Developers And Their Business Counterparts Are Caught In A Trap
They swim in game-changing new technologies that can access more than a billion hyperconnected customers, but they struggle to design and develop applications that delight customers and dazzle shareholders with annuity-like streams of revenue. The challenge isn’t application development; app developers can ingest and use new technologies as fast as they come. The challenge is that developers are stuck in a design paradigm that reduces app design to making functionality and content decisions based on a few defined customer personas or segments.
Personas Are Sorely Insufficient
How could there be anything wrong with this conventional design paradigm? Functionality? Check. Content? Check. Customer personas? Ah — herein lies the problem. These aggregate representations of your customers can prove valuable when designing apps and are supposedly the state of the art when it comes to customer experience and app design, but personas are blind to the needs of the individual user. Personas were fine in 1999 and maybe even in 2009 — but no longer, because we live in a world of 7 billion “me”s. Customers increasingly expect and deserve to a have a personal relationship with the hundreds of brands in their lives. Companies that increasingly ratchet up individual experience will succeed. Those that don’t will increasingly become strangers to their customers.
Buy analytics software, hire marketing scientists, and engage analytics consultants. Now wait for the magic of customer analytics to happen. Right?
Wrong. Building a successful customer analytics capability involves careful orchestration of several capabilities and requires customer insights (CI) professionals to answer some key questions about their current state of customer analytics:
What is the level of importance given to customer analytics in your organization?
Have you clearly defined where you will use the output of customer analytics?
How is your analytics team structured and supported?
How do you manage and process your customer data?
Do you have clear line of sight between analytics efforts and business outcomes?
What is the process of sharing insights from analytics projects?
What type of technology do you need to produce, consume and activate analytics?
The Obama 2012 campaign famously used big data predictive analytics to influence individual voters. They hired more than 50 analytics experts, including data scientists, to predict which voters will be positively persuaded by political campaign contact such as a call, door knock, flyer, or TV ad. Uplift modeling (aka persuasion modeling) is one of the hottest forms of predictive analytics, for obvious reasons — most organizations wish to persuade people to to do something such as buy! In this special episode of Forrester TechnoPolitics, Mike interviews Eric Siegel, Ph.D., author of Predictive Analytics, to find out: 1) What exactly is uplift modeling? and 2) How did the Obama 2012 campaign use it to persuade voters? (< 4 minutes)
The deluge of customer data shows no signs of abating. The perpetually-connected customer leaves data footprints in every interaction with a brand. This presents tremendous opportunities for customer insights professionals and analytics practitioners tasked with analyzing this data, to not only get smarter about customers but ensure that the insights get appropriately used at the point of customer interaction.
When we asked customer analytics users about the challenges and drivers of customer analytics adoption, we found that data integration and data quality continue to inhibit better adoption of customer analytics while users still want to use analytics to improve the data-driven focus of the organization and drive satisfaction and customer retention.
Forrester’s Customer Analytics Playbook guides customer insights professionals, marketing scientists and customer analytics practitioners into this new reality of customer data and helps discover analytics opportunities, plan for greater sophistication, take steps towards building a customer analytics capability and continually monitor progress of analytics initiatives. It will include 12 chapters (and an executive overview) that cover different aspects of customer analytics.
Why? What organization couldn’t benefit from making better decisions? Just ask the Obama campaign, which used sophisticated uplift modeling to target and influence swing voters. Or telecom firms that use predictive analytics to help prevent customer churn. Or police departments that use it to reduce crime. The list goes on and on and on. Virtually every organization could benefit from predictive analytics. Don’t confuse traditional business intelligence (BI) with predictive analytics. BI is about reports, dashboards, and advanced visualizations (which are still essential to every organization). Predictive is different. Predictive analytics uses machine learning algorithms on large and small data sets alike to predict outcomes. But predictive is not about absolutes; it doesn’t gaurentee an outcome. Rather, it’s about probabilities. For example, there is a 76% chance that this person will click on this display ad. Or there is a 63% chance that this customer will buy at a certain price. Or there is an 89% chance that this part will fail. Good stuff, but it’s hard to understand and harder to do. It’s worth it, though: Organizations that employ predictive analytics can dramatically reduce risk, disrupt competitors, and save tons of dough. Many are doing it now. More want to.
Few understand the what, why, and how of predictive analytics. Here’s a short, ordered reading list designed to get you up to speed super fast:
William Shakespeare wrote that “What’s past is prologue.” Big data surely builds on our rich past of using data to understand our world, our customers, and ourselves. Now the world is flush and getting flusher in big data from cloud, mobile, and the Internet of things. What does it mean for enterprises? In a word: opportunity. Firms have taken to big data. Here are my four predictions for key enterprise big data themes in 2013:
Firms will realize that “big data” means all of their data. Big data is the frontier of a firm’s ability to store, process, and access (SPA) all of the data it needs to operate effectively, make decisions, reduce risks, and create better customer experiences. The key word in the definition of big data is frontier. Many think that big data is only about data stored in Hadoop. Not true. Big data is not defined by how it is stored. It can and will continue to reside in all kinds of data architectures, including enterprise data warehouses, application databases, file systems, cloud storage, Hadoop, and others. By the way, some predict the end of the data warehouse — but that’s nonsense. If anything, all forms of data technology will evolve and be necessary to handle the frontier of big data. In 2013, all data is big data.
Rowan Curran, Research Associate and TechnoPolitics producer, hosts this episode to ask me (your regular host) about The Pragmatic Definition Of Big Data. Listen (5 mins) to hear the genesis of this new definition of big data and why it is pragmatic and actionable for both business and IT professionals.
Podcast: The Pragmatic Definition Of Big Data Explained (5 mins)
Big data is driving disruptive change across the economy in business such as healthcare, retail, communications, and entertainment. The potential for firms to use big data to create permanent relationships with customers is huge, and the time to get onboard is now. Big data is driving disruptive change across the economy in business such as healthcare, retail, communications, and entertainment. The potential for firms to use big data to create permanent relationships with customers is huge, and the time to get onboard is now. I was thrilled to be featured in the first episode on a new series, Big Thinkers In Big Data, hosted by TWit network's Sarah
Customer Intelligence (CI) professionals invest in data-mining, predictive analytics and modeling tools and technologies to make sense of the deluge of data. In the past, they've had to adapt horizontally-focused analytics and modeling solutions to a customer intelligence and marketing context. Today, however, they can consider a gamut of customer analytics and marketing-focused analytics providers that have not only analytics production expertise but also domain and role-focused expertise.
We just published our first evaluation focusing on the customer analytics category here: The Forrester Wave™: Customer Analytics Solutions Q4 2012 . After screening more than 20 providers for analytics products specifically catering to customer analytics applications, we identified and scored products from six of the most significant providers: Angoss Software, FICO, IBM, KXEN, Pitney Bowes, and SAS. Our evaluation approach consisted of a 70-criteria evaluation; reference calls and online surveys of 60 companies; executive briefings; and product demonstrations. The core criteria included key dimensions such as core functionality (data management, modeling, usability); analytics production; analytics consumption; analytics activation and customer analytics applications. The evaluation also included the strength of the current product and corporate strategies in the customer analytics market as well as the future vision for this category.
We found that four competencies define the current customer analytics market: