Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Tweets are to Customer Knowledge as….?
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Mining > Tweets are to Customer Knowledge as….?
Data MiningPredictive Analytics

Tweets are to Customer Knowledge as….?

DavidBakken
DavidBakken
5 Min Read
SHARE

…spontaneous complaints and compliments are to customer loyalty management. Like these forms of customer experience feedback, tweets are unsystematic, unorganized, and representative of who knows what underlying sentiments in the broader universe of individual experiences.

Imagine for a moment that we could extract from the Twitterverse all of the tweets about about a particular brand. A little text analysis might help us categorize the tweets along some dimension, such as positive versus negative, and we might dig deeper for specific key words. How many times do the key positioning statements for the brand turn up in these tweets, for example?

Even with an exhaustive set of tweets about our brand (within some time frame, of course) we’ll have only a sample of all the existing conversations about the brand. More important, we don’t know exactly what that sample represents. Most certainly it is not a random sample of all conversations about the brand.

Spontaneous or casually solicited complaints and commendations – long a staple of customer experience management – are a lot like the spontaneous brand conversations we might find in the Twitterverse. There may be substantive …

More Read

Image
Big Data Warning: There Will Be Many Job Casualties!
New Retail Technology Opens Eyes In NYCFuture Of Decorating:…
Predictive Analytics in Software: Focusing on Automation
NYT on Big Data and R
Making Love with Data: Avinash Kaushik’s Strata 2012 Keynote



…spontaneous complaints and compliments are to customer loyalty management. Like these forms of customer experience feedback, tweets are unsystematic, unorganized, and representative of who knows what underlying sentiments in the broader universe of individual experiences.

Imagine for a moment that we could extract from the Twitterverse all of the tweets about about a particular brand. A little text analysis might help us categorize the tweets along some dimension, such as positive versus negative, and we might dig deeper for specific key words. How many times do the key positioning statements for the brand turn up in these tweets, for example?

Even with an exhaustive set of tweets about our brand (within some time frame, of course) we’ll have only a sample of all the existing conversations about the brand. More important, we don’t know exactly what that sample represents. Most certainly it is not a random sample of all conversations about the brand.

Spontaneous or casually solicited complaints and commendations – long a staple of customer experience management – are a lot like the spontaneous brand conversations we might find in the Twitterverse. There may be substantive content, but we know next to nothing about potential sampling error and selection bias. In the case of the tweets we have both a population restriction (only some customers will use Twitter) and a potential selection bias (only some Twitterers choose to tweet about the brand). One of the main arguments for probability sampling of customer experiences (via systematic customer satisfaction measurement programs) is the potential bias in spontaneous customer feedback.

One of my favorite social science metaphors comes from Unobtrusive Measures: Nonreactive Research in the Social Sciences (Webb, et. al., 1966). “Outcroppings,” a concept from geology, are “those points where theoretical predictions and available instrumentation meet.” Think of an exposed seam of coal in a particular geologic formation. A single outcropping doesn’t tell us much about the way in which coal seams formed or the best places to look for coal. More outcroppings lead to better understanding and prediction.

Tweets and other consumer-generated social media content are best used in combination with other outcroppings – systematic research (experiments and surveys of probability samples of consumers) and other behavioral observations (such as transactional data).

One of the unintended (but positive) consequences of the migration to Internet-based survey research has been a critical examination of some fundamental assumptions about survey research. How often did you hear concerns raised about non-response, non-coverage, and non-representative samples when the primary method of survey sampling was random digit dialing. We were lulled into what seems to be a false sense of security by a well-defined sampling frame and procedures that insured that we had a probability sample of telephone numbers.

Of course, survey research always has been only one of many possible outcroppings in the quest for customer knowledge. I’m encouraged that the shortcomings of online survey research and other data created through online behavior has sparked serious debate about how we know what we know in the realm of customer knowledge.

Copyright 2009 by David G. Bakken.  All rights reserved.

TAGGED:customer feedbacktwitter
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

data analytics and truck accident claims
How Data Analytics Reduces Truck Accidents and Speeds Up Claims
Analytics Big Data Exclusive
predictive analytics for interior designers
Interior Designers Boost Profits with Predictive Analytics
Analytics Exclusive Predictive Analytics
big data and cybercrime
Stopping Lateral Movement in a Data-Heavy, Edge-First World
Big Data Exclusive
AI and data mining
What the Rise of AI Web Scrapers Means for Data Teams
Artificial Intelligence Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Could Twitter change customer service?

7 Min Read

Yahoo! CEO Marissa Mayer on Data Portabilty

3 Min Read

The Guy Kawasaki Twitter Bump – Anderson Analytics Facebook Application

3 Min Read

How Big Data Analytics on Twitter Can Help Predict Disease Spread [VIDEO]

1 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data
giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?