Method and apparatus for analyzing and applying data related to customer interactions with social media

Patent metadata

Field	Value
Publication number	US-9519936-B2
Application number	US-201213461631-A
Country	US
Kind code	B2
Filing date	May 1, 2012
Priority date	Jan 19, 2011
Publication date	Dec 13, 2016
Grant date	Dec 13, 2016

Abstract

Embodiments of the invention provide techniques that quantize community interactions with social media to understand and influence consumer experiences.

First claim

The invention claimed is:

1. An apparatus for quantizing community interactions with social media to understand and influence consumer experiences, the apparatus comprising:
a processor configured for analyzing posts on social media for use in customer service to improve brand and customer experience;
said processor configured for determining one or more leading indicators of issues that appear in a consumer contact channel by
mining said posts,
extracting topics from said posts,
selecting those topics that are occurring for a first time or whose frequency of occurring is changing over time,
calculating a term frequency inverse document frequency (TFIDF) score for each feature in each selected topic,
assigning a weight to each TFIDF score,
calculating a score for each of said posts by summing the weighted TFIDF scores of all features within each post, and
selecting top posts from each selected topic;

said processor configured for predicting a trend in future consumer contact channel activity based on said leading indicators and the analysis of said posts;
said processor configured for identifying one or more key influencers from social media; and
said processor configured for, once said key influencers are identified, contacting said key influencers to influence said key influencers.

2. The apparatus of claim 1 , said processor configured for:
using social media for any of identifying a spike in activity, identifying questions around the spike, or, in a call center application, determining plan staffing and planning an issue response in advance.

3. The apparatus of claim 1 , said processor configured for:
applying a model to quantify the influence of said key influencers.

4. The apparatus of claim 1 , said processor configured for:
determining an influencer trust score for each of said key influencers, where Trust Score=w 1 (agreement)+w 2 (Opposition)+w 3 (retweets)+w 4 (References), and wherein the weights are optimized based on a manual ranking of each attribute.

5. The apparatus of claim 4 , said processor configured for:
tracking a subset of said key influencers in a community on a social medium, wherein the subset is composed of those influencers having the highest influencer trust scores;
measuring customer sentiments; and
if a negative trend in customer sentiment is identified, contacting customers with a resolution message to create a positive trend.

6. The apparatus of claim 5 , wherein the subset of said key influencers is determined by aggregating the influencer trust score for each influencer;
wherein each of said influencer trust scores is a numerical sum of instances of a particular attribute; and
wherein said influencer trust scores are normalized to an average number of posts from posters in that population.

7. The apparatus of claim 5 , said processor configured for:
providing a model for scoring said key influencers, in which every post is weighted based upon the influence of a person making the post to yield a net impact score;
wherein factors that make up influence include any of engagement, trust, or a poster's network.

8. The apparatus of claim 5 , said processor configured for:
identifying influence that an influencer exerts over others; and
providing a path to remediation of an influencer's issues that creates a net media positive trend in social media.

9. The apparatus of claim 5 , said processor configured for:
monitoring a particular key influencer on a company's social media; and
mining said particular key influencer's posts to provide a service anywhere function that provides service in a channel of said particular key influencer's choice.

10. The apparatus of claim 1 , said processor configured for:
using knowledge of said leading indicators and said key influencers to respond preemptively, either through a particular key influencer and/or through preparing for issues arising later in other customer care channels;
wherein the key issues are identified using a topic model and/or an issue categorization model.

11. The apparatus of claim 1 , said processor configured for:
preparing one or more channels to serve a customer better and/or to provide a better predictive experience to the customer for both agent-based customer service and self-service.

12. The apparatus of claim 1 , said processor configured for:
providing service to a customer in a customer's channel of choice when and where the customer needs it by text mining social media.

13. The apparatus of claim 1 , said processor configured for:
providing any of a URL, a proactive chat window, or a guided self-service widget to a user to address a problem and/or an issue that the user is facing and that the user posted about in a channel where the user expressed the issue.

14. The apparatus of claim 1 , said processor configured for:
providing a sentiment analyser that identifies sentiment polarity from social media text and/or chats and that provides a sentiment strength score for a given input text.

15. The apparatus of claim 14 , said sentiment analyzer configured for:
using a supervised approach using labelled data;
wherein a set of posts are manually tagged/labelled as negative and positive sentiment posts;
wherein negative sentiment associated with each word in a post is determined by calculating its normalized likelihood on negative posts; and
wherein positive sentiment associated with each word in a post is determined by calculating its normalized likelihood on positive posts; and
using a Bayesian model to calculate a negative sentiment of a sentence by combining an individual word's sentiment scores;
wherein unigrams and bi-grams are used as features.

16. The apparatus of claim 14 , said sentiment analyzer configured for:
using an unsupervised approach using sentiment dictionaries to provide a sentiment score for each of a list of words which are used to express a sentiment;
wherein in a corpus being analyzed for sentiment, words that are part of a sentiment dictionary are identified and their score is obtained;
wherein scores for all sentiment words in a document or a post are summed up to provide a sentiment score for that particular document or post; and
wherein said sentiment score is normalized by dividing by a total number of words in the document.

17. The apparatus of claim 1 , said processor configured for:
providing a snapshot of consumer sentiment across a broad range of issues that may affect a merchant.

18. The apparatus of claim 17 , said processor configured for:
producing said snapshot by assuming each positive or negative term to be equivalent; and
calculating a score as: (#Positive terms−#Negative terms)/(#Positive terms +#Negative terms).

19. The apparatus of claim 1 , said processor configured for:
using words/features in a long tail of a word/feature statistical distribution to identify unique issues and their trends in chats/social media that are associated with unique features;
wherein frequency of any word is inversely proportional to its rank in a frequency table;
wherein a majority of words and, hence, features have very low frequency; and
wherein unique occurrences in time with low frequency represent interesting phenomena.

20. The apparatus of claim 1 , said processor configured for:
providing an insights engine for identifying discriminatory interaction features;
wherein a database for two juxtaposed features is applied to a feature extraction engine which identifies said juxtaposed features as tri-grams, bi

Assignees

• Vijayaraghavan Ravi
• Garikipati Ravi
• Chang Andrew
• Kannan Pallipuram V
• 24/7 Customer Inc

Inventors

• Vijayaraghavan Ravi
• Garikipati Ravi
• Chang Andrew
• Kannan Pallipuram V

Classifications

• G06Q10/40 Primary

  Business processes related to social networking or social networking services · CPC title

• G06Q30/02 Primary

  Marketing; Price estimation or determination; Fundraising · CPC title

• G06F16/2465

  Query processing support for facilitating data mining operations in structured databases · CPC title

• G06Q10/0637

  Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals · CPC title

• G06Q50/01 Primary

  Physics · mapped topic