Weiterlesen »]]> I found this interesting article from Ventana Research,  written by Richard Snow – VP & Research Director.

His research into customer analytics shows three important things: Text analytics are at the early adopter stage; companies still use spreadsheets as their main tool for analysis; and to move companies away from spreadsheets vendors must offer tools that are as easy to use as spreadsheets. That’s no easy task, given the huge volume and varied types of text data companies are generating and the complexity of analyzing unstructured text. However, the research also indicates that this challenge will be met, and a new software release from text analytics vendor Attensity is the type of product that can help companies overcome these challenges.

As with so many things today, the driver is social media. Companies at the very least want to monitor what consumers are “saying” about them on social media, and what consumers are saying produces a lot of unstructured text. Whether tproducts are called text analytics or social media analytics, companies are beginning to deploy systems that allow them to tap into social media and produce analyses of what consumers are saying about their brands, products and services. As they do this, companies are realizing they have a wealth of other sources of text data – email, forms, surveys, Web scripts and others – that can be analyzed to form a bigger picture of their customers, which is now popularly called the Voice of the Customer.

This need for text analytics plays to the strengths of Attensity. The company recently announced Analyze 6.0 , built on the existing Attensity Data Grid and its powerful natural language processing engine, which can deliver insights, discover trends and analyze sentiment from text-based customer interactions.

Major enhancements in this new version make the product easier to use. A new exploration environment gives users the ability to create new types of analysis using drag-and-drop techniques. Users can select the data they want to analyze, filter it by several different types of parameters (such as names, products or scores), design a dashboard that meets their individual requirements either from scratch or using one of the provided templates, and then drop the required reports into the dashboard. Users can select from a set of standard reports or build reports to individual requirements. The software provides extensive visualization tools: Information may be displayed as tables; as bar, pie and line charts; as category trees (such as types of complaints within the category Complaints); and as gauges and something Attensity calls word clouds, which display the key words in a document with color-coding and variable font size. Word clouds allow users to get a quick overview of the content of selected documents, which in turn helps them create or refine categories of interactions based on frequently used words. As one user told me, it allows them “to see what they need to know so they can create the correct analysis rules.” Last but not least, new capabilities allow users to share or distribute reports and analysis in multiple forms using one-click access to email.

The software also supports a number of functional enhancements, starting with new metrics about percentage totals by category or subcategory. The new “hotspotting” feature, which goes beyond normal trend analysis, allows users to see any significant change between the current analysis and a historical trend. For example, some words or phrases may have just started to be used, or a phrase that once occurred 10% of the time now appears 25% of the time. This guides users to look for a significant event that might have brought about the change. The final major functional enhancement allows users to use social media information to enhance their profiles of customers, tracking for example what other products a customer has (and maybe is complaining about). As well, more technical enhancements improve data security and system administration.

In this major release, Attensity has recognized that different generations of people use new sets of words, phrases and acronyms, and that different industries have their own sets of terms. It has enhanced the application’s word-spotting capabilities to recognize text and social media slang and put it into the context of other content. Attensity also announced a banking industry version that recognizes many specific banking phrases and idioms. This version also includes additional multichannel analysis and enhanced workflow capabilities, which for example allow interactions to be delivered to the person best equipped to deal with them.

All of these enhancements add up to a major advance in how Attensity Analyze can be used to derive insight from many forms of text-based data. It addresses key issues for users by making the software simpler to use and includes features that go beyond what spreadsheets can provide.

Recently he wrotes that it is time for companies to change the way they handle customer interactions. Gaining insight into what customers like or dislike using text analytics is a good place to start. Has your company recognized the benefits text analytics can deliver?

Weiterlesen »]]> For years, organizations that wanted to better understand their customers did so by gathering and storing structured data such as customer names, addresses, phone numbers and demographic data, or by conducting focus groups or surveys. Using this information, companies could get a view of the customer and use that information to better target their products and services. Over time, however, both the volume and nature of customer information available to the enterprise have changed dramatically.

