Blogged in collaboration with Rebecca McAdams, Research Associate, serving Customer Insights professionals.
Consumers are connected, constantly influenced by marketing messages, their friend’s social posts, blog posts, reviews, mobile messages, and Twitter posts. In fact, US Adults have an average of three connected devices. Consumers are leaving breadcrumbs of information behind, across multiple channels and devices. Marketers are jumping at the chance to connect with their customers through proactive marketing campaigns and even through non-marketing interactions. But which interactions actually drive impact? What interactions are responsible for sales conversions, and which interactions merely "assist" conversions? CI Pros and marketers are stumped; they must measure these complex interactions to help drive future marketing and media investments and to actually measure their marketing efforts.
By now, we know that attribution is essentially the answer to many marketers’ prayers: more accurate performance metrics, better cross channel insights, and a more informed marketing spend. While the benefits to attribution are clear, many CI pros and marketers still need to make the case for attribution. They need funding, and support from their executives. In light of this aversion to investing in attribution, the recent business case report, Measure the Impact of Cross-Channel Attribution, will help CI pros build the business case you need to convince executives that implementing cross-channel attribution is definitely worth the time, effort, and money. As you follow our guide to building the business case, you will cover all necessary bases by laying out the costs and benefits of attribution, while also planning for any possible risks you may run into along the way.
On May 14, Acxiom announced its intention to acquire LiveRamp, a "data onboarding service," to the tune of $310 million in cash. Several Forrester analysts (Fatemeh Khatibloo, Susan Bidel, Sri Sridharan, and I) cover these two firms, and what follows is our collective thinking on the impending acquisition after having been briefed by Acxiom's leadership on the matter.
On the morning of May 6, 2014, Google announced its intent to acquire Adometry, a leader in the attribution technology space. Later on the same day, AOL announced its intent to acquire Convertro, another top-performing attribution technology vendor. The Adometry acquisition is not surprising, as Google needed to make major investments in its existing attribution offering with some enhanced analytics and insights services, which Adometry can provide. AOL’s acquisition of Convertro was a move to further build out its ad technology stack, hoping to obtain strong attribution algorithm and stellar engineering staff through this acquisition.
Both companies stand to benefit from the acquisition of these small but extremely knowledgeable experts in marketing and media measurement. Two of the biggest benefits for each include:
A strong services staff with deep knowledge of all media and marketing data and, more importantly, the expertise in driving actionable insights in a complicated media-buying world.
An innovative ability to stitch data sources together — online, offline, and mobile — across the buyer’s journey.
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.