LT-Innovate - The Association of the Language Technology Industry: 2014

09 December 2014

LT-Accelerate: A Major Text Analytics-Meets-Multilingual Talkfest in Brussels

LT-Innovate and Alta Plana, headed by text analytics community builder Seth Grimes, combined forces last week (4 - 5 Dec) to launch the first LT-Accelerate conference in Brussels. This attracted a broad range of analytics technologists and user companies to an in-depth conversation about the LT contribution to business opportunities in text and speech analytics, with a discreet emphasis on the multilingual European context.

For those who missed it, there’s a handy summary at Storify. The presentations and pictures of the event are on the event's website. You may also want to check out the @LTAccelerate Twitter channel and hashtag #LTA14.

Basic text analytics is now maturing, with a growing stable of tech companies offering APIs to their NP solutions or dashboards that help user companies make sense of their “unstructured” data. At the same time, the relevance of the binary sentiment analysis models is starting to reach its limits for many users, who henceforth need more insight into how human emotions and intentions are expressed linguistically in the decision-making process. And multilingual text data modelling continues to raise barriers for global players, either due to the inherent structure of languages or to a lack of reach.

Here are four takeaways that we shall explore further in future blogs:

The future of market research: one of the biggest users of text analytics is the Market Research industry (worth $61.45B globally) and currently morphing into a digital player by adopting new technologies of automated listening, mining and engaging. For MR, the future will involve among other phenomena a billion new Chinese tourists (think language, travel tech, tourist infrastructures, and communication generally) – an extraordinary opportunity for almost any business in Europe if they know how to address the challenge.

Getting Down to Semantics: The market opportunity for text analytics covers at least two very different families of data: business-generated text such as that provided by publishers, and every other customer-facing enterprise. And user-generated data, often sourced in social media and customer reviews.

Havas Media showed how they can now classify customer generated data into one of the four stages of the “customer decision journey” on the basis of linguistic cues, with a success rate of some 74%. This allows them to automate the classification of short consumer messages and thereby vitally inform retailers and others about the crucial decision process those customers go through.

On the business content production side, Elsevier demonstrated how they use proprietary semantic technology – known as a Fingerprint Engine - to enrich existing text from authors, patents, and increasingly foreign language data so that specialised STM searches can be apply concepts rather than words alone. This can enable a science author, for example, to find exactly the right journal that matches his research specialty.

We shall come back in a later blog to other semantic solutions in this space.

Generation A to Z: The most unexpected data point in the whole event might well have been the claim by Robert Dale (Arria) that “by 2020, more texts in the world will be produced by machine than by humans.” Three European content generation tech suppliers (Data2Content, Yseop, and Arria) addressed the apparently massive market for automatically generating content from data, rather than about data. The challenges here are to understand data as information (which is where semantics comes in) and then to turn that information into a narrative that tells a story. In a sense, therefore, what natural language generation will be able to do is take the results of data analytics – i.e. data – and use language technology solutions to turn it into content that humans (and also machines presumably) want to read. Watch this space!

Relevant Data is not always Big: Although we were treated to some large numerical data points during the conference - IBM recorded 53 million social media posts during the 64 games of the Brazil World Cup this year and a 50-agent speech contact centre can generate about 11Tb of voice recordings a year – the oil company Total told a story about small data. It highlighted the extremely practical virtues of smart search, analysis and presentation of smallish sets of highly relevant data from a corpus on oil-well safety-standard issues. This showed how you can mine value from text data to optimise knowledge sharing within a business. And it demonstrated that in many cases business clients will want to tailor the solution to their own needs. A useful lesson in how to market certain kinds of text analytics solutions!

21 November 2014

Quick Q&A: On the Earned Media Value of a Brand’s Social Activities

Earned, paid, and owned media are distinct species. If you haven’t laid out cash for a mention of your brand, product, or personnel in a media outlet, whether online or social, you’re deemed to have earned the coverage. (Take “earned” with a grain of salt. You may have laid out big bucks for a publicist or efforts to build your brand’s visibility.) If you’ve bought the coverage — advertising, for instance — that’s paid. And if it’s your outlet, then that media is owned.

Whether media is earned, paid, or owned, you want to measure the extent of attention and the effectiveness of your message. The effort can get quite involved, when multiple channels and multiple exposures are in the mix. The get a precise picture, you have to engage in attribution modeling. When social platforms come into play, the effort can be substantial.

General social business challenges, and technical responses, are central topics at LT-Accelerate, a unique European conference, taking place December 4-5, 2014 in Brussels. We’ll have Roland Fiege of IPG Mediabrands speaking, on methodologies and tools for measuring the earned value of brand social-media activity. If this topic interests you as well, you’ll want to learn more. A quick Q&A I recently conducted with Roland is a start, then I hope you’ll join us in Brussels. First a brief bio –

Roland Fiege is head of social strategy at Mediabrands Social, home of Performly. In his spare time, he is working on a PhD project researching methodologies for measuring the value add of marketing on Facebook and Twitter. And next, -

Our interview with Roland Fiege

Q1: The topic of this Q&A is social media analytics. What’s your personal SMA background and your current work role?

Roland Fiege: My personal SMA background started with consulting projects evaluating social media listening systems back in 2009. In 2010-11, I was part of an international team at US technology company MicroStrategy that developed a solution that analyzed the social graphs of Facebook users to help brands to understand the interests and affinities of their “fans” better.

In my current work role, we analyze user interactions responding to brand messages on social media channels and have developed a model that attributes an monetary “earned media value” to these interactions. This allows brands to quantify and valuate the outcome of their social media investments.
In my current work role, we analyze user interactions responding to brand messages on social media channels and have developed a model that attributes an monetary “earned media value” to these interactions. This allows brands to quantify and valuate the outcome of their social media investments.

Q2: What are key technical and business goals of the analyses you’re involved in?

Roland Fiege: The technical challenges are to keep the solution up to date with ongoing API changes by the most popular social networks and how to loop back “real time” bidding price benchmarks into our systems (vs. a static benchmark). Another challenge is to meet the EU data privacy standards that enterprises,German especially, try to comply with.

Business-wise, the challenge is to establish a common understanding how to attribute and valuate user interactions.
Business-wise, the challenge is to establish a common understanding how to attribute and valuate user interactions.

Q3: And what particular analytics approaches or technologies do you favor, whether for text, network, geospatial, behavioral, or other analyses?

Roland Fiege: We basically gave up on automated text analysis when it comes to sentiment. It never worked in Europe with all the different languages, dialects, irony etc. There was too much manual work involved that clients were not willing to pay for.

Currently we concentrate on the quantification for user engagement and its financial valuation.

Q4: To what extent do you get into sentiment and subjective information?

Roland Fiege: Our experience is that if users like, share, and comment on brand content, it mostly is positive or neutral sentiment involved. Contrary to this, most user posts on brand channels are negative and in correlation with negative customer experiences. Since we measure the monetary value of brand communication, we only measure fans/follower interactions on brand content.

