Big Data and Data Warehouse – a flea market and a pharmacy

Lately I’ve been often faced with questions about differences between the so popular big data initiatives and the traditional data warehousing concepts. Few months ago I tried to distinguish the two in context of overall information management paradigm in a blog post that I wrote for the “IBM.Talking about” blog from IBM Croatia. In the rows that follow, I bring the English translation of the text .
For those of you who are unfamiliar with it, the term big data refers to the overall phenomenon of dramatic growth of available amounts of data due to global acceptance of the Internet, incredible amounts of data from social networks and mobile technologies, as well as due to billions of sensors worldwide and massive digitalization of practically whatever we do. Big data also refers to technologies that are able to process such large amounts of data coming from heterogeneous sources, regardless of whether it is structured such as database transactions records, or in form of unstructured records such as pictures or videos. These technologies are based on specific repositories that are able to store different types of files in their native formats and use principles of massive parallel processing to analyze and process such data.

Structure against the free form; pharmacy against antique store

Compared to data warehouses, big data solutions handle data which is “cheap” per terabyte. It is filthy, not standardized, without a dictionary, scattered in different formats. It is “cheap” also due to considerably lower effort to load it into repositories based on technologies such as Apache Hadoop (which itself is an open source project), but also due to the relatively inexpensive processor units and storage capacity that rely on distributed clusters with relatively high error tolerance. The data in our traditional Data Warehouses is on the other side pretty “expensive”. It has to pass substantial control, cleansing and standardization before it even gets the chance to knock the door of a well structured data warehouse. Compared to the big data cluster, a data warehouse seems like a pharmacy in comparison to a grocery store. In fact, even the mention of a grocery store is too pretentious, we are rather talking about relationships between pharmacies and something without a structured and standardized content – more like a flea market or an antique store.

Boring and exploring

After having had to do with pharmacies (here synonym for data warehouse) for so long, in the era of big data we are starting to visit places that will be supplied with a variety of items at a low entry price, without fancy supply chain, without traceability and complicated regulatory requirements. However in such places, a connoisseur could gain surprisingly lucrative outcomes … just like a data scientist would be able to gain by analyzing large amounts of variety of data forms inside a big data repository. From certified sellers (pharmacists), through severely certified products (pharmaceuticals) to certified point of sales (in many countries pharmacies get permissions by population density), the pharmacies are expensive places per unit sold. We enter there with a recipe (or it is already “brought there” through an IT system) and with unambiguous motives (e.g. stopping the pain). On the other hand, such structure disappears in an antique store. We enter there rarely with a particular intention. On top of that, usually inexpensively furnished shops, offer all sorts of things – from art and books over dishes to useful little things that nobody needs, and precious objects from the distant past. apoteka i staretinarnicaUnlike pharmacies, usually you will not know in advance the nature of outcome of your purchases. You might be keeping something really valuable in your hands. Perhaps, with further research, you can realize that the painting you have just purchased is actually worth a fortune and that you may no longer need to play the lottery. I mean never more! Your visit to the pharmacy will certainly never end with the idea of not ​​playing the Lottery again or terminating the private business you hold. The outcomes from a traditional data warehouse are just as such – boring and predictable. With rare exceptions aside, DWH is generally built with the outcomes known in advance. Users are left to search for relations, understand trends and identify extremes. It will rarely become a journey into the unknown, combining the incompatible and correlating distant phenomenons. That part of the job we leave to the big data.

Comparing the two

Let’s try once again to quickly compare, still generalizing, some aspects of traditional data warehouse and big data solutions. The sole technology implementation, generally is easier with big data. To build the repository it is not necessary to design an extremely detailed data scheme and have ready an exact spot for each byte of data stored based on its type and place in the hierarchy. The logistics of data supply (ETL and data governance) is again much more complicated in traditional DWH. Administration is similar, as well as the learning cycle in adopting the technology.
In case of traditional DWH, for the (already prepared) analytics, there are no experts needed as data is usually packed into predefined syntax and predefined analytical processes which are used by common users – business, scientists, analysts, … With big data this part is much more complicated. The collected massive amount of data needs someone who knows how to filter it in order to reach the value ​​that resides within. Common big data scenarios (e.g. marketing targeting) are often based on “chewing” the data all over again across different dimensions and unstructured attributes. Someone has to distinguish the important from the unimportant, coincidences from rules. He should know filtering techniques and data modeling, be familiar with different tools and algorithms, such as those that are able to connect a person to an image, recognize a script from a picture or understand natural language semantics… Due to its unfiltered nature big data is significantly “more expensive” at this stage .

