Roughly 20 years ago, IBM faced a major fork in the road from the hardware-centric model that defined the computer industry from the days of Grace Hopper. It embraced a services-heavy model that leveraged IBMâ€™s knowledge of how and where enterprises managed their information in an era when many were about to undergo drastic replatforming in the wake of Y2K.
Today itâ€™s about the replatforming, not of IT infrastructure necessarily, but of the business in the face the need to connect in an increasingly mobile and things connected world. And so IBM is in a reinvention, trying to embrace all things mobile, all things data, and all things connected. A key pillar of this strategy has been IBMâ€™s mounting investment in Watson, where it has aggressively recruited and incubated partners to flesh out a new path of business solutions based on cognitive computing. On the horizon, weâ€™ll be focusing our attention on a new path of insight: exploratory analytics, an area that is enabled by the next generation of business intelligence tools â€“ Watson Analytics among them.
Which brings us to last fallâ€™s announcement that IBM and Twitter would from a strategic partnership to develop real-time business solutions. As IBM has been seeking to reinvent itself, Twitter has been seeking to invent itself as a profitable business that can monetize its data in a manner that maintains trust among its members â€“ yours truly among them. Twitterâ€™s key value proposition is the immediacy if its data. While it may lack the richness and depth of content-heavy social networks like Facebook, it is, in essence, the worldâ€™s heartbeat. A ticker feed that is about, not financial markets, but the world.
When something happens, you might post on Facebook, within minutes or hours, blogs and news feeds may populate headlines. But for real-time immediacy, nothing beats the ease and simplicity of 140 characters. Uniquely, Twitter is sort of a hybrid between consumer-oriented social network like Facebook and a professional one like LinkedIn. There is an immediacy and uniqueness to the data feed that Twitter provides, With its acquisition last year of partner Gnip (which already had commercial relationships with enterprise software providers like SAP), Twitter now had a direct pipeline for mounting the enterprise value chain.
So far, so good, but what has IBM done to build a real business out of all this? A few months in, IBM is on a publicity offensive to show there is real business here. It is part way to a goal of cross-trading
up to a quarter of its 140,000 over 10,000 GBS consultants on Twitter solutions. IBM has already signed a handful of reference customer deals, and is disclosing some of the real-world use cases that are the focus on actuals engagements.
Meanwhile, Twitter has been on a heavily publicized path to monetize the data that it has â€“ which is a unique real-time pulse of whatâ€™s happening in the world. Twitter certainly has had its spate of challenges here. It sits on a data stream that is rich with currency, but lacking the depth that social networks like Facebook offer in abundance. Nonetheless, Twitter is unique in that it provides a ticker feed of whatâ€™s happening in the world. That was what was behind the announcement last fall that Twitter would become a strategic partner with IBM â€“ to help Twitter monetize its data and for IBM to generate unique real-time business solutions.
Roughly six months into the partnership, IBM has taken the offensive to demonstrate that the new partnership is generating real business and tangible use cases. We sat down for some off the record discussions with IBM, Twitter, and several customers and prospects ahead of today’s announcements.
The obvious low-hanging fruit is customer experience. As we wrote this in midflight, before boarding we had a Twitter exchange with United regarding whether weâ€™d be put on another fight if our plane â€“ delayed for a couple hours with software trouble (yesâ€¦ software) â€“ was going to get cancelled (the story had a happy ending). Businesses are already using Twitter â€“ thatâ€™s not the question. Instead, itâ€™s whether there are other analytics-driven use cases â€“ sorta like the type of thing we used to talk about with CEP but are real and not theoretical.
We had some background conversations with IBM last week ahead of todayâ€™s announcements. They told us of some engagements that theyâ€™ve booked during the first few months of the Twitter initiative. Whatâ€™s remarkable is they are very familiar use cases, where Twitter adds another verifying data point.
An obvious case is mobile carriers â€“ this being the beachfront real estate of telco. As mobile embeds itself in our lives, there is more at stake for carriers who ear churn, and even more so, the reputational damage that can come when defecting customers cry out about bad service publicly over social media. Telcos already have real-time data; they have connection data from their operational systems, and because this is mobile, location data as well. Whatâ€™s kind of interesting to us is IBMâ€™s assertion that whatâ€™s less understood is the relationship between Tweets and churn â€“ as we already use Twitter, we thought those truths were self-evident. You have a crappy connection, the mobile carrier has the data on what calls, texts, or web access were dropped, and if the telco already knows its customersâ€™ Twitter handles, it should be as plain as day what the relationship is between tweetâ€™s and potential churn events. IBM’s case here was that integrating Twitter with data that was already available â€“ connectionâ€™s, weather, cell tower traffic, etc., it helped connect the dots. IBM makes the claim that correlating Twitter with weather data alone could improve the accuracy of telco churn models by 5.
Another example drawn from early engagement is employee turnover. Now, unless an employee has gotten to the point where theyâ€™d rather take this job and shove it, youâ€™d think that putting your gripes out over the Twitter feed would be a career-limiting move. But the approach here was more indirect: look at consumer businesses and correlate customer Twitter activity with locations where employee morale is sagging. Or look at the Twitter data to deduce that staff loyalty was flagging.
A more obvious use cases was in the fashion industry. IBM is adapting another technology from its labs â€“ psycholinguistic analysis (a.k.a., what are you really saying?) â€“ to conduct a more nuanced form of sentiment analysis of your tweets. For this engagement, a fashion industry firm employed this analysis to gain more insight on why different products sold or not.
Integrating Twitter is just another piece of the puzzle when trying to decipher signals from the market. Itâ€™s not a case of blazing new trails; indeed, sentiment analysis has become a well-established disciple for consumer marketers. The data from Twitter is crying out to be added to the mix of feeds used for piecing together the big picture. IBMâ€™s alliance with Twitter is notable in that both are putting real skin in the game for productizing the insights that can be gained from Twitter feeds.
Itâ€™s not a criticism to say this, but incorporating Twitter is evolutionary, not revolutionary. Thatâ€™s true for most big data analytics â€“ weâ€™re just expanding and deepening the window to solve very familiar problems. The data is out there â€“ we might as well use it.