I’ve been using several geo-based Apps on an iPhone 4S and trying to think through how they could be made more useful in traditional sales and marketing areas.  I manage a team that sells high end industrial machinery to traditional businesses that are not heavy users of Twitter, FB, G+ or other conventional social media applications.  Personally, I use Google Latitude to share location with family and serve as memory-recall to help remember where I was.  4Square is useful for seeing if friends or contacts are nearby and getting recommendations when traveling; it suffers from a narrower contact list than would be desired in a commercial setting.

Apps I’ve I’ve looked at:

Google Latitude and Foursquare have value; Sonar has a *lot* of potential.

1. Google Latitude (“GL” or Latitude): This is Google’s location based product.  It tracks where you are at all times and allows some version of that to be shared (either to the public, G+ users or select friends).  I use it to share my location at all times with my close family.  Sharing is set up to be 100% reciprocal, however, it can be modified.
2. Four Square (“4s”): This is an independent software vendor that provides a downloadable app for the iPhone that allows easy sharing via Facebook or Twitter.  4Square allows you to share one-time usage and location information whenever you check-in (Latitude allows the same check-in capability).  You can friend users on 4Square and sharing here is also reciprocal.
3. Facebook.  Facebook has a geographic sharing capability as part of its posts that lets you identify near by friends as well as your current location.  Facebook has the benefit (for me) of being where I have most of my contacts (other than LinkedIn).
4. Sonar.  Sonar is an app that follows your Twitter followers, LinkedIn contacts and Facebook friends.  It will tell you who is nearby when you are in a location, as well as second-degree contacts; friends-of-friends.  It is a newer app, but has some real potential.
5. Garmin.  This is the iOS app version of a traditional Garmin GPS unit.  If you’ve used one of their products, then you would instinctively know how to use this unit.  It is a good product and has the software of a high-end unit.  This allows tracking of past locations and does a good job of pulling in your iPhone contacts.
6. Others (Find-my-friends) and Google Maps.  Find-my-Friends is Apple’s iDevice-iDevice location sharing app.  Nothing fancy, and it’s universe of potential users is the smallest, making it not terribly useful.  Google Maps is the built-in mapping device installed on the iDevices; it serves as the base material for all Google mapping apps.  It has no social component.

Most common and useful features:

• Location awareness and frequency
  

  	Persistence	Single Instance
  Location Tracking	Latitude has this as does the Garmin for iPhone app.  This allows you to see where you were, when driving, etc. in the past.	This is accessed by the use of a ‘Check-in’ and is possible on both 4Square and Latitude, although 4s’s location library (below) is much larger).
  Location Sharing	Latitude allows you to share with others where it thinks you are (or where you are telling it you are) at all times.  Apple’s FMF also allows this.	Only check-ins are allowed to be shared.  Others can see when you most recently completed this activity.

• Contact list: Latitude does a good job of encouraging you to share with others and showing other Latitude users; it is easy to see how this will fit into G+’s long term ambitions and I expect it will be promoted more over the coming years.  4Square can be given access to Facebook, which is useful, as well as other traditional sources of contact library.  Here, FB actually has a real advantage.  My list of friends on FB is the largest, however they are much less frequent users of geographic services.
  

  Geography on Facebook: Lots of Contacts, Few Check-ins

• Monetization.  I’ve never received any kind of compensation for using Google Latitude.  I know they promote coupons, etc., but it has never happened.  I estimate that in a few months of use, I’ve received about ~$60 in value from discounts (237 check-ins, so $0.25 approximate value), free goods and Amex points from using 4Square.  No question which has been more valuable here.
• 

  Google Latitude and Foursquare had similar commercial location libraries; Foursquare's recommendations were more valuable.

  Location library – count and types:  Latitude only allows you to check in to ‘Google Approved Locations.’  If you’ve used Google Maps any or done similar advertising, you can own a location and promote it via that system.  Google starts with this as its framework for Location curation.  4Square and FB, on the other hand, allow locations to be created by users.  On 4Square it is common to see historical sites, residences, etc.  Google starts with an orientation of selling to a business, the others start with what would be interesting to an individual (business owner or not).

• Location library – information and detail.  A Latitude location is more likely to have a rating (Google Maps’ 5-star system), address and contact information, however most commercial entities for 4Square also have all of this detail.  This is a generalization, but I tend to find the 4Square commentary more valuable, as it is much more probable that  the people have visited the actual location.  Many of the Latitude rec’s come from Google Maps data.

Example of Google Latitude's Dashboard (Like everything from Google, it is in Beta).

• Dashboard.  Google’s Dashboard is good for a professional user who wants to track their location history (this fits me very well).  4Square is more focused on showing what badges you have unlocked and what your fictitious score is.
• API / Widgets:  I’m not a developer and can’t give a refined argument one way or the other here.
• 

  Foursquare promotes usage through badges, points, etc.

  Game-i-fication.  4Square gives you points for doing all kinds of things; they attempt to encourage use of their method and benchmark you against your friend-list.  Google has tried to do similar with its rating of how well you know a place.  Check in a few times and move from visitor to regular.  Continue to check and become a guru of a location (no single mayors like 4Square).

• Creepiness.  4Square has worked to make itself socially acceptable, although several of my friends in the SE US still tell me that it should not be used as it will allow people to know I am out and break into my home.  Fair enough.  If you show anyone Google Latitude they will immediately remark, “That is creepy.”  There is no way around it.  I like that my family can immediately know where I am and vice-versa; that does come with some creepiness penalty.  (I would be interested to know how many people actually use the Latitude badges to put that out in the public domain on a website – my bet is not many.)
• Use methods.  Latitude is good as a form of memory augmentation for those who travel a lot or for those who value precision in remembering where they were.  4Square is good for staying in touch with friends and it has a useful social recommendation engine which has proved valuable.

Just now moving everything over from an ancient iWeb set-up to WordPress.  No quick way to migrate; I expect all older links will be broken.

As part of moving the blog from iWeb to WordPress, I’ve moved over the most popular posts.  This article deserved some additions.  The original .pdf of the article is here.

What is a JA-Market?

JA is an acronym for “Just Another.”  It is based on scenarios where; (i) a layperson is over-whelmed by the options, (ii) a regular practitioner observes that there are a finite set of options, many of which are difficult to tell apart, and finally (iii) that a seasoned practitioner can quickly show that the unique opportunities are highly valuable.

Restaurants and food are everyday examples of JA-problems.  A neophyte walking the streets of Manhattan sees many options.  However, it takes an expert who focuses on the issue to step back and see that there are a finite number of options, and that few of them are truly unique.  If you find yourself in a JA-market, the most important objective is to become an expert as quickly as possible.  Below we discuss methods for accelerating the development of that expertise.

A Case of the JA-‘s (pronounced “jah”)

It all started with JAMMBOG.  At Parish Capital’s 2005 annual meeting we used the term “Just Another Mid-Market BuyOut Group” to describe a phenomena that we observed in private equity.  Most buyout groups look similar.  Mid-market buyout groups (those that buy companies with $50 mm – $250 MM in revenue) looked even more similar.  I don’t think we coined JAMMBOG, but the term struck a chord.  For those in asset management who were constantly scouting for new investment opportunities, JAMMBOG was a real problem – how do you tell these groups apart?

“Just Another” served as a pejorative preposition.  Over four years we evaluated some 2,000 private equity opportunities – about 30% of which could have been labeled JAMMBOG.  Meeting after meeting, call after call, these groups would sit across and try in vain to differentiate themselves from their peers.  Differentiation was a tough assignment.

JAJAA (jah-jah) stands for Just Another “Just Another” Acronym.   “JA-“ exists beyond the walls of the private equity industry.  Since joining Elmarco to run US Sales and Marketing, I’ve been dealing with a pair of triplets; JANTI, JANFI and JANMI.

JANTI (jan-tee) is short for Just Another NanoTech Idea; JANFI (jan-fee) for Just Another NanoFiber Idea and JANMI (jan-mee) for Just Another NanoMaterial Idea.  There should be one for Cleantech – suggestions are welcome.  As a nanofiber equipment supplier, we see a great many ideas wrapped in nano.  Like their distant brethren in private equity they are often difficult to differentiate.  Where the private equity groups were all, “value-added, operationally-focused, bottom line driven and buying proprietary deal flow,” the JANTI triplets have, “easily scalable technology, high surface area, novel properties at market parity pricing.”