Q5: How do you recommend dealing with high-volume, high-velocity, diverse social postings — to ensure that analyses draw on the most complete and relevant data available and deliver the most accurate results possible?

Roland Fiege: We do not only rely on the APIs that Twitter, Facebook and YouTube (Google) provide but also user other (fire hose) data providers to get the most complete picture/dataset, also for retrospective analysis.

Q6: Could you provide an example (or two) that illustrates really well what you’ve been able to accomplish via SMA, that demonstrate strong ROI?

Roland Fiege: What we accomplish: Clients manage to optimize their content strategies in near real time, can compare the performance of their content (agencies) in different regions and countries, and can identify savings potential in the millions. It is the first time brands can calculate the total cost of ownership of their social media channels and have a clear Input vs. Outcome result all condensed into one KPI: Money.

Q7: I’m glad you’ll be speaking at LT-Accelerate. Please tell me about your presentation, briefly: What attendees will learn.

Roland Fiege: In this talk you will learn about the latest methodologies and tools to measure the Earned Media value of a brand’s activities on Facebook, Twitter and YouTube in hard currency.

Q8: Finally, do you have recommendations to share, regarding choice of data sources, metrics, analytical methods, and visualizations, in order to best align with desired business outcome?

I will share those in my presentation in as much detail as possible.

Thank you, Roland, for your responses. I’m looking forward to hearing more, at LT-Accelerate in Brussels.

This Interview has been done by Mr. Seth Grimes, the leading industry analyst covering text analytics, sentiment analysis, and analysis on the confluence of structured and unstructured data sources and founder of Alta Plana Corp.

19 November 2014

How Havas Media Views Consumer & Market Analytics

Our thesis: Language technologies — text, speech, and social analytics — natural language processing and semantic analysis – are the key to understanding consumer, market, and public voices. Apply them to extract the full measure of business value from social and online media, customer interactions and other enterprise data, scientific and financial information, and a spectrum of other sources. The insight you’ll gain means competitive edge, whatever your organization’s mission.

Insight, via business (and research and government) application of language technologies, is the central topic for LT-Accelerate, a new conference that takes place December 4-5 in Brussels.

I recently interviewed a number of LT-Accelerate speakers. My questions broadly cover the topics they’ll be addressing in their conference presentations. This article relays my Q&A with speaker Inés Campanella of Havas Media Group and her colleague Óscar Muñoz-García. I’ll provide a bit of background and short bios and then we’ll get directly to the questions and responses.

Our interview with Inés Campanella and Oscar Muñoz-García

Q1: The topic of this Q&A is consumer and market insight. What’s your personal background and your current work role, as they relate to these domains?

Inés Campanella: I hold a B.A in Sociology and a M.A in Research Methods. Through my work and studies, I specialized in the field of Sociology of Communication and Information Society. Given my background, I’m very keen on new communication models and behavior patterns mediated by new media (i.e., social networks and other social media) and how we can profit from this amazing stream of behavioral data to increase our knowledge of social behavior.

At Havas Media I work as a researcher within the Global Corporate Development Team. My role involves integrating scholarly research and social theory into market research, designing a conceptual framework for insights into online consumer behavior; with a special emphasis in buzz monitoring. One of my main responsibilities is help to build qualitative, business-savvy content classifications to be used in the development of novel content analytics tools. My day-to-day tasks also involve working in practical, hands-on online market research analysis. This twofold approach allows me, and the team I work in, to come up with an innovative and yet pragmatic approach regarding what technology we are able to develop and what technical features we need to improve to meet our clients’ real needs.

Q2: What roles do you see for text and social analyses, as part of comprehensive insight analytics, in understanding and aggregating market voices?

Inés Campanella: Regularly listening to consumers is a task that marketers must undertake in order to know their audience, detect how people feel about them, and cater to their needs and desires. This being said, the new shopping scenario with people massively sharing their thoughts online and performing regular online research about product and brands calls for an assessment of the techniques traditionally used in market research. There are many advantages to this. In comparison with traditional quantitative techniques such as questionnaires, the collection of opinions extracted from social media sources means less intrusion since it enables the gathering of spontaneous perceptions of consumers, without introducing any apparent bias. In addition, the possibility of doing this in real time poses a clear advantage over other techniques based on retrospective data. Overall, this allows for a more efficient and complex business decision making.

So text analysis is and will increasingly be key to market research. Nevertheless, issues such as online privacy, anonymization and the degree of representativeness and objectiveness this data holds in comparison with other methods must be taken into account. We are only beginning to understand how we can combine these approaches in a solid, law-abiding methodological toolbox.
So text analysis is and will increasingly be key to market research. Nevertheless, issues such as online privacy, anonymization and the degree of representativeness and objectiveness this data holds in comparison with other methods must be taken into account. We are only beginning to understand how we can combine these approaches in a solid, law-abiding methodological toolbox.

Q3: Are there particular tools or methods you favor? How do you ensure business-outcome alignment?

Oscar Muñoz: There are many tools for measuring consumer insights in online paid and owned media that are reaching a significant degree of maturity, for instance, Programmatic Advertising and Web Analytics platforms for paid and owned respectively. However, regarding tools for performing consumer analytics in earned media, there is a long road that still lies ahead for offering results that can be easily activated in communication strategies.

Content classification is needed to enable meaningful KPIs (key performance indicators). At Havas Media, we are working on sentiment KPIs that go beyond polarity identification (e.g., classification of emotions expressed by users towards brands and products), on consumer communities research studies via big graph analysis techniques, and on measuring the influence of ad campaigns over word-of-mouth through the analysis of correlations between advertising spent, spots’ audience, and social buzz.
Content classification is needed to enable meaningful KPIs (key performance indicators). At Havas Media, we are working on sentiment KPIs that go beyond polarity identification (e.g., classification of emotions expressed by users towards brands and products), on consumer communities research studies via big graph analysis techniques, and on measuring the influence of ad campaigns over word-of-mouth through the analysis of correlations between advertising spent, spots’ audience, and social buzz.

Q4: A number of industry analysts and solution providers talk about omni-channel analytics and unified customer experience. Do you have any thoughts to share on working across the variety of interaction channels?

Inés Campanella: We live in a digitalized world and this means that we no longer find consumers in one location, environment or channel but, rather, in an ever-increasing variety of them. Traditional customer journeys are no longer valid and, thus, new strategies to engage with consumers — and avoid looking redundant to their eyes — are very much needed.

We have witnessed that, while companies struggle to connect their marketing strategies, they often lack a tool-supported holistic approach that ensures effective multi-channel and multi-device media strategies. Our ultimate goal at Havas Media is to integrate all data sources in order to track consumers across online and offline touch points, gathering information about them with the aim of performing real time automation of communication processes. An example: serving personalized, timely online ads, push messages, and e-mail recommendations. This is completely indispensable if we wish to address consumers in an effective way.
We have witnessed that, while companies struggle to connect their marketing strategies, they often lack a tool-supported holistic approach that ensures effective multi-channel and multi-device media strategies. Our ultimate goal at Havas Media is to integrate all data sources in order to track consumers across online and offline touch points, gathering information about them with the aim of performing real time automation of communication processes. An example: serving personalized, timely online ads, push messages, and e-mail recommendations. This is completely indispensable if we wish to address consumers in an effective way.

