When to consider “Solutions on Demand”?

First, what is the “Solution on Demand”?  This is when you outsource specific business application to some external subject matter experts to manage it for you.

So this is what you do when you want to reduce both risks and the costs in a same time. While this may not be always long term answer – short term tactical benefits are the main appeal of going this route, especially now at a time of slowdown across IT sectors, the times of reduced budgets, and general uncertainty – while at a same time demands of new business applications continue to grow.

Another term for “Solution On Demand” is the term “Software-as-a-service” (SaaS), and a growing  number of organizations are investing in SaaS as the most mature form of cloud deployment. This deployment model is changing the way software services are consumed by the lines of business, spurring the use of business analytics and improving competitiveness. 

IT departments will discover that cloud deployments allow for a renewed focus on core competencies, reduced staffing and zero maintenance – all that while passing all the risks to expert hands.

Send me a comment or contact me directly on goran.dragosavac@zaf.sas.com

Analytics and Data Mining: Use, Abuse and Goldman Sachs

As a practitioner I always hope that the benefits of helping my clients to improve their use of analytical technologies will be somehow passed to their customers. Most of the applications of analytics are geared in that direction.  The whole idea of analytics is to enable you to serve your customers better than your competitor, so that you get in return their purchasing loyalty, and a larger share of their wallets.

However, not all users of analytics think that way. Some are only interesting about what they can get out of it, and customers are there to be milked for all its worth.

One such example is Wall Street’s biggest darling: Goldman Sachs.

It has been known that banks like that have been using analytics and data mining heavily. And not necessarily just as a value add to the customer but primarily to support various trading strategies. Analytics are being used to the abundance of market information to figure transaction flows, trends, whether the market will turn bullish or bearish, and to figure out excess returns.  In other words they are using powerful analytical capabilities coupled with super-computing power to game the system.

While it is illegal to trade on insider knowledge about company finances, these people are trading on insider knowledge about market order flow. That’s how Goldman Sachs and the other biggest houses make so much from trading. Economists have a term for that: “rent seeking” which extracts billions from the market without putting anything back. As one blogger points out: difference between usage of analytics between Google and Goldman Sachs is that Google wants to sell you a book you may be interested in, while Goldman uses analytics to take away all the books you ever bought.

So far, this wasn’t bad business for Goldman Sachs. Their profits soared, $2.3 billion in 2008 and $13 billion in 2009. Never mind the fact that financial markets were shaken and one million people lost their homes and other million lost their jobs.

But of late Goldman Sachs has been under increasing pressure from US lawmakers.  According to US senate sub-committee, which produced 639-page report on the financial crisis and Goldman Sachs role in it - one of the recommendations is that Goldman Sachs top executives are referred to the Department of Justice for possible prosecution.

This report provides the evidence that their sales people were selling securities to clients based on very shaky and volatile bonds, that they knew they will blow-up. So, they unloaded it as fast as they could on their clients, while at a same time there were betting on these bonds to fold. Scam!

Rolling Stone contributing editor Matt Taibbi says: Only reason this is controversial and we even asking if GS’s execs should be jailed, is because this is a financial service company, and things are not as obvious if this would be some other industry. If this was a car dealership – for instance – there would be no question”.

Taibbi continues: “Imagine if you are  Ford dealership and you get a full inventory of Ford Broncos that have brake defect and you decide not only to sell them, but also to give bonuses to your sales people who sell these defective products to encourage them to sell more. And then you go out and take life insurance policies on drivers of these cars you have sold to!”

That’s exactly what has happened here.  

And while there is a lot of evidence against Goldman Sachs, proving the criminal intent of its top executives will not be easy.  Add to that some powerful connections in Washington bought through political donations to the both parties and it becomes clear why not single Wall Street executive has been convicted after the recent financial meltdown.

And as for analytics here – well, you can use a screwdriver to fix a lot of things around the house, but you can also use it to take someone’s wallet.  It is just "a thing”, and how will it be used depends of the user's intent.

