The decline of Apps and the rise of Agents and Clewds
Last week, while presenting a live webinar “The Near and Far Future of Recruiting” I had an epiphany. I was talking about the eventual decline (or morphing) of Facebook. The theory is this: Mobile computing power in 10 years will be server-capable. Add in violation of trust and general mistrust of social networks. The result is peer-peer social networking. No Facebook needed. Everything sits on your mobile device. More private, more secure, total user control and no ads. Facebook may lead the way, but it will be hard to do as they would cannibalize their own ad-driven revenue model.
This was last year’s Epiphany.
What led to the new epiphany was my pontificating on CRM systems. This was a recruiter-centric talk about the future of recruiting. Many recruiter CRMs have connections to LinkedIn profiles. Every one of these, that I have seen, has been implemented incorrectly, not due to any fault of the vendors. In an optimal situation, the data inside the Profile should be mashed up with current CRM data. Instead, LinkedIn requires usage of their API which brings back a canned LinkedIn profile. This is what I call “social linkage”.
The optimal situation would be a pair of “social agents”. While a company may have 1000 company prospects in their CRM, they may only contact 50 in a given day. One “social agent” would automatically refresh the entire CRM on a longer cycle such as once per quarter. Another just-in-time social agent would update the CRM just before the outreach process. Why is this important? LinkedIn is not a definitive data-source; nothing is. What happens when you combine Facebook, Google+, Jigsaw (now data.com), Foursquare, twitter and whatever social network Microsoft comes up with? Are you going to clutter your Salesforce or Microsoft Dynamics interface with 6-8 little snippets, much with redundant information? This gets ugly fast. The optimal implementation is to have a social agent retrieve LinkedIn, Data.com, Google+, Facebook, Twitter information. Next, mash, score, apply analytics to present the information in a way that optimally fits your selling model.