Q5: To what extent does your work involve sentiment and subjective information?

Inés Campanella: To a very large extent. Either when I’m directly dealing with data from social media listening projects or when we’re devising new business coding frames, we’re always trying to elucidate ways to leverage this source of subjective information and make it actionable.

On the other hand, carrying out accurate [sentiment] polarity analysis is essential, but we believe it is equally important to achieve a good classification and detection of recurrent conversation topics between users (e.g., regarding product features). Our deployment of content analytics tries to give answer to all these questions. Otherwise, we would be missing half the story.
On the other hand, carrying out accurate [sentiment] polarity analysis is essential, but we believe it is equally important to achieve a good classification and detection of recurrent conversation topics between users (e.g., regarding product features). Our deployment of content analytics tries to give answer to all these questions. Otherwise, we would be missing half the story.

Q6: How do you recommend dealing with high-volume, high-velocity, diverse data — to ensure that analyses draw on the most complete and relevant data available and deliver the most accurate results possible?

Oscar Muñoz: We deal with volume and velocity by leveraging Big Data processing platforms like Hadoop and the related ecosystem (e.g., HBASE, HIVE, Spark, etc.). To tackle diverse data, we spent a significant part of our computing resources on ETL (extract, transform, load) processes for normalizing, integrating, aggregating, and summarizing data from multiple social media channels (Twitter, Facebook, blogs, forums, etc.) according to a unique schema of linked data about content, users, and related metadata.

Regarding accuracy, our goal is to develop natural language processing (NLP) algorithms that are as precise as possible, to obtain confidence levels similar to other techniques like opinion polls. Unfortunately, this goal cannot be achieved easily. We combine machine learning and deep linguistic analysis techniques in order to find fair balances of precision and recall, but there is still a lot of work to be done.
Regarding accuracy, our goal is to develop natural language processing (NLP) algorithms that are as precise as possible, to obtain confidence levels similar to other techniques like opinion polls. Unfortunately, this goal cannot be achieved easily. We combine machine learning and deep linguistic analysis techniques in order to find fair balances of precision and recall, but there is still a lot of work to be done.

Q7: Could you provide an example (or two) that illustrates really well what your organization and clients have been able to accomplish via analytics that demonstrate strong ROI?

Inés Campanella: We’ve developed business indicators that allow us to better code and interpret social media listening data, namely marketing mix indicators and consumer decision journey stages. Ultimately, this has a very positive impact on ROI. Let me explain this in greater detail.

To monitor in real time and accordingly react to the experiences that customers are sharing, our clients must know the purchase stages in which those consumers are gained and lost, in order to refine touch points, impact consumers at the right time, and achieve the desired result (that is, a transaction). Also, uncovering the exact content of the dialogues that customers are having lets marketers and advertisers keep better track of consumers’ mindsets. We’ve found that the combination of these two categories provides very valuable information for a better positioning of the brand or organization in the market and, therefore, for an improved return on advertising efforts.
To monitor in real time and accordingly react to the experiences that customers are sharing, our clients must know the purchase stages in which those consumers are gained and lost, in order to refine touch points, impact consumers at the right time, and achieve the desired result (that is, a transaction). Also, uncovering the exact content of the dialogues that customers are having lets marketers and advertisers keep better track of consumers’ mindsets. We’ve found that the combination of these two categories provides very valuable information for a better positioning of the brand or organization in the market and, therefore, for an improved return on advertising efforts.

Q8: I’m glad you’ll be speaking at LT-Accelerate. Your talk is titled “Understand Consumers: Mindset, Intentions, and Needs.” Would you please describe your presentation briefly: What will attendees learn?

Inés Campanella: I’m also glad I’ll take part in LT-Accelerate. I will introduce the audience to Havas Media Group current needs, challenges, and practices regarding content analytics. Specifically, I’ll comment on the research we have carried out regarding innovative classification of user generated content (UGC) to improve social media buzz monitoring. In short, I’ll explain the business need for these kinds of classifier and how we can leverage and combine them with other market techniques and insights to improve our understanding of consumers’ mindsets and habits.

18 November 2014

From Social Sources to Customer Value: Synthesio’s Approach

Text analytics is an enabling technology for deep social media understanding. We apply natural language processing (NLP) and data analysis and visualization techniques in an effort to make sense of the diversity of social postings. The social intelligence that results advances customer engagement and informs efforts to meet marketing, customer experience, product management, and reputation management needs.

I interviewed Pedro Cardoso of social intelligence leader Synthesio as part of preparation for December’s LT-Accelerate conference. Pedro will be speaking on language morphology (forms) in sentiment analysis. That’s a fairly technical topic, reflecting Pedro’s role as text analytics director at Synthesio, but one that will help business attendees understand the ins-and-outs of attitudes, opinions, and emotions in social and other text sources.

Pedro’s background: He earned an engineering degree in electronics and control systems and a masters in speech processing. His career path started in Portugal, as a research engineer, followed by 4 years in Japan and 5 years in France. For the majority of this time, he worked on speech processing, mostly relying on machine learning for acoustic and language modeling. For the last 2 years, Pedro has been working on natural language processing at Synthesio in Paris.

Our interview with Pedro Cardoso

Q1: The topic of this Q&A is social media analytics. What’s your personal SMA background and your current work role?

Pedro Cardoso: My background is in machine learning applied to language technology. I started in development of speech recognition systems — language and acoustic statistical models. The focus was not on social media analysis (SMA), even if over the years I did some call-center development, including tests on sentiment analysis in voice. Over the last two and half years, ever since I joined Synthesio, I have been working full-time on SMA.

Currently I am responsible for NLP and text analytics development at Synthesio. Our objective is to create algorithms that help process and analyse social data collected by Synthesio, so that it can easily understood and exploited by our customers. This work includes data visualisation, document topic classification, and sentiment analysis.

Q2: What are key technical and business goals of the analyses you’re involved in?

Pedro Cardoso: Business drives technology, and customers needs drive business.

As mentioned above, our objective in the text analytics group is to find ways to structure and present information from social media sources in a simple way that customers can understand and get value from it. Our focus is on text. We classify and summarize it with the goal of obtaining meaningful key performance indicators (KPIs) from large quantities of data, which would be impossible without technology.

We also develop methods for detecting key influencers and deriving demographic information. This allows our customers to focus their searches on particular groups of social media users.

Q3: And what particular analytics approaches or technologies do you favor, whether for text, network, geospatial, behavioral, or other analyses?

Pedro Cardoso: If we focus on my work, I favor text and also study of network connections between online users. But if the question is what I believe to be the best technologies for SMA, that would have to be text also. Text is the medium, it is what customers use for communication. Network, geospatial, and other analytics are important, but mainly to focus our listening on a specific group. In the end, it is text, what SM users say, that counts.