Over breakfast this morning with a local VC and fellow entrepreneur, we discussed that the RTP area here in NC was going through another JARAG phase.  Just Another Regional Angel Group.  Several are being formed, as there were in the late 1990’s and the early 2000’s.  They are everywhere, difficult to tell apart and it is uncertain how long the will persist.

The same problem existed on our Committee on Forecasting Future Disruptive Technologies.  There are many, many forecasts of “technologies-to-watch” or “big-trends-for-2050”.  How does one sort through the JAFFO (Just Another Future FOrecast)?

The JA-Problem and Solutions

Is there a JAJAP?  A Just Another Just Another Problem?  A JA-problem?

The JAJAP only exists in the eyes of those who are evaluating a market.  Those who are promoting JANTI, JANMI, JANFI or JAMMBOG concepts may not have a problem – they may even benefit from being part of the pack.  In economics we see the ‘theory of minimum differentiation’ also known as Hotelling’s law, which explains why we always see McDonald’s and Burger King across the street from each other.  If you’re in the pack, and the pack is a good place to be, then the rational player would avoid differentiation at all costs.  Any player who emerged would be chased – like the leader of the Tour de France emerging from the peloton.

The JAJAP requires inspection to occur, along with a sufficient number of participants, and a lack of clear differentiation in the participants.  Further, if the JA-players are following Hotelling’s law, then they are actively attempting to render any differentiation.

What should the evaluator do?  What is best for the tech scout?  Or the agent looking to allocate money?

The first goal is to list the facets of the objects that are of interest.  By laying out the components of the object, we begin a process of understanding what the evaluator is looking for.  Second, look to quantify the facets that are most important.  In private equity – returns were an easy area to focus on.  For the JANTI triplets, cost of the end material is usually a good proxy for market suitability and technology maturity.

Finally, in JA-problems, instead of looking for ‘winners’ it is often good protocol to quickly bin out the losers; in the same way a manufacturing operation puts defective parts in the ‘bad’ bin.  When mediocrity is the norm, make a mediocre pile.  The excellent options will stand out.

If you are a participant in a JA-market, the goal if you are mediocre is to ensure that the pack persists.  If someone emerges – imitate them.  If those making decisions about who to work with are confused – make sure the confusion persists.  Fight industry standards.  Hide behind secrecy.   If you are a JA-participant with a solid product and leadership potential, the goal becomes breaking away from the pack.

Summary

For a JA-choice to exist, we would need:

1. At least N options, such that evaluation of N requires skill and time.
2. No master list of the options.
3. Incoherent or incomparable data on the options available.
4. Once a ranking can be assembled, it could be expected that the key variable or facet would show kurtosis; potentially a Cauchy distribution or power-law.

The original .pdf of this document is here.

Introduction

While there are a number of books, articles and case studies that dwell on the investment criteria and portfolio companies of private equity funds, few deal with the challenges of running a firm.  For the purposes of this writing, private equity (“PE”) takes on the classical definition of any non-public equity investment; this includes venture capital, buyout, as well as other illiquid alternative investments.  In this document we outline the primary operating functions of a private equity partnership and use that as a template to map operational improvements.  Our focus in this document is on operational activities, and while there are clear implications for investment strategy, we take that as a given in our examples and leave it for future discussion.

Objectives of Private Equity Operations

In the public markets we encounter a wide variety of rules and regulations that define the acceptable set of behaviors for an investor.  It is through following these rules and regulations that a firm is able to become publicly traded; in exchange for conforming to these rules operating firms are able to access broad capital markets with minimal transaction costs.  Private equity markets are the opposite.   While many laws apply, there is much more flexibility in their application and interpretation.  The goal for a private equity firm is to maximize the impact of this flexibility to create investment advantages for their investors.  The public equity investment is constrained by the legal environment which enables its existence; absent these constraints the private equity investor has a much greater degree of flexibility and a much larger ability to differentiate its investment operations from its public equity cohorts.

Directionality – Upstream / Downstream

Private equity firms serve as market makers between their investors (often referred to as limited partners or “LPs”) and operating companies looking for investors.  If we evaluate the activities based on the flow of funds, the LPs are “upstream” of the PE organization, and the operating firms are downstream.   The activities of the PE firm when pursuing LP investors mirror those of the operating firm when pursuing the PE firm; in this observation we note symmetry in the performance of activities.  For any activities performed by a PE firm, we can assume that they are performed both in pursuit of investments and in pursuit of investors.