Who leaves and who stays?

And finally, a little disappointment to all of those who are fed up with continuously optimizing data warehousing models, with immense work when changes or additions occur, concerns about naughty data derivates and ever changing data sources. Big data and DWH are here to stay together side by side, each in its role, just like a flea shop and pharmacy, complementing each other… at least for some time*.

 

*It is very likely that in the future we will have a single platform for both structured and massive unstructured data. To get to this point some basic technologies such as fast SQL query requests on unstructured repositories should be developed. From the other side the convergence between the two will be further supported by the emerging infrastructure technologies, such as in memory databases, different high performance computing technologies, flash storage and specialized compute architectures.

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Big Data – plenty of it,… but still missing

Spring 2010, at a small Croatian town there is an unusual meeting going on in a factory that we will call PPP. The meeting room at the first floor of the administrative building, just next to the gray production halls, hosted a group of about 10 different people. Individuals in the room take part of a fiery debate about the presentation that’s been projected on the wall. The discussion of a group of people in white coats, most probably production and development engineers, is obviously driven by the two most active members, often arguing with conflicting views among each other. There is a few people dressed in suites. One would guess that those are consultants and the company’s management team. Part of that group is quiet; they are just listening, and nodding from time to time to show their mental presence in the debate. Two persons from the group in suites ask a lot of questions. Some other participants are very active as well. They draw on the board and answer the questions with lively gestures. Those are mostly members of the academic community that take part of one of the EU “cross-border cooperation” projects, which is actually the reason of this colorful meeting.

Data Explosion1

Let’s add sensors
In order to improve the efficiency of the production process and product quality, PPP initiated enrichment of certain phases of production by additional sensors, PLC and SCADA elements. By increasing the number of sensors from 12 to 35 per production machine, PPP started one of numerous initiatives around the world that contribute to the enormous global growth of machine generated data, the one we like to call Big Data. At one point, a temperamental professor with a French beard took stage. He passionately explains to the group recent results gathered from mining of the newly established data sets based on the increased number of sensors. No matter how colorful graphs were clear and despite the insight that was much above the previous findings, it was hard not to recognize the indifference on the faces of other participants in the meeting. Something is missing!

Data model or a Swiss cheese?
The whole initiative should provide, if not revolutionary, then at least usable insights. “We need to close the circle!”. All of a sudden, eyes of the participants were turned on the consultant who had been silent so far. “We need to close the information circle. You have all the parameters of the machine, but you really should start from the goals. You have to ensure traceability and link quality of the products with different stages of the production process and their parameters. Otherwise the new parameters won’t have much to say.” It is difficult to add IT tags to the hot metal castings that are being produced by the machines at PPP, so the data that was supposed to link the quality achieved and the level of waste with the 35 newly established parameters was simply missing.

Big Data: new methods, old constrains
Concluding superficially, Big Data might be perceived as a cure for everything: “now that we have so much information available, it is enough to develop mathematical algorithms and we will find all the answers.” But the truth is exactly the opposite. Today we have plenty of mathematical algorithms – from those that recognize your face, the tone of your voice or your fingerprint to those which understand the context of human speech, but the ways in which we traditionally collect data (processes) are not aligned with the technological capabilities of finding data patterns and filtering it through massive parallel processing (technology). More specifically, Big Data technologies will surely find patterns through a large amount of data, but those will not always propose answers to your problem or give you new relevant insights. In the same way, the data mining in PPP provided insight into the machine behavior such as stability patterns of certain parameters during the production cycles, including some insightful deviations. But it offered no answers about how those deviations and patterns affected the only thing that really mattered – the quality of the product. The answers must be included somewhere within the data set that we explore. They have to take part of the meta model of the entity that we analyze, or we must be able to deduct it from attributes of other entities that are similar enough to the one we study (i.e. the data on the quality of the product of hundreds of similar or identical machines worldwide, in case of PPP).

You can read more in the May 2013 issue of the Mreža magazine (Croatian language only), or later during the year translated to English at Alen’s Thing Place.

This work is Copyright of Alen Gojceta. You are not allowed to use the article, or any of its part in commercial or academic work without citing the author and this link.

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Ode to the seller – who’s doing well?

For many years I’ve been in different types of sales / marketing, business development and sales management roles. Throughout my career I heard plenty of times people showing their basic misunderstanding about the nature of a sales job. I must admit that I get irritated when I hear a sentence that should hurt every genuine sales person, and today I heard these words in three occasions: “Oh you’re very busy, that’s great, it means that you are doing very well”.

