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
    sales and data analytics
    How Data Analytics Improves Lead Management and Sales Results
    9 Min Read
    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
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Attensity Uses Social Media Technology for Smarter Customer Engagement
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > IT > Cloud Computing > Attensity Uses Social Media Technology for Smarter Customer Engagement
AnalyticsBig DataBusiness IntelligenceCloud ComputingCRMMarket ResearchSocial DataSocial Media AnalyticsText Analytics

Attensity Uses Social Media Technology for Smarter Customer Engagement

RichardSnow
RichardSnow
5 Min Read
SHARE

When I last wrote about Attensity I classified it as a “pure play” text analytics vendor, but the latest release of its product has lead me to revise my opinion. Its product Respond uses natural language-based analysis to derive insights from any form of text-based data and among other results can produce analyses of customer sentiment, hot issues, trends and key metrics. The product supports what Attensity calls LARA – listen, analyze, relate, act – which is a form of closed-loop performance management.

When I last wrote about Attensity I classified it as a “pure play” text analytics vendor, but the latest release of its product has lead me to revise my opinion. Its product Respond uses natural language-based analysis to derive insights from any form of text-based data and among other results can produce analyses of customer sentiment, hot issues, trends and key metrics. The product supports what Attensity calls LARA – listen, analyze, relate, act – which is a form of closed-loop performance management. It begins by extracting data from multiple sources of text-based data, (listening), analyzing the content of the data (analyze), linking this data with other sources of customer data, and producing alerts, workflows and reports to encourage action to be taken based on the insights (act).

An increasingly common source of text-based data is social media. The latest announced version of the product, Attensity Respond6, adds additional capabilities to support special media and takes the “act” step further. It has a full Twitter firehose, feeds from most of the other popular social media sites (including Facebook, Google+ and YouTube) and APIs that can extract text from email, surveys, social media forums and blogs. Respond6 then uses natural language analysis to add context to the content, such as determining which words relate to a company (for example, Orange Inc. as opposed to the fruit called orange), different versions of the same name (AA and American Airlines), occurrence of entities (product or company names, locations and times), events, issues (“This product doesn’t work,” “My call to the contact center was a waste of time”), sentiment (“I love this product”) and intentions (“I plan to cancel my contract”). Using this analysis, the product’s rules-based engine determines the appropriate action to be taken to respond to the interaction (such as call the customer back or alert a supervisor). Rules can be set up to match any situation and can trigger a variety of actions, including write to another system, search for information, send out a survey to gather more feedback or ask for support.

Respond6 also can route the record of the interaction, along with other information needed to execute the action, to the person or system responsible for taking the action; for example, it could pass a tweet, with the tweeter’s influence rating, to a social media team to respond, or create a ticket in a CRM system so that a customer service representative would be told to respond. This routing of interactions and actions takes Respond6 beyond “pure play” text analytics and puts it at the heart of what is now being called omni-customer experience management –the movement to provide consistent, personalized customer experiences across multiple channels.

More Read

big data marketing
Have Marketers Taken Big Data Too Far?
Is There Such a Thing as Good Gut Decisions?
Why is Modeling Foundational to Performance Management?
Text Analytics, Big Data and the Keys to ROI
Why clario? Technologies Converge

Attensity has also made some technical improvements to the product.CRM The architecture now supports multitenancy and automatic load balancing, which are especially useful in handling very large volumes of tweets. Reporting has been enhanced to include more visualization options, trend analysis, emerging hot issues, and process and performance analysis.

My benchmark research into the unified agent desktop shows that companies face several challenges in making customer service meet customer expectations. The two most common are that communications channels are managed as silos and that customer-related activities (such as handling customer interactions) are not coordinated across lines of business. These two factors alone make it hard for companies to provide high-quality, consistent experiences across all touch points and all forms of interactions. Respond6 has tools to analyze text-based interactions more effectively and also to enable better responses to them, especially social ones. I recommend that companies evaluate how it can support their efforts to improve customer engagement and the customer experience.

TAGGED:Attensitybusiness analyticsCPMCustomer Performance Managementsocial media
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

sales and data analytics
How Data Analytics Improves Lead Management and Sales Results
Analytics Big Data Exclusive
ai in marketing
How AI and Smart Platforms Improve Email Marketing
Artificial Intelligence Exclusive Marketing
AI Document Verification for Legal Firms: Importance & Top Tools
AI Document Verification for Legal Firms: Importance & Top Tools
Artificial Intelligence Exclusive
AI supply chain
AI Tools Are Strengthening Global Supply Chains
Artificial Intelligence Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

An Analytics Story Problem: When will two trains collide?

4 Min Read

A Lesson in Social Media from the Beatles

5 Min Read
business intelligence benefits for companies trying to get through the pandemic
Business Intelligence

How BI Can Help Enterprises Overcome The Effects Of The Pandemic

6 Min Read
big data and social media analytics
Big DataExclusiveSocial Media Analytics

How Big Data Is Transforming Social Media Marketing

8 Min Read

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

AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
ai chatbot
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots

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?