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Example: Issue Tracking
The Target Data Breach
Dec. 2013 – Feb. 2014
The Example
An illustration of how an issue is
tracked by evolve24 across media
channels, influencers and
geography
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Research Methodology
• This is a sample article set to demonstrate evolve24’s capabilities.
Typical data sets tend to be much larger.
• The dataset examined consists of English-language web, blog,
Facebook, and forum content for the top 50 retailers in the U.S. For
this example, Twitter was sampled at 1%.
• The Target breach conversation was isolated using the date range of
December 18, 2013, to February 28, 2014. Keywords included
credit, debit, card, data, register, store, millions, etc. paired with
terms such as breach, hack, stole, attack, theft. All variations
(plurals, tenses, etc.) were also included. This sample data set
included 87,000 unique articles.
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Key Findings & Recommendations
• The story of the Target data breach spread quickly. Within a few minutes of the blog post, Krebs’ security-oriented followers were discussing it on Twitter.
• Online forums and news sites picked up the story quickly. By the time of Target’s announcement several mainstream media sources had already picked up the news.
• The data spread quickly throughout the U.S. (with particular coverage in California) and the world.
• At first, most discussions were strictly about the breach. As time passed, topics shifted to the number of cards affected and the possibility of personal information being shared on the black market. A few days later, the news that pins might have been compromised also broke.
• Overall, the concern that resonated most with consumers was personal information had been compromised and was now online and available for criminals to use rather than the breach itself.
• The steps Target and the banks took to reissue cards mitigated much of the concern around the issue, but, as of Fall 2014, Target’s reputation had yet to fully recover.
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The Announcement: Krebs on Security
According to examined data, Brian
Krebs first broke the news of the
suspected Target data breach, and
immediately tweeted his story. From
there security minded followers started
to retweet.
Both of Krebs’ initial posts
mentioned “Black Friday,” which
functioned to heighten concern for
consumers. From the outset, the
scale of consumers affected was in
the “millions.”
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The Target data breach story
originated on the Krebs on
Security blog.
Brian Krebs immediately
tweeted about his discovery.
Within the first few hours,
Twitter users and online news
picked up the story. The story
proliferated through Twitter
the first hours post-
announcement.
Krebs blog
& Tweet
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The First 8 Hours
How did the story evolve?
Less that 24 hours after
Krebs, USA Today (early
edition) contacted Target,
Visa and the Secret
Service to confirm the
story.
At 11:00 a.m. Target gives
a press conference and
the story explodes on
Twitter and on the web.
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24 Hours Later
How did the crisis evolve over time?
The First 24 Hours
How did the crisis evolve through media channels?
Krebs
post at
1:30 PM
12/18
Spread from
social to
traditional
Viral growth
begins with
Target press
conference
Less than 24 hours after Brian Krebs’ blog post, news of the breach had spread through
Twitter and social media channels. Traditional media picks up at around the 6-8 hour mark.
Coverage explodes in conjunction with Target’s press conference at 11 a.m. the next day.
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February 2014
How did the crisis evolve through media channels?
Coverage rapidly accelerated from 12/19-12/25 with information sharing in both social and
traditional media. Volume increases at a slower rate from 12/25-1/8 during the holiday week
and spikes on Jan. 9 as news of more cards affected surfaces.
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How did the story spread nationally?
The story originated
from Krebs, based
in Virginia. Within a
few hours the story
spread nationally.
Enter slide show
mode to see the
data spread.
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How did the story spread internationally?
The story originated
from the United
States. Within an
hour, it spread to
Canada and the
UK. Within 3 hours,
to India and then
throughout Europe.
Enter slide show
mode to see how
the story spread
around the world
over the first 72
hours.
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How did topics trend over time?
Initial
conversations
focused on the
facts: credit
cards at risk,
breach affects
40MM
News of other
affected retailers
spreads
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Which topics are consumers more
emotionally invested in?
• Of all sub-topics identified within
coverage of the breach, having
‘personal information compromised
online’ is the most emotional topic. In
other words, the topic most likely to
change consumers’ behavior towards
Target.
