Yesterday, FTC Commissioner Julie Brill published an essay on AdAge.com that calls on data brokers to join -- or, rather, establish -- an initiative called "Reclaim Your Name." The goal of the program would be to provide a single portal where consumers could see what data the industry has collected about them, provide options to opt in and out, and to correct data that might be inaccurate.
While the commissioner's article is a bit heavy on the "big data" rhetoric, her point is well taken: We have entered an era where the volume of data that individuals make available about themselves -- often inadvertently -- is increasing daily. Unfortunately, guidelines for how marketers and the larger data industry collect and use personal data are in short supply. This conflict is one of the major challenges that our industry faces in the coming decade: How can brands excel in the age of the customer if they're constantly under scrutiny about their privacy and data practices?
Acxiom, one of the world's largest data brokers, recently launched its own version of the kind of portal Commissioner Brill calls for. AboutTheData.com lets individuals see a subset of the data Acxiom knows about them, provides correction and opt-out opportunities, and aims to provide consumers with education about the data industry as a whole.
Last year, my colleague Srividya Sridharan published The State Of Customer Analytics 2012 (subscription required). Using the results of her annual customer analytics adoption survey, she uncovered key trends of how customer analytics practitioners use and adopt various advanced analytics across the customer life cycle and highlighted challenges and drivers associated with customer analytics.
This year, I have the pleasure of teaming up with Sri on her yearly survey, to further explore the adoption of advanced analytics, measurement, and attribution. Please read her blog post to learn more about the survey. This survey will explore the adoption and usage of measurement techniques, including attribution, and the adoption of advanced analytics methodologies. With this expanded survey we want to understand how you use and apply measurement and analytics in your organization to optimize both cross-channel marketing campaigns and customer programs.
In particular, we’re fielding questions to understand the goals and challenges associated with measurement and analytics, the adoption and application of measurement and advanced analytics methods, the use of several marketing and customer metrics, the customer insights process and workflow, and the organizational aspects that support measurement and analytics. We encourage you to participate in this survey, as this information will help you benchmark your measurement and analytics adoption efforts.
Last year, we published The State of Customer Analytics 2012 (subscription required) based on the results of our annual customer analytics adoption survey where we uncovered key trends of how customer analytics practitioners use and adopt various advanced analytics across the customer lifecycle and highlighted challenges and drivers associated with customer analytics.
This year, I am teaming up with my colleague and attribution guru Tina Moffett to further explore measurement, attribution and customer analytics practices ranging from the type of attribution techniques in vogue to the adoption of advanced analytics methodologies. With this expanded survey we want to understand how you use and apply measurement and analytics in your organization to optimize both cross-channel marketing campaigns as well as customer programs.
In particular, we’re fielding questions to understand the goals and challenges associated with measurement and analytics, the adoption and application of measurement and advanced analytics methods, the use of several marketing and customer metrics, the customer insights process and workflow as well as the organizational aspects that support measurement and analytics. We encourage you to participate in this survey, as this information will help you benchmark your measurement and analytics adoption efforts.
My eighty-six-year-old mother called me last night to tell me that she’s been “boiling eggs wrong all my life.” It seems she’d watched a cooking show and received some “best practice” advice. My mom is an excellent cook, so this made me realize that no matter how seasoned a veteran you are, there’s no harm (and often some good) in a review of the basics. In that spirit, I am going to share some advice for a question that comes to me quite frequently:
“What are the best practices for leveraging an industry or business award?”
First, deal with the basics: the press release.
Issue a press release. Be sure to include a quote from the awarding body about their judgment process and criteria. If the award is based upon a customer story, work really hard to include a quote from the customer in the press release. It’s OK to use the template that the award giver has probably given you, but make sure the press release is search-engine-optimized for your keywords.
Get aggressive on press outreach. Focus on reporters or social influencers (bloggers, analysts) who have been diffident or unresponsive in the past. If you can offer up an interview with the co-award-winning client, you have a very good chance of getting some coverage.
Post the news on all your social media sites.
If a customer was involved, try to convert to a “customer case study” press release. The barrier to this might be lower now that the customer’s use of your product/service is already public knowledge.
Customer insights professionals have many customer analytics methods (sub's reqd) to choose from today to perform behavioral customer analysis, and new techniques emerge as the complexity of customer data increases. Analysis of customer data involves the use of data-mining and statistical methods that span descriptive and predictive analytics. But how do you decide which customer analysis methods are right for you? How do you plan your customer analytics capability with the right mix of methods that address specific questions and uncover customer insights?