What stands in the very heart of each sales job is its scope or mission. In theory this mission is described as “fulfilling the gap between the market demand and supply”. And people often perceive it literally as it says: individuals in sales organizations walk around fulfilling orders and helping clients get what they need (filling the demand).
The more someone’s market position inclines to monopoly the more this statement is true. In every other case it is about a fundamentally wrong interpretation of the sales role. In saturated markets with high competition, sales force has to fight competitors, customer budgets, brand perceptions and many other things such as economic downturn and all of its consequences. Sellers have to be wise, competent, hard working value creators. They have to use the best marketing and sales tactics just to keep their heads above the water. And they will be lucky enough to be successful if, above all that effort, their organization is able to produce quality products within acceptable price ranges.
That’s why I get so amazed when I hear people so easily and unconditionally connecting “a lot of work” to success, over and over again. Indeed every seller can witness the fact that in sales, such as in so many areas of life, the level of work and creativity invested is in line with their achievement. From the other side, and that’s why I detest the “so you are doing well” statement, the nature of sales implies that the hardest work has to be done when the expected level of business is bad. Sellers have to invest most of their energy, time and wisdom when their sales results are below expectations, or perhaps below levels where an organization’s existence is under threat.

So my friends, next time you see me or anyone from my team being very busy, it will just mean that we are working hard to make things happen – in the way that every sales professional should.

This work is Copyright of Alen Gojceta. You might not use it or any of its part in commercial or academic work without citing the author and this link.

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The Jobs’ impact

As I’ve been engaged in some discussions lately about Steve Jobs‘ heritage, I felt tempted to summarize my opinions expressed mostly through comments to the hbr.org on line community. Some of the thoughts were exchanged before this great personality has passed away.

When browsing through numerous articles, I noticed two dilemmas that I found worth discussing. One was the most frequent and natural question about whether Apple will continue to be such a successful company even after Steve Jobs died. Much more intriguing discussions were about the fact that Steve Jobs’ death solicited enormous interest in press and social media, compared to some other great personalities of our time that passed away – the creator of the C programming language Dennis Ritchie or the winner of Nobel prize in medicine, Dr. Ralph Steinmann who found some essential mechanisms of how the body reacts to infection. I’ll discuss the two dilemmas, starting from the latter.

The media attention

In his hbr.org blog post, Scott Berinato, an editor at the Harvard Business Review, shared his thoughts about huge difference in public and media attention that followed Steve Jobs’ death, compared to the few articles that noticed that Dr. Ralph Steinman passed away.

My comment was that one of the greatest virtues of Steve Jobs was his capability to manipulate media and rise public curiosity. Jobs was not jet an other marketer. His marketing was more than that, it was genuine show business.

He was a performer, a media star. If that wasn’t the fact, the world would have been less keen to recognize his major contribution in leadership and innovation.

By gaining positive publicity we reward sympathy and a stronger public recognition. I’m pretty sure that outside the short-term publicity by press or social media, the work of either Steve Jobs, Dennis Ritchie or Dr. Ralph Steinman will be recognized in the fields where they have contributed the most. They will all be cited and their work elaborated in thousands of pages in scientific, technological and leadership literature for the generations to come. After the lights of the current public attention will turn off, the genuine human heritage of the three great men will remain.

the post – jobs Apple

What about Apple in the post Steve Jobs era? This is among the most frequently asked questions these days. After Steve Jobs announced his retirement from Apple several months before his death, James Allworth, Max Wessel, and Rob Wheeler proposed this question at the Harvard Business Review blog post Why Apple Doesn’t Need Steve Jobs.

The authors argued that the „Jobs’ way“ is already so infused within the Apple culture and that “Today at Apple is going to be exactly the same as yesterday.”

No one is able to predict how will Apple respond in the future years, but I do believe that in either of cases, the future generations will study the „Jobs’ impact“. The core answer about how much a single person can impact a corporate performance hides in the Apple of next decade. Will it keep its market performance and innovation agenda after Steve Jobs haIf Steve Jobs managed to embed the “Jobs’ way” into the fabric of Apple’s culture, then this will be the heritage of a great leader to the rest of us and probably the most searched and cited corporate culture impact of an individual in the future.