• Low emotion/high volume topics like
‘credit cards sold on the black market’
are talked about frequently, but are
less of a concern.
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Who is connected to the conversation?
• Target’s network during the breach is a good example of what a viral network looks like. There is an absence of identifiable clusters talking to one another, meaning no one stakeholder or group is pushing a clear agenda.
• To put this into context, the image on the right is an example of a network with identifiable and connected clusters. This is an example of what a concerted effortlooks like. Had we seen this, we would have suspected a few individuals were driving the story.
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Top Publications
Financial and personal
finance publications led most
of the coverage of the breach
although it did receive
coverage in security blogs
and trade discussions as
well.
Unsurprisingly, most of the
news was negative, though
specific positive coverage
talked to Target’s immediate
announcement of the news
and the steps the company
and various banks took to
mitigate the impact.
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What impact did the breach have on Target?
The data breach eroded consumer opinion about Target and the company was not fully able to recover
over the time period shown. Announcement days saw the greatest loss in consumer opinion.
*evolve24’s Consumer Opinion score calculates and weights the intensity of brand sentiment, the credibility of the source,
and the placement / relevancy of the brand within a given article.
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EVOLVE24 METRICS
Appendix
e24 Online Reputation & Emotion
Online Reputation
evolve24 uses advanced text analytics and patent-pending algorithms to convert social
conversation into a repeatable metric to quantify consumer perception in the online/social
space. Each document (blog, comment, article) receives a reputation score, which is
comprised of the sentiment of the brand, the relevancy of the document towards the
brand, and the credibility of the source.
The reputation score provides a metric for companies to compare its brand performance
over time, and to benchmark against competitors.
Emotion
evolve24’s Emotion Score™ is a weighted measure that calculates the emotional intensity of
a discussion. This measure is based on academic principles of risk communication and
behavioral psychology. The measure offers a scientific and consistent measure for
determining how persuasive a message is on an audience about a given issue or entity. This
provides an indicator of how an audience will react to a given situation. For example, a high
Emotion Score™ indicates that an audience is more likely to react to a given issue becuse
they are emotionally invested.
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• PreCISE™ is a multi-step algorithm designed to start with millions of social and traditional media
observations and arrive at the highest-priority insights in a repeatable, automated fashion.
• Topic Modeling: PreCISE™ applies a state-of-the-art text analytics technology called Topic
Modeling (TM) to discover the topical structures of social media documents and classifies those
documents into different topical categories. TM learns the topical categories from a set of
documents directly; no knowledge about the topical categories is required in advance and in TM,
unlike in other methods, a document can be assigned to multiple topical categories.
• Predictive Issue Ranking: Ranking is performed by combining several per-issue summary
metrics into a single quantitative score for each issue. These per-issue quantitative scores may
then be sorted numerically to determine the overall order of significance of the issues. These
summary metrics, calculated for each issue, include:
• Volume (Core Messages)
• Sentiment (Core Messages)
• Influencer Network Strength
• Speed (Velocity)
• Emotional Intensity
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PreCISE™ - A Predictive Analytic
PreCISE™ Component Overview
The PreCISE™ method combines weighted measures to determine which issues are most likely to
affect your industry and brand. The method is comprised of the following:
Prediction: Better forecasting through combined information
– Predictive Issue Ranking
Core Messages: A measure of messages and sentiment
– Volume: Number of documents for an issue
– Sentiment: Average sentiment score for a given document or stakeholder
Influence: A measure of the entities shaping the outcomes
– Influencer Network Strength: Issues with sporadically-mentioned or weakly-associated
entities are most likely not the imminent targets of focused action in the real world.
Speed: A measure of how quickly the issue is advancing
– Using various weights based on overall article count per day coupled with the speed at which
positivity, neutrality, or negativity is increasing/decreasing.
Emotion: A measure of the potential for the issue to change behavior
– Emotion scores also provide an indication of how likely it is that an audience will react to a
given situation. Higher emotion scores indicate an audience is more likely to react to a given
issue, as behavioral research proves they are more emotionally invested.
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CONTACT US
www.evolve24.com
Karin Kane
Chief Customer Officer
Noah Krusell
Practice Lead, Risk & Reputation