Attensity’s Hotspotting report lets you identify emerging trends

Weiterlesen »]]> Der neue Hypatia Research Report untersucht den Markt für Voice of the Customer (VOC) Lösungen und vergleicht 24 Anbieter, die zunehmend Social-Media-Funktionalitäten in ihre Lösungen integrieren.

Der Report führt aus, dass Unternehmen heutzutage über einen immensen Wissenschatz über ihre Kunden verfügen. „Nun haben sie auch die Werkzeuge, um dieses Wissen zu nutzen“, betont Hypatia und zitiert in diesem Zusammenhang Scott Morrison, Autor des „Dow Jones Newswires“ in San Francisco, mit den Worten: „Travelocity entschied, dass es seine Kunden besser kennenlernen müsste. Daraufhin verbrachte ein Projektteam mehrere Monate damit, einige 10.000 Umfrageformulare zu durchforsten, um herauszufinden, was die Leute mögen bzw. nicht mögen.“ Um diese Herausforderung letztlich zu meistern, entschied sich Travelocity für Attensity-Technologie und kann seine umfangreichen Kundendaten nun nutzen, um wertvolle Einblicke zu gewinnen und auf dieser Basis die richtigen Entscheidungen zu treffen.

Im Rahmen der Hypatia-Untersuchung schneidet Attensity hervorragend ab. Den kompletten Hypatia Report können Sie hier anfordern (Registrierung erforderlich).

Weiterlesen »]]> Buchen Sie unseren 1/2 Tages Workshop und erfahren Sie dabei wie Sie mit Social Media Monitoring  profitieren, mit “Zuhören und Verstehen” im Besonderen!

Inhouse Termine auf Anfrage

• Einführung: Der vernetzte Kunde
• Themenfelder: Von Trend Scouting über Issue Management bis zu Competitor Watch
• Monitoring Prozesse: Listen – Analyze – Relate – Act
• Kennzahlen: Von Relevanz über Impact Analyse zu den richtigen Schlußfolgerungen
• Grenzen künstlicher Intelligenz: Sprachen, Social Language und Technologie
• Risken und Best Practice:  Fallbeispiele
• Live Workshop: Von der “Topic” zu aussagekräftigen Reports und Dashboards

Teilnehmer sind am Ende des Workshops in der Lage Social Media Monitoring richtig einzuschätzen und gewinnbringende Ziele für Ihre individuellen Anforderungen zu definieren.

Kosten auf Anfrage
Halbtag / Inhouse / bis zu 10 Teilnehmer / inkl. Schulungsunterlagen

Weiterlesen »]]>

For years, information that was relevant to an enterprise was numeric and almost entirely found within the enterprise. Sales figures, inventory levels and gross margin numbers were trusted data that was used to drive business decisions.

Managers know that to successfully run a business, you must also take into account a large amount of “unstructured” information. This information includes customer relationship management (CRM) and call center notes, analyst reports and emails. This led to the rise of text mining products to be able to take this “unstructured” information and classify it for use in downstream data analysis.

Recently, more than just the nature of the data has changed. People are turning to social media to communicate their experiences and issues with products and companies, and this information is being added to traditional sources of “voice of the customer” information like surveys, emails, and CRM notes. The Internet has grown into a treasure trove of customer, product, competitive, research and market information.

The Rise Of Search

In response to the rising volumes of data inside companies and the growth of the Internet in the late 1990s, search became a ubiquitous technology in enterprises and for consumers. Search can be a very useful tool for locating a particular document of interest containing a search term.

However, search is inadequate for revealing trends or performing complex operations. For example, if a company is interested in monitoring social media for its customers’ top 10 product issues, it will generally create queries for the company name, their products, and maybe even include keywords of known issues. Then, personnel must manually read through the search results and tally up the issues by hand. Depending on the topic and the brand, the data returned can be enormous and the risk of the inclusion of a lot of “noise” very high.

Manual search and recording isn’t that difficult if you are talking about processing, say, 100 survey comments a month. But it’s impossible to get a near real-time snapshot of what people think about you in social media by using only this method. It’s also difficult to get a uniform manual coding structure to which multiple people can adhere to create an effective, reliable database of information.