In the opposite case, if the “after Jobs” Apple fails (again) instead, this will be the most valuable evidence of all the times of a leader’s impact on organization’s success. I believe that it might start a new era of self-conscious individuals starting great things with trust that they can make the difference – because “Steve did it that way”.

This post is my personal tribute to the person who made the difference. Thanks Steve.

 

KCTXDZFA4ZCP

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The three most read posts on Alen’s Think Place

After more than a year of posting to www.gojceta.com, it seems that the most popular articles were those written with intention to be posted to Alen’s Think Place. In competition with English translations of my articles published in Croatian business and technology magazines during the past decade, the winners were the two posts created out of pure intellectual joy, reflecting my thoughts, without expectation for a financial reward.

The most read article on www.gojceta.com was the story of my experience at the first McCafe’ in Zagreb: “A coffee shop in the hamburger kingdom“. It explored the business model and the McCafe’ service in general.

The other most read article, missing only one visit to equal the McCafe’s score was the story about brand extension of Cedevita multivitamin drink to their line of tea. “Dad does it dissolve in water?” was doomed to be written the day when my son confirmed to me that my own confusion with the brand message goes beyond my own perception.

The third most accessed article was “The bdp triangle – my framework to managing successful proactive telephone campaigns described in an article in Croatian business magazine Lider. Despite the fact that the “triangle” was around 10% behind the two winners, I’m very proud of this concept and I believe that it has deserved the position.

The most read articles at www.gojceta.com:

  1. A coffee shop in the hamburger kingdom
  2. Dad does it dissolve in water?
  3. The bdp triangle

The above articles are copyright of Alen Gojčeta

©2006-2010 Alen Gojčeta

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Brief History of CRM

The history of CRM can’t be observed without considering the development of business applications with contemporary academic research.

Eighties, THE second half

MARKETING SCHOLARS: After the concept of services marketing, particularly developed within the so-called Nordic school of marketing, a new concept has emerged. Early definitions of Relationship Marketing could be found mid eighties (1985 Jackson).

BUSINESS APPLICATIONS: Sales Force Automation (SFA) and Customer Service (CS) applications were still considered as part of the wide family of ERP solutions. Few years later a new and distinct software solution category emerged and SFA and CS became part of so called Customer Relationship Management (CRM) software.

Nineties, the first half

MARKETING SCHOLARS: Relationship Marketing has been studied by Morgan and Hunt (1994) and Reichheld (1996). In 1995 Relationship Marketing was defined by Koiranen as “approach to establishing, keeping and enhancing the long-term relationships with customers and other shareholders.”

BUSINESS APPLICATIONS: Analysts from the first half of the nineties still did not recognize the rising strength of CRM. SFA and CS were classified as a small sub-segment of the ERP market. In 1994 the total CRM software (SFA and CS) market amounted to around 200 million US dollars, compared to the 6.4 billion of the global ERP sales.

Nineties, the second half

MARKETING SCHOLARS: Unlike Relationship Marketing, the CRM was studied relatively late by the academics. First academic definitions of CRM were written relatively late compared to Relationship Marketing. In the 1999 Srivastava, Shervani and Fahey described CRM as a broader concept than Relationship Marketing defining it as “a process that identifies customers, creates knowledge about customers, builds relationships with customers, and forms customers’ perception around the organization and its offerings.”

BUSINESS APPLICATIONS: Towards the end of the nineties, CRM fever heats up. The awareness of the big new market is rapidly growing. Everyone sees the opportunity for a continued growth in a somewhat saturated ERP market. During previous years, ERP applications have generated and stored an impressive amount of data about customers, so indispensible to fuel successful CRM initiatives.

Year 2000 to date

MARKETING SCHOLARS: Despite some terminological dissonance, academics are better aligned with the business practice. Through basic research, they contribute to the development of business concepts that are being embedded within CRM.

BUSINESS APPLICATIONS: Many applications are experiencing their maturity, backed up by better understanding of business processes as well as motivation agendas of individuals and departments within organizations . This maturity was influenced by the evolution of information technologies backed up by cheaper and faster data storage systems, accessible broadband and flexible IT environments, like service-oriented architecture (SOA), SAAS (software as a service) and cloud computing.

2010 and years to come

MARKETING SCHOLARS: The intensive progress in behavioral studies over the past two decades creates the basis for “intelligent” approach to large customer segments. In addition to corporate business systems, such segments are emerging within the social networks of the coming years.

BUSINESS APPLICATIONS: Social networks are opening a new page on the CRM agenda. Facebook, Twitter, LinkedIn, Second Life and other engaging social media networks, enable a more precise segmentation, affinity grouping, customer participation in offer definition and their impact on formulating corporate strategies, in a way that has never been possible before.

