The Obama 2012 campaign famously used big data predictive analytics to influence individual voters. They hired more than 50 analytics experts, including data scientists, to predict which voters will be positively persuaded by political campaign contact such as a call, door knock, flyer, or TV ad. Uplift modeling (aka persuasion modeling) is one of the hottest forms of predictive analytics, for obvious reasons — most organizations wish to persuade people to to do something such as buy! In this special episode of Forrester TechnoPolitics, Mike interviews Eric Siegel, Ph.D., author of Predictive Analytics, to find out: 1) What exactly is uplift modeling? and 2) How did the Obama 2012 campaign use it to persuade voters? (< 4 minutes)
The deluge of customer data shows no signs of abating. The perpetually-connected customer leaves data footprints in every interaction with a brand. This presents tremendous opportunities for customer insights professionals and analytics practitioners tasked with analyzing this data, to not only get smarter about customers but ensure that the insights get appropriately used at the point of customer interaction.
When we asked customer analytics users about the challenges and drivers of customer analytics adoption, we found that data integration and data quality continue to inhibit better adoption of customer analytics while users still want to use analytics to improve the data-driven focus of the organization and drive satisfaction and customer retention.
Forrester’s Customer Analytics Playbook guides customer insights professionals, marketing scientists and customer analytics practitioners into this new reality of customer data and helps discover analytics opportunities, plan for greater sophistication, take steps towards building a customer analytics capability and continually monitor progress of analytics initiatives. It will include 12 chapters (and an executive overview) that cover different aspects of customer analytics.
I just received yet another call from a reporter asking me to comment on yet another BI vendor announcing R integration. All leading BI vendors are embedding/integrating with R these days, so I was not sure what was really new in the announcement. I guess the real question is the level of integration. For example:
Since R is a scripting language, does a BI vendor provide point-and-click GUI to generate R code?
Can R routines leverage and take advantage of all of the BI metadata (data structures, definitions, etc.) without having to redefine it again just for R?
How easily can the output from R calculations (scores, rankings) be embedded in the BI reports and dashboards? Do the new scores just become automagically available for BI reports, or does somebody need to add them to BI data stores and metadata?
Can the BI vendor import/export R models based on PMML?
Is it a general R integration, or are there prebuilt vertical (industry specific) or domain (finance, HR, supply chain, risk, etc) metrics as part of a solution?
What server are R models executed in? Reporting server? Database server? Their own server?
Then there's the whole business of model design, management, and execution, which is usually the realm of advanced analytics platforms. How much of these capabilities does the BI vendor provide?
Did I get that right? Any other features/capabilities that really distinguish one BI/R integration from another? Really interested in hearing your comments.
I’m excited to announce that our new research on how firms use customer analytics was just published today. The new research reveals some interesting findings:
Customer analytics serves the customer lifecycle , but measurement is restricted to marketing activities. While customer analytics continues to drive acquisition and retention goals, firms continue to measure success of customer analytics using easy-to-track marketing metrics as opposed to deeper profitability or engagement measures.
Finding the right analytics talent remains challenging . It’s not the just the data. It’s not the just technology that hinders analytics success. It’s the analytical skills required to use the data in creative ways, ask the right questions of the data, and use technology as a key enabler to advance sophistication in analytics. We’ve talked about how customer intelligence (CI) professionals need a new breed of marketing scientist to elevate the consumption of customer analytics.
CI professionals are keen to use predictive analytics in customer-focused applications, Forty percent of respondents to our Global Customer Analytics Adoption Survey tell us that they have been using predictive analytics for less than three years, while more than 70% of respondents have been using descriptive analytics and BI-type reporting for more than 10 years. CI professionals have not yet fully leveraged the strengths of predictive analytics customer applications.
Earlier this week Dell joined arch-competitor HP in endorsing ARM as a potential platform for scale-out workloads by announcing “Copper,” an ARM-based version of its PowerEdge-C dense server product line. Dell’s announcement and positioning, while a little less high-profile than HP’s February announcement, is intended to serve the same purpose — to enable an ARM ecosystem by providing a platform for exploring ARM workloads and to gain a visible presence in the event that it begins to take off.
