Sales Force Culture

Recently I read the book: The Complete Guide to accelerating sales force performance. I discovered it reading this interesting blog post from Sten Tamkivi. The book covers many interesting aspects related to sales forces. The last chapter is related to building a sales force culture. Some of the interesting points from the book are the following:

  • A sales force culture is the unwritten rules guiding salespeople and sales managers whenever a decision is made. A strong culture provides a framework for sales people so that they can make good decisions automatically
  • Culture starts and develop communicating norms and values, but the most powerful communication medium are the actions of the sales team. If a manger wants to communicate a value, the best approach is alligning words and actions
  • To understand a sales force culture a good starting point is to observe its work style: how they make decisions, how they interact with colleagues and customers. In fact a sales organization objective is to make decisions on some specific sales force drivers: market segmentation, sales team size, people recruiting and training, etc.. These decisions will define a priority scale of values, influencing the sales force activities. Culture and work style will create a reinforcing loop, culture will reward and influence a specific work style, and the work style will reinforce the culture. Which culture and work style will be developed in a specific company will depend on many factors like: product, market, management team, etc..
  • A new sales force culture starts with its first employee, how he will hire, manage, and reward will define the culture. Once a culture is defined, making a change will not be easy, it will require a good leader who will be capable to communicate a new vision

Which is the culture at your company?

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Notes from Sten Tamkivi Blog on Building and Managing Effective Sales Organizations

The past days I spent some time reading some great blog notes from Sten Tamkivi.

The notes are a diary about the Sloan Stanford program that Sten followed in 2013. The Sloan program is a general management program developed in 4 quarters. Searching on the quarter courses list I have found that the spring quarter has some great information regarding Sales and Business Development, two areas that are important to me. In particular there is a specific course on developing sales organizations: STRAMGT 351Building and Managing Effective Sales Organizations (James Lattin, Peter Levine). From the Stanford website we can learn that the focus of the course is on the challenges and key issues associated with the creation and management of a professional sales organization. The course is organized to follow the development of the sales function from strategic inception through to execution and implementation: choosing a go-to-market model (e.g., direct sales, VARs, OEMs, hybrid models); building and structuring the sales organization (e.g., sales learning curve, organizational structure, allocating territories and quotas); and managing the sales force (e.g., hiring/firing, compensation, forecasting, culture). These topics are analyzed in the context of both early stage ventures and later stage enterprises. The course classes are complemented with cases, guests interviews, articles and the following book The Complete Guide to Accelerating Sales Force Performance: How to Get More Sales from Your Sales Force.

The course notes are divided in weeks (week 32, week 33, week 34, week 35, week 36, week 37, week 38, week 39week 40). For each week Sten is providing some important points discussed during the class. After reading the various notes if I had to select some key points that should drive a sales manager activity I would select:

  • Hire well focusing on cultural fit, technical and communications skills
  • Manage the activities using a reverse-engineered chain of sales metrics, aligning individual actions to the key drivers of the sales economics
  • Create a fertile environment where communications flow is optimal, priorities are clear, thinking big is natural, and most energy is directed externally

Which is your experience in sales organizations? Which activities you believe are important to build an effective sales organization?

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Notes on: Introducing statistical learning

Few months ago I was reading chapter 1 from “An Introduction to Statistical Learning (ISL)” by James, Witten, Hastie and Tibshirani. These are some notes.

Statistical learning refers to a vast set of tools for understanding data. These tools can be classified as supervised or unsupervised. Supervised statistical learning involves building a statistical model for predicting an output based on one or more inputs. With unsupervised statistical learning, there are inputs but no supervising output; nevertheless the goal in this case is learning the patterns in such data.