Investment Process Activities

We outline five phases of the investment process;

1.       Sourcing. Sourcing is the act of identifying and pursuing investment opportunities.  The actual investments, and resulting portfolio, emerge as a subset of the prospects evaluated during deal sourcing.

2.       Diligence. Diligence is the act of evaluating an investment opportunity.  Only well qualified deals will pass through diligence and into deal structuring.

3.       Deal structuring. Deal structuring is the combined act of negotiating, creating legal documentation and closing of a transaction.  While deal structures have numerous conventions; the fact that private equity transactions are in fact private, negotiated deals provides substantial variation in the way they are closed.  Differences in legal conventions between agents, lawyers and accountants create great influence in deal execution as well.

4.       Operations. During the Operations phase the investment is managed following the closing of the transaction.  The methods of exerting influence are identified during the deal structuring phase, and may include fees, board positions, preferred returns, pre-identified rules, advisory boards, etc.  The operations phase can be the longest and most involved phase of the investment process, and this ability for differentiation in activity is definitionally absent in public equity investments.   Of the five investment phases outlined here, operations is the most interesting for future decomposition and analysis.

5.       Exit. Exit is the act of selling the investment and bringing the relationship to a close.  In the public markets it is a rare case where an investor can create an advantage in the way they exit a holding; in the private markets the timing, method, structure and even the purchasing party are all variables which impact the value of the investors’ returns.

Operational Template

When we combine the directionality of investment activities (upstream and downstream) with the five investment process activities, we find a 2 x 5 grid which allows us to analyze the discrete functional areas of a PE firm.  By laying out the capabilities, we can (i) conduct orderly diligence of their capabilities, (ii) create side by side analysis between firms, and (iii) look for ways to improve the operations of a firm.  The grid below is a rough outline; further materials are available upon request.

Strategic Implications

The strategy of a PE firm is dictated by its assets under management, the transaction pipeline and the expertise of its operators.  Given two firms with the same strategy, a relative ranking across the 2 x 5 grid will demonstrate areas where the firms could be expected to have differentiated performance.

Conclusions

Private equity firms are small, rare and highly differentiated.  Understanding the activities of a firm with fewer than 20 employees, of which there are fewer than 2,000 in the US, and which invest in dramatically different types of businesses makes comparison between firms difficult.  Our 2 x 5 operational template provides a first attempt to decompose, define and codify the activities that go on within these firms.  We believe it will serve as an important first step in improving LP diligence as well as GP investment performance.

The original .pdf of this write-up is here.

The use of simulations to model behavior has increased in popularity; with increased computing power and software capabilities simulations have been able to replicate complex real-world behaviors.  For a thorough text on the subject, see Philip Ball’s Critical Mass: How One Thing Leads to Another.  Two simulations using cellular automata, also known as intelligent agents, which are frequently referenced are the sandpile game and the forest fire game.  Based on the behavior of the agents in this work; we set forth a game which would allow for basic modeling of industry and firm behavior by following a similar game.

In the Grazing Game, we take the view that there are three key elements; (i) the field, or game board, (ii) resources, allocated across the field that vary over time, and, (iii) the players, which make decisions about where they would like to be on the field based on their own capabilities and the status of the resources.  At each step of the game, players evaluate the field using their attributes to detect resources, move accordingly based on their attributes and then come to rest.  Players seek to achieve some target value of Karma in their moves.  We outline several methods by which the game can be made more complex and also look at how the results could be used to evaluate industry and firm competitive behavior.

The game board, consisting of adjacent areas.  The field is the location upon which resources are allocated and the positions that players desire to achieve.

Resources are allocated to the field.  The number of resources can vary, as well as their individual characteristics.

Players seek to attain positions on the field which will give them a certain relative value.  The players have different abilities (ie resource perception and movement) and may pursue different resources.

