Drawing I made to support explanation about the change in existing BI paradigm with introduction of Big Data
Drawing I made to support explanation about the change in existing BI paradigm with introduction of Big Data
I am writing this post for three reasons:
Rainy September, late evening. I am surrounded by a group of strangers having late dinner at a hotel in the center of Vienna. All of us are here for the same reason – we missed our late flights and the airline company assured the dinner and accommodation. A young French couple is sitting at the opposite table. The young woman is talking to the waiter. I can hear the conversation: “You want to say that we have the business class and now we have to pay extra for some cheese on my pasta?” The clerk’s answer is neat and clear: “Yes exactly”. Cold and factual service is what I really got enough today. I’m taking my smartphone and tweeting the following.
— Alen Gojceta (@agojceta) September 1, 2014
It all happened a couple of weeks ago when I was flying from Belgrade to Bucharest over Vienna. To make more productive the next day, I decided to take a less comfortable evening flight over Vienna instead of the direct one in the morning.
My flight from Belgrade was getting late, and I missed the next plane. This was not the worst thing one could have experienced, but somehow I really felt that no one in the service chain really cared whether I would miss this plane or not. Moreover, I believe that I could have caught it if it was different. Here is the story about my perspective and suggestions on how Austrian airlines could have done it differently. I use the “idontcare” (I don’t care) tag from my tweet as a synonym for the missing service experience.
Just after we landed to Vienna, while some of the passengers were still at their seats, I told the flight attendant that my boarding for the next flight ends in 20 minutes. She responded by telling me just to go through the regular procedure. Without any additional information – will I make it or not. At the time, I was sure that by that she meant: “You should make it by passing the normal procedure”. Tone of her voice or expression of her face did not insinuate that I should expect anything else. It turned out lately that this was my first idontcare that evening.
Second #idontcare I experienced in front of the craft. While we were waiting for the cabin luggage, passengers to Frankfurt were invited into a van waiting aside. Some of us with tight schedules also asked the attendant about our options but we were told just to proceed to the bus and gates. The Frankfurt van only increased my expectation-perception gap. “This was the way I should have been treated” was the only thought that could have come to my mind.
Third #idontcare I experienced at the security check. My time was running out when I reached the security check. I saw that I had perhaps just a few minutes to make it to the gate some 30 meters away. From my position in the queue I told the security person that my boarding is about to close. She just shook her shoulders and said that I had to wait. Some nice granny was the first to show some compassion and let me in front of her and her husband. However, the security check took it’s time with the second turn with my shoes passing through the x ray. I missed the gate closing for just about 4 minutes.
An airline might argue “But this is not about us, security check is an independent organization”. I do not take that! Airport security does not take part of bureaucratic government agencies any more but rather private security firms. I would not agree with an argument such as “This is under airport authority, and we are just an airline”. It is up to the airline to develop a business model that assures certain service level (with delivering on schedule as its minimum) including the whole ecosystem – security, airports, food providers, check-in process, travel agencies, on-line booking services, and even (to go to extremes) air traffic control authorities if needed.
It is also about the way we associate brand with service: Austrian airline – Vienna – Vienna airport. Now tell me who do you associate the security service with. Got it?
Some airports do not require additional security checks when switching flights. Austrian airlines (ups sorry Vienna airport) does. Competition sets expectations (once again).
When I reached the gate, the person standing there, just concluded that “The boarding is completed. You have to go to the service desk”, pointing to the nearby booth with 5 or 6 working places and 2 or 3 persons servicing clients.
Although short – maybe 6 or 7 people, the queue at the both was very slow. After 10 minutes, I started feeling uncomfortable: “What if there is a next plane departing in minutes? What if I’m missing it by standing in this queue, what if…”. The problem was I could not check any status as there were no displays in my sight, and I was afraid to leave the row that was becoming seriously long behind me. A young woman cried because she missed the same flight, a group of people in front of me discussed about what they should do. After long waiting, the group of people in front of me was serviced in seconds as they obviously stood in a wrong row. Overall, at the service desk the “idontcares” reflected in significant lack of information as well as indifference about the impact of the process on the passengers. While standing there, you feel blind, dump and not really serviced. Next time in such situation, I’d like to understand what is happening, how long will it take and what are my options.
