"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.
Google recently announced, on Tuesday, plans to offer its Attribution Modeling Tool through Google Analytics via a public white list. The Attribution Modeling Tool was previously offered through the Google Analytics Premium product at an additional cost. The move to make its Attribution Modeling Tool available through Google Analytics for free indicates that Google is aggressively looking to extend its current analytics and measurement capabilities. Specifically, Google’s Attribution Modeling Tool allows users to:
Work with data they’re already tracking in Google Analytics. That means no additional setup or work for your IT department, marketing, or analytics groups. Flip the switch and you’re on. You can input and view values across channels, including affiliates, display ads, paid and organic search, and email.
Customize the attribution model. Google Attribution Modeling Tool provides either last-click or rules-based attribution models to their users. Google allows the user to have control of their attribution model, allowing the user to compare various models to each other, including the contributed value of channels, campaigns, and various other dimensions.
Access the Attribution Modeling Tool for FREE. We all love free things. All users have to do is sign up for the tool and the tool is available through the Google Analytics product. If you want more information about the tool, Google is hosting a webinar, which will give an overview of the capabilities.
Marketing Manager: “Net Promoter Score is the one number we need to grow!”
Customer Intelligence Manager: “Nonsense! ‘Satisfaction’ predicts customer loyalty better than ‘likelihood to recommend’ – it says so in the wonky business journals I read!”
Marketing Manager: “You don’t understand how business works!”
Customer Intelligence Manager: “You don’t understand how math works!”
The sad thing is that in a micro sense they’re both right, but in a macro sense they’re both wrong. The reason? They’re each taking an inside-out point of view based on their own specialties.
Where NPS Fits In A Customer Experience Measurement Framework
In our research into customer experience measurement, we see many organizations that use Net Promoter Score. Some use it poorly because – like the fictional marketing manager above – they don’t understand the limitations of what NPS can do.
Here’s how they should think of it: Customer experience is how customers perceive their interactions with a company along each step of a customer journey, from discovery, to purchase and use, to getting service. NPS measures what customers say they’ll do as a result of one or more of those interactions. It’s what Forrester calls an “outcome metric.”
But outcome metrics are just one out of three types of metrics captured by effective customer experience measurement programs. The best programs gather and analyze:
I've noticed a disturbing trend in one of the markets I study. Thirty percent of marketers say their top social media goal is creating brand impact, but only 10% tell us they measure brand impact — a gap of 20 percentage points. But then while just 4% say sentiment or engagement are their top goals, a whopping 26% measure these numbers —leaving us with an almost identical gap of 22 percentage points, but in the other direction. It’s clear what's happening here: Marketers are using sentiment and engagement numbers as a proxy for brand impact surveys.
Deep down I love the idea of measurement proxies. A properly constructed and proven proxy could be a cheap, quick, and effective stand-in for direct measurement of things that are quite frankly hard to measure — like brand impact.
But there’s a big problem here: I've been looking pretty hard for good measurement proxies for a while now, and I’ve found very few that could be described as "properly constructed and proven." And I'm pretty sure none of the marketers in our survey have proven their proxies — because if they'd tried, they'd have almost certainly failed.
The Oil And Gas Information Technology Innovation Dilemma
The hydrocarbon logistics chain of natural gas and crude oil connects globally distributed exploration and production sites with industrial and private consumers via pipelines, tankers, rail cars, and trucks with massive intermediate buffering storage and conversion facilities (tank farms, refineries, gas plants); it is the lifeblood of our energy supply chain today and for the coming decades.
More than 75 million barrels of oil and 300 billion cubic feet of natural gas are produced, transported, and consumed all over the globe — every day. Along the complex transportation chain, these special bulk products, both liquids and gases, are transferred between the different modes of transportation, resulting in a number of challenges based on complex measurements of product volumes and masses:
Measurement accuracy. In an ideal world, we would always determine the mass of crude oil and natural gas at each measurement point; however, due to the large quantities involved, weighing is possible only at the very end of the logistics chain. Consequently, we have to live with measurement data that typically carries an uncertainty of 0.1% to 0.5 %, depending on the measurement devices’ intrinsic accuracy.
But saying that raises the question: If the number of fans or followers you have doesn’t tell us whether you’ve succeeded as a company, then what does it tell you? And if your CEO shouldn’t be worried about the number of wall posts you’ve generated, then who should be paying attention to this number?
Since last summer, I’ve been using a structured model to help my clients focus on delivering the right social media marketing data to various stakeholders inside their organization. Social media programs throw off so much data that the key to measuring and managing your programs well is focusing each stakeholder on just the pieces of data that are relevant to helping them do their jobs. If part of your job is measuring the success of your social media marketing programs, then you need to start segmenting the stakeholder groups you’re providing that data to and tailoring the type of metrics, the volume of metrics, and the frequency of reporting you provide them.