The Beginning Of A New Age For AI

These days it seems like you can't open a newspaper (ok, web browser) without coming across an article on artificial intelligence. Well publicized breakthroughs like Google AlphaGo's unprecedented victories over human Go champions have heralded the promise of a new golden age for AI. Add to that the personification of personal assistants in Apple's Siri and Amazon's Alexa coupled with Salesforce's “resurrection” of Albert Einstein and the rampant proliferation of AI-related startups - and the AI buzz becomes more of a cacophonous clamor.

To put it mildly, this is confusing for businesses, who are trying to determine what is real and what is mere snake oil. Will AI achieve its transformational promise, or will it join the trash heap of over-hyped technologies?

Forrester believes AI will significantly disrupt the way organizations win, serve, and retain customers... eventually. To do this, it will take massive amounts of data to train artificially intelligent systems to perform their jobs well enough to replace their human counterparts.

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Fighting FOMO: Join The Customer Analytics Party

The first U.S. presidential debate was the most watched in history, with 84 million people tuning in.  Sure, many of us wanted to educate ourselves before practicing our solemn duty as democratic citizens in November.  However, many of us also didn’t want to miss out on what (hopefully) promised to be a once in a lifetime political event .  We were motivated by FOMO.

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Benchmark Your Customer Analytics Maturity

Customer insights professionals consistently ask me what other companies are doing to turn their customer data into actionable insights.  To answer this question, Forrester partnered with Burtch Works, an analytics executive recruitment agency, to survey customer analytics and measurement professionals about their current efforts.  I’m quite thrilled to share the results in my State of Customer Analytics 2016 report.

This goal of this report is to give CI pros, marketers, and anyone tasked with gleaning insights from massive amounts of customer data a concrete snapshot of what others are doing in the space.  Here are a few of the key questions we set out to answer:

  • What are the top data sources companies are using for analytics and measurement?
  • What types of analyses are they doing?
  • How and where are they applying insights?
  • What challenges do they face?

In analyzing responses, we segmented companies based on their customer analytics sophistication so readers can see what separates leaders from laggards.  My hope is that as you read through this report, you will be inspired to evolve your own customer analytics maturity.  Please feel free to reach out to me via inquiry if you’d like to discuss how to do so.

What Should I Order? A Tasting Menu Of Customer Analytics Techniques

The primary objective of customer analytics is to transform data into valuable insights that impact organizational goals.  With an abundance of organizational goals, petabytes of data at their disposal, and a whole slew of potential techniques for analyzing that data, customer insights professionals often (quite ironically) find themselves in a state of analysis paralysis.

Forrester’s updated TechRadar™: Customer Analytics Methods, Q2 2016, originally published in 2014, aims to help CI pros by highlighting 15 customer analytics techniques their peers are using to extract insights from their data.  In this edition, we have emphasized the need for customer entity resolution, a foundational precursor to many of these techniques.  We have also broadened sentiment analysis to text analytics to reflect the move toward more actionable categorization of unstructured data.  And we have updated examples of relevant technology and services vendors, the estimated cost of implementation, and our assessment of where each technique sits on the analytics adoption curve.

The techniques run from descriptive to predictive, and employ structured, unstructured, and geospatial data.  Potential use cases run the customer lifecycle gamut from acquisition to personalization to loyalty and retention.  Since customer analyses don’t exist in a vacuum, the report describes the interrelationships and dependencies between different techniques. 

CI pros who face an ever-expanding list of stakeholder requests should use read this report to help plan and prioritize customer analytics projects.

The “Quantent” Quandary

Last week, I had the opportunity to attend a teleconference highlighting IBM Watson’s success stories over the past year.  Most of them are under NDA, so I can’t go into the details, but I will say they covered an incredibly broad range of use cases.  One use case that I was hoping they would cover and didn’t was content analytics for marketing, aka “quantent.”

In the customer analytics arena, we often talk about “getting the right message to the right customer at the right time.”  This is only partly true.  Well-built and rigorously tested propensity models will deliver you the right customer and the right time.  Behavioral segmentation models may even specify the best channel to use to deliver the message.  But that still leaves the message itself.  Whatis the right message?

Content analytics begins with entirely different data than customer analytics, and the two analytical streams merge just prior to the point of action.  Whereas customer data contains information about customer profiles, transactions, and behaviors, data about content characterizes tone, length, wording, dates, products mentioned, type of offer (if applicable), and other key themes within the content itself.  Most importantly, content that has been subject to A/B testing also creates data about the success of the message on an individual customer basis.

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Hot Off The Press – The Forrester Wave™: Customer Analytics Solutions, Q1 2016

We have all this valuable data about our customers, but we need to make better use of it.

This is the most common theme I hear on inquiry calls, at conferences, and in advisory sessions.  At this point, companies are fully aware that their data contains enormous value.  In fact, I like to think that data has a potential value much like the concept of potential energy in physics.  In physics, the conversion of potential energy to kinetic energy requires force.  In business, customer analytics is the Force that unlocks the hidden value in your customer data.

Because customer analytics often relies on advanced machine learning algorithms, it used to be the domain of statisticians who could write code in R or Python.  Today, thanks to the 11 customer analytics solution providers in The Forrester Wave™: Customer Analytics Solutions, Q1 2016, customer insights professionals are applying these techniques to their data to address key business objectives.  This report, which is only available to Forrester clients, evaluates the customer analytics solutions of Adobe, AgilOne, Angoss, Alteryx, FICO, IBM, Manthan, Pitney Bowes, SAP, SAS, and Teradata.

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Welcome to my customer analytics blog!

Greetings!  My name is Brandon Purcell and I am the new Senior Analyst serving Customer Insights professionals at Forrester.  I will cover customer analytics which uses advanced analytics to analyze customer data to optimize customer-focused programs and initiatives to drive acquisition, retention, cross-sell/upsell, loyalty, personalization, and contextual marketing. I am a recovering customer analytics practitioner and come to Forrester from a boutique consulting firm where I led a team of data scientists that helped our clients solve their urgent business challenges by harnessing the latent value in their customer data.  A few highlights from my former life:

  • I helped develop a best in class Voice of the Customer program at one of the country’s largest banks
  • I created and led many trainings in business applications of predictive analytics
  • I built a patented algorithm that uses geospatial data to predict a person’s future location
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