You don’t need to be a fine woodworker to sit in a chair. An inability to precisely construct an angled mortise and tenon joint does not preclude you from resting your feet. Similarly the time is rapidly approaching where you won’t need to be a marketing scientist to deploy analytics. Ignorance of neural networks will no longer impede your ability to use them to improve a campaign. The democratization of predictive modeling or other trends involving the intersection of customer analytics and marketing technology is much of what I will cover for Forrester Research.
In my new role as a senior analyst I look forward to helping Customer Insight professionals increase marketing and business returns through becoming more intelligent enterprises. This might involve guiding clients on technology decisions, organizational strategy, or benchmarking to their peers. What topics would you like to see me cover?
SAP today announced plans to acquire KXEN, a provider of predictive analytics technology. The terms of the deal are not known. This is an interesting development for both companies and highlights the focus on the democratization of predictive analytics, especially for marketers. The proposed deal puts the spotlight on two shifts in the analytics landscape:
Expert user to casual user. Our research shows that finding top analytics talent is a key inhibitor to greater customer analytics adoption. As a result, users expect analytical tools to cater to nontechnical, nonstatistician business and marketing users.
Buy analytics software, hire marketing scientists, and engage analytics consultants. Now wait for the magic of customer analytics to happen. Right?
Wrong. Building a successful customer analytics capability involves careful orchestration of several capabilities and requires customer insights (CI) professionals to answer some key questions about their current state of customer analytics:
What is the level of importance given to customer analytics in your organization?
Have you clearly defined where you will use the output of customer analytics?
How is your analytics team structured and supported?
How do you manage and process your customer data?
Do you have clear line of sight between analytics efforts and business outcomes?
What is the process of sharing insights from analytics projects?
What type of technology do you need to produce, consume and activate analytics?
Forrester is launching new research looking at how firms and companies can better use data and analytics. Please help us make this research better by taking our survey. We want to hear from you whether you use data extensively or not, and your responses will be extremely valuable. Plus you get a free Forrester report (not to mention the warm glow you'll get from helping out).
In addition, we appreciate any efforts to spread the word: Forward this to anyone who uses - or could use - data as part of their job.
On behalf of the Forrester team, thank you very much!
"Let's just say I'm not lost when it comes to data . . . but I could be more found . . ." – (eBusiness team member at a top 50 US bank)
Digital teams are surrounded by data and metrics — from KPIs to customer analytics. Yet I often hear from clients who wish they were just a little more comfortable knowing what the data is really saying, or which metrics are most important.
We just published a brand new report on The Mobile Banking Metrics That Matter which outlines how mobile strategists at banks can put the right metrics in place and work with their analytics teams to get data outputs that guide them toward smart business decisions.
Writing this report got me thinking about which books, blogs, and articles I’ve found most useful when it comes to really getting data and metrics. Here are five I think might help you too:
The Tiger That Isn’t. Probably my personal favorite book about stats and measurement. Written for a mainstream audience, the book works as a guide to thinking through what a given stat or data point really means — and when to trust or doubt such data. It’s also a great read, full of interesting nuggets and statistical oddities (like how the vast majority of people have an above-average number of legs). The book’s thesis is that people who consume data should be skeptical but not cynical about statistics. From there, it helps the reader more easily contemplate and act on the data and metrics they encounter.
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.
Customer Intelligence (CI) professionals invest in data-mining, predictive analytics and modeling tools and technologies to make sense of the deluge of data. In the past, they've had to adapt horizontally-focused analytics and modeling solutions to a customer intelligence and marketing context. Today, however, they can consider a gamut of customer analytics and marketing-focused analytics providers that have not only analytics production expertise but also domain and role-focused expertise.
We just published our first evaluation focusing on the customer analytics category here: The Forrester Wave™: Customer Analytics Solutions Q4 2012 . After screening more than 20 providers for analytics products specifically catering to customer analytics applications, we identified and scored products from six of the most significant providers: Angoss Software, FICO, IBM, KXEN, Pitney Bowes, and SAS. Our evaluation approach consisted of a 70-criteria evaluation; reference calls and online surveys of 60 companies; executive briefings; and product demonstrations. The core criteria included key dimensions such as core functionality (data management, modeling, usability); analytics production; analytics consumption; analytics activation and customer analytics applications. The evaluation also included the strength of the current product and corporate strategies in the customer analytics market as well as the future vision for this category.
We found that four competencies define the current customer analytics market:
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.
Companies adopt advanced analytics tools and techniques to convert data into intelligence and drive key customer-facing business decisions. We see that customer intelligence (CI) professionals involved in customer analytics broadly perform three activities:
Generate analytics: Create and produce analytical insights using analytical tools and technologies.
Apply analytics: Choose the appropriate analytical methodology for the business problem and apply it to the context of the customer lifecycle.
Activate analytics: Use analytical output and insights to optimize customer experiences and to drive customer growth, share of wallet, retention, and lifetime value.
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.