The Emergence Of Keyword-Based Text Mining

Using search becomes even more complicated when it is difficult to define a small set of keywords that can get you the answers you seek. Other actions difficult to do with the search engine approach include having an engine automatically route to product management various product suggestions, or having customer service be quickly and automatically alerted to messages indicating an intent-to-leave.

A similar situation used to exist in the world of physical goods. When there was a limited variety of goods sold at the corner shop, it was not a big deal to manually inventory, sort, and count goods. But, as the volume and variety of goods increased, and as just-in-time delivery of those goods became more expected, the process needed to become automated. So the barcode was invented and standardized as a standard way of expressing the “aboutness” of a product.

Likewise, with the rise of unstructured data volumes and the need for just-in-time delivery of information, a new system was needed that would go beyond keyword indexing. This new system was text analysis – a way of “barcoding text” that could express information about that text (what it is, where it is located, how it relates to the words around it) and allow it to be mined for information and moved to the appropriate place based on its “aboutness.”

The first text analysis systems were just basically extensions of the search indexing principle. “Categories” were defined by creating sets of keywords that could be used to define a specific characteristic. This sort of keyword-based system finds sentences like “my room smelled bad” or “this room is really stinky” and classifies those sentences into a category of “smelly bedroom” on which a report can then be generated.

Challenges Inherent to Keywords-Based Classification

Language is a tricky thing. While a keyword-based system will accurately locate and mark these examples, it begins to have accuracy issues when dealing with more complex text. These accuracy issues are around both

• precision (the number of items correctly labeled as belonging to a class [true positive] divided by the number of elements incorrectly labeled as belonging to the positive class [false positive]);and
• recall (the number of true positives divided by the total number of elements not marked but that actually do belong in the class).

Another problem arises with the complexity of keyword-only-based category definitions. For example, a retail store might like to track electronics department “neatness” from survey and other comments. So they laboriously construct a category looking something like the below:

electronics department, electronics dept, TV department, electronics area

AND

bedraggled, begrimed, black, contaminated, cruddy, crummy, defiled, dirty; disarrayed, dishabille, disheveled, dreggy, dungy, dusty, filthy, foul, fouled, greasy, grimy, grubby, grungy, horrible, icky, lousy, messy, mucky, muddy, mung, murky, nasty, pigpen, polluted, raunchy, scummy, scuzzy, slatternly, slimy, sloppy, slovenly, smudged, smutty, sooty, spattered, spotted, squalid, stained, straggly, sullied, undusted, unhygienic, unkempt, unlaundered, unsanitary, unsightly, unswept, untidy, unwashed, yucky, uncluttered, clean, cleansed, clear, delicate, dirtless, elegant, faultless, flawless, fresh, graceful, hygienic, immaculate, laundered, neat, neat as a button, neat as a pin, orderly, pure, sanitary, shining, simple, snowy, sparkling, speckless, spic and span, spotless, squeaky, stainless, taintless, tidy, trim, unblemished, unpolluted, unsmudged, unsoiled, unspotted, unstained, unsullied, untarnished, washed, well-kept

Not only does this approach require a good deal of imagination but it is also quite brittle. Even using this extensive list of keywords to define a category, there are still many false positives and misses:

In addition, by using only keyword-based classification, you miss chances for discovery of issues. For example, if you have many people complaining about tattooed employees, but you haven’t previously thought to create a “tattooed employee” class, you might never know this was a problem.

Unfortunately, these types of deficiencies are not always readily apparent in a demonstration of a keyword-based system. Many times, a keyword-based software manufacturer will take sample data provided by the customer and then perfectly fit the keyword categories to the task at hand. Only after a company has run real-world data through the system unaided by the manufacturer do these types of issues begin to surface.

A New Type Of Text Analytics: Exhaustive Extraction

In answer to the brittleness, a new type of text analytics system arose that not only takes into account keywords, but the context of those keywords.

This type of “exhaustive extraction” is the ability to automatically extract people, places and things and their roles and relationships in text. It looks at words and their surroundings, diagramming sentences and phrases in much the same way as the human mind interprets them. It parses this content to extract facts, relationships and sentiment from this data.

