Catching criminals off-guard

I recently listed an old HP mini computer on Craigslist. One “John Clara” reached out to me via text and tried to sucker me in. Funny thing is that at the time of this post I am still a PayPal employee. How amusing that this sucker is trying to scam a PayPal employee. This is how the conversation went:

+12242028827: HP Mini 110-3510NR Netbook laptop notebook - $145 (san jose downtown) 7:30 PM
+12242028827: still for sale? am john „i want you to get back to me with the condition,and your firm price 7:30 PM
Me: Yes, Condition is like new. Software factory reset, so super fast and no junk software. 7:51 PM
Me: Last price is $140 7:52 PM
+12242028827: I’m okay with your asking price I’m buying it for my cousin in abroad , I want you to get back to me with your PayPal email address and some detailed 7:52 PM
+12242028827: pictures of this item so that I can pay in asap. I’ll add the shipping fee to the total payment I’ll be sending Thanks. 7:52 PM
Me: what’s your paypal ID ? 7:53 PM
+12242028827: johnclara939@gmail.com 7:54 PM
+12242028827: also send me pics to my email johnclara939@gmail.com 7:55 PM
Me: Cool. Pics are on the craigslist posting 7:55 PM
Me: I can’t really ship it because of various reasons. 7:56 PM
+12242028827: tell me the reason 7:57 PM
Me: Btw. I work for PayPal just so you know 7:57 PM
+12242028827: so what does that mean to me now? 7:57 PM
Me: Can you pick up in person or have someone pick up? 7:58 PM
Me: I am cool with you using paypal. Please pay to this cell phone 8:01 PM
Me: No bringing in email addresses 8:01 PM
+12242028827: what are you saying since 8:02 PM
Me: I am saying I am ready to do the deal if you can pay me with paypal 8:04 PM
+12242028827: i told you already 8:06 PM
Me: ?? 8:07 PM
+12242028827: get back to me with your paypal email address for the payment 8:08 PM
Me: ***************@**.com 8:09 PM
Me: Or you can send payment to 814 *** **** using the paypal app 8:09 PM
+12242028827: okay 8:10 PM
+12242028827: send me the pics to my email,johnclara939@gmail.com, so i will proceed with the payment now,get back to me when am done 8:11 PM
Me: Cool. Let me confirm I have received the payment. 8:12 PM
Me: No receipt of payment yet. The pix are available on the post right here btw http://sfbay.craigslist.org/sby/sys/4****08994.html 8:15 PM
Me: Are you still there ? 8:18 PM
+12242028827: what 8:19 PM
Me: Did you send the payment already? 8:19 PM
+12242028827: E**@***.com,can you just stop all these rubbish 8:22 PM
Me: Yeah. I am serious. 8:22 PM
+12242028827: can you check stop doing rubbish 8:22 PM
Me: I told you I work at PayPal 8:23 PM
Me: That’s my legit email 8:23 PM
+12242028827: okay,thank you,give me 4hours 8:24 PM
Me: Ok 8:25 PM
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How to form good data questions

Consider the following questions:

BAD: How many customers churned (went dormant) last month?

GOOD: What percentage of our customers are expected to churn in the next few months and why?

BETTER: What short term impact can we have on our business by reducing customer churn and what options do we have to do that?

Needless to say, bad questions get bad answers. As business leaders we often approach our data science and analytic teams with very specific questions. In most cases asking specific questions gets us very specific answers, which alone is useless. In this case, the good and better questions are likely to generate some very useful and actionable analysis.

Good data/analytics questions seek to get three things out of the answers or analysis. These are:

  1. Insight - Something you didn’t know before
  2. Action - Asking for possible solutions
  3. Time - In a time-frame that your actions can have an impact

What you want is information (not data)

INSIGHTFUL
The answer should provide some new thought or idea that was not apparent before. For example,being told about your organization’s market share that you didn’t know before or your customer life time value. To ask questions that generate insight you should almost never ask for absolute numbers. Instead, ask for ratios and percentages of how data points compare.


ACTIONABLE
An insight is still pretty useless if you can’t do much about it. If you were simply told what your market share was, that’s not so useful. However, if you were told of a market share shift because your competitor was introducing a new product at a lower price point, that would be actionable.


TIMELY
Ask questions that don’t generate a rear view mirror answer. While historic data can help generate trends, it is the ‘prediction’ that’s valuable. For example, instead of finding out your current market share, it might be better asking what is the predicted market share in the next few months. Another important aspect of timeliness is the reproduction and periodic rerun of the analysis so that it continues to provide insights. You want your analysts to automate this process as well so that they are not spinning their wheels each week or month doing the same thing.

Time value of business insights

Business insights extracted through data mining are valuable for a very short period of time

Just because your organization has Saleforce.com or an Oracle database doesn’t mean you should be going out and hiring a team of data scientists to work full-time. The value of insights that these professionals generate, tends to diminish over time if no new data-sets are being populated or expanded as a result of growing business operations.

Data-sets, like gold mines, are finite and vary based on the three V’s (volume, variety and velocity). Looking from an organization’s leadership perspective, insights discovery typically takes place in four stages.

  1. Initial Stage (Finding the gold mine)
  2. Data Discovery
  3. Decision Making
  4. Monitoring

The value an organization gets out of data sets varies over time, but for most typical engagements the process is very similar. The figure below is an abstract view of how much value is generated over time (Note that this figure is not cumulative) during each of the aforementioned stages.

image


Finding the gold mine

Today most companies are sitting on Enterprise Resource Planning (ERP) and CRM Customer Relationship Management (CRM) tools that over time become data gold mines. Even small tools like Quickbooks, IBM Rationale Clearquest and Clearcase are storing away valuable log files that can unlock tremendous efficiency gains for organizations. The problem is that most organizations do not realize, or even understand, the significant value their data-sets can generate for them.