Some examples of problems that statistical learning techniques try to solve are:

  • Regression problems with a quantitative output: trying to predict a quantitative output like the wages of a sample of individuals from the value of other variables like age, calendar year, and education
  • Classification problems with a qualitative or categorical output: analyzing the Standard & Poor’s 500 (S&P) stock index daily movements over a 5-year period between 2001 and 2005, to predict whether the index will increase or decrease on a given day using the past 5 days percentage changes in the index
  • Clustering problems: analyzing gene expression measurements for cell lines. Instead of predicting a particular output variable, the goal is determining whether there are clusters among the cell lines based on their gene expression measurements. In these cases we can try to represent the cells in a limited number of dimensions and then find similarities between the cells so that we can group them in clusters. The accuracy of this technique is related to the selection of the appropriate dimensions

The term statistical learning is fairly new. Following some important events in the evolution of this field:

  • Early nineteenth century: Legendre and Gauss published papers on the method of least squares for predicting quantitative values
  • 1936: Fisher introduces the linear discriminant analysis to predict qualitative variables
  • 1940: various authors developed logistic regression
  • Early 1970s: Nelder and Wedderburn coined the term generalized linear models for an entire class of statistical learning methods that include both linear and logistic regression as special cases
  • Mid 1980s: Breiman, Friedman, Olshen and Stone introduced classification and regression trees, and were among the first to demonstrate the power of a detailed practical implementation of a method, including cross-validation for model selection
  • 1986: Hastie and Tibshirani coined the term generalized additive models for a class of non-linear extensions to generalized linear models, and also provided a practical software implementation

Lessons learned:

  • Statistical learning refers to tools for understanding data
  • These tools can be classified as supervised or unsupervised
  • Supervised SL involves building a statistical model for predicting, or inferring an output given some inputs
  • Unsupervised SL, there are inputs but no supervising output. The goal is to learn from relationships

In which fields you would like to apply the statistical learning tools?

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Notes on: Arthur Clarke

The first of January 2014 Marc Andreessen sent 3 tweets mentioning the Clarke’s laws. This event triggered my curiosity in learning more about him.

If you like technology and if you have the opportunity to work in the telecommunications sector there are high chances that you will know about Arthur Clarke because of his contribution to the idea of global communications using satellites in a geostationary orbit. In fact Clarke in 1945 wrote a paper where he describes the possibility to transmit radio signal from satellites. The title of the paper was: Extra-Terrestrial Relays – Can Rocket Stations Give Worldwide Radio Coverage?

Reading the Wikipedia webpage you can find many interesting facts about the life of Arthur Clarke. One of the traits that got my attention about his life is related to his capacity to be a creative and a rational person, with interest in science fiction writing, travel explorations, science and technology. He was the author of the Odyssey 2001 and with Robert Heinlein and Isaac Asimov, was one of the “Big Three” of science fiction writers. During his life Clarke published a number of non-fiction books covering concept like space travel, communications, and the future of technology. In the book Profiles of the Future, published in 1962,  Clarke describes a timetable of inventions and ideas up to the year 2100. The same work also contained “Clarke’s First Law” and text that became Clarke’s three laws in later editions. Another book that got my attention is, Voices from the Sky.  First published in 1965, the book is divided into three sections: space travel, communications satellites, and a section ranges widely over the side implications of the space age.

Being a prolific writer and public speaker Arthur Clarke is also famous for many inspiring quotes. Following you can find a selection that got my attention:

  • How inappropriate to call this planet Earth, when it is clearly Ocean
  • Before you become too entranced with gorgeous gadgets and mesmerizing video displays, let me remind you that information is not knowledge, knowledge is not wisdom, and wisdom is not foresight. Each grows out of the other, and we need them all
  • In my life I have found two things of priceless worth – learning and loving. Nothing else – not fame, not power, not achievement for its own sake – can possible have the same lasting value. For when your life is over, if you can say ‘I have learned’ and ‘I have loved,’ you will also be able to say ‘I have been happy

Learning about Clarke, is learning that the first step toward innovation is imagination.  If you had to imagine a timetable of future technologies from now to 2100, how would you imagine it, how you would draw the profile of the future?

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Notes on: An Introduction to Statistical Learning

In the past months I have spent some time to learn more about data analysis. After some research I found a very interesting book titled “An Introduction to Statistical Learning (ISL)” by James, Witten, Hastie and Tibshirani. Some of the authors (Hastie, and Tibshirani) are a reference in the field having written “The Elements of Statistical Learning (ESL)” and having developed important models for statistical learning. While reading the book I’ll write some notes and will share them on this blog.