For example, let’s look at the sentence “It was even a smoking room, but I could not smell anything.” An exhaustive extraction uses the linguistic structure of the sentence and automatically extracts both entities (such as the location “a smoking room” and the person “I”) and relationships or events, as interpreted in what is called a triple: I:smell[not]:anything.

This indicates a non-smelly room event that can be compared with other events that are found and analyzed much like structured data.

Likewise, “the room was clean, on a smoking floor, and smelled fresh” is properly coded: the room:smell:fresh. Again, this is correctly interpreted as a positive event:

Finally, in the example “the room was clean, but the hallway did smell of smoke”, the correct extractions are again made:

By using this type of advanced linguistic understanding, which is done automatically and with no reliance on keywords, we identify and extract the true meaning of a customer’s comments.

Voice Tags

Voice tags refer to additional information about an extraction that can change its meaning. The change can be subtle or extreme, and provides customers crucial insights into their data. There are seven different voice tags:

• Question [?] voice indicates that the sentence from which the fact was extracted was in the form of a question.
• How can I get free shipping with future orders?

free shipping : get [?]

• Condition [if/then] voice can be utilized to find these priceless customer service opportunities to mitigate circumstances and to persuade customers to retain their loyalty to the company.
• I would shop much more frequently if you offered free shipping.

free shipping : offer [if/then]

• The function of the intent [intent] voice is to depict people’s intentions or desires. This voice gives heightened insight into voice of the customer as it reveals what a person wants, threatens, or tries to do.
• I plan to shop here often.

I : shop [again] [intent]

• The function of a Negation [not] object is literally to negate the meaning of the verb:
• I didn’t find what I was looking for.

what I was looking for : find [not]

• The Augment [more] voice detects emphasis and helps differentiate between degrees of sentiment
• Your selection is extremely limited

selection: limited [more]

• The function of the recurrence [again] voice is to indicate that the action in the sentence has happened before, or is happening in an ongoing, recurring fashion.
• Your prices are still high

price : high [again]

• The indefinite [maybe] voice can be used to represent suggestions or requests.
• I wish you would offer incentives like coupons.

incentive : offer [maybe]

Returning to our previous retail example, the combination of technologies changes the results to look like this:

As you can see, a true exhaustive extraction system reveals a wealth of information contained within every customer communication or relevant social media interaction, without requiring the anticipation of every permutation and combination of words you want to analyze.

For example, a keyword-based system might tell you that there are a lot of customers talking about cupholders, but it isn’t sophisticated enough to also reveal, unaided, whether those customers are saying they want bigger cupholders, more cupholders, or that their cupholders are breaking.

Advanced text analysis software also employs sophisticated linguistic capabilities like anaphora resolution, which enables the system to automatically resolve a pronoun back to its proper noun. For example, when I use the sentence “The salesperson was really great; she helped me to understand the product,” it will correctly resolve that it was the salesperson who helped me to understand the product – that I was the one who was having difficulty, not the salesperson.

These newer business intelligence systems should also be able to interpret colloquial language, emoticons and other “social speak” so prevalent in online discussions, with no additional effort on the part of the user — something that simple keyword-based systems cannot do.

Conclusion

The keyword-only data-mining model for classification can be flawed and time-consuming for true business decision-making. For any enterprise that needs to use insights found in unstructured data for action such as customer service, it’s critical to use deep, automatic analytics to parse, process and classify text. Only the ability to understand the true meaning and relationships between people, places, things and issues provides accurate enough output on which companies should take action.

About the author

Catherine van Zuylen serves as the vice president of product marketing for Attensity, which provides text analytics solutions for Customer Experience Management. Attensity helps the world’s leading brands leverage multi-channel customer conversations as a business asset. Catherine has more than 15 years of experience in defining and implementing new positioning, product, and branding strategies. Catherine can be reached at cvanzuylen@attensity.com. She blogs at http://blog.attensity.com/ and tweets under @catevz.

Quelle: Dashboard INSIGHT

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Nothing to do directly with my business of advanced text analytics but interesting where we go…..

