Over the past few months, following publication of my "Customer Insights Center of Excellence" report , there’s been a significant uptick in questions by insights and analytics teams who want to talk to us about CoEs. That’s a positive sign that firms are feeling the crunch to get more value from their insights functions. What’s the evidence for that conclusion? What can we learn from who’s asking about insights CoEs? And most importantly, what really matters in how you organize?
Before we dig in to answers, let’s set the bar on what “great” looks like in truly customer obsessed organizations: they use data for insights to improve customer experience that matters most to business outcomes. As my colleagues James McCormick, Brian Hopkins, and Ted Schadler write in their recent report, "The Insights-Driven Business," customer obsessed businesses act on insights in closed loops, at speed, and at scale in all parts of the firm. They embed analytics and testing directly into operating teams. And, firms who implement these approaches run faster and fleeter than you. The pressure is on from insights-driven organizations.
Is your business digital? Like Domino’s Pizza, do you realize that you are not a product or service business, rather you are a software and data business that provides products or services? Do you exploit all of your customers' data to know them inside-out? Are customers flocking to you because you are driving every engagement with insight about them? If the answer to any of these questions is not a resounding, “Yes!”, then you are losing revenue and shareholder value.
In Forrester’s new report, The Insight Driven Business, my colleagues Ted Schadler, James McCormick and I identify a type of business that ignores the "data driven" hype. Instead, insights-drivenbusinesses focus on implementing insights - that is actionable knowledge in the context of a process or decision - in the software that drives every aspect of their business. This is a big shift from most firms that fret over big data and technology. Instead insights-driven businesses focus on turning insights into action. The big data and technology pieces come along naturally as a consequence.
To gauge the economic impact of insights-driven businesses, Forrester built a revenue model that conservatively forecasts insights-driven businesses will earn about $400 billion in 2016; however, by 2020 they will be making over $1.2 trillion a year due to an astonishing compound annual growth rate between 27% and 40%. Given that global growth is less than 4%, how will they pull this off? Plain and simple, they’ll do this by understanding customers more deeply and using that insight to steal them from their competition.
The questions below may sound familiar to you. I hear them from leaders of business insights teams of all kinds, from quant to qual, digital analytics to database marketing, customer analytics to voice of customer, market research to competitive intelligence, campaigns to customer service, behaviorial to predictive, B2C to B2B, CPG to pharma – you name it:
"I lead our [name the insights area[s] here] team. We’re struggling to get our business and operational areas to take action on insights – heck, sometimes we don’t even know what happens to the insights we provide. How do we change this?"
"Our insights teams work in silos that have built up over the years. The teams are good at what they do. But how do we pull together and combine our different flavors of insights to get more customer understanding? How should we organize?"
"I've been asked to re-organize [or, I'm new and I've taken over] our insights areas. I need to give a presentation to the C-team about what I'll propose. Any ideas on a framework I should use?"
The Background – Linux as a Fast Follower and the Need for Hot Patching
No doubt about it, Linux has made impressive strides in the last 15 years, gaining many features previously associated with high-end proprietary Unix as it made the transition from small system plaything to core enterprise processing resource and the engine of the extended web as we know it. Along the way it gained reliable and highly scalable schedulers, a multiplicity of efficient and scalable file systems, advanced RAS features, its own embedded virtualization and efficient thread support.
As Linux grew, so did supporting hardware, particularly the capabilities of the ubiquitous x86 CPU upon which the vast majority of Linux runs today. But the debate has always been about how close Linux could get to "the real OS", the core proprietary Unix variants that for two decades defined the limits of non-mainframe scalability and reliability. But "the times they are a changing", and the new narrative may be "when will Unix catch up to Linux on critical RAS features like hot patching".
Hot patching, the ability to apply updates to the OS kernel while it is running, is a long sought-after but elusive feature of a production OS. Long sought after because both developers and operations teams recognize that bringing down an OS instance that is doing critical high-volume work is at best disruptive and worst a logistical nightmare, and elusive because it is incredibly difficult. There have been several failed attempts, and several implementations that "almost worked" but were so fraught with exceptions that they were not really useful in production.[i]
As a customer insights / analytics / digital measurement pro, do you experience any of these challenges? And what can you do right now to make progress with them?
I can’t keep up with requests from my stakeholders for analysis and insights. Does the volume of requests and your team’s capacity seem increasingly out of whack in your organization?
Our customer data isn’t where we need it to be – we can’t get a comprehensive view of our customer. You’re not alone. Marketing and technology teams struggle to align objectives, roles, budget, projects and process, and timelines to maximize value from customer data. Marketing decision-makers report several reasons they are failing: too many data sources (44%), lack of access to technology to manage data source integration (38%), lack of budget (35%), lack of skills to support integration (34%), organizational silos (27%), and lack of an executive sponsor (23%).
