATTING THE
.CSV

11. UPLOADING THE
.CSV: LABEL AND
BODY
When uploading social media data, choose the .CSV file containing your social media posts.
Quid will ask what the “Label” and “Body” should be. Label is the text that will appear when you
hover your mouse over the node, and “Body” is the text that the Quid algorithms will read to cluster
the content and analyze its sentiment.
For short social media posts, choose the content of the post for both Label and Body. This allows for
easy and quick identification within the visualization.

12. After selecting Label and Body, quickly check the Advanced Mapping Options to ensure Quid is
reading the data correctly.
The most important thing is to check that Quid is reading your date/time data as dates. If it is not,
click on the teal text and select “date.” If a yellow exclamation point appears, click on it to discover
the issue with the data.
Quid will also warn you if there are blank values in your data. It is generally not a problem to
visualize metadata where there are a few blanks – these nodes will just be left out of visualizations
visualizing that element of metadata.
UPLOADING THE
.CSV: ADVANCED
MAPPNG
OPTIONS

13. Quid will then prompt you to select industries that are relevant to your data. This helps Quid analyze
your data more effectively. For instance, selecting healthcare enables Quid to extract medical
conditions that are mentioned within your text.
Quid will also ask what type of data is being uploaded. For this use case, always select social media.
This will enable Quid to understand and extract key elements specific to social media, like hashtags.
UPLOADING THE
.CSV: INDUSTRY
AND DATA TYPES

14. OPTIMIZING THE
NETWORK
Before naming the clusters, optimize your network by letting Quid know what words should not be
considered when forming clusters. These are words that cause clusters to form around topics that
are not useful to your analysis.
Go to Regenerate Network, and then type in the words that you wish the clustering algorithms to
exclude. These will often be:
• The names of the brands you are analyzing (it’s not useful to have a cluster of content that
frequently repeats the name of the company)
• Units of time (people will use ”years” to talk about ongoing problems or initiatives, but it would be
more interesting to have clusters around those particular ongoing problems or initiatives)
Once you have listed out the words, regenerate the network. Then take a quick look through some of
the clusters to see if there are other keywords you will want to block out. It can take a few tries to get
the kinds of clusters you are looking for.

15. Ignore List:
Boost List:
smells, attention to detail, booked, online, schedule, thick, dark, roots, kind, personable, highlights, location, neck, arm, scalp,
blow, dry, pure privilege, skin, skin care, hair care, welcoming, cutting, curly, frizzy, smell, expensive, waxing, wax, conditioning,
deep conditioning, pedi, mani, salt, scrub, shampoo, conditioner, wedding, makeup, selection of aveda, atmosphere, environment
IGNORE LIST BOOST LIST
hotel, concept, moment, family, check, stars, sell aveda products, sell
sell aveda, love the aveda, yelp, couldnt, extra, thing, received, finally,
finally, knew, town, making, front, completely, youre, offered, client,
client, looked, person, listened, takes, ago, told, extremely, times,
decided, today, needed, high, people, feeling, lot, year, felt, absolutely,
absolutely, asked, leave, owner, love aveda, moved, left, area, coming,
coming, exactly, better, day, aveda salons, find, wanted, feel, salons,
salons, definitely, im, going, years, place, job, experience, aveda salon,
salon, arent, heather, chicago, stephanie, sunday, nyc, despite, lol,
lol, actual, pm, system, ahead, st, guys, figured, exact, needless, fair,
fair, sister, imagine, current, company, clear, supposed, bottom,
achieve, carry, addition, matter, decent, cup, weekend, earth, usual,
usual, number, negative, korean, ease, provided, studio, single,
remember, san, provide, ashley, jessica, basically, happen, stopped,
stopped, note, amount, saturday, sarah, min, park, school, opinion,
opinion, guy, bring, total, house, cutting my hair, naturally, lucky,
lucky, talking, real, honestly, honest, mom, bob, literally, la, yesterday,
yesterday, surprised, idea, happened, stuff, talked, husband, perfectly,
perfectly, met, top notch, notch, mind, seriously, theyre, set, half, true,
true, recommend this salon, wrong, fact, type, works, ended, regular,
regular, worked, super, sweet, awesome, highly recommend, minutes,
minutes, beautiful, loved, bit, room, visit, worth, hot, couldnt, care
smells, attention to detail, booked, online, schedule, thick, dark, roots,
roots, kind, personable, highlights, location, neck, arm, scalp, blow,
blow, dry, pure privilege, skin, skin care, hair care, welcoming,
cutting, curly, frizzy, smell, expensive, waxing, wax, conditioning,
conditioning, deep conditioning, pedi, mani, salt, scrub, shampoo,
shampoo, conditioner, wedding, makeup, selection of aveda,
atmosphere, environment
CURATING THE NETWORK

16. CATEGORY IGNORE LIST
Emotive Words
love, cheap, good, awesome, bad, nice, lots, lot, lot of stuff, nice, amazing, awesome,
awesome, cheap, better^5, fun, happy, excellent, absolutely, favorite, lol
Verbs Stated, alleged, called, emailed, left voicemail,
Topic related words
Brand Names Aveda, Yelp, Amazon
Proper Nouns
HOW TO CREATE THE IGNORE LIST?
Create categories of concepts you’d like Quid to ignore!

17. INSIGHTS:
NETWORK VIEW
Like most analyses, the first view that will be immediately useful is the network view.
Set up: Nodes could represent social media posts (as pictured below) or nodes could represent
themes (clusters) for a cleaner, more concise visual.
Insights:
• What topics do these
brand(s) /influencer(s) focus
on the most often?
• What topics are central to
the messaging?
• Are there some topics that
the brand/influencer
discusses that are not
related to its other
messaging?
• Is a topic of particular
interest (i.e. diversity) being
discussed alongside other
corporate priorities (i.e.
innovation)?

18. Q u e s t i o n : H o w h a s Av e d a ’s b r a n d r e c o g n i t i o n c h a n g e d o v e r t i m e ?
From the network map, select “Timeline” visualization dropdown menu.
1
Increase
in overall
volume
2
Notice what’s
increased and
decreased
overtime

19. Identify brand
differentiators
Q u e s t i o n : W h a t t o p i c s r e p r e s e n t Av e d a ’s l a r g e s t o p p o r t u n i t y / b i g g e s t w i n s ?
Change the visualization view to the bar chart. Change “Bars Represent” to “Clusters.” Toggle the
tab from “Axes” to “Nodes” and change “Color By” to “Rating (uploaded).”
Identify
opportunity areas:
clusters with high
number of low
ratings
1 2

20. Q u e s t i o n : W h a t s a l o n l o c a t i o n s h a v e t h e m o s t / l e a s t r e v i e w s ?
Toggle axes to represent locations to look at perception of salons at different locations