Recently there has been interest on image analysis. People share more and more pictures. Sharing the picture of a brand logo or a product carries a strong brand loyalty message. Still, we need better image processing techniques and to learn how to best use information from images, in particular how it combines with text, in case of comments.

Social media allows us to focus on particular customers and groups, it allows us to have more personalized communications. In these cases, technologies such as demographic analysis and group detection gain favor, but discussing further, we would be getting off-topic.

Q4: To what extent do you get into sentiment and subjective information?

Pedro Cardoso: Automatic sentiment analysis is a great part of what I do as text analytics director. Our team is responsible for the development of automatic sentiment analysis at Synthesio, and has developed internally current support for 15 languages offered as part of the product.

Subjectivity is a very complicated subject, and one that I believe no one has managed to solve. To understand subjectivity, you need first to understand well the user and the context in which a message was written. After all, the real meaning is in the person’s mind. We are still not there, and it might take a long while to get there.

Pedro Cardoso: We have developed data crawlers that ensure we can capture, enrich and standardize data from different sources worldwide should they come from largest social networks (Twitter, Facebook, Sina Weibo, VKontakte, etc.), mainstream media sources, and blogs or forums (thanks to a dedicated sourcing team of 5 people). This approach allows us to deal with several million social mentions each day and to provide for each of them a sentiment assessment, a global influence ranking (proprietary algorithm), and potential reach (another proprietary algorithm), on an ongoing basis and in near real time. It takes less than 2 minutes for a data to be crawled, parsed, enriched and pushed into client interfaces. Once structured with both metadata and enriched data, our clients can then access their dashboard. They can either work on global data volumes for main KPI tracking and trend analysis and/or on focused subsamples for deeper human qualitative analysis.

Q6: Could you provide an example (or two) that illustrates really well what Sythesio’s customers been able to accomplish via SMA, that demonstrate strong ROI?

Pedro Cardoso: Sure. One of our clients in the automotive industry has achieved, through deep analysis of first-customer feedback in European forums, identification of key barriers when it comes to acquiring an electric car. Based on the lessons, they had the ability to create a far more efficient digital and social media campaign. ROI was there for reducing costs before the campaign both in terms of message crafting and media planning. ROI was there after the campaign, which drove far more traffic to the Web site, and to dealerships for test drives, than previous efforts.

Another example we can give is a telco company that uses Synthesio for both listening and engaging directly with its clients on social networks, regarding client questions and complaints. By defining a precise listening scope and by clustering, combined with precise workflows for answer validation and publication, the client was able to measure ROI based on average answer time for any given question. By socializing answers to most frequent topics they also built up a C to C advice platform, which allows top users to directly address other customers questions. ROI is also achieved via fewer inbound calls to the call center.

Q7: Do you have recommendations to share, regarding choice of data sources, metrics, analytical methods, and visualizations, in order to best align with desired business outcome?

Pedro Cardoso: At Synthesio we hold two key principles when it comes to social data and metrics.

We believe social analytics and intelligence have to be global. We have sources covering more than 200 countries, networks crawled natively in more than 50 languages, etc.

And they have to be simple. We built business oriented metrics, comparable KPIs, and customizable interfaces to make sure that every single client within a company (from PR to marketing, from CRM to sales) can access the right data at the right moment.

Furthermore we know that social analytics can’t be envisaged as another data silo. That’s why we pay so much attention to openness and interconnections with other digital marketing tools (such as consumer review platforms like Bazaarvoice, owned communities platforms like Lithium, social marketing platforms like Spredfast, etc.), CRM (Salesforce.com, Microsoft Dynamics, etc.), or BI (IBM, etc.) tools used by our clients. Our open API helps them to both push data to such tools but also integrate data from other sources to get a 360° view of customer feedback, for instance.

Last recommendation we would like to share is “Don’t get too focused on data: Next step is people.” To better measure ROI, our clients have to go back to where it all began: Business is conducted by people and not by a data set. Being customer centric for better targeting, better personalization of messages, and better understanding of the brand relationship is what guides all of our present and future developments. Even though our roadmap is our best kept secret, be prepared to see more demographic profiling, audience targeting tools, and sales oriented measurement and anticipation metrics.

Q8: I’m glad you’ll be speaking at LT-Accelerate. Your topic is fairly technical — exploiting languages’ morphology for automatic sentiment analysis — noting that we do have a range of presentations on the program. Would you please tell me about your presentation, briefly: What attendees will learn.

Pedro Cardoso: The first thing we need to understand is the definition of morphology. Morphology of a word defines its structure: the root, part-of-speech, gender, conjugation, etc. And this is the first giveaway of the presentation.

Continuing, I will show how the use of morphological information of words helped us at Synthesio in building sentiment analysis, in particular for less represented languages, those that offer less labeled [training] data. Also, it is an important part of the system for agglutinative languages, whose vocabulary is theoretically close to infinite.

That wraps up this interview. I’m looking forward to Pedro Cardoso’s LT-Accelerate presentation. If you’re intrigued by what you read here, please do visit the conference Web site to learn more. And I hope you’ll join us 4-5 December 2014 in Brussels.

17 November 2014

The Voice of the Customer × 650 Million/Year at Sony Mobile

We understand that customer feedback can make or break a consumer-facing business. That feedback — whether unsolicited, social-posted opinions, or gained during support interactions, or collected via surveys — captures valuable information about product and service quality issues. Automated analysis is essential. Given data volume and velocity, and the diversity of feedback sources and languages that a global enterprise must deal with, there is no other way to effectively produce insights.

Olle started in the mobile phone business 1993 as a production engineer. He has held many roles as a project and quality manager. He was responsible for the Ericsson Mobile development process and for quality at a company level. Olle is currently quality manager in the Quality & Customer Service organization at Sony Mobile Corporation. Olle is responsible for handling feedback from the field.Consumer and market analytics — and supporting social, text, speech, and sentiment analysis techniques — are subject matter for the LT-Accelerate conference, taking place December 4-5, 2014 in Brussels. We’re very happy that we were able to recruit Olle Hagelin from Sony Mobile as a speaker.

Our interview with Olle Hagelin

Q1: The topic of this Q&A is consumer and market insight. What’s your personal background and your current work role, as they relate to these domains?

Olle Hagelin: My responsibility is to look into all customer interactions to determine Sony Mobile’s biggest issues from the customer’s point of view. We handle around 650 million interactions per year.

Q2: What roles do you see for text and social analyses, as part of comprehensive insight analytics, in understanding and aggregating market voices?

Olle Hagelin: I think text and social analyses can replace most of what is done today.

Everyone’s customer will sooner or later express what they want on the Net. And opinions won’t be colored by your questions. You just put your ear to the ground and listen. You probably want to ask questions too but that will be to get details, to fine tune — not to understand the picture, only to understand what particular shade of green the customer is seeing out of 3,500 shades of green.

