We've been talking about Adaptive Intelligence (AI) for a while now. As a refresher, AI is is the real-time, multidirectional sharing of data to derive contextually appropriate, authoritative knowledge that helps maximize business value.
Increasingly in inquiries, workshops, FLB sessions, and advisories, we hear from our customer insights (CI) clients that developing the capabilities required for adaptive intelligence would actually help them solve a lot of other problems, too. For example:
A systematic data innovation approach encourages knowledge sharing throughout the organization, reduces data acquisition redundancies, and brings energy and creativity to the CI practice.
A good handle on data origin kickstarts your marketing organization's big data process by providing a well-audited foundation to build upon.
Better data governance and data controls improve your privacy and security practices by ensuring cross-functional adoption of the same set of standards and processes.
Better data structure puts more data in the hands of analysts and decision-makers, in the moment and within the systems of need (eg, campaign management tools, content management systems, customer service portals, and more).
More data interoperability enables channel-agnostic customer recognition, and the ability to ingest novel forms of data -- like preference, wearables data, and many more -- that can vastly improve your ability to deliver great customer experiences.
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, 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.
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.
There’s no doubt that, to consumer marketing professionals, data about the users of mobile network are highly valuable. But AT&T is finding that enterprise application designers, corporate security & risk professionals, corporate trainers and CFOs are very interested in this data as well - so much so that the US-based network operator is turning access to and collaboration on its data into a new business service.
Under the guidance of Laura Merling, VP of Ecosystem Development & Platform Services (and formerly of Mashery), AT&T Business Solutions is embarking on an ambitious plan for sharing its data in a secure programmatic fashion leveraging RESTful APIs. It had previously shared it data in a more informal fashion with selected partners and customers but found this approach difficult to standardize and repeat on a larger scale. It also has participated in data collaboration efforts such as the well-known hackathon with American Airlines at South by Southwest earlier this year.
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.
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?
Last week, I had the pleasure of attending the Future of Consumer Intelligence conference in San Francisco. This week, when I reflect back on the conference topics and energy, I realize how fitting San Francisco was as the location of the event: Much like the essence of the city itself, the conference speakers and attendees showed ingenuity and optimism around the challenges and opportunities that the market research industry faces. I also thought about the same conference that I attended last May (IIR Market Research Technology Event 2012) and the key themes that I gathered and blogged about: Big data is here, integrating survey and behavioral data is powerful, and behavioral economics has huge implications for market research. For me, the big difference between last year’s conference and this year’s is this: A year ago, market insight professionals were sizing up their challenges with the future of market research. This year, they are taking the bull by the horns and embracing both the challenges and opportunities that technology in market research presents. Here are the main themes I gathered from the event:
As an analyst on Forrester's Customer Insight's team, I spend a lot of time counseling clients on best-practice customer data usage strategies. And if there's one thing I've learned, it's that there is no such thing as a 360-degree view of the customer.
Here's the cold, hard truth: you can't possibly expect to know your customer, no matter how much data you have, if all of that data 1) is about her transactions with YOU and you 2) is hoarded away from your partners. And this isn't just about customer data either -- it's about product data, operational data, and even cultural-environmental data. As our customers become more sophisticated and collaborative with each other ("perpetually connected"), so organizations must do the same. That means sharing data, creating collaborative insight, and becoming willing participants in open data marketplaces.
Now, why should you care? Isn't it kind of risky to share your hard-won data? And isn't the data you have enough to delight your customers today? Sure, it might be. But I'd put money on the fact that it won't be for long, because digital disruptors are out there shaking up the foundations of insight and analytics, customer experience, and process improvement in big ways. Let me give you a couple of examples:
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.