 

Social Networks' role in CRM

Social networks in CRM ecosystems enable a more precise segmentation and customer involvement. ©2010 Alen Gojčeta, Mreza magazine April 2010

This is the excerpt in English from the article published in the issue of April 2010 of Croatian magazine for IT professionals “Mreža“. The work above us the copyright of Alen Gojčeta and the Mreža magazine. The CRM history is part of the findings of the research done for author’s master degree thesis “Segmentation models in CRM”, 2006.

 

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About my Commentary Printed in Harvard Business Review Jan/Feb 2011

I had an honor to have the edited version of my commentary to Harvard Business Review case study Preserve the Luxury or Harvest the Brand? printed in its January / February 2011. The commentary was part of the gojceta.com initiative and it is embeded in my web 2.0 “personal strategy”, which is partly described in one of my posts.

Snapshot from the Jan/Feb 2011 HBR

I posted the comment to the hbr.org community in October 2010 after having read the intriguing case study. The complete text is available in the Jan/Feb 2011 issue of the Harvard Business Review, or on the HBR.org. In short, the case was about an imaginary French winery Chateau de Vallois. Vallois is a typical family business with simple business model combining vineyards cultivation with wine making. They are traditional in producing high quality wines and selling it through the traditional channel which exists for centuries in Boreaux. The negociants are wine merchants that diminish the risk of wine placement, but take the majority of the sales margins in return.

The youngest member of the family, after her MBA study and engagement with a leading consulting firm, wanted to make some change and start branding and selling wine on a much larger scale, targeting lower market segments. The young women’s idea engaged the family into discussion. Others were concerned about Vallois’s ability to market, their capacity to go for large scale, the relationship with negociants and similar.

The authors of the case study Daniela Beyersdorfer and Vincent Dessain wanted to hear opinions about going with a more affordable wine or stay with exclusivity.

This was my original commentary to HBR.ORG Vallois case study

Personally, I would’t go from exclusivity to wide market. We learned lessons from different industries such as car producers or fashion brands where this was often a bad idea.

I would rather use an even more exclusive wine*, sold directly from the chateau as brand exstension. Brand extension is fair even if adding several percent to profits of a business. Let’s say, in this case a new wine would make only 5% of the total quantity. Its margins would be double than the usual shipments due to direct sales and would add a 30% due to exclusivity. In this case the 5% of the revenue could contribute with additional 13% to the margin, assuming their selling price as base price and that the new production wouldn’t generate excessive additional cost (vine making is their core business anyway).

The new brand extension with exclusive price, channel and quantities would be a lever to increase demand for other wines purchased by negociants. The 5% of the total quantity wouldn’t create problems with the lack of grapes and would not harm the negociant’s market, but rather increase the whole brand value through scarcity of the newly introduced wine. The new wine would allow the owners to gain marketing expertise and establish the new business model with a low risk approach.

*In the original text it stands “vine” instead of “wine”

What was my point?

The approach I suggested was keeping the family business set up, while giving them the opportunity to start building their sales and marketing capability. The approach with even a more prestigious vine brand, sold directly from the vinery, would have been focused on profits rather than revenues.

How did I reach the above calculation?

Negiociants used to resell Chateau de Vallois’s wines with around 100% margin. If the Chateau would have had produced additional 5% of a more exclusive wine with higher margin of 7 value points from 5 quantity units, compared to the 135 value points they gain from 100 quantity units through negocitants (supposed cost ratio is 80 value points per quantity of 100 for both vines), it would contribute with 13% of additional profit margin (7 of 55).

New prestigious wine Actual wine
Quantity units 5 100
Production cost (value points) 4 80
Price sold out (value points) 12 135
End consumer price (value points) 12 200
Ch. de Vallois margin (value points) 7 55

What did I mean by lessons from different industries where brands have eroded due to switching from serving exclusive segments to wide markets?

Different exclusive car producers have given up their exclusivity to address a wider market. Not many of them have succeeded. Instead, many have struggled for years to regain the original brand proposition. Some of the examples were Porsche’s front engine GT models discontinued in 1995, or Jaguar’s trip into middle class car production.