Q3: Are there particular tools or methods you favor? How do you ensure business-outcome alignment?

Olle Hagelin: You will always prefer the tool you use/can. For our purposes what we get from Confirmit and the tool Genius is perfect. But again it is to find issues, to mine text to find issues and understand sentiment of issues. If you are a marketing person it may be that other tools that are better.

Business-outcome alignment is a big statement and I don’t try to achieve that. If it comes, nice, but my aim is only to understand customer issues and to ensure that they are fixed as soon as possible. And I suppose the in-the-end result is business-outcome alignment?

Olle Hagelin: Yes. Do it. I do. Sorry. Politically correct: Sony Mobile does and has since 2010. All repairs, all contact center interactions, and as much social as possible. As said above, we handle around 650 million interactions per year.

Q5: To what extent does your work involve sentiment and subjective information?

Olle Hagelin: A lot although it could be more. Especially to determine which issues hurt the customer most. Identifying the biggest, most costly issues etc. is easy, but to add on pain-point discovery would be good.

Sentiment/subjective analyses are used frequently to look into specific areas but not as part of the standard daily deliverable. Hopefully everyday will be in place in a year or two.

Olle Hagelin: This can be discussed for days. But in short: Look at what you have and start from that. Build up piece-by-piece. Don’t attempt a big do-it-all system because it will never work and always be outdated. If you know only one part well — say handling either structured data or unstructured data — don’t try yourself to take a big bite of the other part, the part you don’t know well. Instead, buy help and learn slowly.

Sony Mobile works to split the data up into structured and unstructured parts. We work with them separately to identify issues first and then compare. We know structured data well and got very good support and help with the unstructured part. After four years we can do a lot ourselves, but without support from Confirmit with the hard unchewed mass of unstructured data — Confirmit handles text in the language it is written in (no translations) — we wouldn’t be able manage.

The end result is to make it quick and easy to get to the point.

After working with this data many years, we now have a good understanding of what issues that will be seen in all systems and which will not.

Q7: Could you provide an example (or two) that illustrates really well what your organization and clients have been able to accomplish via analytics, that demonstrate strong ROI?

Olle Hagelin: Two cases that we fixed quickly recently –

First is an issue when answering a call. The call always went to speaker mood. We identified the problem and it was fixed by Google within two weeks – it was an issue in Chrome.

Another one was several years ago: A discussion about a small and in-principle invisible crack in the front of a phone stopped sales in Germany. After we issued a statement that the problem is covered by warranty and will be fixed within warranty coverage, sales started again. It turned out almost no one wanted a fix! As I said, you had to look for the crack to see it.

I have many more examples, but I think for daily work, the possibility of quick-checking social to see whether an issue has spread or not has been the most valuable contributor. And that ability keeps head count down.

Q8: You’ll be presenting at LT-Accelerate. What will you be covering?

Olle Hagelin: I’ll show how Sony Mobile uses social and also text mining of CRM data to quickly identify issues, and how we get an understanding of how big they are with complementing structured data.

Added to this, the verbatim from customers can be used as feedback to engineers so they can reproduce issues in order to fix them.

14 November 2014

Social Media Analytics Innovation: Q&A with Shree Dandekar, Dell

It’s common to talk of the “customer journey,” of the path an individual takes from needs awareness, via research and evaluation, to purchase and, in the case of a happy customer, loyalty and a lasting relationship. The customer journey may involve multiple channels and touchpoints.

Social touchpoints are among the most important, at every stage of the customer journey. We explore them in this interview with Shree Dandekar, general manager for social analytics at Dell and a speaker at the LT-Accelerate conference, December 4-5, 2014 in Brussels.

The ability to understand, measure, and shape social influence and advocacy is hugely important. You need software to do the job right, software that automates collection, filtering, and analysis of social and online text in conjunction with network and market analytics. Techniques are rapidly evolving, making social media analytics innovation a topic of great interest for brands and agencies across industry.

The social business challenge and technical responses are central topics at LT-Accelerate. I’m very much looking forward to Shree’s presentation, about tools and techniques for social ROI. If this topic interests you as well, you’ll want to learn more. An interview I recently conducted with Shree is a start, then I hope you’ll join us in Brussels.

Shree has been at Dell for 14 years, in roles covering software design, product development, enterprise marketing and technology strategy. He is responsible for developing and driving the strategy for Dell’s predictive analytics and BI solutions.

Our interview with Shree Dandekar

Q1: The topic of this Q&A is social media analytics. What’s your personal SMA background and your current work role?

Shree Dandekar: I am the GM for our social analytics offering and have been responsible for taking Dell products in this space to market.

Q2: What are key technical and business goals of the analyses you’re involved in?

Shree Dandekar: Given that we are in the business of offering social analytics to our customers, our technical and business goals are tailored around that. Specifically, technical goals are focused on making our social analytics product robust enough to support our customers’ needs. This does include making sure we capture the right sentiment, glean the right insights, and prep the data to ensure both business and social context information can be surfaced in an efficient manner. Our business goals are to make sure our customers can realize their “social nirvana” by identifying themselves on the social analytics journey and making the right investments in moving to the next level.

Q3: And what particular analytics approaches or technologies do you favor, whether for text, network, geospatial, behavioral, or other analyses? [You don't have to cover all these analysis types.]

Shree Dandekar: We use predictive analytics algorithms to derive insights from Social Media data. Dell has invested significant IP in building its text and natural language processing (NLP) capabilities and our social media analytics offerings is directly built on top of that foundation. Dell also recently acquired a leading predictive analytics player: Statistica. Statistica Text Miner is an extension of Statistica Data Miner, ideal for translating unstructured text data into meaningful, valuable clusters of decision-making “gold.” As most users familiar with text mining already know, real-world data comes in a variety of forms, not always organized or easily ready to analyze. Text mining digs for the underlying information not readily apparent in traditional structured data. These data sources can be extremely large as well. Statistica Text Miner is optimized and has recently been further enhanced for working with such data.

Q4: To what extent do you get into sentiment and subjective information?

Shree Dandekar: Dell joined forces with a leading text analytics provider to leverage sentiment and text analytics. Their patented NLP engine uses a mix of rules and dictionaries to break down and analyze customer feedback text, and to score it on an 11-point sentiment scale for added granularity and measurement. The sentiment and text analytics solution enables Dell to make sense of the vast amount of customer feedback data available. In order to make the insights relevant to Dell’s business and understand brand health through the voice of the customer, the social analytics team developed a proprietary metric, the SNA metric. This metric is an indicator of purchase intent, giving Dell a clear view into customer advocacy of the Dell brand. Once the social media data is collected, analyzed, and scored for sentiment, it is then scored against Dell’s SNA scale.

