text analytics

10月 052012
They say golf is a simple game. The objective is straightforward; get a little white ball into the hole while minimizing the number of shots. Yet, anyone who has ever stepped up to the tee understands that there are many other factors at play. I can’t tell you why, but [...]
9月 212012
Did you know we have a blog dedicated to the voice of professors and students?  Generation SAS is a blog forum, encouraging submissions from all those working in school setting, to share their thoughts and research. Knowing that universities around the world are teaching text analytics using SAS, I’m thrilled [...]
9月 102012
Math and analytics are back “in vogue,” says Kathy Lange, member of the Americas Business Analytics practice at SAS. Since she was little, Kathy has seen the world as one big math problem, and her devotion to mathematics is overwhelmingly clear in this lively interview. Read on below, and be [...]
8月 112012
Dan Zaratsian, senior associate technical consultant in the analytics group, is an expert web-crawler—and wakeboarder—who works one-on-one with customers using math to mine sentiment from textual data. An electrical engineer with a mathematical mind, Dan thinks the text analytics he does is awesome—and so do we. Read on for a [...]
7月 172012
With increasing adoption of text analytics, organizations are expecting more from their analysis. They are demanding answers to questions that basic word clouds and niche sentiment analysis tools cannot answer accurately. Not only are they asking how people feel about their products and services and who’s talking about what, but [...]
7月 102012
Ernest Cu, CEO, Globe Telecom

Ernest Cu, CEO, Globe Telecom

No one questions that getting your messages through to customers is very, very hard.  Some say organisations have lost control altogether and that today's model has customers owning your brand marketing.  A recent article on Globe Telecom highlighted how important analytics is in the search to be heard in a very noisy market. Ernest Cu, CEO of Globe Telecom, and recently announced as CEO of the year by CIO Asia, discusses the six ways to better align yourself with your customers. I thought I would share some insights on how to kick start your customer alignment journey to improve your cut through under this new decision making model.

1. Collect as much of the right data as possible

Through all the noise it's key to identify the relevant data.  Look for information that describes customer behaviour, life stage and environment.  Begin with the obvious like transactional data and customer profile detail from your CRM.  Then look for less core, such as support touch points, web interaction, email and voice.  You will need to be able to look into text based analytics in order to draw insight from some of these sources.  While a little more complex, the combination of structured data with unstructured data provides more powerful insights into the customer buying process.

2. Create a 360 degree customer view that includes data from every relevant source

Most CRM systems hold a very narrow perspective of your customer, traditionally lacking granularity on interactions, service, transactions and networks.  In the 1990s we failed to achieve a single customer view through attempts to store everything into a single CRM application.  With data now strewn across the enterprise in various application silos, attempts have been made to virtually aggregate a single customer view.   Master data management capabilities allow us to see a single view of the customer without needing to physically centralise the data. These capabilities allow us to easily tap into information silos without the mess of data movement, and reduce the fear felt by line of business managers. It is very important in making sure not to annoy your customers - ever had an email or call from a company offering you a product you already have?

3. Build customer intelligence on top of the data warehouse

Leading companies are moving towards analytical data warehouses (ADW). The business intelligence approach to slicing and dicing data is now a commodity, and gaining the competitive edge means using business analytics on your data. The key is to look for relevant data that impacts decision making.  An ADW is an environment where analysts can prepare, model, explore and test different hypotheses against the data.  For example these hypotheses could look to support understanding into a customer’s propensity to buy a product based on age, marital status, existing basket contents, location and event.

4. Automate inbound and outbound communications

All the insight in the world is useless if it’s not used.  The return on investment comes from bringing the insight to the point of decision. To this end look at how you can expose the business rules supporting your insight at the point at where your customer interacts with your company. It could be a pop up on your website or a customised script for the call centre operator.  This type of responsiveness, and your ability to sustainably move faster than your competitors, requires automation with real-time decision management of the end-to-end analytical process from preparation-to-insight-to-action

5. Add scope and analytical sophistication as you go

Business analytics is a journey.  There is incremental value as you progressively add more analytic capability.  Start off looking at trend analysis on which factors most influence what behaviours.   Then move on to looking at different ways to gather more information, like text-based data sources or external sources like social networks.  As you become more comfortable, look at more advanced analytical techniques, for example, using customer relationship/network information to improve your profiling, segmentation and targeting to track customer influence network. As the market environment is always changing the shelf life of a model is limited.  As such you should always look at new models to challenge your existing champion model to ensure you are getting the highest impact.

