After my daughter gave me some lessons and I did the homework and took the tests, I could then turn on the phone and get to the home screen. Being so empowered, I decided it was time to make a call.  On my old Blackberry, I hit the speed dial key and the phone began ringing — one button and I was talking. I figured a smart phone might have the call already ringing before I made the decision to call. HA!

These are the steps I had to take on the new Droid: Press the button to turn on the phone. Swipe the lock to open the phone. Go to the phone icon. Go to the contact list. Find the person. Hit the green phone button.  What is so smart about that phone?

It can do so much more but the problem is it doesn’t do what I need nearly as efficiently as my old phone.  For all this inconvenience I pay more each month for a “data charge”.  The phones are free but over the course of the contract, I am paying three times what the phone costs!  Not a price increase but a data charge. . . Brilliant!

Give me a couple months and the frustration will become just another day and I will get used to the many swipes, punches and curses needed to do what I need it to do.  Of course with all my new apps,  I will be able to find Orion in the sky and quickly tell you how many calories I consumed last month.  Please don’t call me though, as I really can’t answer the phone.

This article seeks to highlight some useful, but typically ignored synergies between science, engineering, business and retail. Karl Popper elegantly described the purpose of science as a process which generates predictive theories.  Science, economics and all kinds of business applications rest critically upon a common need; the need to accurately forecast a complex and dynamic future.

While the goals of science, economics and business are worlds apart, the actual process of forecasting is common to each. Similar challenges in forcasting allow lessons learned in one discipline to be applied in another.

Here, we’ll look at what forecasting insights can be gleaned from raindrops and market drops to help make our retail profits a little more stratospheric.

The most basic, even instinctive, method of forecasting involves guessing what will happen. Humans naturally learn to link certain events together. A midwestern corn farmer might say “knee high by the fourth of July.” If the crop isn’t tall enough by the given date, the farmer knows in advance that the crop has gotten a bad start and will therefore yield a weak harvest. Other times, the process is more intuitive, what we call “gut instinct.’ A person might “have a bad feeling” about some situation, even if he’s unable to explain the rationale behind it to another person. Recognizing patterns is something people do without even trying. It’s nearly impossible to look at a word written on a page, for instance, and not “read” it.

Of course, this kind of guessing is one of all kinds of human flaws and limitations. It lacks the dispassionate rigor of an actual scientific experiment. Just knowing that the crop isn’t high enough tells a person nothing about what caused its short stature. Even worse, it offers no clues to fix the problem. Gut instinct is difficult to transfer from one person to another. It creates dependence on a person, rather than a process and it’s horribly subject to the constraints of a single person’s memory and intellect. Using only gut instinct is better than nothing, but even at it’s best it is imprecise and prone to error. Stock outs sometimes and markdowns others is the result.

Of course, some of these problems can be solved by using past trends and performance to predict future results.

This approach is a little more precise and predictive if the system varies the same way it has done in the past. We’re no longer relying on the feelings of one individual and emotions are checked somewhat by stubborn little numbers. But it’s still less than ideal.

The professionals that spoke on climate research at a talk I attended recently amazingly faced the same problems that we faced in trying to predict future sales and performance for retailers. They started, as we did, using statistics.  Statistics and trends are useful in a somewhat stable or controlled environment. In statistical terms its changes can be depicted by a bell shaped curve or some other known distribution.   When climate change was affected by increasing CO2 the statistics based on the past could no longer predict the future.  Retail , also, is a constantly changing environment. When the recession hit, trends based on past performance were completely invalid.

The solution the climatologists brought to bear to help understand our dynamically changing environment was to make mathematical models of how the system worked. Over the years the model for climate change was modified to include surface temperatures, then atmospheric makeup including CO2, methane, and other gasses. Moisture content, then ocean temperatures were added. Then solar radiation coming in and out was added and so on. As each new variable was added to the model, a more accurate prediction was possible.  The true test was to back test to see if the model predicted what happened in the past. The final test is to see how accurately it predicts what happens in our actual, uncertain future.

We went through similar trials and tribulations to develop our Winning@Retail™ software. It contains both analysis of past performance using statistics and mathematical models  that account for the effects of the economy, local buying habits, inventory levels and much more to get an accurate prediction of future sales.  With each new variable added to the model the predictions improved.  Several independent tests have measured our ability to predict sales at 94% or better.

Just as knowing the future of climate change can help us prepare for the coming challenges, knowing future sales allows us to identify the right inventory levels and predict cash flow in the business.  If we don’t like the outcome, we can use the models to chart a new course based on a solid forecast of coming trends. Rather than just seeing a bad crop coming several months ahead of the harvest, we can consider how to nourish a business so that it continues to be fruitful and productive. The use of predictive models is the best approach to inventory planning.  POS systems and many spreadsheet approaches use statistics to project the past into the future.  Their susceptibility to sudden shocks and changes causes waste, errors and inefficiency, often when they are most painful.  The better your data and analysis, the better the predictions and the better the results will be.

If you’ve ever flown a major airline, you’ve probably been frustrated over the variations in the seat prices. How can the airlines sell basically the same seat on the same flight to a business traveler for $600 and a student for $200? Why should it matter if you stay over a Saturday night at your destination or if you fly there and back the same day?

One answer is marketing. Airline personnel spend a good deal of time and money figuring out who their customer is, how to market to him/her and what he/she will pay. They know whether the passenger is traveling for business or pleasure and they know who exactly is a frequent flier of theirs. What does this have to do with you if you are an owner of a small to mid-size business?

A lot! You should have as much similar information about your customers as possible. Does your database let you know if customers buy for business or leisure? Do you know if you are a preferred business for their needs or not? Have you ever figured out how much each customer spends at your store each year? When? And on what?

You should already know the easy stuff about your customers such as their birthday, size, color preference, family information and work information. If you are going to be more effective than competitors at attracting customers, you must know even more about them.

Turn your sales staff members into detectives and have them find out the key information about your customer base. Give them a way to record the data. Implement a strategy to use the information to improve your CRM and your personal marketing campaign. Armed with an arsenal of information on your individual clients, you should be able to customize the customer’s shopping experience at your store!