Probabilities and Statistics Pay-Off for Marketers
August 20, 2010 by Stics·
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Conditioning Probabilities
When observing our customers, sometimes evidence can look pretty persuasive and sometimes it can be down-right compelling. And yet, without proper attention to underlying statistical properties in the customer data, you might form an entirely false impression of your customers. It is not uncommon for marketers to ask this question, at least once in their career.
“How did I make such a wrong conclusion from such compelling evidence”?
The answer lies in its numerical underpinnings, what statisticians call “conditioning”. Conditioning can cause you to reach the wrong conclusions and send lots of marketing dollars down the rabbit hole and take you to the Mad Hatters Tea Party.
The unhappy results of conditional probabilities exist in all facets of animal and human behavior. These are typically complex scenarios of layered assumptions, so I am using this simple example, of one type of casino player, to demonstrate how the application of incorrect assumptions can lead to lost revenue and profitability.
Casino Gambler Example
Let’s assume one percent of the casino gamers who played at your property were cheaters. That percentage might be high (Cheaters = 1%), but this is an example after all. Let’s also assume that when a player is a cheater, he or she will decline to fill out a loyalty Rewards Club card application about ninety nine percent of the time. (If cheater, 99% decline application.)
Now comes the interesting part. Let’s say that you see a suspicious player, approach him or her, ask the player to fill out a rewards application, and the player declines. What are the chances that that player is a cheater?
Intuition vs. Reality
Intuition would suggest a high likelihood of cheating. But the reality is different. There is only about a one in thirty-three percent chance that this individual casino player is a cheater. So consider the statistical probabilities before you decide that the player is not worthy of a revenue generating comp or a coupon.
Here’s a table that describes the situation exactly. You have total players showing up in green. In red are the 1% or 100 are cheaters. 99 of those 100 cheaters were not willing to fill out an application, so your test was very good.
Wrong Assumptions Equal Lost Profit
So here is the big question. If someone refuses to fill out an application (the red boxed group), should you refuse to serve them or market to them? The answer is No for the following reasons.
- First, there is only roughly a 1 in 33 chance (more precisely 99 of 3,399) that the player is a cheater.
- Second, there is a 3,330 out of 3,399 chance that they are a good player who will be profitable.
- And finally, these 3,300 players are likely to be even more profitable than reward applicants.Even when making generalizations about customers, it is essential to understand the effect of your assumptions on your conclusions. Especially when formulating policy, it really pays to know your probabilities and statistics.
If you found this interesting or want to learn more about the power of statistics for marketing, feel free to contact Stics. We are happy to help.
Why Data Segmentation Is Not Enough
People Are More Complicated Than Their Data Segmentation Parameters
When evaluating customer wants and needs, there are many ways of looking at data from the sublime to ridiculous. Commonly accepted ways of looking at customer data include generating a “gut feel” from experience, sampling at random,looking at exceptions, reviewing periodic totals, and SQL selection. SQL, the most common tool used by database marketers, stands for Structured Query Language, but what it boils down to is averages or data segmentation. It gives you general information about groups of people, often by age or income.
Let’s say, for instance, that you want to find people that fit a certain category, such as empty-nesters. To use data segmentation to find these people, you might search for people in the 50 – 60 year age range and within a certain income bracket. SQL would look at your data to find the people who fit both of these parameters.
The problem with SQL is that you’re potentially overlooking the right people. In this example, your data segment would be missing all of the empty nesters who are under 50 as well as those who fall outside your chosen income range. SQL is just a fancy way to put people into buckets. The problem is, not everyone you seek is in the obvious bucket.
People are more complicated than just their income or age, but when analyzing populations, you can become trapped by these segmentation tools and rules.
Where Your Customers Really Are
The truth is that you’re not really looking for empty-nesters within certain parameters (shown in brown on the chart) or even empty-nesters in general. You are looking for people who will be receptive to what you have to offer (shown in yellow on the chart).
To find these potential customers, you need to stop relying on buckets and start looking at the bigger picture. And to do that, you need statistics. Statistics can help you find the best people that correspond to your potential customer base.
Predictive Analytics: Better than Buckets
The most effective form of selection would be to use the most information and use it optimally. With this light, predictive analytics can be seen as the clear winner for selecting target prospects or customers.
Predicative analytics looks at the bigger picture of marketing. It has the capability to consider all kinds of factors, rather than just one of two. It also looks at patterns in behavior, and makes predictions about future behaviors. This lets you more accurately pinpoint your potential customers, as well as stop marketing to people who are not interested in what you have to offer.
That sounds like a much more effective way to do business, right? The problem is that you can’t do it by yourself. Predictive analytics is a science, and it can take years to develop accurate predictive models. You don’t want to make the top 10 mistakes a novice with predictive analytics would make. That’s why turning to a reputable predictive analytics provider can save you time and money.
At Stics, we have spent year’s fine tuning our software and processes so our customers get extremely accurate predictions. We can take your data and give you insightful information about potential customers and the effectiveness of future marketing campaigns. With Stics predictive analytics solutions you can make your business more efficient and cost-effective.