Facets in Iteration 1 of the Game

1. A simple grid
2. Field has no boundaries (ie spherical surface)
3. Locations may have limited carrying capacity
4. Single resource
5. Random log normal distribution
6. Resource changes in distribution after each play
7. Ability to perceive resources (distance)
8. Steps to move in a single turn

Facets in Iteration 2 of the Game

1. Rather than simple grid, locations have a range of connections
2. Create artificial boundaries
3. Multiple resources
4. Resources may have force-multiplier effects
5. Resources may shift at different rates
6. Different attractions to different resources

Facets in Iteration 3 of the Game

1. Locations may have long distance ties to far parts of the board (travel)
2. Resources may have combinatorial values (derivatives)
3. Resources may have greater ability to attract players (publicity)
4. False and/or negative resources
5. Ability to understand derivatives
6. Ability to communicate between players
7. Players may have no ability to determine resources; may simply mimic nearby players
8. Karma calculations may change over time

Rounds

The players will move (or attempt to move) each turn.  The game will be played for thousands of turns; allowing Karma calculations at the end of each turn.  By varying the characteristics of the field, resources and the players, we will be able to interpret which methods maximize player Karma.  As we vary the game characteristics over time, we will find better analogs to competitive landscapes faced in industries, allowing for simulation of strategies.  By overlaying this with the different characteristics found in players, and eventually in communities of players, we will be able to see what strategies maximize player performance and Karma.

Constituents

1.The field

• Assume a geometrically uniform area (ie a square) or playing field.  There are no end or corner locations (imagine playing on the surface of a sphere).
• Entrants (players) are deposited at random on the map in a log normal fashion.  (X = # of entrants / Y = # of locations with that many entrants is log normal)
• New entrants enter in a log normal fashion based on the presence of existing participants
• Spots on the map have a relative value (values could be of anything, imagine ‘goodness’ or the amount of a desired resource.)  (Such that; X = # of locations with a given value of goodness / Y = Amount of Goodness = log normal)
• There is a change in goodness value at a location over a time period.  (This change is bi-directional, such that it moves up and down. Such that X = % of locations that experience a given level of change and Y = % of change in a given time period.)
• Amount of time that goodness persists at a location (Such that if we were to evaluate the duration that a spot were to hold the ‘Max Goodness Value’ title, X = duration of time that a max holder held that title and Y = number of max holders with that duration of time)

2.The Players

• Entrants attempt to pursue geographies of high value (again, assume the values are measures of goodness).
• Entrants are capable of perceiving goodness (Such that; X = an entrants ability to perceive goodness and Y = number of entrants with that given amount of capability)
  • Ability to perceive goodness of a specific location is the function of distance to the location, this too is log normal
• Entrants’ desire to pursue goodness follows a log normal distribution (Such that X = % of entrants that have a desire to move and Y = % of entrants that have a given desire level)
  • This too changes over time in a bi-directional log normal fashion
• Speed of pursuit follows a log normal distribution.  (Such that X = % of entrants able to move at a given speed and Y = speed at which entrants can move)

3.Play of the Game

• FIRST TURN
  • Populate board with initial values
  • Populate board with initial players
  • Allow players to calculate
  • Allow players to move
• SECOND TURN
  • Calculate players relative happiness value
    • Happiness = F (Desire, [Value*Target – Value*Current])
    • Calculate Happiness of the entire board, SUM (ALL PLAYERS)
  • Recalculate board values
  • Allow players to calculate
  • Allow players to move
• N TURNS
  • Calculate players relative happiness values
  • Repeat (ii) – (iv) above

4.Objectives

• Measure changes in aggregate happiness over time
• Are their trends?
• Measure changes in aggregate happiness under different values of the variables
• Do some variables have a greater impact?

5.Other modifications

• Introduce carnivores that want to be where the herbivores are
• Introduce herding or flocking behavior
• Introduce types of geographic value (again, Lognormal distribution) and different kinds of consumers (LND here, too).
• Introduce Communication between geographies and between players
• Players may desire to be near each other or far away from each other (bi-directional)
• Meta-geographic values – ability for some geographies to support multiple sub-categories at certain times
• Introduce race to the players
• Introduce coordination among the players

Evaluating Long Term Results

1. How often and how far are players moving?
2. How much stability is there?  Over 500, 1 000, 10 000 moves?
3. What variables influence performance the most?  Do any of them matter?
4. What variables change the play of the game?

If we introduce different rules to the agents, what improves performance?  If we put the agents through an evolutionary process, culling weak players at random, which strategies