Dell’s platform is based on a four-core Marvell ARM V7 SOC implementation, which it claims is somewhat higher performance than the Calxeda part, although drawing more power, at 15W per node (including RAM and local disk). The server uses the PowerEdge-C form factor of 12 vertically mounted server modules in a 3U enclosure, each with four server nodes on them for a total of 48 servers/192 cores in a 3U enclosure. In a departure from other PowerEdge-C products, the Copper server has integrated L2 network connectivity spanning all servers, so that the unit will be able to serve as a low-cost test bed for clustered applications without external switches.
Dell is offering this server to selected customers, not as a GA product, along with open source versions of the LAMP stack, Crowbar, and Hadoop. Currently Cannonical is supplying Ubuntu for ARM servers, and Dell is actively working with other partners. Dell expects to see OpenStack available for demos in May, and there is an active Fedora project underway as well.
Any big data or analytics conversation would be remiss without the mention of "data scientists." Much has been written about data scientists– who they are, who they should be, and where to find them. My colleague James Kobielus wrote an interesting series of blog posts about the skills required to become a data scientist.
From a customer intelligence (CI) perspective, we outlined four segments of CI professionals — marketing practitioners, marketing technologists, marketing scientists, and customer strategists. Of these, marketing scientists typically orchestrate the customer and marketing analytics function. They manage the reporting, analysis, and predictive modeling processes using marketing and customer data.
In a CI context, we find that the role of the marketing scientist has evolved from being a pure data analyst drowning in data analysis to that of an analytics translator — someone who is equally comfortable with building advanced predictive models and also adept at embedding the output of the models into customer-facing processes. What type of marketing scientist does your analytics team have?
We recently published a report on why "Customer Intelligence Needs A New Breed Of Marketing Scientist" (accessible to Forrester clients). In the report, we highlight ways to develop analytics translators across the staffing cycle — starting from attracting the right talent, nurturing the relevant skills, training with new skills, and incenting them based on business impact.
Cloud computing continues to be hyped. By now, almost every ICT hardware, software, and services company has some form of cloud strategy — even if it’s just a cloud label on a traditional hosting offering — to ride this wave. This misleading vendor “cloud washing” and the complex diversity of the cloud market in general make cloud one of the most popular and yet most misunderstood topics today (for a comprehensive taxonomy of the cloud computing market, see this Forrester blog post).
Software-as-a-service (SaaS) is the largest and most strongly growing cloud computing market; its total market size in 2011 is $21.2 billion, and this will explode to $78.4 billion by the end of 2015, according to our recently published sizing of the cloud market. But SaaS consists of many different submarkets: Historically, customer relationship management (CRM), human capital management (HCM) — in the form of “lightweight” modules like talent management rather than payroll — eProcurement, and collaboration software have the highest SaaS adoption rates, but highly integrated software applications that process the most sensitive business data, such as enterprise resource planning (ERP), are the lantern-bearers of SaaS adoption today.
Are you interested in business intelligence, wonder about the future of the analytics market or have a question on advanced analytics technologies?
Then join the Forrester analysts Rob Karel, Boris Evelson, Clay Richardson, Gene Leganza, Noel Yuhanna, Leslie Owens, Suresh Vittal, William Frascarelli, David Frankland, Joe Stanhope, Zach Hofer-Shall, Henry Peyret and myself for an interactive TweetJam on Twitter about the state of advanced analytics on Wednesday, December 15th, 2010 from 12:00 p.m. – 1:00 p.m. EDT (18:00 – 19:00 CET) using the Twitter hashtag #dmjam. We’ll share the results of our recent research on the analytics market space and discuss how it will change with new technologies entering the scene and maturing over time.
Business intelligence is the fastest growing software market today as companies are driving business results based on deeper insights and better planning, and advanced analytics is the spearhead of BI technologies that can untap new dimensions of business performance. But what exactly is ‘advanced’ analytics, what technologies are available and how to efficiently use them?
Much more detailed information can be found in the blog of Forrester analyst James Kobielus who will lead us through the discussion during the TweetJam. Above you see an overview graphic listing the different elements of advanced analytics today, taken from his blog.
Here are some of the questions we want to debate during our TweetJam discussion:
What exactly is and isn’t advanced analytics?
What are the chief business applications of advanced analytics?
#SCRM (the hash our group uses to communicate on Twitter) group embodies the very essence of what social media is about: genuine authentic, direct and real conversations. Being a participant and a practitioner, I thought I would share my observations and thoughts... not just at this conference, but what I have seen in the actions and behaviors of this group over the past year or more... And these foreshadow a world that is being created right now as you are reading this...