Perhaps you’ve heard him in meetings — he is the one questioning your results. Perhaps you’ve seen him at his desk surrounded by tombs and tables in an effort to lower incremental sales calculations — he calls it reducing bias. Perhaps you’ve hoped he will not be assigned to your project — he delivers lower lift estimates than his peers. He is the measurement curmudgeon.
How do you detect if a measurement curmudgeon resides in your office? Listen for the following clues/questions:
Is that control group really comparable to the experimental group? Isn’t it biased toward less engaged customers and inflating your measured lift?
Wasn’t that concurrent with our fall promotion? Isn’t that event likely accounting for most of your positive results?
Haven’t sales been trending up? Did you incorporate that trend into your analysis?
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.
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:
Within the modern applications era, regardless of whether new software applications are being developed and delivered for mobile, tablets, or the Web, the truly successful app-dev leaders will be those who focus on delivering constant value and incremental improvement to their business. That is a totally different perspective from “I need to keep control of my team’s productivity to make sure that we stick to our estimated costs, scope, and project dates.” Of course, the interest in cost is never going away, but app-dev leaders today have a great chance to enhance their conversation with executives and business stakeholders and add value to the conversation.
However, as the recent research I just published, Agile Metrics That Matter, proves, while some of the most advanced Agile teams do use new progress, quality, efficiency, and value/benefits metrics (these to a lesser degree), some software development industry luminaries have worked and are working on new methods to measure value in software development. But it’s still early days!
I’d like to summarize here some good old practices on establishing metrics that count together with some of the new findings of the research:
More news from Mountain View on Tuesday, where Internet powerhouse Google released the much-anticipated Data Driven Attribution (DDA) feature for its Premium users. The release of Google’s DDA approach comes as no surprise to the analytics and measurement community. The world of attribution measurement is constantly evolving and new attribution approaches, new players, and new tools regularly enter the market, enabling marketers to select the right attribution tool for their business needs. It was only a matter of time before Google released a persuasive, more advanced measurement offering.
First, the Data Driven Attribution feature is only available for Google Analytics Premium users. It has several notable features worth highlighting:
Google DDA’s approach is statistically driven methodology. Google’s DDA approach is a huge improvement over its rules-based Attribution Modeling tool (which is available for FREE for Google Analytics users). The DDA approach uses probability modeling to best estimate the values of each interaction. The approach itself is transparent, understandable, and Google is extremely open about how it calculates the value parameters.
"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.
Last week, I had the pleasure of attending Forrester's Forum For Marketing Leaders in London and met some members of the Forrester Leadership Board (FLB) for Customer Insights (CI) professionals. I was eager to share my research on attribution measurement and (selfishly) get their point of view on measurement successes and challenges in Europe. Here are a few key takeaways from our CI colleagues across the pond:
Attribution measurement is a growing topic among European firms. When I met with the FLB members, I was delighted to learn that attribution is being widely adapted in most organizations, with the same challenges that we face in America. In fact, it seems that the firms I spoke with adapted attribution for quite a while, and they’re really looking to advance their attribution approach in the near future. Overall, they are making significant investments in the right data, resources, and tools to have a more sophisticated measurement approach.
Marketing professionals are more and more accountable for proving value, and making investment recommendations and decisions, based on business and marketing performance. Marketing mix modeling is quickly being adopted across different industries as the preferred way to measure, forecast, optimize, and plan marketing budgets.
Today, I am pleased to announce the publication of The Forrester Wave™: Marketing Mix Modeling, Q2 2013. This evaluation is a result of countless hours of vendor reviews and assessments, in-person briefing reviews, customer calls, fact-checking, and intensive research work. This Forrester Wave will help firms create a shortlist of providers, based on their unique business needs.
After long days and nights, I am glad to share with you the key takeaways that emerged from the Forrester Marketing Mix Modeling Wave:
Wide arrays of firms are adapting marketing mix modeling. Marketing mix modeling is the traditional approach to uncover value and build a marketing plan for consumer packaged goods companies. However, other industries, including financial services and retail, are quickly taking an interest in adopting this approach because they need a more scientific, holistic way to understand marketing and business performance. As a result, we see an upsurge in adoption across different industries.
Cross-channel attribution. For customer insights and marketing practitioners, attribution is a white hot measurement topic. It’s viewed as the best way to measure effectiveness of marketing and media campaigns; a way for firms to assess…truly assess… the value of the customer journey. For the past 18 months, I have been living and breathing this topic and today I am happy….no, I’m elated…to announce the official publication of the Cross-Channel Attribution Playbook.
What’s a playbook, you ask? Well, a playbook is a framework to help organizations develop expertise around a specific business topic. The Cross-Channel Attribution Playbook helps marketers and customer insights professionals to take strategic steps in building an attribution strategy within their organization. It includes 12 chapters, including an executive overview, which covers different aspects of developing and managing a cross-channel attribution measurement framework. The four “chapters” specifically help organizations:
The analytics community is experiencing a rebirth. A renewal. A renaissance. Why? Data is bursting from every corner, from every device, allowing brands to deliver relevant messages and offers to its customers. So, being an analytics connoisseur is important now more than ever. I mean, who else is going to play with all this data . . . and actually enjoy it?
Organizations must develop relevant marketing strategies across devices -- to different customers -- and have the advanced measurement and analytic frameworks to fuel decisions. And the perpetually connected customer is forcing organizations to act quickly, so near-real-time insights are paramount. My past research addresses this, specifically, how analytics professionals can use attribution as a way to understand the true value of each interaction point. This is even more complex because of the increase in cross-device usage. As a result,analytic pros are using savvy ways to connect information and to measure cross-device impact and incremental value.