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Shine a Light on Your Hidden Data

If you watch the technology around sales and marketing closely, you’ll know that beacon technology is all the rage. Stores can purchase beacon broadcasting equipment, and when shoppers enter their stores with beacon-enabled apps, the apps will respond to the beacon signals – even if not in use. Stores see nirvana in pushing sale offers and the like to customers who are already on the premises. And of course, it is expected that some mainstream apps (Twitter is often cited, though this is unconfirmed) will become beacon-enabled as well.

Beacons represent a concrete manifestation of the larger frenzy surrounding geolocation. Everyone wants to know where consumers are at any given moment, as epitomized by big players such as Foursquare, which has evolved from its gimmicky “check ins” to become more of a location-driven discovery service.

That’s why I was so intrigued by Foursquare’s most recent product announcement called Pinpoint. Shifting its focus from where people are now, Pinpoint is going to mine valuable insights around where people have been and let companies use it for precise ad targeting.

Details about Pinpoint are scarce right now, but Foursquare is smart to start mining its historical data. At the lowest level, it means that Foursquare can help, say, Starbucks target lots of Starbucks customers. Useful, but not too sophisticated. If Pinpoint can roll up businesses by type (such as pet food stores), it starts to get a lot more interesting. But the real home run would be to be able to divine purchase intent. If someone visits three car dealers in a short period of time, you suddenly have an amazingly valuable sales lead. And mining insights like this is now practical with Big Data tools.

But the real insight here is that your history data isn’t just ancient history: it provides the multiple data points you need to find patterns and trends. Knowing that a company replaces its CEO every 18 months or so is a hugely valuable insight that you can identify simply by comparing your current data to your historical data. At a minimum, you’ve got a powerful sales lead for recruiters. But that level of volatility might be a signal of a company with problems, thus creating useful insights in a business or competitive intelligence context. We’ve all heard about the predictive powerful of social media sentiment analysis. You may have equally valuable insights lurking in your own data. All you need to do is shine a light on them.

How Starbucks in Mall of America looks to Foursquare

How Starbucks in Mall of America looks to Foursquare


Sentimental Journey

Sentiment analysis represents a real opportunity for many data publishers to add new, high-value, proprietary and even real-time insight to their data products. But sentiment analysis has inherent strengths and weaknesses you need to appreciate when considering if there is a sentiment analysis opportunity for your data products.
A wonderful example of sentiment analysis at work is represented in a new service called the Twitter Political Index. Working with two respected political polling organizations, Twitter analyzes over 400 million tweets per day, to determine how people are feeling about the two presidential candidates. The key word here is "feeling," because that's what sentiment analysis is all about -- guessing how people feel about a topic. It's not easy, and it is particularly complex for tweets, which are short and often lack context. Moreover, most sentiment analysis tools go well beyond simply binary like/dislike assessments and try to gauge the degree of like or dislike. It's tricky stuff, but the potential applications are numerous and exciting.
This is often the point where many people will start questioning such issues as whether or not Twitter offers a representative sample, and what level of precision and confidence sentiment analysis can offer. These are valid questions, but be careful not to fall into the trap described by research industry veteran Ray Poynter, who notes that all new research methodologies are invariably measured against the standard of perfection. This implies that all current research metholodogies are perfect, which is far from the case. When using tools such as sentiment analysis, you need to consider the application, then pick the methodology, seeking the best fit possible.

That's why data publishers should be thinking about sentiment analysis. You don't need to analyze every tweet; indeed you don't necessarily need Twitter at all. Sentiment analysis can be applied to research reports, blog posts and press releases. And if you can help your customers better understand how the world currently views a company or product, for example, you can deliver a useful new layer of insight that differentiates you from the competition, makes your products more valuable, and can be acquired and implemented fairly quickly and economically.
And particularly with new research methodologies, I think it's useful to remember the saying, "don't let the perfect become the enemy of the good." Building powerful new data analysis tools is a long journey, one that both publisher and customer are taking together.



The Strategic Use of APIs

Looking for change, challenge, growth? Increased innovation across your organization? New content models and revenue? A new audience acquisition strategy? The ability to knock out the competition? Then think about giving third party developers to access to your content and data in a structured and open manner via APIs -- Application Programming Interfaces. APIs represent a way for publishers to develop new sources of revenue by increasing content distribution fueled by technology and bringing outside ideas in.

Consider Twitter and the constellation of products created by third party developers in its orbit. Twitter provides users up-to-the-minute content on a continuous basis and generates ad revenue through sales of promoted tweets. Twitter is a relate-able and familiar model to publishers.

