Monthly Archives: March 2014

Consumer Centric vs. Product Centric vs. Profit Centric…

To start with you can never run an organization just thinking about profits.  It is the end result.  If everything is done well and if the products/services are uniquely differentiating, it will happen.  It will happen gloriously.

But the newbies to entrepreneurial work will always think about profit and it is a bad approach from a great contributor to the society, the entrepreneurs.

So what is left is, Product centric approach to running an organization or consumer centric approach to running an organization?  Before I go further answering this, consumer centricity is a better phrase than customer centricity because, generally to be a customer, they should be part of your current buyers list, which excludes working with the opportunities of “new customer acquisition”.

Coming back to the question of which is a right path for an organization, I want to bring this nicely done comparison chart from www.marketculture.com.

As you can see, the product centric approach is stuck and infatuated in the rails of two dimensional plane – the products, products, products, engineering, development, and perfection of product…, – with out realizing that the whole spirit of working and connecting with people is more important than anything in life and consumers are the ones who give you right ideas as to what matters and what do not.

The consumer centric approach lifts you up and make you fly in three dimensional world of consumers, products, and profits.

If the organization follows product centric, think about how difficult it is to think about cross-selling different products to high value consumers, just as an example.  But if you are consumer centric, you are fluidly moving in and out of products needed for the consumers, because you are focused on the consumers and they are the ones who pays your bills and raises your profits.

Systematic Approach To Data Analysis of Business Opportunities … Part 3 – ETL and Analysis

In this note, I will continue with the fifth and the sixth steps in “Identifying and implementing Solutions”.  These steps are, “Extracting, Transforming, and Loading for analysis”, and “Analyzing data”.

“Extracting, Transforming, and Loading for analysis”, is also known as ETL.   From a general enterprise architecture point of view, to start with, the following from wikipedia is a great process flow model.

More detailed information is provided here:  http://en.wikipedia.org/wiki/Extract,_transform,_load.

More to come…

Systematic Approach To Data Analysis of Business Opportunities … Part 2

Locating the right data and defining the right measurement to work on the verifiable hypotheses are not equally loaded in our favor.

The search for data has been a cognitive process much longer in our psyche than carefully understanding and using right measure.  We are easily fooled by the argument of how we explain by quoting or not quoting the data source provided by others, often not even knowing the veracity of the source, and we willingly or easily believe.  However, we hardly spend enough time to question, how is the measure defined?

The famous problems of people getting confused in regard to use of joint probability vs. conditional probability, or when to use mean, median or mode, when to use arithmetic mean vs. geometric mean vs. harmonic mean are enough to make people pass clueless, and often times even above college level educated people.

Being analytical is the next higher level of cognition and it requires systematic thinking and systematic statistical principles and concepts.  While every one would like to be analytical in thinking, deciding with least bias and knowing and keeping the amount of error in prediction to be minimal is a deeper cognitive process.

The famous Monty Hall problem in which two goats and a car behind three closed doors are given for contestant to select two doors but get distracted , was a challenge of a 1000+ PhDs.  See the summary article I created and provided in the link, http://blog.crmportals.com/2014/03/25/even-math-professors-fail-in-this-simple-game-why-2/

Also, we all know how important the measurement, OBP was in the Moneyball assignment, and in fact, it was the organizational strategic metric.

In terms of locating the right data, there are four sources.

  • Application/Registration/Inquiry data – Prospect data
  • Transaction data
  • Third party – syndicated data (geo-demographic, lifestyle, attitudinal, behavioral data)
  • Survey data (special enterprise initiated vs. existing panels)

In terms of defining the right measure to use for analysis, it is tightly connected to the hypotheses on hand.   If it is a simple classification problem where we hypothesize that high value customers are highly educated and making above average income, this measurement of interest is a conditional probability.

On the other hand, if the hypothesis is that those with two or more children of age less than 15 years old are more likely to be a loyal customers in the next 3 years, then the measurement is likelihood but it is more subtler than the previous example; it is a loyalty survival probability.

The way these two probabilities are defined, calculated, interpreted, implemented, and the value they bring to the organizations, as well as how the consumers need to be engaged are all very different.

So pay close attention to the hypotheses and make them help identify and define the right measure.

Continue with Part 3….

Ref: Joint-Marginal-ConditionalProbabilities and Bayes Theorem – From httpocw.metu.edu.tr

Systematic Approach To Data Collection and Data Analysis of Business Opportunities … Part 1

Seven Steps to a Systematic Analytics Approach from Conceptual Ideas to Implementation

Besides the steps, going through this will also help understand how prioritize research questions, collection of high priority doable hypotheses, and define and acquire right data.