A battle is happening in the Silicon Valley. Two software giants, Google and Facebook, are competing to be the leader of search in the coming years. Facebook won’t give Google access to its social graph data. In response, the search engine giant is working with just about every other social network in an effort to make Google search social.

February 17th Google launched an upgrade to its search engine, adding social results for users signed in with their Google accounts. In addition to traditional website search results, searchers will start seeing results from their friends on Twitter, Blogger, Flickr, LinkedIn, etc.

For a full understanding of these new changes, check out this new video explaining social search improvements from Google.

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Der Bundesverband Digitale Wirtschaft (BVDW) e.V. veröffentlicht zehn Thesen zur Zukunft von Social Media in 2011. Die Fachgruppe Social Media im BVDW sieht wesentliche Veränderungen in vielen Unternehmensbereichen wie Marketing, Öffentlichkeitsarbeit, Marktforschung, Kundenbindung, Personal und Produktentwicklung. Vor allem mittelständische und kleine Unternehmen werden 2011 Social Media intensiver nutzen.  Die 10 Thesen der Fachgruppe Social Media im BVDW sind kostenlos als Download unter www.bvdw.org erhältlich.

10 BVDW-Thesen zur Zukunft von Social Media in 2011:

Social Media wird in Unternehmensorganisationen integriert
Die Frage nach der richtigen Organisationsform für den Bereich Social Media wird ein größeres Thema. „Hobby-Lösungen“ mit Praktikanten haben ausgedient. Die Phase eines Hypes oder Trends ist vorbei, Social Media wird zum Alltag und will disziplinübergreifend organsiert werden. Der Bereich der Unternehmenskommunikation wird hierbei über alle Abteilungen hinweg eine zentrale Rolle spielen.

Employer Branding 2.0
Employer Branding in Social Media wird zum essentiellen Bestandteil für Unternehmen bei der Rekrutierung von neuen Mitarbeitern. Statische Personalbereiche auf Homepages und reine Anzeigenportale waren gestern. Nicht nur Kunden werden anspruchsvoller, auch Bewerber setzen auf User Generated Content in ihrem Entscheidungsprozess und informieren sich über die Unternehmen anhand ihrer eigenen Präsenz und ihrer Bewertung sowie der Aktivitäten ihrer Mitarbeiter in Social Media.

Die Produktentwicklung wird sozialisiert
Die Entwicklung neuer Produkte und Dienstleistungen wird auf breitere Beine gestellt. Eine extrem breite Wissensbasis als Input zur Produktentwicklung und extrem schnelles Feedback intern wie extern führt zunehmend zu “Adaptive Engineering”. Bald könnte die Zeit langwieriger Mafos (Befragungen, Beobachtungen etc.) vor der Einführung/Entwicklung neuer Produkte vorbei sein.

CRM wird nicht mehr sein, was es war
Letztes Jahr lief das Beziehungsmanagement noch weitgehend unsozial ab. In 2011 werden wir eine zunehmende Verschmelzung verschiedener CRM-Bereiche mit Social Media-Komponenten erleben. Leadmanagement, Kundenservice und Kundenbindung sind die ersten CRM-Felder, wo Social Media eine zunehmende Rolle spielen wird. CRM-Systemanbieter werden dafür sorgen, Daten und Prozesse aus Social Media systematisch nutzbar zu machen.

Unternehmen aus der zweiten Reihe springen auf
Bisher haben große Brands die Entwicklung von Social Media Marketing getrieben. Spezielle Social Media-Budgets sind gesamten Werbebudgets gefolgt. 2011 werden auch die „small“ bis „medium“ Spender auf den Zug aufspringen. Mittelstand, Industrie, B2B, Verbände, NGOs – für sie gilt es aus den Fehlern der Großen zu lernen und einzuschätzen, wo Social Media ihnen wirklich nützt. Der Mehrwert von Social Media wird dabei oft in anderen Unternehmensbereichen gesehen und weniger direkt im werblichen Bereich.