Shree Dandekar: Dell is using this patent-pending software (SNA) and integrating it into all aspects of the business from product development, marketing, Net Promoter Score (NPS) diagnosis, customer support/service, sales, and M&A. Measuring more than 1.5 million conversations annually, the system provides the ability to drill down to very granular parts of the business in real time. It serves as a source of uniform distribution and assimilation of customer feedback for multiple business functions. This enhances Dell’s avowed policy of customer centricity and direct feedback. And, since it updates in real-time, SNA accelerates customer feedback on important topics enabling shorter response cycles without negatively affecting the brand health.

Q6: Could you provide an example (or two) that illustrates really well what you’ve been able to accomplish via SMA, that demonstrate strong ROI?

Shree Dandekar: The Dell social media analytics portfolio includes the patent-pending Social Net Advocacy (SNA) metric. SNA is designed to measure the net advocacy of a brand or topic, calculated from the sentiment and context of social media conversations (see figure). Dell uses SNA internally to help the company deliver an enhanced experience to its customers. SNA is integrated within the Dell Social Media Command Center, which enables the company to monitor and react to online conversations in real time.

Dell measures SNA at the brand level and also extends this measurement to more than 150 topics representing various aspects of the business. Online conversations are analyzed for topics including products, services, marketing, customer support, packaging and even community outreach efforts. Each of these conversations influences brand perception and therefore affects the overall advocacy or health of the brand. SNA enables organizations to understand, quantify and contextualize online feedback, leading to informed business decisions that help improve the overall customer experience. Organizations can integrate customer feedback in near-real time for short response cycles — meaning that an organization can quickly connect with a customer and discuss relevant solutions.

The customer feedback derived from the SNA program is delivered across the entire organization, from departments such as customer care and quality control to marketing and product development. The real time analysis and measuring of social data has allowed Dell to proactively quell any public concerns before they grow into potentially larger issues. Moreover, Dell is able to add context to the sentiment and SNA scores such as understanding whether the customer is a brand advocate or not.

For example, within hours after the launch of a specific Dell product, the social analytics team saw a declining trend in SNA (decreased by more than 50%). When the analyst team looked further into the issue, they found a significant number of social media conversations expressing anger over the pricing for the new product. They turned to Dell’s chief blogger who quickly wrote a post explaining the situation and rectifying the price concerns. Within one day, Dell was able to return to original sentiment levels. Moreover, the general manager didn’t even need to be brought into the issue- employees are empowered to make quick and informed decisions.

Q7: Finally, do you have recommendations to share, regarding choice of data sources, metrics, analytical methods, and visualizations, in order to best align with desired business outcome?

Shree Dandekar: With the explosive growth of social media, customers are increasingly taking their conversations to online platforms such as Twitter, Facebook, community forums, wikis and blogs. Because social media has the power to influence brand reputation, daily engagement with people who are discussing an organization’s brand has become a critical step for understanding the market — and in some cases, converting detractors into brand advocates.

Through social media analytics, organizations can determine who is doing the talking: Are they customers, influencers or others? They can find out when specific events caused positive or negative conversations and also measure general brand sentiment on a daily, weekly and monthly basis. This rich data enables enterprises to obtain real-time customer insights that can help solve complex business challenges.

The development of a social media analytics strategy can be thought of as a journey that begins by listening to online conversations. The next steps are to collect, record and analyze the data, and then monitor trends. Finally, heuristics and business algorithms are applied to the data to derive actionable insights. This journey from an ad hoc approach to a highly optimized solution does not happen overnight but in increments, as an enterprise develops analytics maturity. To achieve this maturity, business leaders need to make the right investments in technology, and then invest in training people and creating a social media analytics culture within the organization.

Through social media analytics, organizations can determine who is doing the talking: Are they customers, influencers or others? They can find out when specific events caused positive or negative conversations and also measure general brand sentiment on a daily, weekly and monthly basis. This rich data enables enterprises to obtain real-time customer insights that can help solve complex business challenges.

The development of a social media analytics strategy can be thought of as a journey that begins by listening to online conversations. The next steps are to collect, record and analyze the data, and then monitor trends. Finally, heuristics and business algorithms are applied to the data to derive actionable insights. This journey from an ad hoc approach to a highly optimized solution does not happen overnight but in increments, as an enterprise develops analytics maturity. To achieve this maturity, business leaders need to make the right investments in technology, and then invest in training people and creating a social media analytics culture within the organization.

Thanks, Shree. Readers, if you’re intrigued by Shree’s take on social media analytics, please check out the LT-Accelerate program and consider joining us in Brussels!

13 November 2014

Consumer & Market Analytics: Q&A with Lauren Azulay, Confirmit

Our thesis: Language technologies — text, speech, and social analytics — natural language processing and semantic analysis — are the key to understanding consumer, market, and public voices. Apply them to extract the full measure of business value from social and online media, customer interactions and other enterprise data, scientific and financial information, and a spectrum of other sources. The insight you’ll gain means competitive edge, whatever your organization’s mission.

Insight, via business (and research and government) application of language technologies, is the central topic for LT-Accelerate, a new conference that takes place December 4-5 in Brussels.

I recently interviewed a number of LT-Accelerate speakers. My questions broadly cover the topics they’ll be addressing in their conference presentations. This article relays my Q&A with speaker Lauren Azulay of Confirmit, a customer, employee, and market insights solution provider.

Our interview with Lauren Azulay

Q1: The topic of this Q&A is consumer and market insight. What’s your personal background and your current work role, as they relate to these domains?

Lauren Azulay: My current role is Senior Product Manager for Text and Social Analytics at Confirmit, where consumer and market insight is at the heart of what we do. Text Analytics gives insight into the voice of the customer and the social analytics provides insight into the voice of the market. Previously, I was the Product Manager of the Channel and Brand insights platform for YouTube multi-channel network, Base79, where we discovered and uncovered meaningful patterns in data for brands and channels wanting to increase their exposure on YouTube. Before that I spent 7 years as Head of Product and then Head of Internationalisation and Billing on a social networking product, where consumer and market insight drove our new product development and launch in new territories.

Q2: What roles do you see for text and social analyses, as part of comprehensive insight analytics, in understanding and aggregating market voices?

Lauren Azulay: The voice of the market includes your competitors, independent analysts and commentators, or just consumers who may or may not be your customers. Understanding the buzz and sentiment around key topics or issues across all these voices can help marketing, sales, services and product functions within a business. Social analysis is also good for early issue detection which can protect you from brand damage, reduce service costs and improve customer satisfaction.

Q3: Are there particular tools or methods you favor? How do you ensure business-outcome alignment?

Lauren Azulay: Extracting social insights requires capturing not just the text but all of the metadata associated with each post or comment, such as the conversation data, author, etc. Because of the large volumes, statistical techniques are better than rules-based approaches to categorization and sentiment analysis, as they can process very large volumes of text much faster.
The right visualisations are important for bringing the data to life, and the ability to correlate the social insights with other business and customer data has the potential to offer significant value.

Lauren Azulay: A centralised customer hub is essential. In the end, the solution will most likely be a hybrid combination of different technologies, as storing and managing social data is a different technical problem to solve than storing transactional data. So it’s important for any hub solution to easily integrate with other customer or data hubs within an organisation, and each one can be targeted at the problem it is best suited to solve. This is the only way businesses will be able to achieve the holy grail of a truly unified customer experience across all channels, including social.

