X-Event Marketing

Dr. PG Madhavan developed his expertise in Data Analytics as an EECS Professor, Computational Neuroscience researcher, Bell Labs MTS, Microsoft Architect and startup CEO. Overall, he has extensive experience of 20+ years in leadership roles at major corporations such as Microsoft, Lucent, AT&T and Rockwell and startups, Zaplah Corp (Founder and CEO), Global Logic, Solavei and SymphonyEYC He is continually engaged hands-on in the development of advanced Analytics algorithms and all aspects of innovation (12 issued US patents with deep interest in adaptive systems and social networks). More at www.linkedin.com/in/pgmad

This blog is a coming together of a bunch of my past blogs and my reading of a recent book by John Casti called “X-Events”.

PG Blog: “What does ‘Emergent Properties in Network Dynamics’ have to do with Shopping?”; http://pgmadblog.blogspot.com/2012/10/what-does-emergent-properties-in.html?m=1

PG Blog: “Network Dynamics & Coupling: Shannon’s Reverie Reprised . . . “ http://pgmadblog.blogspot.com/2012/11/network-dynamics-coupling-shannons.html?m=1

Book by John Casti, "X-Events: The Collapse of Everything";

http://www.amazon.com/X-Events-Collapse-Everything-John-Casti/dp/B00BJEP6B4

Reading X-Events book triggered ideas from some of my past academic work (http://www.jininnovation.com/SoF.PDF) combined with my present startup, “Syzen Analytics” (http://www.SyzenAnalytics.com/), leads me to suggest an interesting way to create “pocket” X-events in Retail Commerce via X-Event Marketing or “XM”!

Let us start at the beginning . . .

1.     Shannon understood the importance of rare events in conveying information. He sought a mathematical formulation to capture this important property of our brains and came up with the definition of “entropy”. As I had made clear in the original blog, this is a fictitious reverie I made up to give one the guts to turn other valuable physical/ practical insights into mathematical forms!

2.     Similarly, from my past research in Neuroscience, I had some strong impressions that have refused to go away despite the passage of decades. One is the activity that precedes the most significant X-Event in each of our lives, Death! Unreplicated and unpublished observations in our neurophysiology labs had shown that one of the surprising events that happen before a mouse with brain in-dwelling electrodes dies is the all-out firing of “complex spike cells” (which when the mouse is alive and performing “place cell” tasks are painfully difficult find and record). In other words, the “natural state” of the brain seems to be excitatory and the work of a healthy brain seems to be to assert control and coordination by keeping the *inhibitory tone* high so that all hell does not break loose (by cells firing away with no coordination or control). I speculate a regime-transition of the sort shown below in death: the right-most picture is indicative of brain cells firing away in an uncontrolled fashion (just before a major “X-Event”, death or epileptic seizure) with the left-most indicative of a properly functioning brain (“nicely” coupled). The next story will illuminate the idea of the middle picture where the “system is uncoupled” with the system poised for all its energy to pile into a single “mode” and create a major suppression of inhibition in the brain.

3.     Following “Shannon’s approach”, how do I make these intuitions mathematical? Let us look at the right-most picture. As I outlined in my “Shannon’s Reverie Reprised” blog, everything is firing away and the potential field will show up equally across the scalp, much like a single “wavefront” that reaches all the regions of the scalp simultaneously. Imagine you are at a beach looking out to the sea and gentle waves are rolling in – let us say in parallel to the beach. If you are standing knee-deep in the water and look in a direction parallel to the beachfront (i.e., up or down the beach), the spatial frequency in your “look direction” (or the frequency of “corrugation”) is 0 cycles/meter! Much like the ocean waves, the single wavefront of activity is nearly “constant” across the distributed neocortex and its relevant strength can be thought of as the power at zero frequency. I happen to know that power at zero frequency is called “Scale of Fluctuation” or “θ” in random field theory. From the previous work of Eric Vanmarcke (“Random Fields”, 1983, with an updated edition in 2010), θ is defined below. I refer you to my blog and academic paper mentioned at the outset for the gory details!

 

Is Theta as powerful and useful as Shannon’s entropy . . . time will tell. As you see in my academic publication, Theta does have some intriguing properties in the case of 2^nd order linear time-invariant systems which may indeed prove useful in the future! Adding results from my simulation and data analysis of machine tool chatter, my summary observation is that:

Large value of Theta can be good or bad. It indicates “Coupling”: *good* coupling as a nicely functioning brain (or lathe) or a *bad* coupling as in death (or chatter)! The key here is that low value of theta can be a “predictor” of impending X-Event!

As you can imagine, if our speculation above holds up, we may be able to predict X-Events (or Taleb’s “black swans”) such as 2008 Great Recession or Tohoku earthquake and eventual tsunami. In the case of Tohoku, a few extra hours of warning may have helped Fukushima engineers reach a consensus and move the power generators to a higher location (thus avoiding the nuclear disaster).