Social Media ist mehr als Facebook
Letztes Jahr war geprägt durch den großen, blauen Riesen. Großteile der Budgets in Deutschland sind in Facebook Apps und Media geflossen – der Return of Invest war bisher ungewiss. Die Nutzer werden neue, dynamische Plattformen und Applikationen für sich entdecken, um themen- und kontextspezifische Beziehungen zu führen. Marken werden die gute alte Homepage sozialisieren, um der Community auch im eigenen Garten etwas zu bieten. Spitze Themenplattformen wie Blogs und Foren werden den „Longtail“ wieder ins Gespräch bringen.

„Social Intelligence“ wird zum Erfolgsfaktor
Social Media Monitoring war bereits in 2010 ein großes Thema. 2011 wird es darum gehen, die Tools weiter zu optimieren. Aber viel wichtiger wird die Frage sein, wie und wofür das Wissen eingesetzt werden kann. Von einer adaptiven Aussteuerung der Kommunikation über die Produktentwicklung bis zur Kundensegmentierung – die Daten aus dem Social Web bieten unzählige Möglichkeiten.

Die ROI Diskussion geht weiter
Die Messbarkeit der Maßnahmen wird auch in 2011 weiter ein Thema sein. Was ist Erfolg und wie messe ich ihn? In den letzten Jahren ging es für Marken darum, dabei zu sein. In Zukunft wird es darum gehen, was Marken für Ergebnisse erzielen. Social Media wird ein typischer Aspekt des Kommunikationsalltags werden.

Neue Berufsbilder entstehen
Die Nutzung von Social Media erfordert von den Mitarbeitern der Marken neue Fähigkeiten. Gelerntes Marketingwissen ist nur noch in Teilen anwendbar. Mit Social Media wollen neue Tools und Infrastrukturen bedient werden, zudem muss ein neuer Kommunikationsstil geprägt werden. Community Manager sind die neuen „Arbeiter“ und gleichzeitig die neuen Erfolgsfaktoren der Kommunikation. Marken dürfen es nicht verpassen, diese Positionen gemeinsam mit der Unternehmenskommunikation und anderen Fachbereichen der Unternehmensorganisationen intern aufzubauen.

Das Ende der Kampagne, wie wir sie kannten
Die für Marken und Agenturen gelernte Kampagnenarbeit ist ein Auslaufmodell. Ideen müssen mehr können, als Aufmerksamkeit zu generieren – Engagement ist gefragt. Media wird nicht mehr gekauft, sondern verdient. Der Launch ist nicht das Ende der Arbeit, sondern der Anfang.

Quelle: BVDW

Web 2.0 für Pressearbeit und Unternehmenskommunkation

Weiterlesen »]]> Welche Trends werden 2011 für Schlagzeilen sorgen, mit welchen Veränderungen in den Social Media Bereichen ist zu rechnen? Welchen Einfluss haben Smartphones, Local based Services und Livestreaming? Was bedeuten die Entwicklungen für die traditionellen Printmedien und das Fernsehen? Welches ist der nächste Schritt von Social Media nach erfolgreicher Einbindung in die Unternehmenskommunikation?

Social Media Schweiz hat sich hin gesetzt und hat diese und andere Fragen für uns beantwortet.

Die 11 Megatrends in der Übersicht

Quellangabe:  Social Media Schweiz

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Wien (pts/21.10.2010/10:35) – Die Nachrichtenagentur pressetext hat ihre Drei-Länder-Tour unter dem Titel “Kommunikation 2020 – The Company ist the News” am Mittwoch in Wien mit einer weiteren ausgebuchten Veranstaltung abgeschlossen. “Auch wenn sich Social Media zum Trend des Jahrzehnts entwickelt, so muss jedes einzelne Unternehmen doch seine eigene Strategie im Umgang mit sozialen Netzwerken entwickeln”, erklärte pressetext-Marketingchef und Initiator Wilfried Seywald.
…..

Der Event am Mittwoch in Wien wurde von den Referenten Thilo Baum (Klartextexperte), Martin Wolfram (NewsOnVideo), Jörg Fuchslueger (Social Media Monitoring Experte) und Wilfried Seywald (pressetext) bestritten.

Quelle: pressetext