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Looking back on the Conversational Marketing Summit which concluded Tuesday evening in NYC, I can see a couple of strong trends concerning the technology efforts in this space.
First is the role of social media analytics in convincing brands how to morph their message to fit better with the conversation happening around their brand. One example was MasterCard. Most of us are familiar with their “priceless moments” campaign that actually counter-distinguished their card from your priceless moments: “Last cookie in the cookie jar: priceless — and for the rest, there’s MasterCard.”
But recently, MasterCard used listening platforms to determine that observing priceless moments was not what resonated the most with their customers; rather, it was when their card could be found enabling priceless moments. Analyzing what customers said on Twitter, Facebook, etc., is what let them discover this. They have changed their messaging accordingly, with a positive impact on sales.
Similarly, Nokia, prior to re-entering the US market with their Lumia 900, used NLP to analyze social media and determine what (if any) positive opinions existed toward them among American consumers. They found out that many of us still have a lot of nostalgic fondness for our first phone, which in many cases was a Nokia, and that we remember those older generation Nokia phones as being more sturdy and more reliable than the fancier smartphones we have today. This gave Nokia critical leverage points in constructing a brand message that would have staying power in the US market.
What these two examples have in common is that neither of them would have come about without (1) analytics on Big Data and (2) semantics or NLP being applied to such data. A consciousness is growing among big brands that these technologies are critical today for any serious online marketer.
Another thing that got people’s attention was “growth hacking.” When this came up in John Battelle’s interview of Ron Conway, it spawned a chorus of tweets. In case you’re not familiar with the concept, a growth hacker is a coder who pairs up with a product marketing person (or perhaps hybridizes both skill sets) to analyze user data and then do A/B testing to find ways of accelerating customer acquisition or engagement. The contention at CM Summit was that this is a “must have” role for anyone serious about gaining momentum on the Web.
The counter-point to this was a couple of speakers (I won’t name them) poking fun at the recently-trendy job title in the Valley of “data scientist.” It was asserted that “we don’t really know what a data scientist is,” the implication being that some folks are making it up as they go along. But the data scientist’s role certainly overlaps that of the “growth hacker”, since both spend most of their time analyzing the data. Perhaps a “growth hacker” is just an “applied” data scientist, i.e. a data scientist with a specific focus.
The way this relates to conversational marketing is that (just as in Nokia’s and MasterCard’s case studies) you need to discover what your audience (or your potential audience) really wants to talk about, or learn about, or engage with. A growth hacker brings science to that discovery process.
A final technical theme — really a technical challenge — was Luminate’s seizing upon John Battelle’s neologism of “the imagesphere.” The point to appreciate here is how rapidly our mode of expression on the Web is moving away from text (or at least, away from verbose text) and toward images (witness Instagram, Pinterest). But technologically, we have far fewer ways to analyze and manipulate images, compared to what we can do in the world of text, where we’ve made a much heavier investment in our tools. This means that manipulating the imagesphere is perhaps the next frontier within the geekosphere.
Luminate claims to have a head start in charting that territory, and they made a compelling presentation, with an eighty-three slide deck arguing strongly that the way conversations are happening on the Web is increasingly more visual than textual. Those who neglect to factor this in to their long-range marketing plans, do so at their own peril. But facing the challenge means investing in technologies that can do “image mining” to extract meaning in an automated and scalable way. Such technology is in its infancy.
Ah, so many worlds left to conquer!