Goran Dragosavac

"Buzz" Analytics

The other day, at one of our regular coffee chats with the senior partner here, he dropped the word, “Buzz Analytics”.
“Heard about it”, I said, “but how important is it?”

“It’s a very effective tool”, he replied, and went on to quote Shane Atchison of Zaaz, who says “the explosion of online dialogue isn’t going away, and you can either sit on the sidelines or try to proactively influence and manage the situation.”

“Or else”, he went on, “as Neil Mason of Clickz Network puts it “blogs and forums are sources of unstructured consumer data on brands. In some ways, it’s no different than surveying consumers for brand opinions, other than that the opinions are unadulterated. This is a valuable source of intelligence for companies concerned about their brands’ reputation.”

“Buzz Analytics tools” the senior partner added, “put together all the information in a rational manner of what’s being said about brands and their competition online. So what what we have here is a software monitoring the various feeds set up and determines the sentiment and essence of what’s being said using natural-language text-processing algorithms.

After which, the software reports what’s being said about you and your competitors.”

“Hmmm”, was all I could manage”, I sipped my coffee. I think it was more because I was at a loss of words.

Later, as I sat down to catch up on more about web analytics, it reminded a lot of market research, where data is collected, analyzed and communications developed in accordance.

Yes, buzz analytics is here to stay, dear reader, as tools get even smarter by the day, and companies start competing more fiercely for eyeballs online.

--------------------

This article has been written by Wigbert Piedade, aka Wiggy, who is a Creative Director at Pigtail Pundits. A writer and film maker, Wiggy dreams of utopia; he hopes to one day reside in a village by the sea and head to the city on weekends.

How to Wake Up Dormant Customers

Maintaining the relationship with a customer is a costly exercise, and not all the customers provide the same value to an organization. There are some customers who buy little, the value of their purchase is low and this is unlikely to change, regardless of the type of stimuli. The organization loses money by investing in this type of relationship.

Then, there are other customers with whom a stronger relationship and better customer interaction would result in more profitable relationship and higher value to the organization. These two groups of customers differ in their buying potential.

Recently, I was tasked with providing assistance to one financial company with the goal to estimate purchasing potential of about a million customers who have rarely been contacted in the past.

Due to the prohibitive cost of the acquisition programs, and the fact that it is cheaper to sell to an existing customer than to a newly acquired customer, this South African financial organization has decided to re-acquire a segment of their dormant base that was rarely contacted in the last 5 years. The intention was to use advanced analytics to separate customers whose buying potential was spent, from the customers who would still respond given appropriate marketing stimuli.

So, this was two-step process. On a first step we needed to quantify customer buying potential, through building simple “look-a-like” model, which would assign probability to purchase. So for customers with potential above a specific threshold we needed to produce cross-selling model which would give us a probability of purchase of specific product. After that was done, we were not only able to isolate customers who still had buying potential, but moreover – we were able to tell which product should be offered to which customer so that this potential is realized.

After modelling was done, we have done some pre-implementation testing which was successful, and that opened the door for full-on implementation. About 12 months later,  collected numbers have indicated that 24% off dormant segment was re-activated.

Regards to all readers,
Goran

Advanced customer segmentation

Customer/market segmentation is one of these topics that are defined in a multitude of different ways. And while all these definitions are in a way correct - they tell more about the person ‘s understanding of the subject – rather than of the subject itself!

The biggest point of contention is whether segmentation is the business application, or it is an analytical method. So, to those users who think that segmentation is just synonym for clustering technique – I would say – it is a good starting point, but there is so much more into it.

And once different facets of segmentation are understood (natural segmentation - versus business driven - versus segmenting on specific business dimensions) - then you can start appreciating all the different directions where this application can take you. Add to that substantive knowledge of market, customers and data sources together with effective data preparation and your ingredients for success are coming together.

Why customer segmentation? Well, customers with similar attributes tend to behave in similar ways, more often than not. This fact is particularly evident in customer relationship management, marketing, and risk management. People within same life-stage segment tend to buy certain-types of products, so promoting products that go with that specific group can lead to successful marketing. In credit and insurance industry, good customer segmentation can lead to minimum exposure to risk. Similarly, in catalog sales, customers can be selectively targeted to reduce marketing cost.