In this first stage, while no value is extracted from data-sets, an acknowledgement has to happen by the organization’s leadership recognizing the value of data. The leader then assembles a team of business analytics and data science professionals that are asked to explore the possibility of finding golden nuggets in these data sets.

There are many different ways data brings value to an organization: transparency, platform for experimentation, customized actions, and automation of decision making. A right combination of skill sets is also required to analyze this data.

Discovery Stage

Following the formation of a team, Data discovery is the phase where a number of processes happen. Business analysts look at the data and try to determine what analytics frameworks should be applied to the data; what Key Performance Indicators (KPIs) to extract; and what potential business cases can be constructed. Business analysts and scientists are not only examining the data but also determining the best ways to perform robustness and data hygiene checks.

Once some inroads are made into data structure and cleaning, rich insights and trends start to emerge. Many insights at this point are interesting but less are useful and actionable. This stage is where logged data is converted to daily active users, transaction amounts per day, useful sales ratios and histogram plots of various segments.

During this exploratory stage, a lot of visualization tools become useful as well. Time series and historical analysis are conducted to see how things are changing over time. Predictive analysis can be done to see where the organization is headed. In summary, this stage brings a tremendous amount of transparency to leadership uncovering opportunities for data to play a role.

Decision Making

After a significant amount of brainstorming with data-sets, analysts and leaders define a clear framework that separates out vanity metrics from ones that are useful and actionable. This is an important step because two very important things happen: (1) determination of what data the organization needs to consider, and more important, what data it does not (2) putting together a system in place that determines the right intervals to repeat the insight generation process.

Determining what metrics to focus on is a very critical step because it ties analytics to the organization’s strategic plan. A connection to the strategic plan also means that leaders of the organization have to openly assimilate the metric into the goals of the organization. The framework becomes important because it helps members of the organization understand how they contribute to the goal.

Another important aspect at this stage of the process is the development and deployment of data visualization that is accessible by all members of the organization. This enables democratization of data and allows everyone to be on the same page.

Monitoring Stage

After a visualization tool has been setup, organizations start to monitor their data. The organization also gets aligned with a certain analytical framework. Leaders get updated on the statistics of data periodically, and they take actions when things get out of trend. The amount of value being generated at each data generation cycle is at best incremental and data scientists and analytics don’t have much more value to add besides making sure data updates are accurate. Organizations that become smart enable reporting by exception.

CONCLUSION

Hiring a data science or business analytics team makes sense if an organization is going through tremendous growth and continues to generate new data sets. If this is not the case, data scientists are only valuable for a very small stage of the insights generation process. As a leader of an organization not undergoing tremendous growth, you must carefully think about your goals and the depth of goal mine you think you are about to mine

In their efforts to compensate for the unreliability of human performance, the designers of automated control systems have unwittingly created opportunities for new error types that can be even more serious than those they were seeking to avoid

- Chris Hart, acting chairman of the National Transportation Safety Board (NTSB) talking about the cause of the Asiana crash.

Unforeseen consequences of automation

Since the advent of autopilots life for pilots has become much easier. This was widely driven in the aerospace industry by something called Crew Resource Management which has a goal of focusing the crew on only a manageable number of tasks at a time. Adversely it has an impact potentially making pilots complacent and depend on autopilot systems more than the systems are capable of.

As businesses start to gather more data and move towards automation they are going to start facing the same kind of problems. Leaders of organizations trying to run lean may rely too heavily on automation resulting in unforeseen disasters.

Ways to get screwed by taxi drivers in Buenos Aires

On our recent travel to Argentina we primarily used taxis as a means of transportation in and around the city. I think I learned every possible way a taxi driver can deceive and cheat a passenger. Here are the top methods that were used on us on five different trips by various drivers. 

1. Currency conversion

Argentina has two exchange rates: one official and the other unofficial. Officially you get 8 souls per dollar and unofficially 11 per dollar. Upon arriving at the airport we only had american moola in our pockets and the taxi driver convinced to pay him 30 US dollars instead of local currency. Obviously trip would have been cheaper in local street rate.

2. Take you for a longer ride

This is a classic one where if you don’t know the way they take you the longer route. How did we know we were getting screwed? We had google maps pulling directions for us and the taxi driver was obviously ‘taking us for a ride’.

3. Take you for a very slow ride

Aware of the shenanigans of the long ride, I started giving turn by turn directions at the next occasion. This guy caught on, and tried a brand new trick. Started driving 5 miles per hour below speed limit. For a trip that google maps predicted 40 minutes, it took us an hour and 5 more dollars. 

4. Meter rates

Another classic one is a faster meter. For a trip that cost 10 dollars one way, cost us $20 returning the same route.

5. Charge extra for your bags

After all these lessons learned you would think we should be negotiating rates in advance. That did not work either, even after negotiating the price, the taxi driver asked for more for ‘additional’ services for unloading bags etc. 

All said and done any money we saved during our awesome negotiations with Peruvians, we lost to Argentina. Chalking another one up to #costofdoingbusiness

P.S. Shout out to Peruvians for the being one of the nicest and politest people. 

stanfordbusiness:

Founder of personalized men’s clothing service Trunk Club Brian Spaly (MBA ‘07) offers advice on how to build a great business: http://stnfd.biz/wgw7r #StanfordGSBAlumni

What a great quote! With so much capital to go around these days, we may be going through a time with too many vitamin products and too little aspirin solutions.