The book is developed in 10 chapters and includes labs implemented using the popular statistical software package R. Following the chapter list:

1) Introduction: introduces the statistical learning history, the content and the premises of the book

2) Statistical Learning: introduces the basic terminology and concepts behind statistical learning

3) Linear Regression: reviews linear regression

4) Classification: discuss two of the most important classical classification methods, logistic regression and linear discriminant analysis

5) Resampling Methods: introduces cross-validation and the bootstrap, which can be used to estimate the accuracy of a number of different methods in order to choose the best one

6) Linear Model Selection and Regularization: consider a host of linear methods, both classical and more modern, which offer potential improvements over standard linear regression. These include stepwise selection, ridge regression, principal components regression, partial least squares, and the lasso

7) Moving Beyond Linearity: discusses a number of non-linear methods that work well for problems with a single input variable. These methods can be also used to fit non-linear additive models for which there is more than one input

8) Tree-Based Methods: investigates tree-based methods, including bagging, boosting, and random forests

9) Support Vector Machines: describes a set of approaches for performing both linear and non-linear classification

10) Unsupervised Learning: describes a setting with input variables but no output variable, and present principal components analysis, K-means clustering, and hierarchical clustering

The book and other additional information can be downloaded at:

Statistical Learning MOOC was also offered by Trevor Hastie and Rob Tibshirani in January 2014.

It is interesting to note that the book preface ends with this quote: It’s tough to make predictions, especially about the future (Yogi Berra). Do you agree?

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Business Development

What is BD? Sales ? Strategy ? Marketing ? The Wikipedia definition is: Business development comprises a number of tasks and processes generally aiming at developing and implementing growth opportunities within an organization.

To understand in practical terms the meaning of BD and the activities developed by BD professionals I believe that the best way is to listen from the guys that are implementing BD in the field.

This post from Holger Luedorf, (BD at Foursquare) describes 15 basic rules that he follows at FS: 1) Create clear BD targets related to core company goals (e.g. grow the user base, expand internationally, outsource a critical technology etc.) or more explorative (e.g. M&A), 2) Structure your approach, this includes market sizing, market and competitive analysis, and a clear timeline. Identify partners and how to reach them (e.g. give intros hoping to receive intros), 3) Solve problems, help partners reach their goals, 4) Be prepared, research the companies you want to partner with, this means scanning a lot of industry press and frequently meeting with peers to share information, 5) Understand the partner organization, who are the decision-makers, which teams or managers are heavily weighing in, who is responsible for the long-term execution of the partnership, 6) Build a hierarchy of touch points, in certain scenarios bottoms-up approaches might work better in other cases it might be better to pitch top-down knowing that an executive is passionate about certain topics and will strongly influence the decision making process of her organization, 7) Always be responsive, 8) Don’t rush, don’t annoy, pitch, have a solid follow-up providing additional data points but then let it sit for a period of time, before sending a reminder, 9) Can’t close? Regroup, analyze, and adapt if possible, 10) Own your partners, not just deals, when BD is a separate function from Partner Management, it is easy to play good cop, bad cop, 11) Don’t over-commit, internally or externally, 12) Build strong relationships with key partners over time, 13) Be present as a company, 14) Relay partner feedback back into your own organization (e.g.: partner meetings generate a lot of information like product critique, observation of what the competition is doing) 15) Make sure you have solid legal support. Digging in the comments there is also intersting information. Following some notes: a) Hiring BD professional: I am always looking for:
 1) Personality (obviously you need someone personable, trustworthy, and who fits with the rest of the team)
, 2) Hustler mentality, you want someone pro-active who can think on the spot and is creative in her approach, 
3) Reputation & network (someone that already has a good reputation & network, whether it be from college or previous jobs)
, 4) Quantitative skills (number skills will come in handy when researching, negotiating, and forecasting), b) Startup KPI: Transparent, weekly reporting e.g.: – New Meetings: 2
- New Term Sheets: 1
- Signed Deals: 0, c) Segmenting BD. The Foursquare BD team has split up its team in these groups: 1) Mobile distribution (OEMs, carriers, platform app stores)
, 2) Media deals (publishers, sports & entertainment companies), 
3) International (doing early-stage BD deals abroad, similar to what we did early on at Foursquare)
, 4) Strategic/Monetization (e.g. Amex)
, 5) Platform (the Foursquare platform is becoming increasingly important for 3rd party service and there is high demand for our venue & Explore API).

