Facebook connected us in a vast network – this is only a first step. The deep reason for the fascination with social networking can be understood from an example. Shoppers are enmeshed in an ever-changing network of social interactions and preferences; desirable behavior can be made to emerge by perturbing the interactions within the network.
Insight #1: Network embedded data.
Today in Analytics, data are treated as isolated bits of information. In reality, data exist in *embedded* forms in preference and influence networks of the shopper as well as distributed in time and space. There are few if any analytics techniques that explicitly exploit embedding – if new ones do, such techniques will be very powerful.
Insight #2: Closing the Analytics Loop.
Analytics extract meaningful patterns and information from raw data. But what do we do with those insights? The promise of Analytics will be fulfilled only when we close the loop via actions that lead to profitability in the broad sense. Emergent Marketing is an example of closing the loop of “analytics” drawn from retail data and shopper network activity back to the customer generating purchase activity and additional new analytics.
Insight #3: Emergent Marketing.
Emergent Marketing is a low-level multi-node intervention in a human social network that creates the emergence of a ground-swell of a desirable activity (“emergent” activity) without identifiable one-to-one causality. In a “crawl-walk-run” approach to developing full-fledged Emergent Marketing techniques, JIT Branding is a “slow crawl”.
Insight #4: Just-in-time Branding.
JIT Branding using Linear Influence Model (LIM) is a primitive Emergent Marketing tool. LIM models minimally incorporate the dynamics and the ability to perturb the network in a fine-grained manner. The design and number of “contagions” for the emergence of desirable “ground-swells” of activity are also open issues at this time.
· The true value of LIM modeling for JIT Branding is that key shoppers’ influence functions are estimated from historical data. In general, these influence functions are not available and naïve guesses have to be employed (such as exponential decay).
Insights from analytics can be “closed” in multiple ways: via the shopper, via merchandising and via many stages of the supply chain. Our focus is on closing the loop via the shopper; they involve advertisements, discounts or loyalty programs plus new approaches.
1. Proximity Marketing using Passive Organic Search™ - implemented (New).
2. Viral Marketing using Shop Ally™ - ready for implementation (relatively NEW).
3. Emergent Marketing using JIT Branding™ - technology ready (brand NEW).
More details at . . .
o “What does ‘Emergent Properties in Network Dynamics’ have to do with Shopping?” http://pgmadblog.blogspot.com/2012/10/what-does-emergent-properties-in.html
“Social-Mobile-Cloud Framework” http://pgmadblog.blogspot.com/2012/07/so-mo-clo-framework-for-cus-dr.html
o "Business IPTV Service – A New Cloud Business Vertical" http://pgmadblog.blogspot.com/2009/09/advertisers-are-always-looking-for-ways.html
Dr. PG Madhavan was CTO Software Solutions at Symphony Teleca Corp. Previously, he was the CTO & VP Engineering for Solavei LLC and the Associate Vice President--Technical Advisory for Global Logic Inc. PG has 20+ years of software products, platforms and framework experience in leadership roles at major corporations such as Microsoft, Lucent, AT&T and Rockwell and startups, Zaplah Corp (Founder and CEO), Solavei and Aldata. Application areas include retail, mobile, Cloud, eCommerce, banking, enterprise, consumer devices, M2M, digital ad media, medical devices and social networking in both B2B and B2C market segments. He is an innovation leader driving invention disclosures and patents (12 issued US patents) with a Ph.D. in Electrical & Computer Engineering. More about PG at www.linkedin.com/in/pgmad