What is the first step in segmentation? It is to know your segmentation objective. What is the goal that you want this application to take you to? Do you want to just to see your natural, data driven-segments among your customers?. Do you want to better explain your existing business-driven segments (who are my “gold-card” customers for example, what do they buy, and where do they come from)?  Or, do you want to segment your customers based on their buying potential, value, risk, propensity to attrite, or something else?

After you know your segmentation objective it is just a matter of translating it into data driven analytical process, supported by business knowledge.  Once you have a segment your population you can now act on these segments in and measure their movement and stability.

So, is that all to it? No -this is just beginning to more advanced segmentation.

If you imagine segment in a circular shape, you can imagine that there is inner and outer layer. Inner layer you can describe as your core segment members who are typical for that segment. Then you have an outer layer which is far less stable. Any segment migration strategies are done on outer layer. And that’s where the fun begins. So, depending on the direction of the portion of the outer layer you can now do two basic things. You can try to stop movement in a specific direction or you can try to encourage movement by running specific (marketing) stimuli on this sub-segment.

So not only that you are able to group your customers, and understand what makes them similar, but you can entice sub-segment movement, or even  stop it – if that is what would go in line with your initial objectives.

So, hopefully this article gave you some more insight into the power of segmentation and what can you do with it.  But how would you do it? Well, this may be in my next article, so – I hope to see you again on my blog!

Rescue of data miner

Recently I was called to assist one of the junior data miners at one telco company.  Junior was buried under an avalanche of data. Breathing heavily, she seemed like planet was resting on her. Model that she tried to build had thousands of variables and millions of rows.  Her problem had a more potential solution than the Milky Way has planets.

Smoke was coming out of her quad processing computer, and I was fearing the worst.

There was no time to be wasted. It was only one solution, and that was to go back in time and start fresh.

So, we unplugged computer and left a room. She desperately needed some electrolytes in her system.  

Once, she was back to okay, we started our trip from beginning with slow steps.

Just two of us, with the pen and paper.

Soon, there were more people… and more information.

This time we understood better where we were, and where we wanted to go.  And we knew why are we going on that trip, and what will await us when we get there.

Soon, we also found out that there are many things that we could carry on that trip. Too many!

Then we call it a day.

The next day, we met again.

And it told her, that we need to make our trip lighter and more mobile. More flexible.
So, we decided that we will drop a few things, combine the rest, so that our step as light as the butterfly wings.

This time we wanted no avalanches.  

So, when we started the engine, plugging back her computer – she was very surprised to realize that we were already half way to our destination.

The rest of the trip was most enjoyable, and we managed to get to our endpoint sooner than we thought.

What was the difference this time?
Starting point was the same. The endpoint was the same.  The reason for the trip was the same. Means of travel was the same.  It was only one difference!

The route we took.  

Use the Analytics to Find a Perfect Match

  It is known that internet dating has become lucrative multi-billion dollars industry.  Part of the service is helping date seekers to find “ideal” match. So how do they do that? Well, dating service companies like match.com use sophisticated algorithms to profile its date-seeking base and then match them against similar profiles. But, it goes far more sophisticated than that. There are some new Facebook applications that create pseudo-DNA profile of its users in terms of preferences for music, hobbies, movies, as well as topical preferences using their Twitter threads and match them against each other to find a match.



Critics may say that very important dimension by using analytics and data mining to choose the match – is omitted. Dimension of physical appearance! Well , wrong! Some of these applications are able to do visual mining to extract certain characteristics and match them against similar characteristic of the match. For example - If person in the photo is smiling likely match will be another smiling photo of opposite gender.

  

So, now you have in a mix known characteristic, preferences, physical characteristics, topics and themes of interest and “DNA” is ready to go on a search for the ideal partner. While conceptually all this is sound, and technologically possible to do – cynic in me is just giggling. Data mining has never been “exact science” so, before I take it any more seriously – I would like to see some match results (preferably from “confusion” matrix.)