I have listened to a lot of conversations about how we are worse off than our parents, how the United State’s Economy is doomed, and how United State’s position as a World Power is doomed. This month I had more conversations than usual about the Great Stagnation.
It is easy to get discouraged about the Great Stagnation. Is the sky falling? It might be, how would I know? Also, after a hard day it is attractive to think that the entire system is broke and we are all doomed to stagnation
However, In the back of my mind I knew things couldn’t be stagnating. Infact, things are changing faster than ever. Confusion, ambiguity, fear and uncertainty are not the result of stagnation. They are emotions caused by rapid growth and new paradigms.
We have failed to realize that we now live in a world that looks like a 1970’s Science Fiction Dystopia. Some people take advantage of the changes, others choose to ignore the change. The result is that we feel anxious, but are not sure why.
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Is the US Economy Doomed to Stagnation?
The Future is Now
There is something very strange about Contemporary Analysis. It’s not something that can be easily identified or quantified. People have often remarked on the fact that we are different even though they weren’t able to put their finger on what exactly made us different.
Perhaps you’ve noticed. On any given day, a person might wander into our offices and overhear a conversation about artificial intelligence, or a spirited debate over metabolic intelligence versus programmable intelligence. Often, the ideas that capture our attention haven’t yet entered into the imaginations of the public at large. Sometimes we tend to discuss the trends of the future as if they’ve already happened, and it’s these types of clues that have aroused suspicion. And so, we have decided to come clean and confess.
We are from the future.
We’ll forgive you if this claim is met with skepticism, after all, the first rule of time travel is that you just don’t talk about the future. But we’re not interested in following that rule, we want to talk about the future. There are a few things we think you should know.
We want you to know that in the future, things aren’t so bad. In most ways the future looks surprisingly similar to the present. The major difference is that “the new” is neither good nor bad. People will be creating all sorts of new things in the years to come, just like we always have. You do not need to be scared of “the new.” There will always be new ways of thinking, fueled by new ideas. There will be new problems but also new solutions.
At CAN we were raised in this bold, new world. It’s what we’ve always known. We grew up with computers, the internet and a plethora of digital interface devices at our fingertips. We navigate these new landscapes with relative ease and radical speed.
Predictive Analytics is the future. The future is now. How will you use it?
Why you should update predictive models
After writing my previous post, “How Often Should You Update Predictive Models”, it was appropriate to followup with a post regarding the consequences of not updating predictive models.
Predictive models use the patterns in historical and transactional data to identify risks and opportunities. Since the conditions and the environment are constantly changing the accuracy of predictive models need to be monitored. Once a predictive model no longer reflects reality it needs to be updated. Most of the time this is because the assumptions behind the model need to be updated.
Take for example a community bank. Internally every new transaction, deposit, withdrawal, application, or transfer creates new data. For most individuals, these transactions are occur several time every day, and that means you’re compiling thousands of new data points. Over time the customers environment is changes, this is reflected in each data point collected. Did they get a raise or a new job? Is there car breaking down? So although this community bank may have a relatively modest customer base, their customers are experiencing change all the time.
Also, their are external changes that impact a customer’s behavior. For example interest rates change, new competitors enter markets, competitors invest in marketing, consumer confidence changes, and competitors merge. It makes sense then that they would need to update their predictive models to keep up with all of these changes. When these changes start to represent structural changes a new model needs to be developed.
For a typical community bank, strategic sales, marketing, and planning decisions happen at least once a quarter. If a bank doesn’t update their predictive models in preparation for these events, they are at a high risk of using obsolete information when making decisions.
What are the consequences of using this obsolete information?
- Your pricing models don’t reflect changes in the competitive environment.
- You recommend outdated products.
- Your marketing material isn’t targeted at the right groups. They might not exist any more.
- Your business development team begins chasing the wrong types of leads. For example, it might not be a profit environment to pursue new home mortgages.
So if you’re planning on making an investment in predictive analytics, make sure you consider the implications of using your data as well as the consequences of using outdated information.
How Often to Update Predictive Models
Everyday new information is being created in your business. Your customers are buying more, subscribing or unsubscribing, and before you know it your customers today are seemingly different than the customer you had the day before.
As these new patterns emerage its important to periodically take time to investigate your data, update your predictive models, and challenge the assumptions about your business going forward. But how often should you do this? To answer that question, consider the following:
- How often is my data changing?
- How often do I plan on making decisions with the data?
History of Predictive Analytics: Since 1689
Many people credit the rise of predictive analytics to the technological advances of the last 50 years. However, The history of predictive analytics starts in 1689. Its true that record keeping standards, relational databases, faster CPUs, and even newer technologies such as Hadoop and MapReduce have made predictive analytics an accessible tool for decision making. However, the history of predictive analytics show that it has been used for centuries. (more…)
Predictive Analytics is Not a Crystal Ball
Its common to see predictive analytics as a sort of “crystal ball” for your business. This crystal ball image makes for great marketing. Unfortunately, predictive analytics is not a crystal ball.
It will not provide the correct prediction every time. Its primary purpose is to help you make better decisions by giving you the power to unlock the patterns inside your data. When performed correctly this gives you the ability to simplify decisions. When performed incorrectly it can spell disaster for your company.
Predictive analytics is both an art and science. It requires a combination of both empirical and subjective experience to verify that models reflect reality. This is why CAN takes into consideration three main aspects when building predictive models: Data, Theory, and Math. In our experience your predictive models will not reflect reality if all of three of these aspects are not held up. (more…)
The New Frontier of Data Science
Most of us think of our world as having already been explored. After all, the days of Magellan and Columbus are literally history, and today we can pull up Google Maps to view satellite and street-level images of every square mile of our planet within seconds. The generations before us sailed new seas, crossed continents and mapped lands that were completely foreign to them. Future generations will be exploring the cosmos and travelling to distant planets. And so it seems as if there aren’t any bold new frontiers for the explorers of our time, but that’s not true.
We live in the digital age, discovering new frontiers using computers, data and the Internet. This world is growing in complexity and we are venturing out to map it and settle it. According to Google’s Eric Schmidt, we now create as much new data in 2 days, as we did from the dawn of civilization up to 2003. We produce 5 exabytes of data every 2 days. (1 exabyte = 1000 petabytes = 1,000,000 terabytes)
This new landscape of data science can be as foreign and complex to many of us as the Great Plains were to the early settlers. Where do we begin? Where are we going and how do we get there? What resources do we have to gain from this bold, new world? (more…)
Predictive Analytics improves M&A Activity
There have always been two major ways to expand your business: Grow it, or Buy it. This brings up some interesting questions about which is more beneficial. The correct answer is usually based on cost of customer acquisition and customer lifetime value. Right now, with the cost of client acquisition being so high, companies are turning to buying distressed businesses. One, it eliminates competition, and two, the customers can be acquired “on sale”. While mergers and acquisitions are common across all industries, there seems to be a significant propensity for growth by buying in the banking industry.
The unique problem that is causing an increase in the ” buy them” thought process is that in banking their revenue generating power has dwindled with the decline of interest rates. Not only that but as clients leave for competitors by natural attrition, there is a dire need for new customers. Buying seems to solve both of these.
While it may solve the issue of new customers at a reduced cost, how to transfer the old customer base to the new bank has always been a major problem. First, you have a bevy of new customers who have not gone through your buying process. You have no idea who they are and why they are in the product they are in. Secondly, you can fix problem number one by keeping the staff from the bought bank, but they’re not sure if the customers are in the correct products anymore either because they don’t know what products they have to sell. (more…)
Rethinking Business Intelligence: Information or Decisions
Traditional business intelligence leaves executives with the same amount of work, but with even more information to sort through. The number of decisions, the unit of work, is not diminished.
Traditional Business Intelligence asks, “What information do you need to make better decisions?” The outcome is hopefully beautiful well designed reports and dashboard that support decisions. The problem is that you still have to make decisions.
Decisions are work. Having more information doesn’t reduce the amount of work required to make decisions. In fact, it makes decisions more work. More information does not create less work.
The flaw is thinking that the business decisions are calculations. (more…)
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