Another great post regarding BD is: Who You Gonna Call? Navigating the Existential Crisis, from John O’Farrell. The post describes the story of a crisis at Loudcloud/Opsware, and the important role of the BD guys orchestrating and executing the deal that saved the company.  The author explains that strategic business development is not sales, is an investment in systematically mapping and networking your ecosystem to drive transformational opportunities. BD is focused on a very few high-impact events a year rather than a large volume of quarterly transactions, so it should have separate goals and incentives from those of sales. Strategic BD should be low headcount and high impact, led by a senior professional operating at a peer level to Sales, Engineering and other company functions.

A part II of this post reinforce this perspective, showing how BD is important to:  1) leverage key company assets (e.g.: management team, engineering, marketing, sales, legal and finance functions) driving the process while the rest of the company continue focusing on daily operations, and 2) gaining massive industry insights during the process of evaluating future acquisitions.

How you manage BD in your organization? Which BD frameworks are you using?

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Observing a sail yacht with a telecommunications engineer perspective

The relationship between the sea and telecommunication started many years ago, in fact, as discussed in a previous post, the inventor of the telecommunications industry, Gugliemo Marconi, used the yacht Elettra as his wireless innovation lab.

If we observe a yacht with the perspective of a telecommunications engineer we will see a great number of antennas. I like the wind, so I’ll post a photo of a sail yacht, but the same will apply for a motor yacht.  The main difference is that even the largest sail yacht will have issues to allocate very large antennas (e.g.: C Band antenna with dish diameter >1.5m).

IMG_6212

If we ask which bands are used by the devices installed on a large sail yacht, we will find that most of the radio spectrum is used. This is an initial attempt to answer the previous question, using the IEEE US bands and some of the devices that you can find on large yachts (including the omnipresent iPhone!):

Band Frequency Devices
HF band 3 to 30 MHz SAILOR 6300 MF/HF, TX 1605 kHz – 30 MHz, RX 150 kHz – 30 MHz
VHF band 30 to 300 MHz SAILOR 6222 VHF DSC, TX 156 MHz -157 MHz, RX 156 MHz -163 MHz
L band 1 to 2 GHz SAILOR 500 FleetBroadband,TX 1626.5 – 1660.5 MHz, RX 1525.0 – 1559.0 MHz, SAILOR SC4000 Iridium, TX 1616 – 1626.5 MHz, RX1616 – 1626.5 MHz, SAILOR 6110 mini-C, RX 1525 – 1545 MHz, 1626.5 – 1646.5 MHz, Iphone 5S Cellular bands/Cisco 819 Router 850, 900, 1700/2100, 1900, 2100 MHz + LTE bands
S band 2 to 4 GHz Iphone 5S Wi-Fi bands/Cisco 2600 AP , 802.11a/b/g/n Wi-Fi (802.11n 2.4GHz and 5GHz)Furuno FAR 28X7, 3050MHz – 30 MHz
C band  4 to 8 GHz Iphone 5S Wi-Fi bands/Cisco 2600 AP 802.11a/b/g/n Wi-Fi (802.11n 2.4GHz and 5GHz), Sea Tel 9711, TX: 5.850-6.725 GHz, RX: 3.4-4.2 GHz
X band 8 to 12 GHz Furuno FAR 28X7, 9419 MHz – 30 MHz
Ku band 12 to 18 GHz Sea Tel 4012 GX, TX 13.75-14.5 GHz, RX 10.7-12.75 GHz
K band 18 to 27 GHz NA
Ka band 27 to 40 GHz Future. Intelsat’s EpicNG, Telenor’s Thor-7, Inmarsat’s Global Xpress, and O3b
V band 40 to 75 GHz NA
W band 75 to 110 GHz NA
G band 110 to 300 GHz NA
H band 220 to 325 GHz NA

Few weeks ago I had the opportunity to follow a presentation from Vinton Cerf at Campus Party. He was discussing about global connectivity, and he mentioned a Google project to use satellites transmissions over the 60GHZ band, to increase the bandwidth delivered to users. I’m sure that in the future we will see more innovation coming from the space and we will see more technologies that will improve the way people at sea connect with information. Stay tuned!

Do you know about other innovative technologies working in the extremely high frequency band?

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