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Summary of the first year of Alen’s Think Place

The 2010 was the first full year of Alen’s Think Place is represented by 12 peaces of written work:

  • 5 translations of my articles published in Croatian business magazines during the past decade. Mostly on CRM.
  • 5 classical blog posts. Some of those were quick thoughts, and some other were excerpts from my recent articles
  • 2 reviews of positioning / branding approaches (McDonalds and Cedevita)

Alen’s think place is meant for business professionals, mostly for those who deal with sales, marketing, CRM and business strategies in general. I hope that you have found value for yourself and that you will keep finding it at www.gojceta.com.

CRM education – the follow up

11/23/2010
I used the www.gojceta.com to write the follow up of the CRM seminar I hosed. The conclusions are interesting to any professional dealing with CRM.

The CRM seminar redesign – the whys and hows

10/24/2010
In this post I announced the facelift of my CRM seminar and shared some thoughts about the approach.

The “bdp Triangle” – my Article on challenges of telesales campaigns – (English translation), Liderpress, 11/2005

09/09/2010
The “bdp Triangle” is among my best sales concepts….

Cedevita tea: “Dad does it dissolve in water?”

06/27/2010
I just couldn’t resist to write the article about this unconsistent branding approach…

Redefining the concept of Alen’s Think Place – the imperative of consistency

06/15/2010
A small “Alen’s ThinkPlace” strategy: consistent content to the consistent audience using a single language…

My Article on Organizational Gaps in CRM (translated to English), Banka magazine, March 2003

04/26/2010
The article elaborates the problem of a “CRM organization”…

Story about Mr. S and the failed CRM project

04/11/2010
Small insight in my latest article published in Mreza magazine through a story inspired by a true event…

7 wisdoms for a successful CRM implementation

03/16/2010
English preview of the implementation part of my April article – the 7 wisdom for a successful CRM implementation…

My Article on CRM customization (translated to English), Banka magazine, September 2002

01/31/2010
The concepts from this article from 2002 are just today becoming really mature and useable…

“Tasting” the McCafe’ business model

01/18/2010
In 2010 this was one of the most read articles at www.gojceta.com. Check why…

My article on IVR systems – PART 2 (translated to English), Banka magazine, May 2002

01/11/2010
A pretty long article about Interactive Voice Response set in two parts. Still relevant, but with the major change about the handy nature of mobile Internet access. Read first the part one below :-)

My article on IVR systems – PART 1 (translated to English), Banka magazine, May 2002

01/02/2010
Part one of the above article.

All of the work above is copyright of Alen Gojceta. If you use it in academic or professional publications, please cite the author and the respective sources.

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Market Segments – Value for Them and Value for Us (English translation), MarketingUP, 05/2007

Twenty percent of customers make eighty percent of sales, Vilfredo Pareto (1911)*

The popular “20/80″ metaphor of the Pareto principle reflects its simplicity and broad applicability. This is one of the most used and most cited principles in economy. Anyone who ever tried to trade on an open market can recognise this pattern.

Segmentation allows creation of manageable, homogenous groups of customers

Segmentation is a wide marketing topic, which, among other, helps understanding the value of each customer to your organization, and vice versa – it creates insight to what are the aspects of your products and services that your customers value the most. Such awareness enables adapting of business strategies to different homogeneous market  groups that we call segments. Differentiating customers with regards to the value that they ‘deliver’ to the organization became particularly important in recent decades with highly saturated markets and consumers that show great immunity to the large amounts of marketing information they have been exposed to. The marketing response to such market conditions is basically answering the question: “how to keep the most profitable customers in the most efficient and cost-effective way?”. Driven by the above mentioned transformation in market environments and by changes in technological capabilities of modern information and communication systems, the evolution of marketing has yielded some new and innovative concepts. Among those, the services marketing represents a fundamental change in the traditional market approach, followed by important concepts such as niche marketing, relationship marketing and the customer relationship management (CRM).The latter two concepts are mutually overlapping and complementing each other. Boundary between them is a wide “zone” of common postulates and shared principles. In this ‘zone’, the understanding of the value that customers ‘deliver’ to the organization, take a very prominent place. A number of segmentation tools and techniques exist that are used to support strategies aimed to identifying and retaining the most profitable groups of customers (customer segments).