Q5: To what extent does your work involve sentiment and subjective information?

Lauren Azulay: We have many customers that have deployed sentiment analysis for text from both voice of the customer data and social media data. For example, Sony Mobile have been using social media to detect consumer issues as they come up in order to improve their products and services, protect their brand and to avoid costs.

Lauren Azulay: The key to analysis is to understand the data sources and how they all fit together for your business and your market. In order to deal with large volumes of diverse data, such as social data, you need to know your objectives and be very focused on the project goal. This drives the data sources you analyse and the categorisation model you apply. The data structure is also important, as mentioned before, so that you make sure that all relevant metadata is stored along with the text. Categorisation of all texts can be performed very quickly using Boolean search techniques. Knowing the volume of interactions by category gives you the buzz, which can then be tracked over time and can quickly highlight trending topics.

However, performing sentiment analysis on all the data captured is not practical, even with a high-performing statistically-based sentiment algorithm. Statistical sampling, performed correctly, can ensure an accurate sentiment and can be obtained by category, even if there are thousands or millions of text strings within each category. Research we have conducted shows that a collection of a million documents can be analysed in a very short time using a sample of 20,000 documents, with a high degree of accuracy.

In addition, with the right analytical tools and the right visualisations, humans can interpret the results and explore the root causes for specific topics. Human analysis is very important in pulling it all together and drawing the overall conclusions.

Q7: Could you provide an example (or two) that illustrates really well what your organization and clients have been able to accomplish via analytics that demonstrate strong ROI?

Lauren Azulay: Sony Mobile, through their social media ‘listening’ service, uncovered more than 15,000 unique issue reports in a year. This gave them the ability to prioritise the 3 main issue categories and focus on a process of remediation. This has saved the company tens of thousands of dollars and improved customer satisfaction, which has been measured through social media.

Q8: You’ll be presenting a lighting talk at LT-Accelerate. What will you be covering?

Lauren Azulay: Social analytics in action!

Thanks Lauren. Readers, if what you’ve read here sounds interesting, please do visit the LT-Accelerate Web site to learn more about the conference. We’ve designed the conference as a venue for learning, networking, and opportunity, for technologists and business users alike.

12 November 2014

The Analytics of Digital Transformation, per Tata Consultancy Services

Next month’s LT-Accelerate conference will be the third occasion I’ve invited Lipika Dey to speak at a conference I’ve organized. She’s that interesting a speaker. One talk was on Goal-driven Sentiment Analysis, a second on Fusing Sentiment and BI to Obtain Customer/Retail Insight. (You’ll find video of the latter talk embedded at the end of this article.) Next month, at LT-Accelerate in Brussels, she’ll be speaking on a particular topic that’s actually of quite broad concern, E-mail Analytics for Customer Support Centres.

As part of the conference lead-up, I interviewed Lipika regarding consumer and market analytics, and — given her research and consulting background — techniques that best extract practical, usable insights from text and social data. What follows are a brief bio and then the full text of our exchange.

Dr. Lipika Dey is a senior consultant and principal scientist at Tata Consultancy Services (TCS), India with over 20 years of experience in academic and industrial R&D. Her research interests are in content analytics from social media and news, social network analytics, predictive modeling, sentiment analysis and opinion mining, and semantic search of enterprise content. She is keenly interested in developing analytical frameworks for integrated analysis of unstructured and structured data.

Lipika was formerly a faculty member in the Department of Mathematics at the Indian Institute of Technology, Delhi, from 1995 to 2006. She has published in international journals and refereed conference proceedings. Lipika has a Ph.D. in Computer Science and Engineering, M.Tech in Computer Science and Data Processing, and 5 Year Integrated M.Sc in Mathematics from IIT Kharagpur.

Our interview with Lipika Dey –

Q1: The topic of this Q&A is consumer and market insight. What’s your personal background and your current work role, as they relate to these domains?

Lipika Dey: I head the research sub-area of Web Intelligence and Text Mining at Innovation Labs, Delhi of Tata Consultancy Services. Throughout my academic and a research career, I have worked in the areas of data mining, text mining and information retrieval. My current interests are focused towards seamless integration of business intelligence and multi-structured predictive analytics that can reliably and gainfully use information from multitude of sources for business insights and strategic planning.

Q2: What roles do you see for text and social analyses, as part of comprehensive insight analytics, in understanding and aggregating market voices?

Lipika Dey: The role of text in insight analytics can be hardly over-emphasized.

Digital transformation has shifted control of the consumer world to consumers from providers. Consumers — both actual and potential — are demanding, buying, reviewing, criticising, influencing others, and thereby controlling the market. The decreasing cost of smart gadgets is ensuring that all this is not just for the elite and tech-savvy. Ease of communicating in local languages on these gadgets is also a contributing factor to the increased user base and increased content generation.

News channels and other traditional information sources have also adopted social media for information dissemination, thereby paving the way for study of people’s reactions to policies and regulations.

With so much expressed and exchanged all over the world, it is hard to ignore content and interaction data to gather insights.

Q3: Are there particular tools or methods you favor? How do you ensure business-outcome alignment?

Lipika Dey: My personal favourites for text analytics are statistical methods and imprecise reasoning techniques used in conjunction with domain and business ontologies for interpretation and insight generation. Statistical methods are language agnostic and ideal for handling noisy text. Text inherently is not amenable to be used within a crisp reasoning framework. Hence use of imprecise representation and reasoning methodologies based on fuzzy sets or rough sets is ideal for reasoning with text inputs.

The most crucial aspect for text analytics based applications is interpretation of results and insight generation. I strongly believe in interactive analytics platforms that can aid a human analyst comprehend and validate the results. Ability to create and modify business ontology with ease and view the content or results from different perspectives is also crucial for successful adoption of a text analytics based application. Business intelligence is far too entrenched in dashboard-driven analytics at the moment. It is difficult to switch the mind-set of a whole group at once. Thus text analytics at this moment is simply used as a way to structure the content to generate numbers for some pre-defined parameters. A large volume of information which could be potentially used is therefore ignored. One possible way to practically enrich business intelligence with information gathered from text is to choose “analytics as a service” rather than look for a tool.

As a researcher I find this the most exciting phase in the history of text analytics. I see a lot of potential in the yet unused aspects of text for insight generation. We are at the confluence where surface level analytics has seen a fair degree of success. The challenge now is to dive below the surface and understand intentions, attitudes, influences, etc. from stand-alone or communications text. Dealing with ever-evolving language patterns that are also in turn influenced by the underlying gadgets through which content is generated just adds to the complexity.

Lipika Dey: Yes, we see many business organizations actively moving towards unified customer experience. Omni-channel analytics is catching up. But truly speaking I think at this point of time it is an aspirational capabilty. A lot of information is being pushed. Some of it is contextual. But I am not sure whether the industry is still in a position to measure its effectiveness or for that matter use it to its full potential.

