AI is a hot topic in financial services. And its easy to see why. Increasing margins on transactions, decimated by compliance costs and low interest rates, reaching new market segments, and automating routine tasks, makes AI innovation attractive. And in one sense, FinServ has always been about algorithmic innovation.There is no higher potential ROI than beating the market.Advanced analyticsfor program trading have been banging away at this goal for decades, with a rich base of advances.
Over the holidays, I was a guest on the Modern Customer Podcast, a wonderful podcast hosted by Forbes’ blogger Blake Morgan. She describes the podcast as providing “surprising and counter-intuitive insights on customer experience, social customer service and content.” No pressure there, then. During our episode, Blake and I discussed the ways that increased usage of self-service has begun to dramatically transform the jobs of customer service personnel and contact center agents.
At heart, my argument goes like this: customers have begun to use, and in some cases even prefer, non-agented interactions. They use knowledgebases, FAQs, mobile customer self-service, chatbots, and peer-to-peer communities in increasing numbers. This means that:
Because self-service solves many of the simpler issues that customers have, the inquiries that do make it through to contact center agents are the more complex, difficult, or relationship-dependent ones. So, contact center agents now need to be prepared for solving harder problems than in the past.
Because most customers that actually do reach a contact center agent will have tried to self-serve and failed, they will more frustrated than they were in the past. In a world where the phone and even chat are actually escalation channels, agents start three steps back by the time they say the word, “Hello.”
To understand how open customers are to receiving messages from brands in social media, the question has to shift from “How social are our customers?” to “How social are our customers in their path to purchase?”
Given the amount of time consumers spend on social networks, marketers intuitively know they need to be present on social media but many still struggle to pin point exactly:
Why they need a social presence - or rather, how they can be relevant on social media,
How much resources to invest in social media,
And where to invest these resources.
Forrester has developed the Social Technographics Framework to help marketers address exactly these questions. Using Forrester data to analyze the social behavior of various consumer groups and their inclination to use social touchpoints in their interactions with brands, the framework helps marketers determine:
How important social media should be to their marketing plan
When their audiences rely on social touchpoints in their customer journey
What social touchpoints their audiences use, and to what ends
Stop! Before you invest even 10 minutes of your precious time reading this blog, please make sure it's really business intelligence (BI) governance, and not data governance best practices, that you are looking for. BI governance is a key component of data governance, but they're not the same. Data governance deals with the entire spectrum (creation, transformation, ownership, etc.) of people, processes, policies, and technologies that manage and govern an enterprise's use of its data assets (such as data governance stewardship applications, master data management, metadata management, and data quality). On the other hand, BI governance only deals with who uses the data, when, and how.
One of my kids gave me the book The Martian for Christmas. He knew that I had loved the movie and thought that I might enjoy a deeper dive. Check. I highly recommend this book, even if you have seen the film. Beautifully outlined and beautifully written, the book lets you bathe in astronaut Mark Watney's perils and ingenuity as he tries to stay alive on Mars alone after having been left for dead. Plus, the tech is very, very cool, and according to NASA and the physics community, it's generally accurate.
And there are some bigger lessons that all of us can use here on Earth:
1) Anything can be fixed. When your strategy isn't performing, your product is fading, or your market is changing, diagnose and repair. There is always a way.
2) Fix, then monitor. Watney would repair, but he was always running diagnostics -- he never trusted that things would operate dependably. He was always checking back in to verify.
3) Solve the first problem, then go on to the next, and on to the next. When a company is transforming to be digital and customer-obsessed, Forrester has found that leadership often doesn't know where to begin. The Martian's lesson is to just start. Resolving many small issues and maintaining forward velocity will lead to big results.
4) Always keep duct tape on hand. Watney saves himself and his equipment a bunch of times with gray tape. The duct tape of digital transformation is MVP -- minimum viable product -- building something basic and then improving the hell out of it. It's a hack, like tape, but it keeps you in the game.
The CRM market serving the large enterprise is mature. The market has consolidated in the past five years. For example, Oracle has built its customer experience portfolio primarily by acquisition. SAP, like Oracle, aims to support end-to-end customer experiences and has made acquisitions — notably, Hybris in 2013 — to bolster its capabilities. Salesforce made a series of moves to strengthen the Service Cloud. It used this same tactic to broaden its CRM footprint with the acquisition of Demandware for eCommerce in 2016.
These acquisitions broaden and deepen the footprints of large vendors, but these vendors must spend time integrating acquired products, offering common user experiences as well as common business analyst and administrator tooling — priorities that can conflict with core feature development.
What this means is that these CRM vendors increasingly offer broader and deeper capabilities which bloat their footprint and increase their complexity with features that many users can't leverage. At the same time, new point solution vendors are popping up at an unprecedented rate and are delivering modern interfaces and mobile-first strategies that address specific business problems such as sales performance management, lead to revenue management, and digital customer experience.