Business opportunities abound as the speed of how the consumers act and react to the markets and how the markets acts and reacts to consumers’ behaviors feed each other.  These opportunities have their own life cycles , of creation and destruction.

There are two major challenges organizations face, and analytics as a strategy comes to the rescue.

(1) The complexity of business processes are connected to the changing environments of how consumers act and react, and in turn contributing to the new life cycles of value generation (consumer dynamics), the creation of new generations of products arising out of the old ones (product dynamics), and together how organizations, governments, and consumers push and pull to receive their share of the value in the market place (market dynamics). Yet organizations have to abide by the ethical considerations of operations.

(2) While these complex interactions are going on in the market,  the previous generations of concepts of organizational and market efficiencies are becoming standard best practices in all organizations, eliminating any differences in uniqueness of products and services due to those best practices and associated market efficiencies.

The next generation of value differentiation among organizations seems to be being relevant, timely, and personal.   That is being relevant to consumers in terms of right product, at the right time, with a right price on terms of personalized offers.  While these concepts have been in vogue in the last 20 years, they have come to occupy a central place in the new highly connected, socialized real time world.

Connecting the ideas of being relevant, timely, and personalized with the dynamics of business processes mean we collect right data, make it available for analysis, do pertinent analysis, and use the insights and implementation of analytical results in the right way.

From the point of view of an organization that is trying to be competitive using analytics as a key strategy, the patterns of activities on how these opportunities are identified and leveraged are explained as a seven step processes, listed as (1) spotting the opportunities, asking right research questions, (2) identifying the verifiable hypotheses, (3) defining and locating the data sources, (4) defining and confirming the right measure to use, (5) preparing the data, (6) analyzing, and (7) presenting for implementation.

In the following, these concepts are explained.

1. Identifying and articulating the business opportunities

Each and every dynamism among consumers and markets may look like a great opportunity, but not all opportunities are the same in terms of value it can bring to the organization and the ripeness of the market situations that will yield itself for value creation.  The interesting thing is identification of right opportunity itself is a mini data analysis problem.  Typically organizations use return on investment to rank opportunities and among the highly ranked opportunities, select the one that is ripe for execution and implementation, popularly stated as picking low hanging fruits.

2. Converting business/process related verifiable research questions leading to verifiable, computable, and attributable hypotheses

Continuing on the path of analytics as a key strategy, once we identify the right opportunity to pursue, we have to unpack this organizational opportunity into components of analytical steps.  The first step is asking right research questions from different angles and spotting the most probable and most valuable questions that would help us seek for more valuable details that can be attributed to consumers’ behaviors so that consumers can be engaged in a right way through various campaigns.  See the reference at the bottom, on developing great research questions.  There is a systematic collection of thoughtful activities moving from business process questions to verifiable hypotheses.

“Hypotheses are suppositions or proposed explanations made on the basis of limited evidence as a starting point for further investigation” (Google definition)

For example, if our limited historical observation points out that the minimum monthly sale happens during the holiday season, a season where consumers spend the maximum amount of disposable income, it is a missed opportunity for the organization.

However, it could be because our products and services are not tuned to the holiday season (product dynamics), or because we are not having the right campaigns during those months (consumer dynamics), or because we are not matching the competition efforts (market dynamics).

This is a missed opportunity with maximum possible value to the organization because the whole economy is buzzing with almost one fifth of spending by consumers during the holiday season.

The testable hypotheses are,

–          There are high value consumers who are looking for our products during holiday season

–          Our products captures or represents holiday moods and sentiments

–          Our average marketing efforts in terms of time and money, for high value customers are lower than our competition

–          The average marketing reach out by channels of communication to our high value consumers need to be matching at least the levels of what the competition is spending.

Note that hypotheses are the links that explicitly connect the data to the research questions and hence to the opportunities. The opportunities are realizable in terms of being relevant, timely, and personalized, if the measurement arising from important hypotheses are estimated for each and every one of the consumers, truly becoming a one to one marketing solution.

3. Defining data sources

4. Defining a right measure to use

5. Extracting, Transforming, and Loading for analysis

6. Analyzing

7. Stating conclusions including the levels of uncertainty in conclusions and caveats

Once we settle on our hypotheses, we are ready to define the right measure and the right data.  Most of the times, the challenges of locating right data is not that much compared to not giving enough careful attention to defining the right measure.  Continue in the next section, …

Reference:  Developing great research questions by Earlene E. Lipowski

Even Math Professors Fail in This Simple Game – Why ?