It is true that a multitude of sources help in generating a more comprehensive view of a consumer, both as an individual as well as a social being. Interestingly, as data is growing bigger and bigger, technology is enabling organizations to focus on smaller and smaller groups, almost to the point of catering to individuals.

As a researcher I see exciting possibilities to work in new directions. My personal view is that the success of omni-channel analytics will depend on the capability of data scientists to amalgamate domain knowledge and business knowledge with loads and loads of information gathered about socio-cultural, demographic, psychological and behavioural factors of target customers. Traditional mining tools and technologies will play a big role, but I envisage an even greater role for reasoning platforms which will help analysts play around with information in a predictive environment, pick and choose conditional variables, perform what-if analysis, juggle around with possible alternatives and come up with actionable insights. The possibilities are endless.

Q5: To what extent does your work involve sentiment and subjective information?

Lipika Dey: My work is to guide unstructured text analytics research for insight generation. Sentiments are a part of the insights generated.

The focus of our research is to develop methods for analysing different types of text, mostly consumer generated, to not only understand customer delights and pain-points but also to discover the underlying process lacunae and bottlenecks that are responsible for the pain-points. These are crucial insights for an enterprise. Most often the root cause analysis involves overlaying the text analytics results with other types of information available in the form of business rules, enterprise resource directory, information exchange network etc. for generating actionable insights. Finally it also includes strategizing to involve business teams to evaluate insights and convert the insights into business actions with appropriate computation of ROI.

Lipika Dey: Tata Consultancy Services has conducted several surveys across industry over the last two years to understand organizational big data requirements. The findings are published in several reports available online. (See the Tata Consultancy Services Web site, under the Digital Enterprise theme.) One of the key findings from these surveys was that many business leaders saw the impending digital transformation as siloed components affecting only certain parts of the organization. We believe that this is a critical error.

The digital revolution that is responsible for high volumes of diverse data arriving at high velocity does not impact only a few parts of business — it affects almost every aspect. Thus our primary recommendation is to harness a holistic view of the enterprise that encompasses both technology and culture. Our focus is to help organizations achieve total digital transformation through an integrated approach that spans sales, customer service, marketing, and human resources, affecting the entire universe of business operations. The message is this: Business processes need to be rethought. The task at hand is to predict and prioritize the most likely and extreme areas of impact.

Q7: So what are the elements of that rethinking and that prioritization?

Lipika Dey: We urge our clients to consider the four major technology shifters under one umbrella. Big data initiatives should operate in tandem with social-media strategy, mobility plans, and cloud computing initiatives. I’ve talked about big data. The others –

Social media has tremendous potential for changing both business-to-business and business-to-consumer engagement. It is also a powerful way to build “crowdsourcing” solutions among partners in an ecosystem. Moving beyond traditional sales and services, social media also has tremendous role in supply-chain and human resource management.

Mobile apps are here to transform the way business operated for ages. They are also all set to change the way employees use organizational resources. Thus there is a pressure to rethink business rules and processes.

There will also soon be a need for complete infrastructure revision to ward off the strains imposed in meeting data needs. While cloud computing initiatives are on the rise, we still see them signed up by departments rather than enterprises. The fact that cloud offerings are typically paid for by subscription makes them economical when signed up by enterprises.

Having said that we also believe there is no “one size fits all” strategy. Enterprises may need to redesign their workplaces where business will work closely with IT to redesign its products and services, mechanisms for communicating with customers, partners, vendors and employees, business models and business processes.

Q8: Could you say more about data and analytical challenges?

Lipika Dey: The greatest challenges while dealing with unstructured data analytics for an enterprise is to measure accuracy, especially in absence of ground truths and also effectiveness of measures taken. To check effectiveness of actionable insights, one possibility is to use the A/B testing approach. It is a great way to understand the target audience and evaluate different options. We also feel it is always better to start with internal data — something that is assumed to be intuitively understood. If results match known results, well and good — your faith in the chosen methods increase. If they don’t match — explore, validate and then try out other alternatives, if not satisfied.

Q9: Could you provide an example (or two) that illustrates really well what your organization and clients have been able to accomplish via analytics, that demonstrate strong ROI?

Lipika Dey: I will describe two case studies. In the first one, one of our clients wanted to analyze calls received over a particular at their toll-free call-center. These calls were of unusually high duration. The aim was to reduce operational cost for running the call center without compromising on customer satisfaction. The calls were transcribed into text. Analysis of the calls revealed several insights that could be immediately transformed into actionable insights. The different types of analyses carried out and insights revealed were broadly categorized into different buckets as follows:

Content based analysis identified that these calls contained queries pertaining to existing customer accounts, queries about new products or services, status updates about transactions, and eventually requests for documents.
Structural analysis revealed that each call requested multiple services and for different clients, which eventually led to several context switches for search of information, thereby leading to high duration. It also revealed that calls often landed at wrong points and had to be redirected several times before they could be answered.

Based on the above findings, a restructuring of the underlying processes and call-center operations were suggested with an estimated ROI based on projected reduction in number of calls requesting for status updates or documents to be dispatched etc. based on available statistics.

In the second case study, analysis of customer communications for the call-center of an international financial institution, done periodically over an extended period, revealed several interesting insights about how customer satisfaction could be increased from their current levels. The bank wished to obtain aggregated customer sentiments around a fixed set attributes related to their products, staff, operating environment, etc. We provided those, and the analysis also revealed several dissatisfaction root causes that were not captured in the fixed set of parameters. Several of these issues were not even within the bank’s control since those were obtained as external services. We correlated sentiment trends for different attributes with changes in customer satisfaction index to verify correctness of actions taken.

In this case, strict monetary returns were not computed. Unlike in retail, computing ROI for financial organizations require long-term vision, strategizing, investment and monitoring of text analytics activities.

Q10: I’m glad you’ll be speaking at LT-Accelerate. Your talk is titled “E-mail Analytics for Customer Support Centres — Gathering Insights about Support Activities, Bottlenecks and Remedies.” That’s a pretty descriptive title, but is there anything you’d like to add by way of a preview?

Lipika Dey: A support centre is the face of an organization to its customers and emails remain the life-line of support centres for many organizations. Hence organizations spend a lot of money on running these centres efficiently and effectively. But unlike other log-based complaint resolution systems, when all communication within the organization and with the customers occur through emails, analytics becomes difficult. That’s because a lot of relevant information about the type of problems logged, the resolution times, the compliance factors, the resolution process, etc. remains embedded within the messages and that too not in a straight forward way.

In this presentation we shall highlight some of the key analytical features that can generate interesting performance indicators for a support centre. These indicators can in turn be used to measure compliance factors and also characterize group-wise problem resolution process, inherent process complexities and activity patterns leading to bottlenecks — thereby allowing support centers to reorganize their mechanisms. It also supports a predictive model to incorporate early warnings and outage prevention.

Thanks Lipika, for sharing insights in this interview and in advance for your December presentation.