We’re leaving money on the table because our different analytics and insights teams work in silos. Here’s a simple digital measurement example of this: one digital team is responsible for driving visits to the website. Other teams are responsible for maximizing on-site conversions. They work in their own separate silos. A more efficient and effective approach: work together to identify the characteristics of customers most likely to convert, and work on driving that group to the site. That type of silo breakdown needs to happen more.
Delivering broad access to data and analytics to a diverse base of users is an intimidating task, yet it is an essential foundation to becoming an insights-driven organization. To win and keep customers in an increasingly competitive world, firms need to take advantage of the huge swaths of data available and put it into the hands of more users. To do this, business intelligence (BI) pros must evolve disjointed and convoluted data and analytics practices into well-orchestrated systems of insight that deliver actionable information. But implementing digital insights is just the first step with these systems — and few hit the bull's eye the first time. Continuously learning from previous insights and their results makes future efforts more efficient and effective. This is a key capability for the next-generation BI, what Forrester calls systems of insight.
"It's 10 o'clock! Do you know if your insights support actual verifiable facts?" This is a real challenge, as measuring report and dashboard effectiveness today involves mostly discipline and processes, not technology. For example, if a data mining analysis predicted a certain number of fraudulent transactions, do you have the discipline and processes to go back and verify whether the prediction came true? Or if a metrics dashboard was flashing red, telling you that inventory levels were too low for the current business environment, and the signal caused you to order more widgets, do you verify if this was a good or a bad decision? Did you make or lose money on the extra inventory you ordered? Organizations are still struggling with this ultimate measure of BI effectiveness. Only 8% of Forrester clients report robust capabilities for such continuous improvement, and 39% report just a few basic capabilities.
From discussions with our clients in the financial services industry (FSI) in Asia Pacific, we’ve noticed that their digital agenda has changed dramatically over the past 18 months, shifting from a consideration of acquisitions and distribution channels to a broader business transformation imperative.
In fact, leaders at banks and insurance firms are increasingly realizing that:
Customer experience is fast becoming the only competitive differentiator.
Banks and insurance have to accelerate their ability to innovate and deliver new sources of value to customers faster.
Yes, I think someone’s banging on the door. Pretty hard actually.
In fact, it’s deafening.
The knocking is empowered digital media buyers. The slowness to answer is the media ecosystem of publishers, media agencies, and broadcasters.
I shared the video below a week ago on LinkedIn and people clearly like it. It’s the parable I just stated, but acted out. Listen to Gabe Leydon of Machine Zone (big digital media buyer) slam the media ecosystem. It’s painful. Cathartic. Iconoclastic. Focus on two segments: 11:00 -> 11:45 and 12:55 -> 13:55.
This is the advertising ecosystem’s reckoning with the age of the customer. The customers want to cut through all of the layers of BS that advertising has traditionally wrapped itself up in.
I had a few takeaways given Leydon’s analysis:
Media businesses are trying to be technology platforms, but are mostly houses on fire.
Analytics agencies are the new media agencies.
Media agencies are just houses on fire.
If you’re a marketer, pull your media-buying capabilities close to your chest. Invest in better analytics. And do everything in your power to get a measurable, direct-to-consumer sales channel on its feet, if only to provide insights to the marketing that feeds your indirect channels.
We've seen another acquisition in the shifting eDiscovery market this week as kCura, the developer of Relativity, announced its acquisition of Content Analyst Company, the brains behind the CAAT analytics engine (kCura’s press release is here). The acquisition is not entirely surprising. kCura has been relying on the CAAT engine to power its analytics offering for eight years. According to kCura, use of its Relativity Analytics offering “has grown by nearly 1,500 percent” since 2011, with more than 70% of current kCura’s customers with licenses.
What does this acquisition mean for kCura, its customers, and Content Analyst Company customers?
One of the reasons for only a portion of enterprise and external (about a third of structured and a quarter of unstructured -) data being available for insights is a restrictive architecture of SQL databases. In SQL databases data and metadata (data models, aka schemas) are tightly bound and inseparable (aka early binding, schema on write). Changing the model often requires at best just rebuilding an index or an aggregate, at worst - reloading entire columns and tables. Therefore many analysts start their work from data sets based on these tightly bound models, where DBAs and data architects have already built business requirements (that may be outdated or incomplete) into the models. Thus the data delivered to the end-users already contains inherent biases, which are opaque to the user and can strongly influence their analysis. As part of the natural evolution of Business Intelligence (BI) platforms data exploration now addresses this challenge. How? BI pros can now take advantage of ALL raw data available in their enterprises by: