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Increase Your Share of Wallet

By definition “Share of Wallet” is the percentage of a customer’s expenses for a product that goes to the firm selling the product. For Banks, Share of Wallet constitutes the number of financial services products a customer has with them relative to all of the financial services products they currently have. For a gas station, it may be the number of times a customer fills up with them in a month relative to the total times they filled their gas tank up total that month. Learn how we helped a Credit Union increase their share of wallet.
Share of Wallet is widely used today as a loyalty metric in many industries. Although its based on rough estimation it gives companies the ability to seemingly compare themselves to their competitors and their industry as a whole. However, because the information is gathered through market research and surveys, its at best a rough estimation and better information can be gleaned after a certain point once companies start gathering more information about their customers from internal and external sources. Simply tracking Share of Wallet though also doesn’t allow companies to improve upon its product offerings and marketing strategies. It should also be fairly apparent whether you are able to compete on Price, Quality, and/or Convenience.
So why not find ways to make your current customers more loyal rather than continually push surveys to simply track their level of loyalty?
Today, most companies are sitting on a goldmine of data that is being underutilized. Customers provide new information each day to companies about their status in the marketplace. With enough customers, this information creates patterns that can not only help you identify least loyal customers that are most likely to go to your competitors, but also help identify those most loyal and help you focus your sales and marketing efforts and dollars on the right groups.
This data can then be further applied to help you grow loyalty by determining who to push your sales and marketing information to based on:
– Which customers are the best fit for your products or subscriptions
– Which customers are likely to need certain products next
– Which customers are likely to need complimentary products based on recent purchases
– Which customers are the best fit for loyalty or reward programs
Today most of your data is centralized in a way to help you accomplish this. Your CRM, accounting systems, and ERP by default are structured to connect the wealth of customer data that is continually being created each day.
So by using patterns in your data to guide the right customers to the right products and services, you continue to grow loyalty and thereby increase Share of Wallet. As an added bonus, you are also optimizing the limited sales and marketing resources you’ve been given.

Learn how you can use Predictive Analytics to increase your share of wallet by downloading our ebook, Predictive Analytics: The Future of Business Intelligence.

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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…)

On Entrepreneurship, Risk and Uncertainty

Entrepreneurs live with risk and uncertainty. They don’t have a choice. The future is up to them. They are responsible for their successes and failures, and success is never permanent.  Therefore, Entrepreneurs have to learn to handle the risk and uncertainty of having to be responsible for their company and employees.
I have been fortunate. I have spent the majority of my life as an entrepreneur. In fact, I have never had a “real job”. I started my first real company when I was in elementary school, and sold it when I was 20.  I have spent most of my life focused on building successful and sustainable companies.  My early start allowed me to adjust gradually to the risks and uncertainty of being an entrepreneur.
When I started I had nothing to lose. I started a business because the people I knew needed a service and I had time. Gradually, the risks and uncertainty increased. In order to increase profits I started to take on more and riskier projects. They required hiring more employees, purchasing more equipment, investing more money, and taking more risks. Over the years, I have been forced to learn to handle the risk and uncertainty of being an entrepreneur, both in the good and bad times.  I have made and lost money, employees, and capital.
In good times, I learned to stay paranoid. In High School, after getting overly confident I learned the importance of Andy Grove’s quote, “Success breeds complacency, complacency failure, only the paranoid survive.” (more…)

Net New Sales: Sell More by Selling Less

“Selling more by selling less”, is a phrase that most salespeople write off.  They can’t believe that you can increase sales by focusing less on acquiring new clients, and more on your current customers.  This is a basic misunderstanding and over simplification of the sales equation.  Most salespeople focus only on new people. They fail to recognize the huge crowd of people that have already decided to become clients.  They forget about their current clients. They have already been sold, and are waiting to be called again.  But you have already sold them you say?  Sure, weeks, months, and sometimes years ago.  Don’t your think they are ready for something else?  They are.  Salespeople just don’t know how to resell to someone that already knows their game, and therein lines the trouble. Learn how we helped increase customer loyalty by reselling current clients. 
Sales is not about new sales, it is about net new sales.  To increase net new sales, salespeople have to also be concerned with reducing the number of their current clients that are lost each month to customer churn.
Current clients.  I find it fascinating that sales people never think of their current clients as leads for more sales. I think this stems from bad Customer Service Management systems that do not allow salespeople to keep track of when customers are ready to be sold another product, and management’s lack of understanding about how salespeople should be trained and compensated.  But why?  Why cant sales people just focus on new sales.  Because new sales are only half of the equation.

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Diapers, Beer, and Data Science in Retail

When asked for white papers or case studies on how predictive analytics works, I often give a few stories on how different industries use analytics to find patterns in their data and then apply that knowledge to their existing data to predict what future trends are going to happen. Learn about how we applied predictive analytics to politics. 

I get asked specifically about legends that roam the retail world:  the study that found that milk is the most purchased item so it is always in the back of the store, making you walk by everything thing else they have before you get there, the fact that women’s shoes are always on the way to men’s clothes, and the fact that bananas are at the front of stores because they are found to be an impulse buy.  The one that seems to get the most requests though is the one that men who buy diapers for their kids are most likely to have beer also in their carts.

It doesn’t seem that far-fetched. (more…)

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Occam's Razor and Model Complexity

When using predictive analytics to develop a model it is important to understand the principles of model complexity.  Occam’s Razor is a concept that is frequently stated, but not always fully understood.  The basic idea is that “All else being equal, simpler models should be favored over more complex ones.”  It is concept we both embrace and approach with caution so that it is not misused.
First, let’s flesh out the concept of Occam’s Razor beyond the simple aphorism given above as it can apply to predictive analytics.
Suppose I flip a coin ten times, and I get a run that goes “HHTTTHHTTT”.  After observing the coin flips I assess that there are two possible models for the behavior of the coin:

(A) The coin is fair and has a 50/50 chance of getting either heads or tails on each flip.  The observed run was just one of 1024 possible results of the ten coin flips.

(B) The coin flips are deterministic and will land in a repeating pattern of “HHTTT” which perfectly fits with the results of our sample of coin flips.

Without further experimentation I have no certain way of knowing which model is actually true.  If I were to flip the coin five more times, if I got anything other than “HHTTT” all confidence in (B) would be gone, the same cannot be said for (A).  This is because (B) is a much more complex model then (A).  It other words, it would take much more evidence to be confident in (B) over (A).
Keeping this concept in mind is important when developing predictive models. (more…)

Use Email Signatures, They are Important

I get emails all the time from clients, potential clients, and people who want me to buy something from them. What constantly amazes me is the lack of email signatures. I know for a fact that all email programs allow you to make an email signature with your name, rank, email address, phone number, Skype number, LinkedIn page, web page, blog address, and business address.

Why then, don’t people use them? It can’t be because they are lazy. Not having one loses you business.  After all, it is not always best to respond to an email with an email, especially when a topic is new, complicated, or sensitive. (more…)

Using Mean Absolute Error for Forecast Accuracy

Using mean absolute error, CAN helps our clients that are interested in determining the accuracy of industry forecasts. They want to know if they can trust these industry forecasts, and get recommendations on how to apply them to improve their strategic planning process. This posts is about how CAN accesses the accuracy of industry forecasts, when we don’t have access to the original model used to produce the forecast.

First, without access to the original model, the only way we can evaluate an industry forecast’s accuracy is by comparing the forecast to the actual economic activity. This is a backwards looking forecast, and unfortunately does not provide insight into the accuracy of the forecast in the future, which there is no way to test. Thus it is important to understand that we have to assume that a forecast will be as accurate as it has been in the past, and that future accuracy of a forecast can be guaranteed.

As consumers of industry forecasts, we can test their accuracy over time by comparing the forecasted value to the actual value by calculating three different measures. The simplest measure of forecast accuracy is called Mean Absolute Error (MAE). MAE is simply, as the name suggests, the mean of the absolute errors. The absolute error is the absolute value of the difference between the forecasted value and the actual value. MAE tells us how big of an error we can expect from the forecast on average.

One problem with the MAE is that the relative size of the error is not always obvious. Sometimes it is hard to tell a big error from a small error. To deal with this problem, we can find the mean absolute error in percentage terms. Mean Absolute Percentage Error (MAPE) allows us to compare forecasts of different series in different scales. For example, we could compare the accuracy of a forecast of the DJIA with a forecast of the S&P 500, even though these indexes are at different levels.

Since both of these methods are based on the mean error, they may understate the impact of big, but infrequent, errors. If we focus too much on the mean, we will be caught off guard by the infrequent big error. To adjust for large rare errors, we calculate the Root Mean Square Error (RMSE). By squaring the errors before we calculate their mean and then taking the square root of the mean, we arrive at a measure of the size of the error that gives more weight to the large but infrequent errors than the mean. We can also compare RMSE and MAE to determine whether the forecast contains large but infrequent errors. The larger the difference between RMSE and MAE the more inconsistent the error size. The following is an example from a CAN report,

Using Mean Absolute Error, Mean Absolute Percentage Error, and Root Mean Square Error to evaluate forecast accuracy.

While these methods have their limitations, they are simple tools for evaluating forecast accuracy that can be used without knowing anything about the forecast except the past values of a forecast.

Finally, even if you know the accuracy of the forecast you should be mindful of the assumption we discussed at the beginning of the post: just because a forecast has been accurate in the past does not mean it will be accurate in the future.  Professional forecasters update their methods to try to correct for past errors.  However, these corrections may make the forecast less accurate. Also, there is always the possibility of an event occurring that the model producing the forecast cannot anticipate, a black swan event. When this happens, you don’t know how big the error will be. Errors associated with these events are not typical errors, which is what RMSE, MAPE, and MAE try to measure. So, while forecast accuracy can tell us a lot about the past, remember these limitations when using forecasts to predict the future.

To learn more about forecasting, download our eBook, Predictive Analytics: The Future of Business Intelligence.

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