XM:

In this blog on X-Event Marketing (“XM”), my purpose is different. I want to *create* desirable, “pocket” X-Events to beneficial ends!

One of the preconditions of “interesting” dynamics in systems is sufficient “complexity”. This can be taken to mean multiple actors with high degrees-of-freedom, dense interconnections with linear and non-linear coupling and in general, hard to analyze! One such natural system is the “shopper network”.

A modern-day shopper is enmeshed in an ever-varying network of preferences and influences (from friends and family) and embedded in a network distributed in time and space – just the type of complex systems where “interesting” dynamics can arise. We want to create desirable “pocket” X-Events in the shopper network with the purpose of increasing business value for the shopper or the seller or both.

This is the so-called “Purchase Funnel” that an individual shopper goes through when executing a product transaction. The marketing, merchandizing and offer activities are devised to impact the shopper at predetermined points of importance in the shopper’s decision making process. For example, a well-conceived offer delivered via shopper’s mobile phone at the point of “Desire” may precipitate a Purchase “Action” – a win for the seller (in this case both the retailer and the manufacturer of that product; for example, Walmart and Procter & Gamble, respectively).

This was one shopper. Now, you have to consider the shopper being embedded in a network. At this point, it may be useful for you to review my earlier blog, “Social Network Theory” http://pgmadblog.blogspot.com/2013/08/social-network-theory.html, where Barabasi’s work in Network Graphs and the following terms are discussed:

1. Clusters (of friends who “hang out”).

2. Weak links (to your long-forgotten high school classmates).

3. Hubs (politicians and others with massive number of contacts).

Clusters and hubs are some examples of the influence on an individual shopper. There are many such shoppers, all interconnected, when studying the dynamics with a view to creating a “pocket” X-Event. Even this does not complete the complexity picture – the shoppers are distributed in space (you are in NYC and your buddy from Seattle tweets about a cool product that he bought) and time (good and bad product reviews reaches you at various times and not in a synchronized fashion).

That the Shopper Network is sufficiently complex to engender “interesting” dynamics must be beyond a doubt by now! The picture above may be a reasonable representation of the Shopper Network (WITHOUT the time element captured – we will need a video showing the undulations in the ”heat map”). The “heat” shown as ‘yellow’ spots indicate the total dollars spent each day on a particular product on a particular day, aggregated to a county for all of USA. Clearly, this will vary from day-to-day.

How do we capture the dynamics of the Shopper Network picture shown above in a tractable manner so that we can understand the dynamics and then track the effect of any manipulations we do in the network so that we can close the loop to adaptively adjust our manipulations to achieve a desired effect?

This is the essential question of XM. Of course, there are other important questions such as the type and timing of manipulations (Offers to selected customers? A country-wide branding effort? Is it a new product or an existing one? And so on). As a systems engineer, I will find a partner who is more qualified than I am in answering the marketing questions; I will focus on Systems Analytics tools to create a quantitative execution infrastructure to deploy marketeers’ ingenuity!

Engineers love scalars! I suspect it is because having a single control variable is easier to manage than many. Probability theory is replete with reduction to scalars – I am talking about mean, variance, correlation coefficient, Entropy, Theta, . . . The full underlying information is available in the joint probability density functions but they are a bear to handle! So, we reduce it to a scalar – clearly, in the process, we have thrown away MUCH information but what is left is what we plan to use; so our justification for selecting and using a scalar for a particular engineering task is very *operational*.

We will use the scalar, Theta, to answer Shopper Network analysis and control question I asked a few paragraphs earlier.

Scenario: New Product Introduction

Procter & Gamble is going to introduce a new detergent and wants to create a ground-swell of purchase interest (our “pocket” X-Event).

·         P&G introduces the product quietly and collects the heat-map data from T-log data of their partner Retailers.

·         Sysan (our startup) processes the heat-map data: total dollar spent each day on the new detergent, aggregated to a county for all of USA.

·         Sysan calculates Theta (a single number) as the baseline measure.

·         P&G Marketing has developed 2 major tools for the launch of the new detergent: (1) nation-wide TV ad campaign and (2) a 10% cash discount for the first 1 million customers in US.

·         P&G runs a 1-week TV ad campaign and while collecting T-log data.

·         Sysan monitors Theta for the week.

·         P&G suspends the TV ad campaign and starts the cash discount offer.

·         Sysan monitors Theta and finds that Theta is entering a “Decoupled” regime with low values of Theta.

·         Sysan advises P&G to resume the TV ad campaign in an effort to trigger “bad” coupling among various counties of US.