The breadth and depth of CRM capabilities available from vendor solutions makes it increasingly challenging to be confident of your CRM choice. In the Forrester Wave: CRM Suites For Enterprise Organizations, Q4 2016. we pinpoint the strengths of leading vendors that offer solutions suitable for enterprise CRM teams. Here are some of our key findings:
As enterprise architecture (EA) practices mature and the demand for EA services grows, interest in enterprise architecture management suites (EAMS) continues to also grow. A lot has happened to the EAMS market since the September 2015 Forrester EAMS Wave, from divestures by certain major players (e.g., IBM) to takeovers (Planview of Troux, Erwin of Casewise). Before making a choice of EAMS tool, EA leaders need to take a step back and assess how they can demonstrate value, and then select the most appropriate toolset.
In Forrester’s most recent research, we have identified that although there are approximately 60 EAMS tools vendors, they can be categorized as follows:
· Architecture modeling tools (AM). Vendors in this category aim to capture the architectural landscape and the relationships between them.
· Technology asset management tools (TAM). This is a further evolution of the AM tools and includes additional functionality that is typically found in CMDB type solutions, but it also includes the management of technology projects.
· IT portfolio management tools (ITPM). This category of tools is less focused on the asset management and more in line with capturing technology strategy, the associated target architecture state, and the portfolio that will deliver the strategic objectives. Additionally, there will be significant features to enable investment decisions to be made and portfolio scenarios to be analyzed.
Moore’s Law was bound to catch up with us. Loosely applied, it says that technology grows more complex every year. Human brains do not. People can’t keep up with monitoring, debugging, and managing today’s technology. Users’ rising expectations make it even worse: they want features and fixes in minutes, not days or weeks. Technology may soon get away from us.
The American comic strip character Pogo put it this way: “we have met the enemy and he is us.” In this case, our enemy is also our best ally. Surely we can harness technology’s power to help us keep it under control. We can, we are, and we will. Predictive analytics, common for decades in other industries, is now a growing force for monitoring and managing business technology, and has the potential to put us back in control of our runaway technology.
The least sophisticated analytics predicts what instrumentation is appropriate for a server based on what software it’s running or what kinds of network traffic is going in or out. For example, is database software found, or are SQL queries going in and out? This analytics drives automation that reduces manual administrative work.
Moderately sophisticated analytics predicts trouble based on simple trends like CPU utilization rising, memory consumption rising, or free storage declining; and drives capacity planning before a resource crisis occurs.
Really sophisticated analytics watches multi-variate trends such as cycles of high user demand (for example monthly sales campaigns) coupled with performance expectations and resource constraints, to drive automated resource scale-up (to sustain best performance) or scale-down (to reduce over-provisioning costs).
Operations teams value stability. Uptime is golden. So it’s no surprise that operations teams buy finished, complete, documented, supported tools from vendors they can hold accountable. Ops people already have their hands full dealing with complex apps, infrastructure, and users – they don’t need to be hassling with flaky do-it-yourself tools. Even so, most operations teams still wind up with a mixture of tools from multiple vendors plus home-built integrations and scripts.
Development teams, on the other hand, are developers. If they need a tool to do exactly what they need, they’ll build one – and share it with their friends. As agile development has grown into continuous integration and continuous deployment, developers collaboratively created tools to automate tedious tasks and accelerate the application lifecycle. Customer obsession relies on speed, and speed relies on automation. The open source collaborative model has been very effective at creating the tools that support high frequency agile releases.
The DevOps phenomenon brings together these two teams and their divergent cultures. Yes, stability still matters; but what matters more in the age of the customer is agility through the entire software lifecycle, including the ops portion of release, deployment, and support. The success of collaborative open source tools in development suggests that operations may be headed the same way. And in the last year a lot more of my clients are asking about open source APM tools as an alternative to commercial solutions. I’m also seeing APM vendors more involved in contribution, participation, and use of open source. As Sam Cooke sang, “a change is gonna come.”
As a music lover, this has been a year of goodbyes for me, with many of my teenage heroes like David Bowie, Prince, and earlier this week, George Michael, passing away. It makes you realize how fast time moves on, and nothing lasts forever. As I’ve shared before, I love this time of year: Thinking about what has been, and having a world of opportunities in front of us. And I can’t wait to see what next year will bring.
This year, there were a number of surprises and new developments that nobody predicted. And 2016 was the year of Pokémon. As my colleague Anjali Lai shared in an earlier blog post about this phenomenon: “The Pokémon Go phenomenon is not only about adopting technology or using new, cutting-edge features; it is also about designing a sticky experience that is enabled by the ways customers are changing.”