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Rethinking Business Intelligence: Information or Decisions

on January 21, 2013

Share of Wallet & Predictive Analytics

on August 7, 2013

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.  Read more…


What if you knew your target audience well enough to market to them exactly?

Your organization may serve a variety of different demographics — and that’s a good thing! But the factors that motivate one type of customer can be completely different from another customer choosing the same product or service. The key is determining your market segments, and learning what messages will have the most impact for each demographic.

By micro-targeting your customer base, you are able to reach the people that need or want your service, with marketing campaigns tailored to what matters most to them. Instead of sending a generic message to everyone on your list, you are able to send specific messages to targeted groups — increasing your likelihood of response, and ultimately, your overall conversion rate. Read more…



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Where in the world CAN you find us?

on May 23

Check out Data Science Central

on May 11

Customer segmentation analysis is essential. No company has just one type of customer. Customer segmentation analysis allows you and your data to capture this reality. Capturing reality is a pre-requisite to using data to make decisions. Each customer segment needs to be understood, marketed to, and tracked.  Download our case study. 

It is time to stop thinking about your “customer” and start thinking about your “customers”. Don’t let your marketing and customer metrics, hide valuable facts and insight in aggregated data and averages. The next level of marketing analytics is to calculate and track metrics for each customer segment. Customer segmentation provides you a window through which to understand why people do what they do. This gives you enormous power when trying to improve customer lifetime value, increase customer loyalty, reduce the cost of customer acquisition.

Read more…


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Examples of How to Improve Your Customer Experience

on September 18, 2012

Get More Sales from Your Current Customers

on December 3, 2012

How to Increase Customer Lifetime Value

on December 10, 2012

While it is important to focus on new client acquisition, it is equally and perhaps more important to focus on improving your relationships with your current customers. This will help you improve your customer lifetime value. Customer lifetime value is the amount of net profit you receive from each customer. As a general rule, the average customer lifetime value needs to be 3 to 5 times the average cost to acquire a customer.

Improving customer lifetime value will help you have a sustainable and profitable business. To stay in business, the net profit from each customer has to be more than the cost to acquire each customer.

Customer Lifetime Value is the average net profit that can be attributed to a company’s entire relationship with a customer. Read more…


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Where in the world CAN you find us?

on May 23

Check out Data Science Central

on May 11

Data is no good if you can’t get it quickly enough and act quickly enough on it. Its all about getting the data fast and acting on it fast
– Andrew Pole

Predictive analytics solves many differnt problems in a many facets of business. The New York Times published an article in February about the retail chain, Target. Target knew that if they could predict buying patterns in a certain group of consumers, they could influence those consumers purchases. Target was smart, they knew exactly who they wanted to go after.

Andrew Pole, a statistician working for Target, created a pregnancy-prediction model that was able to track spending habits and predict when a woman is pregnant. Pole said that, “We knew that if we could identify them in their second trimester, there’s a good chance we could capture them for years. As soon as we get them buying diapers from us, they’re going to start buying everything else too. If you’re rushing through the store, looking for bottles, and you pass orange juice, you’ll grab a carton. Oh, and there’s that new DVD I want. Soon, you’ll be buying cereal and paper towels from us, and keep coming back.” Read more…


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Where in the world CAN you find us?

on May 23

Check out Data Science Central

on May 11
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 mens 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. Read more…



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