·        This pushes Theta into a high-value, the coupling between counties get tight and a “pocket” X-Event occurs where there is a ground-swell of purchases of the new detergent country-wide!

NOTE that in this fictitious example, P&G would have pretty much done the various marketing campaigns that I speculate here on their own. The KEY point is that P&G does NOT have a scalar (or any) measure to get immediate feedback regarding their efforts, combined for the whole country. The ability to quantify “closed-loop” intervention is the value of our humble scalar, Theta, in X-Event Marketing!

Clearly, what manipulations are best applied to the network and when is an open question. Utilizing Theta, Sysan is developing methods using “massive simulation methods” (for background, please see: John Casti, “Would-Be Worlds: How Simulation is Changing the Frontiers of Science”) that can predict the effect of various interactions at various instances of time and points in space, thus providing guidance for the most effective use of vast sums of money required for ad campaigns and discount offers.

Stay tuned for our initial results . . .

PG

Emergent Marketing: A New Force in Social Commerce

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) and Solavei. Application areas include mobile, Cloud, eCommerce, banking, retail, 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


“Emergent Marketing” is a fundamentally new approach different from the traditional “resultant” marketing that is based on cause-and-effect at the individual level. In networks, one of the most studied phenomena is that of “infection” spread when a contagion is dropped into a network of humans; the result can be traced back to its cause – this is the hallmark of “resultant” marketing. Contra-distinct from this approach, 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.

Let us start at the beginning. From high school Physics, we know that atoms come together to form molecules and they have palpable physical properties; for example, H2O in the form of water can flow as a liquid and you can get wet. When a state transition occurs to water (such as due to cooling), ice emerges whose “solid” property was unsuspected when H2O was a liquid – this is called an “emergent property”. Solid-state Physics has countless examples of such phenomena and one of the necessary conditions for it is densely connected simple entities. The brain is an example of such a densely interconnected neuronal network; such networks exhibit variability over time. Variability over time or dynamics is often an essential property of such networks, exemplified by the recent discovery (popularized in the press during September 2012) of the role played by “junk” DNA in the expression of “coding” DNA responsible for various diseases. There is a more informative discussion of these basic concepts in my recent blogs, “What does ‘Emergent Properties in Network Dynamics’ have to do with Shopping?”, http://pgmadblog.blogspot.com/2012/10/what-does-emergent-properties-in.html and “So-Mo-Clo Framework for CUS”, http://pgmadblog.blogspot.com/2012/07/so-mo-clo-framework-for-cus-dr.html

The last paragraph introduces three concepts: networks, dynamics and emergence. In my previous Emergent Properties blog, I noted that “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 the shopper 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.”  For the first time, the concept of “Emergent Marketing” was introduced in this context.

Emergent Marketing:

As mentioned in the first paragraph, Emergent Marketing arises when one goes beyond one-to-one “resultant” marketing designed to provoke a certain purchase activity in a shopper.

A simple model to understand marketing approaches is the Purchase Funnel. The right hand side of the funnel in the figure shows the various intentions of the shopper as she traverses through the funnel. Marketing techniques aspire to move her from “Awareness” phase as quickly as possible to the “Action” or purchase phase. The left hand side shows the coarse buckets of marketing activities traditionally undertaken to achieve this funnel transition – Branding and Direct Response. By the Direct Response methods, the marketer is trying to induce a specific one-to-one response (as indicated by the word, “Direct”) that will culminate in a purchase. On the other hand, “Branding” is more nebulous; branding can take years to take hold and start to bias your purchase decisions.

When you think some more about “Branding”, it appears to have many of the features of “Emergent Marketing” that I had mentioned in the first paragraph! Branding is a multi-node intervention that creates a ground-swell of interest in a product or service but with a long delay.

In this blog, I want to focus on the Interest-Desire-Action phase of the Purchase Funnel. Ads in this phase have the desirable properties of immediacy and measureable efficacy; both are very important to the Brands that pay for advertisements. Especially, the ability to correlate ‘ad spend’ to point-of-sale receipts is a super-important metric; Brands need it (“Half of the ad expenditure produces results; the trouble is no one knows which half!”). Even during one of the worst down years of the Great Recession, worldwide advertising spending was huge (estimated at approximately $654 Billion, $54 Billion of which was for online ads); it will be good to know which of the Billions worked!

Among the many Emergent Marketing techniques possible, we want to start by building on what we already know about branding ads and create what I term “Just-in-time Branding” (JIT Branding). JIT Branding will exist in the Direct Response phase of advertising and will have immediacy and measurable efficacy.

Just-in-time Branding:

Retailer’s interest is to precipitate a specific buying behavior within a specific customer. But we do not approach it on an individual basis because we know better now - we have to consider him as a node in a network that includes his social network which creates latent influences and also his “Shopper Attribute Map” made up of his likes and dislikes.