The general #idontcare during my experience with the missed flight is the overall absence of empathy in all interactions in which I was involved with Austrian airlines representatives. Yes, by that I meant the Vienna airport and security employees also – sorry again. I do not blame Austrian for not having designed the customer facing processes around empathy. However, I do mind the ease in which I was left to spend the night in Vienna, without anyone even pretending to try to help or at least care.
Finally by writing this, I am answering to the Austrian airlines tweet, although I told them I would not (#idonthavetime), I have invested time and energy to “co-source” the design of Austrian’s “#idotcare” processes. However, I’m not sure that it will make any difference. By my experience, I suspect that this correspondence will remain just another cross on the to do list of some “social media specialist” who did his or her job by finally showing some compassion and care thus avoiding bigger harm to the image of the brand.
@agojceta We're so sorry for the troubles, Alen! Can you tell us what exactly happened? We'd love to follow-up with you!
— Austrian Airlines (@_austrian) September 2, 2014
There are just a few companies which have the culture and the business model integrated responsive and actionable in the way that a client complaint would have made any changes, and I’m really not sure that Austrian Airlines with its service ecosystem (the craft crew, the security check, the service desk, the lady at the gate, the airport stuff,…) is among those.
Austrian Airlines, surprise me!
Big Data and traditional data warehouses. How to compare them? What are the differences and the roles? Who wins? No one actually.
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.
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.
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.
Unlike 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.
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 .
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.
Big Data is a big is a Big buzzword. Although it brings huge opportunities, the “hype talks” might miss lead you to wrong decision that Big Data is cure for all. The truth is pretty simple – Big Data can give those answers that are hidden within the analyzed data set.
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.
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.
Many people, including sellers, perceive sales job as filling the market demand. They forget that sales is the major value creation engine and that it has to engage most when the orders are not so abundant.
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.
After Steve Jobs has passed away, many articles were written about the impact of this great individual. In this post, I give my view on the two of many dillemas presented lately in press articles and blog posts.
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.
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.
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.
The three most read articles at www.gojceta.com during the past year
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:
The above articles are copyright of Alen Gojčeta
©2006-2010 Alen Gojčeta
The post presents the history of Customer Relationship Management (CRM) within the context of academic research and business applications. This is the excerpt English from the article published in Croatian in April 2010 in “Mreza”, magazine for IT professionals. The CRM history is described from its starts in mid eighties to day, with a view on the years to come.
The history of CRM can’t be observed without considering the development of business applications with contemporary academic research.
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.
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.
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.
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.
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.
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.
I had an honor to have the edited version of my comment to Harvard Business Review case study “Preserve the Luxury or Harvest the Brand?” printed in its January / February 2011 issue. Here is the case…
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.
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.
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|
|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.
The first full year of Alen’s Think Place is represented by 12 peaces of written work. Here are the summaries and links to 5 business articles translated to Englsih, 5 blog posts and 2 reviews of positioning / branding strategies…
The 2010 was the first full year of Alen’s Think Place is represented by 12 peaces of written work:
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.
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.
In this post I announced the facelift of my CRM seminar and shared some thoughts about the approach.
The “bdp Triangle” is among my best sales concepts….
I just couldn’t resist to write the article about this unconsistent branding approach…
A small “Alen’s ThinkPlace” strategy: consistent content to the consistent audience using a single language…
The article elaborates the problem of a “CRM organization”…
Small insight in my latest article published in Mreza magazine through a story inspired by a true event…
English preview of the implementation part of my April article – the 7 wisdom for a successful CRM implementation…
The concepts from this article from 2002 are just today becoming really mature and useable…
In 2010 this was one of the most read articles at www.gojceta.com. Check why…
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 🙂
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.