By allowing third party developers access to its content, Twitter invited innovation from the outside in, increasing the use and value of its content and boosting its revenue. Third party development using Twitter’s API makes Twitter even more useful and draws a larger user-base to its content. Twitter’s ongoing evolution holds valuable lessons for those producing and distributing content.

Innovation, Increased Data Use, Expanding Audiences - APIs provide external talent the ability to develop novel useful new pathways to your content which increases data use and revenue and helps companies innovate and evolve past its competition. Providing access to content and data in a structured and open manner for third party development provides the opportunity to design entirely new ways for existing customers as well as new customers to experience content.

Successful publishers understand the importance of aligning content to the capabilities new technologies bring. It’s a tough job since publishing as an industry has traditionally under-valued and under-funded R&D and struggles with accepting external ideas. APIs represent the next step in developing new ways of presenting and pricing content as well as meeting the expectations of an audience which is constantly growing in technological sophistication.

Monetizing APIs, Controlling Access to and Pricing Content -APIs offer endless possibilities to monetize content which are limited only by the imagination of app developers. Technology exists for controlling the access to and securing content as well as the tools necessary for monetizing it.

Old-timey revenue and pricing models publishers are already familiar with: ad-supported, transactional and subscription as well as somewhat newer models like DaaS (Data-as-a-Service) can be implemented in conjunction with systems for tracking and billing for data usage.

APIs and Expectations - Across industries and businesses APIs are redefining how companies develop their products and conduct business and the steadily escalating growth of APIs will influence and shape expectations about how content is accessed, used and priced.

-- Nancy Ciliberti



Twitter and LinkedIn Battle for Eyeballs and Loyalty

On June 29, Twitter and LinkedIn decided to end a partnership that began in 2009. Before the 29th, tweets had the ability to flow seamlessly from Twitter to LinkedIn. That's no longer possible. Twitter has restricted its API to prevent tweets from posting to LinkedIn user profiles. LinkedIn users can still create updates to publish to Twitter - it is a matter of clicking a button and it happens.

More significantly, the separation is a story which illustrates the difference between how collaboration looks on paper and how it plays out in practical terms when collaborating companies mature and change and business models uncomfortably bump up against one another.

If you are a regular reader, you are likely an information provider. As an industry, publishers are familiar with business model conflict and the Twitter and LinkedIn the split is not surprising.

The pairing made sense for convenience reasons: compose once and publish twice. A seamless flow of tweets from Twitter to LinkedIn added aspects of community that LinkedIn, with its origins as a structured database, had lacked from its inception.

As a website for professional networking, LinkedIn succeeds in its ability to connect people. Once connections between people on LinkedIn are made, the ability to share information is limited. LinkedIn Groups have found wildly varying degrees of success. (InfoCommerce LinkedIn Group members: please check out my colleague Megan's question posted earlier this week regarding how helpful you find LinkedIn Groups and weigh in).

But is this loss of seamless "tweet flow" truly a big loss for LinkedIn? Arguably not.

Although the collaboration enabled sharing of information between LinkedIn connections, the pairing was not without its problems. Pacing and content between the sites were a less than ideal match. Overall, LinkedIn is much slower paced than Twitter. The Twitter partnership produced significant amounts of content for LinkedIn. Yet Twitter users who tweet often (say 15 or more times a day) tend to stand out and can crowd or eclipse LinkedIn generated updates displaying on the site. Perhaps this is the reason why hiding tweets on LinkedIn was an option.

Further, tweets aren't entirely consistent with that which should be shared on a professional networking site. Twitter content that doesn't play well to an audience of business connections could carry more significant consequences than just personal embarrassment. And even anodyne tweets, because of their economy of space, still offer ample room for miscommunication.

LinkedIn and similar sites using Twitter's API have created a range of value-added products from Twitter clients to analysis tools. These products have improved Twitter's value and reach. Even though LinkedIn was never really a destination to go to read tweets, LinkedIn and others using Twitter's API may have funneled some traffic away from Twitter which presents a challenge when money is in the mix. Twitter's revenue model relies on ad money (promoted tweets).  

As International Business Times' Valli Meenakshi Ramanathan notes: "Though the end of the partnership was nothing new in the social network landscape as search giant Google moved away from the microblogging sweetheart recently, leaving Twitter to spruce up its search function to stay on track, the changes did call for LinkedIn having to redefine its strategy and operations."

Bottom line, this is a battle of eyeballs, user loyalty and control of content. And while LinkedIn might look like the loser, it probably is time for LinkedIn to put more effort into enhancing its value proposition, rather than papering over the issue with a tidal wave of tweets.

-- Nancy Ciliberti