Even Math Professors Fail in This Simple Game – Why ?

The title is a funny disparaging statement and people say that because it is sensational and to add to that, the experiment explaining person actually works with a professor to prove this point.

The truth is it knocks off even highly trained mathematicians and statisticians, and yes even mathematics and statistics professors because some times we trust our intuition or do not listen extremely carefully.

Most of the times our intuition works, but not always. Of course, never undermine the power of careful listening.  It is better said than followed.

You will love this little video and see why you should also rely on mathematics (am i excited to say, rely on probability and statistics).  This is called ‘Monty Hall Problem’.

Ok, here is the interesting thing for real world application.  Where do you think you can use this as an application in real life.

I am collecting some examples.  My immediate example is the following:

If government regulations change and hence associated uncertainty distribution changes (specifically because at least one of the options available to you has been removed) then you should also change your strategy (move away from the existing strategy) what seems to be an equally likely situation!

For application to cognitive dissonance in free choice paradigm for explaining how human beings are not tricked into false choices in preferences, see, http://www.nytimes.com/2008/04/08/science/08tier.html?_r=0, and as an extension claiming the whole collection of psychological tests.

It becomes complicated because in many real life situations not all events are equally likely.  But then there is a way to figure out the likelihood of success.

No BIG data or power data are required here; this is just a human folly. You need a good mathematician or statistician to help you resolve such issues and remember when in doubt you just at least do simulation so that one does not have to be put in an embarrassing situation of being totally incorrect.

I love to hear your examples.

At least people will see the higher order intelligence why Mitt Romney is allowed to change his mind or if allowed to change why he would prefer that (popularly called flipping)!

updated 22Jul12:

Here is a snippet from  Wikipedia (ref: below).

“After the Monty Hall problem appeared in Parade, approximately 10,000 readers, including nearly 1,000 with PhDs, wrote to the magazine claiming that vos Savant was wrong (Tierney 1991). Even when given explanations, simulations, and formal mathematical proofs, many people still do not accept that switching is the best strategy.”

Here is the simulation applet to experience yourself

Here is an excellent summary by Wikipedia

Here is NY Times’s sensational story

More Details on Monty Hall Problem

Essential Microsoft Videos in Preparing Powerful Dashboards

For overall overview, see the demos by Microsoft in http://www.microsoft.com/en-us/powerbi/default.aspx#fbid=kB3LlM0Qit7

The Excel 2013 is a significantly improved tool for Dashboards and it is a serious contender of Tableau.

Here are some best well presented EXCEL BI (can not believe that EXCEL can be a BI power tool, you are thinking, right?!), take a look at these videos and you will be surprised how much EXCEL has come along.

Getting Started with Power View in Excel 2013

Relationships in PowerView Tables

Introduction to PowerPivot by Microsoft …

Using Advanced Features of Data Explorer and Building Power View with Bing Maps and Animations

Introduction to Power Map Within Microsoft BI Excel Tools

 

Special Readings for Consulting in Analytics Solution Development and Implementation – DL 498

Required Readings

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I have been writing these articles on CRM over a period of 10 years, and I will move some of the following to the above required list:

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 Predictive Modeling: Myths on Increasing the Predictive Power

 Modeling Process Flow

 CRM Intelligence – Real Time CRM Intelligence and Real Time CRM Best Practices

 Sampling Methods, Inferences, and SAS Procedures for Discrete – Categorical – Response Data

 Interaction term vs. interaction effect in logit and probit models – using STATA to compute the interaction effects – Too technical and you may skip

 Web analytics best practices for marketing and creative – Part 1

 Analysis of Longitudinal Data – Part1.ppt

 A Class of Natural Plots for Marketing Regressions – N-plot.pdf

 Top 10 – Minimal SAS Tutorial Documents – Examples – A Statistician List

PROC SQL Lecture

 Strategies for CRM and Direct Marketing Analytics – Critical Approach and Guidelines

Bayes Theorem, Autoregressive Modeling, and Marketing Optimal Messaging

 Real Time Analytics – Basics of Updating Algorithms

 CRM portals, Analytics, and Direct Marketing Principles are Critical for a Real Time CRM-Direct Marketing Platform

 Pharmaceutical Marketing Mix Models

 Some Principles of Building Pharmaceutical and Non-Pharmaceutical Acquisition Models

 Pharma CRM – Pharmaceutical CRM

 Pharmaceutical CRM – Why Patient Relationship Management is key for product differentiation and competitive advantage?

