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?
Many have commented on the 14 product enhancements announced at last week’s Google Analytics Summit (GAS), but I attended to learn about their new Data Driven Attribution (DDA) tool. Why travel to Mountain View, CA “just” to focus on a new advanced attribution tool?
Digital pathways are rarely last click. Imagine a consumer who clicks on a banner ad sending them to your YouTube video. They watch the first 45 seconds and then enter your website through natural search. Do you really want to give zero credit to the YouTube ad? Assuming this pathway is common, should you increase or decrease your banner ad budget? Now with Google’s DDA (or competing tools) you can get an accurate answer for each touch point.
Pathways are rich with insights—in theory. Now imagine your team is struggling to optimize YouTube across a set of products. Also imagine you could measure how the influence of YouTube varied across journeys based on what was purchased, lifecycle stage and persona. Armed with those insights your team could develop a content creation schedule or define the role of YouTube in new product launches. Unfortunately Google’s DDA is limited in pathway comparisons, but I predict expansion of that functionality in the next 12 months. I applaud Google for its simple interface, but marketers need more options than the limited demographics and attitudes available today.
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:
Last week I had the privilege of participating on the Advisory Board for the Retail Marketing Analytics Program (ReMAP) at the University of Minnesota, Duluth (UMD). Perhaps the best part of these sessions is the opportunity to meet with the students, many of which will be tomorrow’s marketing scientists.
During a few conversations on this visit, I was asked how to secure an entry-level position that would involve lots of cool predictive analytics. I want to focus on one of the answers I shared — don’t tell anyone you’re doing predictive analytics. What do I mean? Imagine you’re a freshly minted analyst in the following situation:
Your manager asks you to quickly evaluate who responded to a promotion.
You have many factors to investigate (because you have lots of data).
You have very limited time to find a great answer and build a deliverable.
The required deliverable needs to be simple and free of analytic jargon.
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)
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?
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.
Yesterday, Acxiom, one of the world's largest data brokers and a key player in the marketing services ecosystem, launched an important new consumer service (still in Beta) called "About The Data." It's an initiative to show consumers some of the data that Acxiom has compiled about them, to provide education around how certain types of data are sourced and used, and to let users correct and/or suppress the use of these datapoints for marketing purposes.
This is a big deal. Why? Because it's pushing Acxiom (and, frankly, the entire third-party data industry) way out of its comfort zone on a few levels.
First, this is not a company that is used to dealing with consumers on a mass scale. Acxiom's DNA is fundamentally B2B; learning how to communicate to, and design tools for, individual consumers is a massive undertaking, and it shows in the UI. For example, when I attempted to register my address with a "#" preceding my apartment number, the format was rejected without any indication that symbols were disallowed in that field. As a tech-savant, it only took me one more attempt to figure that out, but not all consumers are so savvy. Similarly, clicking the "Home" button on the navigation bar logs users out without any notice or warning.