January 2014 Newsletter

Will you be capitalizing on your Data in 2014



 Descriptive, Predictive or Prescriptive .  Where are you on the Analytics curve? 



The first stage of business analytics is Descriptive analytics, which still accounts for the majority of all business analytics today. Descriptive analytics answers the questions what happened and why did it happen. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Most management reporting - such as sales, marketing, operations, and finance - uses this type of post-mortem analysis.


The next phase is Predictive analytics. Predictive analytics answers the question what will happen. This is when historical performance data is combined with rules, algorithms, and occasionally external data to determine the probable future outcome of an event or the likelihood of a situation occurring.  But predictive is not about absolutes; it doesn't guarantee an outcome. Rather, it's about probabilities. For example, there is a 76% chance that this person will click on this display ad. Or there is a 63% chance that this customer will buy at a certain price. Or there is an 89% chance that this part will fail. Organizations that employ predictive analytics can dramatically reduce risk, disrupt competitors, and save tons of money.


The final phase is Prescriptive analytics, which goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the implications of each decision option.  Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options.
Predictive Analytics provides you with Insight into Customer Lifetime Value and Propensity to Quit

How much are your customers worth and what is their anticipated lifetime values?


The fact is customer lifetime value is becoming a more commonly-used metric among companies. Of course, a key factor to customer lifetime value is retaining customers.


Using predictive analytics and customer data to segment customers can help a company identify the most profitable customers and enable it to focus its engagement and marketing efforts on those customers that will make the biggest impact on the company's business.


Segmentation is a critical aspect of examining and acting on customer lifetime value.  This allows you to focus on those customers with the highest value to your organization.


Companies whose most valuable customers have higher customer lifetime values and also purchase more often can use predictive analytics to determine the type of messaging and offers that will tend to resonate with these customers. In addition, predictive analytics can enable these firms to take the actions that will lead to additional purchases therefore increasing loyalty.



Propensity to quit is the next step.  Once you know the value of a customer it's important to then identify those customers who have a high value and a high propensity to quit.  


Predictive Analytics models provide you with this insight by analyzing all the variables you collect on your customers and through regression analysis determine the variables that have a positive or negative impact on Customer retention.


Wouldn't you want to know if any of your high value customers have a high propensity to quit and be able to take action to retain them before they leave. 




2014 the year of Analytics and Big Data .  To Outsource or Not 


The surge of interest in big data has led to growing demand for analytics teams. Having big data capabilities can help companies become more efficient and improve overall competitiveness. Companies with superior data analytics capabilities have found ways to build long-term advantages.


Assembling analytics teams, however, is difficult. For one thing, many companies lack the in-house knowledge and experience needed to put together an analytics team. What's more, the labor market for analytics professionals has grown increasingly tight. Fortune recently reported, "Online help-wanted ads for data analysis have shot up 46% since April 2011, and 246% since April 2009, to over 31,000 openings now, according to job-market trackers." The shortage of analysts - particularly those capable of developing and leading world-class teams that can enable a company to create a competitive advantage from its data and analytics - is driving organizations to consider outsourcing their analytics activities.


IDC research shows that the following factors will act as key enablers for increased third-party outsourcing of business analytics services:

  1. Lack of internal (end customer) analytics resources, such as mathematicians, business analyst, data modelers, statisticians, and data scientists.
  2. The fast pace of new technologies including automation around analytics and their link to social and mobile will make it difficult for the end customer to build and deploy teams in view of investment required to build/buy infrastructure and talent.
  3. As business analytics providers successfully consult, deploy, and manage business analytics solutions for their customers, they will be able to demonstrate an increased number of transformative use cases.

"Talent gap and lack of knowledge base in the analytics space will continue to force businesses to rely on service providers to fulfill their business analytics needs in the near future"



This is where we can help.  Our expertise in developing predictive models allows companies to capitalize on their data and to be in a position to act upon the outcomes in a timely manner.   




  1.   Wikipedia - Descriptive, Predictive and Prescriptive Analytics
  2.  IDC Research - April 2013


To learn more, please visit us at www.tdtanalytics.com 
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