Even though infection spread is not a good analog for Emergent Marketing, let us try to utilize it since much of the earlier work in networks has been the modeling of the spread of infections in a population. Each node has a different “influence function” – a node “infects” a varying number of other nodes (out of total of ‘N’ Nodes). If there are ‘K’ contagions, the Linear Influence (or Infection) Model (LIM) is -

v = Mi;   where M - Influence Matrix (KxN);  v – Volume of Infection (Kx1);  i – Node Infection (Nx1)

Matrix Algebra has so many powerful tools, fast algorithms and practical insights that a lot of useful information can be extracted by the expert from this simple equation. In general, singular values and vectors (and the related eigenvalues and vectors) of matrix, M, can tell you which nodes are the major “influencers” or hubs, how big their influence is and even what forms the bases of their influence in some cases! You can start populating the Linear Influence Model (LIM) with historic data collected from your real network and apply the results and insights to your business to improve or control the viral spread of your marketing message (or “contagion”).

JIT Branding using LIM is still a primitive model of true Emergent Marketing. 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 emergence of desirable “ground-swells” of activity are also open issues at this time.

What is next in Emergent Marketing?

In a “crawl-walk-run” approach to developing full-fledged Emergent Marketing techniques, JIT Branding is a “slow crawl” at best. Unlike traditional branding, our main insight is that we have to explicitly utilize the facts that (1) shoppers are embedded in social and preference networks, (2) these networks are dynamical and (3) control knobs and levers at a fine-grain node and link level have to be perturbed to engender desirable emergent activities.

One promising avenue is likely to be synchronization in dynamical networks; it is a powerful method to generate global and local “ground-swells” (sometimes with undesirable outcomes such as epileptic seizures in the brain or outages in the Internet!). More advanced Emergent Marketing techniques will exploit this avenue and other perturbation methods in dynamical social networks to create desirable short-term product affinity for a selected cohort of shoppers.

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 additional new analytics. We may have tapped a rich vein in analytics-driven retailing here; whether it will taper off or hit the mother lode remains to be seen. 

Google Neuroscience

Following up on my last blog on “Google Googly” (http://pgmadblog.blogspot.com/2009_11_01_archive.html), I take a deeper look at why the Google business model works and what other businesses can learn from it.

Google is into everything these days but Search and AdWords are still their “bread and butter” business. Search was their initial focus and it took a while for them to figure out how to monetize it via AdWords. As I mentioned in the last blog, their core DNA is this: Wherever the user is searching “content”, Google wants to provide the most popular results while proffering some paid-for ads.

Why has this model been so very successful? It has to do with neuroscience – how our brains work!

Once we have made a purchase decision and acted on it (made the purchase), it is pretty obvious that we will have little interest in additional ads. The “Purchase Funnel” on the left shows the various relationships. “Branding” has an important place in advertising but it is a long drawn-out awareness creation process. Plus there are no easy metrics such as CPM (one of the many so-called “click metrics”) to connect the ad to influencing the eventual consumer decision and action. Google happened to have landed, by design or luck or both, in the perfect spot in the Purchase Funnel!

When we “Search”, we are in the information collection phase (by definition). We are willing to explore and click through various informational pieces on the screen such as text ads – if they seem relevant and related to the information we are after, the more likely that we will click through. But after such explorations, consumers want to move on to the Decision and Action phases if their search was driven by a purchase need. Once we have finished the Action, we have no real patience for more ad watching – we have made our purchase and we want to enjoy it; this isn’t the time for more awareness-generating branding-type ads either. We are in a satiated state after a purchase action and our brains are not looking for more information to process.

In summary, in the information acquisition phase, we see significant consumer “traffic” and the click through of many links in an effort to gather the best information. This is Google’s realm – they provide very good search results and provide many good opportunities to click through, thus generating massive revenue via AdWords business model. But once I click through to a final destination such as a Wall Street Journal article by Mossberg, I am keen to read it rather than pay attention to any ads; I am in the satiated state – I do not care about banner ads that create awareness about random products, let alone make the effort to click through other ad links. Ads in the IDD (Interest, Desire, Decision) stages are most directly useful to the consumer and most monetizable via measureable “click” metrics.

This explains to me why Google business model works so well for them and why content business cannot monetize increased traffic (provided by Google) to their “terminal” sites. It is neuroscience!

Are there other cases where Ads in IDD (AIDD) will work? Look for opportunities when consumers are “searching” in a purchase context; at that time, provide them with “useful” ads and generate metrics to monetize.

I will reveal an AIDD business that parallels Google in the mobile world in my next blog. In the mean time, think of other cases where AIDD will work. Let us hear about it . . . after you have patented it. J

PG