The value exchange as foundation of business

The purpose of markets as well as the foundations of the marketing concept lies in exchange of value. How much are you aware of the value provided to your organization by each of clients you serve? It’s a good question indeed, because the value is not allocated only within the tangible parameters, such as actual sales figures. It is the difference between costs and revenues, but much more than that. Doing business with certain clients for your organization can be matter of image, reference, or some other “intangible” benefits. Some clients, even if they do not contribute significant revenue to your business, can be your good messengers to a wide number of potential customers. On the other side, some others will take a significant place in your business books and stand up with figures, but a deeper analysis will discover that they are actually “value destroyers”. There are different ways in which your customers can destroy value. Those can be reflected in requests for (unreasonable) product customizations or frequent urgent deliveries. Other aspects of destroying value could be wide exploiting of customer service rights, taking advantage of long delays in payment and by generating similar expenses or other extraordinary pressures to your organization’s resources. We often justify such actions by customer’s revenue figures. However, we should ask ourselves: is it the volume of transactions on our bank account the purpose of our business? Or is it perhaps the amount that remains after the transactions are completed, through a longer period of time?

Value based segmentation

There are different methods of recognizing the value of customers for the purpose of grouping them into segments. These methods range from the most primitive measurement of sales figures to complex models that include the allocation of the current and future value creation such as potential referrals or future use of other products in your portfolio. In practice, the value of a client is measured by different surrogate measures. Some well known segmentation methods are RFM (Recency, Frequency, Monetary value), usage analysis and Customer Lifetime Value.

Within Customer Relationship Management strategy, the most suitable segmentation is the one based on the so-called Customer Lifetime Value. It is used to define the general approach to the customer set. The Customer Lifetime Value is a complex, synthetic value gained by allocation models that take into account both present and future value exchange factors. To simplify identification of present and future “value creators” marketers seek to identify visible client attributes that indicate his or her tendency toward specific behaviors that affect value creation. For example, within a costly customer lifetime value segmentation project, conducted by one of America’s leading insurers, among other findings, they understood that the size of individual’s US credit score represent a very strong “value creation” predictor.

Segmentation takes into consideration different views on customers

ABC method – earn the status

Director of Customer Service department of one of Croatian telecom operators, when arguing the substantial investment in segmentation and distinctiveness of customer service levels, said: “We started the investment when we realized that we couldn’t afford any more the highest service levels to all of our customers.” What the telecom operator actually did in that occasion was to use the ABC method to diversify approach to their customers based on their value creation. The best customers were entitled to the so-called premium service. When deciding about the granted quality of service, the CRM system was able to distinguish not only those which created value, but those who were destroying it as well. The investment in a customer’s service level was reciprocal to his or her contribution to the profitability as the operator’s measure of sustainable success.

For a better segment “visualization”, the value levels within the ABC method are often marked by descriptive terms such as “bronze”, “silver” and “gold”.

In the late nineties, the former Swiss monopoly telecommunications operator Swisscom, started a loyalty program to protect its market position during the market liberalization process*. The objectives of the program were focused on keeping the leader position, retaining the most profitable customers, while trying to avoid price wars with the newly introduced competitors. The loyalty program was delivering certain benefits to its members. Based on the data collected through the program, Swisscom was able to analyze more than 20 target groups. Four segments were chosen based on the analysis. Using the ABC analysis method, different approaches were deployed towards each of the four segments:

- Premium customers: keep them loyal at all costs

- Profitable customers: keep them loyal and intensify cross selling

- Customers with medium consumption: offer packaged services, cross sell

- Unprofitable customers: there are no benefits without increasing consumption

The ABC method can easily be recognized by clients in the banking industry. The personal banker (or clerk) service is meant to be a lever of investing in “value creators.” The remaining clients are left with the option of waiting in queues within the branches, the ATMs or other self-service systems. For the “worst ones”, which can be described as “the value destroyers”, the preferred channel of business is – the one with the competition.

Customer segmentation based on benefits

The above described segmentation will differentiate customers according to their contribution and their potential profitability. It is obvious that it puts the needs of sales organizations in the focus (which client is better for ME).On the other side, it is good to know what customers find most valuable in our offer or our general approach. We’d like to know that in particular, for those that we value the most.

Contemporary markets are often perceived as collections of different business models (within the organizational buyer) and lifestyles (in case of consumer markets). Such determinants of our clients define the reasons why they would accept our value proposal. Benefit segmentation is a mirror in which we try to figure out how our customers segment their “suppliers market” based on their perception of value. In contrast to value based segmentation, the segmentation based on benefits puts the needs of customers in the center of the segmentation effort.

A business organization, whose business model is based on low costs or minimal inventory, will value your ability of flexible, frequent and timely delivery options. Two persons that purchase the same vehicle will base their decisions on completely different reasons. While one will value a prestigious brand and design, the second will be making the purchase based on safety features and high quality service network. This understanding of value that the customer perceives within our proposal can have a powerful impact on adapting the product / service, the marketing approach, as well as the pricing strategy.

