AgilOne Blog

We are constantly pushing to improve our underlying algorithms and make them as adaptive as possible. Taking a step back, our problem generally is to fit classes of models and algorithms to customer data sets of varying data quality. In addition, we need to automate this so that we can scale delivery of our offerings from a business perspective.

Are you looking for answers, and not getting help from your internal analytics and business intelligence team? Is there a fundamental question that even your company’s biggest data nerd can’t answer?

Well, if you present a problem to Wharton's Research Center for Customer Analytics, you won't get an answer right away, but you’ll be guaranteed maximum brainpower and a decidedly objective view of the issue.

For decades marketers have debated whether marketing be more art than science or vice versa.

If you’re like me, Forrester’s new report by analyst Fatemeh Khatibloo, Navigating the Future of Customer Intelligence is sure to light a fire under you, if not several fires. Customers are completely changing the game, and if we don’t change with them, we will miss key opportunities and our businesses may not even survive the disruption we all find ourselves in.

I’m very excited to share this Email Marketing Scorecard with you, a piece by leading Forrester analyst Shar VanBoskirk. This 15-page report is packed with tips to help you evaluate your email marketing programs and improve your use of email as a marketing optimization tool. The email marketing game is always changing, and Forrester continues to evolve its methodology so you can make your campaigns more effective, incorporating criteria about how easy it is to share email with friends and colleagues, how engaging email is on mobile devices, and how using interactive elements such as video can help.

We all like to be liked. But obsessing over Facebook “likes” and last-click attribution does not a social media marketing strategy make, nor is it any kind of litmus test for customer lifetime value. An eMarketer.com survey stated that 60 percent of marketers used “the number of people linking as friends, followers, or placing ‘likes’” on a brand’s Facebook page as the means of measuring social media marketing success. But according to Moontoast, only 20 percent of marketers found quantifiable ROI from social media. Coincidence? Probably not. Revenue attribution is notoriously hard and last-touch attribution leaves us with an incomplete set of clues.

Recently I came across a Computer Weekly article that confirmed what I already knew: the use of predictive analytics is definitely on the rise. In the article, Brian McKenna summarized Accenture’s latest research on analytics in the enterprise. Since 2009, the use of predictive analytics has tripled. Considering that many executives still make decisions based on gut feel, this increase, from 12% in 2009 to 33% in 2012, is significant. Nick Millman, digital, data and analytics lead at Accenture, said, “There has been a reasonable shift from gut feel to data [as a basis of decision-making], and that will accelerate.”

Tom Davenport is a key thought leader in the analytics space whose work I have been following. When I saw he had written an article called “Ready for Analytics 3.0?” I jumped on it. Davenport gives us a guided tour of the journey from Analytics 1.0 to 2.0 and now 3.0. He traces the evolution of analytics, talking about changes in the types of data in use, internal, external, structured, and unstructured, as well as advances in technology such as in-memory databases and predictive analytics software. Every bit as interesting is the movement of analytics from a back-office function to the center of the C-suite. As I read this, I thought about how true this is for those of us in marketing – analytics is right at the center of every marketing plan and every conversation.  

Introduction

Basketball great Michael Jordan said, “Talent wins games, but teamwork and intelligence wins championships”. I fundamentally believe that the way a company thinks about their way of working and the structure  of teams can either become a millstone around the neck or a competitive advantage. I received quite a few questions on how we develop software. So in this blog post I will talk about how we think about engineering teams at AgilOne for building marketing focused big data predictive applications.

Observation and definition of the question

Although often over quoted, the term Life Time Value (LTV) is one of the most underutilized but fundamental marketing concepts. The concept is based on the belief that the customer is the unit value for an enterprise and the sum total of customer value is equal to the enterprise value ( see Customer Equity).   How useful is this concept for those involved in the daily grind of running a marketing department?