6. Make analytical insights available at customer touch points across marketing, sales and service

Insight must be shared across the business departments.   The service side and the sales side can learn from each other and it's important that analytics provides a platform to collaborate and share that insight.  As you develop your analytical capabilities ensure you look at an effective communication and visualisation plan that simply identifies how the analytical insight is aligned to the different customer based key performance indicators.

Recent economic events, like the global financial crisis, have left customer confidence at an all-time low, and they are looking for new brands to trust in.  The social internet has moved marketing from a push to a pull model.  Under the new model, leading organisations like Globe Telecom are leveraging analytics to improve their customer alignment.  Competitive advantage will rely on the ability to look for the relationships and patterns in data that impact the new customer decision making process. This is where analytical talent is crucial to sustained success.

tags: analytics, Asia, big data, business analytics, business intelligence, customer experience, customer intelligence, Philippines, Singapore, social media, text analytics, trends, value
6月 282012

The world has reached the point where billions of digital comments are posted daily on public web forums, blogs and social media sites such as Facebook and Twitter. In March 2012, Twitter announced that it had 140 million active users, sending 340 million tweets per day, amounting to an exponential volume of ‘letters’ – 140 characters at a time – sent worldwide.

Despite these millions of words communicated in text conversation daily, it is possible to analyse words and phrases to provide a thorough understanding of the hot topics of discussion, as well as society’s sentiment, from all around the world. These public comments express sentiment about employment, transportation, cost of living, dissatisfaction and anything else that’s on people’s minds. If we collect and analyse all of these public comments with respect to current events (financial crises, natural disasters, sporting events), clear and common sentiments begin to emerge.

There is a need, greater than ever before to understand in near real-time what is happening in the world. Social media chatter shapes actions and sentiments very quickly and too often, by the time evidence is collected of what is happening at the household level, harm, for example, has already been done. This is ironic considering we are swimming in an ocean of real-time information.

A recent project by the United Nations and SAS uncovered that social media conversations reveal much more than the person’s current mood. Increased chatter about postponing vacations, cutting back on groceries and increased use of public transport predicted an unemployment spike in the countries monitored. While these relationships are not surprising in themselves, the analytics quantified the amount of time these conversations usually precede an event. For example, chatter about delaying vacations typically preceded unemployment spikes by five months in the US, thereby providing adequate notice to respond appropriately to upcoming events.

Why is this research so important? Sales and marketing executives, fraud specialists, risk advisors, journalists and advertising agencies could all leverage text analytics to avoid pain or gain competitive advantage by better understanding the consumer voice. An example could be a health insurance company being alarmed by a policy holder telling them, “It’s taken you three days to issue me a new policy quote. I am not happy with your pricing on the policy package, so I will look into other insurers. Goodbye!”

Making sense of social media chatter and using text analysis to meet business goals is a three step process:

1. Listen

The first step in any social media strategy is to listen and understand, ‘How can I be sure what I’m hearing is accurate and relevant to my business?’. Conversations must be categorised to align with business goals to understand overall perceptions and other relevant issues such as faulty products, communication breakdowns and so on.

Technologies such as web crawlers drive this insight from conversations by extracting and mapping key terms on the topics relevant to the business. This is enabled by downloading relevant posts from key sites, overlaying word taxonomies and extracting the terms from textual data.

2. Leverage

Listening informs on what is being said. To more effectively listen, one needs to understand how it is being said. For example, listening might tell a hotel brand that customers are talking about their hotels on a review website. However, to leverage these insights to their full capacity they would need to reveal the finer details such as are customers positive about the check-in experience and negative about room cleanliness?

It is useful to pinpoint the tone of conversations visually – seeing the ebb and flow of sentiment in a chart is no different from tracking the highs and lows of a stock day by day. Among the enabling technologies that drive social media analytics, sentiment analysis discerns tone and provides web-based reporting that can be used across the business.

3. Engage

Finally, to close the loop, one must engage with the customers taking the time to speak about the company in web forums and on social media websites. If the problem pops up in a forum, close the loop within that forum and within the company. Follow up and follow through can be just as important, if not more important in social media as they are anywhere else. Enabling technologies include real-time alerts and workflow that direct emerging issues to the appropriate departments who are most relevantly positioned to respond.

Businesses need to acknowledge that Facebook, Twitter and other such social media are no longer the domain of the young or limited to personal commentary. They have transcended to being the voice of the community and are increasingly influencing public sentiment.


tags: customer intelligence, social media, text analytics