Segmentation based on value will give us the answer to the question about who are the customers that are worth our best effort, while the segmentation based on benefits will help us to understand what this effort should look like. Modern marketing segmentation concepts keep confirming – Vilfredo Pareto was right.

*The idea use of Vilfredo Pareto’s principle in this article was  inspired by Art Weinstein’s “Hanbook of Market Segmentation – Strategic Targeting for Business and Technology Firms”, The Harworth Press, 2004


The original of this article has been published in MarketingUP, 05/2007  magazine. The article and the above English translation are copyright of Alen Gojceta.

The Swisscom business case is described in: Brown, Stanley A.; Customer Relationship Management – a strategic imperative in the world of e-business; John Wiley & Sons Canada Ltd; Toronto, 2000.

If you decide to use this article or its parts for academic or professional work, do not forget to cite the author and the source.

© 2011 Alen Gojceta

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CRM education – the follow up

Past Thursday I had an opportunity to host a seminar on CRM for a group of experienced professionals. I have announced the education in my recent post, describing challenges and uncertainties around the topic and the potential audience. This post is written in form of a follow-up letter to the seminar attendees, but it might be interesting to many of you who deal with CRM and who are interested in what were the major take-aways from the intensive 6 hours CRM education.

Dear attendees,

thank you for taking part of this seminar that showed to be very productive and interactive, due to contribution of all of you.

All of my concerns before the education about the homogeneity of the group vanished when I received the first list with your names, your companies and business functions. The group was very compact in terms of CRM understanding as well as your experience in managing customer relationship or implementing CRM systems. A bank, several telecommunication companies and two CRM vendors made a perfect audience for a focused and productive education.

I really enjoyed the experience and I hope you did as well.

The customer's hidden attributes

There are ten points that I want to stress out and that I’d like you to keep in mind as points to take away from the lecture:

  1. No one needs CRM because it is fancy (’cause others „have“ it too) unless they plan to waist time and money
  2. CRM often doesn’t need „implementing CRM“. In many cases, I’d rather advice you to take a look at you core processes and make it fast, responsible and transparent.
  3. Basics of a healthy customer relationship management lies in you customer focused corporate culture in opposite to the one that arises from product or process orientation (remember our first exercise?)
  4. When you work on aligning your company’s agenda with that of your customers, don’t forget about different motivations of your departments as well as talents and motives of individual employees
  5. Manage customer experience through managing their perception. Perception is often tightly related to expectations. Take care! Expectations are set by your organization’s „CRM processes“, your marketing communication, as well as by your competitors.

    The "Moments of truth" exercise

  6. Manage touch points. Those are the essential „places“ where customer experience occur. Try to use „Moments of truth“assessment in combination with customer expectations or even emotions as a powerful tool to manage total customer experience.
  7. Customer data derive from customer oriented processes not vice versa
  8. „Critical point of CRM implementation“ is the one where you know what do you want to achieve, why do you want it so much and what is the frame within you are able to do it
  9. The message of the story tale „Wolf and the three piglets“ is that we have to build solid basis for a lasting survival (business) model. The same is with social media and their use in CRM ecosystem (sCRM): invest time, engage to get them engaged
  10. …ah yes, segmentation. Some of you stated that you didn’t get enough. You asked for more. More of theory, more of tools, more of segmentation methods. It is homework for me and a great feedback from your side. Thank you.

When talking about segmentation, is not only about splitting customers into (more) manageable groups. And especially it is not just

The list of touch points from one of the exercises (in Croatian)

about distinguishing them based on their spending (value based segmentation). It is about what does your offering or your organization mean for different customer groups (benefit segmentation). It is about events from the CRM ecosystem that create dynamic

segmentation attributes and micro segments. The segmentation is about the general approach to certain customer profiles, as well as small operational activities. This is in particular case for the segmentation of the CRM era where you are able to track in real time what your customers are doing, experiencing or even saying.

About trends of the future, remember that today’s products can become powerful interactive touch points. Use QR codes in combination with Web 2.0 tools.

I encourage you to try in your everyday work what you have learned. Think customer. Think expectations. Think experience at touch points. Think about service – the fast one, responsive and transparent.

Thank you for your active contribution and for sharing your time, energy and experience with all of us.

Alen

P.S. Feel free to comment about your experience or suggestions for further improvements.

© Alen Gojceta 2010

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