 CRM Portal, CRM Data Capture, CRM Analytics – Rapid Implementation of Real Time CRM – Part II – Operational CRM

 Pharmaceutical CRM – Differentiate the Product or Die or Do Customer Care.html

 Lead Generation – Consumers – Patients – Optimal Channels for Better ROI – US Mail, Web, Media

 CRM Portal, CRM Data Capture, CRM Analytics-CRM Implementation Requirements

 Marketing Portals – An integrated marketing gateway to a marketing operation

 Optimal Sample Size for Direct Marketing – Some Basics

 Neural Network Approach to Model building – CRM analysis

 Hierarchical Cluster Analysis

 What are the types of CRM analytics?

 Web marketing intelligence – Do you know the effectiveness of your advertisement dollars spent with GOOGLE and OVERTURE sponsorship placements

 Contents of  real time CRM for rapid implementation – Customer Perspective – Part I

 Strategies for CRM and Direct Marketing Analytics – Marketing Options Model

 ROI Trends – Free Tools for Real Time Web Marketing Analytics

 Real Time CRM, Right Time CRM, Customer Relevant Time CRM – Your Priority is…

 Data Hub, Information Hub, Knowledge Hub – Integrated Real Time CRM system.pdf

 Basics of Real Time CRM Intelligence and Real Time CRM Best Practices

 CRM Data Mining – Methods of Dimensionality Reduction and Choosing a Right Technique.

 What is meant by best practices in CRM and/or database marketing.

 Why Real Time CRM is Feasible, Not an Information Overload, and Worth It?.

 Is it CRM software or CRM vendor or CRM implementation?.

 Evaluating your email (e-mail) marketing best practices and fast track adjustments.

 Test for evaluating the state of best practices for your CRM implementation and solution for fast track alignment.

 Web Marketing is Key for Direct Marketing.

 Real Time Analysis, Content delivery, and Data Capture Powers CRM portals.

 Building Real Time CRM intelligence: Some Key Analytics and Strategies.

 TEN REASONS WHY MODELS MAY FAIL.

 Architecture for e-Commerce strategy – Questions and Answers Demystifying Structurally Strong E-Commerce Architecture..

 Using Customer Care to Differentiate a Pharmaceutical Product From Its Competition.

 Why Companies Need Analytics? – Facets of Getting to Useful Analytics.

 Foundations of Database Best Practices for Analytics in CRM Setup.

Where does quality meet CRM?

 

Conducting A Sample Survey – Proposal Document Template

Sampling proposal – template document to complete

Two additional things that strengthens a professional document.  As part of fair amount of work for a two weeks, I am not expecting this…!

* A sample survey meta data check list.  This list is from your Survey Methodology book “Groves M. Groves, et al”.

Survey_meta_data_check_list

* A project plan using Gantt Chart.

Researching for Analytics Success Stories – How to Utilize Northwestern Resources…and Other Resources

2007 – Northwestern joins Google Scholar Program; what does it mean?

http://classrooms.tacoma.k12.wa.us/tps/jleiman/documents/download/google_scholar_northwestern_library.pdf?id=69739

How to use Northwestern University Google Scholar.

http://files.library.northwestern.edu/schaffner/scholar_demo_skin.swf

Also, watch the following regarding Northwestern resources, especially for MSPA program.

http://nwuniversity.adobeconnect.com/p5mg1vo1zdi/ (Requires NU login ID and password)

Remember, Eric Siegal’s book has 147 success stories and there are many in Davenport and Harris, as well as Bill Frank’s books. So pick up key words from a story and see whether there are more details in a Google search.

IBM Resources:

152 Use cases of analytics success stories.

SAS Customer Success Stories:

http://www.sas.com/en_us/home.html;

Oracle Analytics Success Stories

http://www.youtube.com/playlist?list=PL2D3BAED0D29B2820

SAP Analytics Success Stories

http://global.sap.com/uk/campaigns/50successstories/index.epx

CSC Analytics Success Stories

http://www.csc.com/big_data/success_stories

Splunk Analytics Success Stories

https://www.splunkstorm.com/stories

A video presentation utilizing these are provided in

http://nwuniversity.adobeconnect.com/p6awqnrzs8d/ (this requires a NU login ID)

Again, this is to enhance your information.  These ideas were shared and discussed in the Stage I presentation.

But made it as a special searching session using a video presentation.