Using our Forrester TechRadar™ methodology we are kicking off research that will address many of the questions above as well as explore:
The current state of each customer analysis method, its maturity, market momentum, ecosystem interest and investment levels.
The potential impact of each method on your ability to understand and predict customer behavior
The customer analytics methods to be included in this report range from behavioral customer segmentation to propensity models, social network analysis, next-best offer analysis, lifetime value analysis, customer churn analysis to name a few.
If you are interested in participating in this research as an end-user/client, expert or customer analytics technology or services vendor reach out to me directly at ssridharan [at] forrester [dot] com.
Thanks in advance for your participation! All research participants will receive a copy of the published report.
The standard pricing model for email marketing — the CPM — may soon change. Industry consolidation, commoditization, and growing data volumes threaten the standard. Buyers may soon confront models that range from a platform license (all-you-can-email) to total utilization (data + messaging) to seat-based models. In November, I will publish research into the rationale for model changes, evaluate different candidate models, and explore the repercussions of the change.
I need your help. Price changes will have dramatic and difficult to predict effects on customer experience, marketing practices, the vendor landscape, and even the structure of the marketing organization. For example, an all-you-can-email model may, paradoxically, reduce email volumes in the long run, if it removes barriers to adoption of cross-channel programs.
This potential shift from channel-specific to cross-channel is one of the more interesting consequences of a model change. I’d like your reactions include:
What is the best pricing model given the challenges you face (performance, cross-channel, real-time, mobility, etc.)?
Who in your organization might be affected by the change?
How do you anticipate the purchase process (RFP, selection, negotiation, contract review) might change as a result of a model change?
If you faced no pricing limits on email, how would your strategy and operations change?
If vendors moved to a platform model — e.g., including other modules such as web recommendations, push notifications, or behavioral targeting with email — how would your strategy and operations change?
The end of a quarter forces me to reflect on what I learned in regards to my coverage area: measurement and attribution. From customer insights (CI) pros and marketers, I saw an increased interest in advancing their measurement approaches. On the attribution front, there is an appetite to learn about specific methodologies, use cases, ongoing attribution management strategies, and attribution applications to marketing/media buys. On the vendor side, I saw more advancement in tools, approaches, and offline and mobile data integration. I predict attribution — and general consumer and marketing measurement — will continue to be a hot topic for marketers and CI professionals well into 2014. Specifically, I expect to see more attribution adoption and usage of attribution to measure customer purchase paths and to learn more about customer behaviors and motivations.
In the meantime, let me recap the Q3 2013 measurement takeaways:
I remember my first day at high school. Yikes it was scary. The older kids were BIG! The teachers were BIG (the phys ed teacher was even a little mean), the school was BIG . . . Everything felt so BIG! But as the year ticked by, l became familiar and comfortable with my classmates, teachers, and the school -- the place shrunk to a more comforting size.
Today marketers feel about data as I did about my first day at big school -- it’s BIG. There is lots of it, and it’s coming at them from many directions and in many forms. But data does not feel so big and daunting to the marketer who recognizes their customers buried in the fog of big data. The fact is, customer recognition is the key for marketers to make sense of big data; and it is at the heart of all effective marketing activities. I write about this in my most recent report: “Customer Recognition: The CI Keystone.”
So what is customer recognition?
Recognition associates interactions with individuals or segments across time and interactions. The strength of recognition is gauged on its ability to associate interactions to anything from individuals to a broad segment; and to persist those associations across different touchpoints over time.
Keys are needed for recognition at touchpoints. There are many types of keys, ranging from IP addresses, to cookie-based TPIKs, to phone numbers and customer account numbers. At Forrester we call them touchpoint interaction keys (TPIKs)
Tag management tools are much more than the management of tags. Strategic use can:
give control of digital marketing campaigns to marketers – relieving significant IT burden,
significantly reduce digital marketing implementation and operational costs,
garner support for digital marketing programs – even in highly regulated firms – by offering detailed multi-stakeholder visibility and control of scripts and digital data,
reduce the “stickiness” and dependence on digital technology vendors, and
enable digital data syndication, which in turn drives dynamic segmentation and bottom-up attribution programs.
Forrester is currently assessing the tag management capabilities of top global brands, advising on their strategies and guiding them with their digital marketing road maps. Also; tag management research is ongoing with a few papers due for release later this year.
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.