We've been talking about Adaptive Intelligence (AI) for a while now. As a refresher, AI is is the real-time, multidirectional sharing of data to derive contextually appropriate, authoritative knowledge that helps maximize business value.
Increasingly in inquiries, workshops, FLB sessions, and advisories, we hear from our customer insights (CI) clients that developing the capabilities required for adaptive intelligence would actually help them solve a lot of other problems, too. For example:
A systematic data innovation approach encourages knowledge sharing throughout the organization, reduces data acquisition redundancies, and brings energy and creativity to the CI practice.
A good handle on data origin kickstarts your marketing organization's big data process by providing a well-audited foundation to build upon.
Better data governance and data controls improve your privacy and security practices by ensuring cross-functional adoption of the same set of standards and processes.
Better data structure puts more data in the hands of analysts and decision-makers, in the moment and within the systems of need (eg, campaign management tools, content management systems, customer service portals, and more).
More data interoperability enables channel-agnostic customer recognition, and the ability to ingest novel forms of data -- like preference, wearables data, and many more -- that can vastly improve your ability to deliver great customer experiences.
IBM recently kicked off its big data market planning for 2014 and released a white paper that discusses how analytics create new business value for end user organizations. The major differences compared with last year’s event:
Organizational change. IBM has assigned a new big data practice leader for China, similar to what it’s done for other new technologies including mobile, social, and cloud. IBM can integrate resources from infrastructure (IBM STG), software (IBM SWG), and services (IBM GBS/GTS) teams, although the team members do not report directly to them.
A new analytics platform powered by Watson technology. The Watson Foundation platform has three new functions. It can be deployed on SoftLayer; it extends IBM’s big data analysis capabilities to social, mobile, and cloud; and it offers enterprises the power and ease of use of Watson analysis.
Measurable benefits from customer insights analysis. Chinese organizations have started to buy into the value of analytics and would like to invest in technology tools to optimize customer insights. AmorePacific, a Hong Kong-based skin care and cosmetics company, is using IBM’s SPSS predictive analytics solution to craft tailored messages to its customers and has improved its response rate by more than 30%. It primarily analyzes point-of-sale data, demographic information from its loyalty program, and market data such as property values in the neighborhoods where customers live.
It is no surprise that a significant amount of work is being done at marketing service providers (MSPs), customer engagement agencies (CEAs), and direct and digital agencies to differentiate their organizations abilities. The vendor landscape is crowded and competitive, which creates a complex environment for marketers and customer intelligence professionals. How can they choose the best partners for their key business needs?
I am happy to have joined the Customer Insights team at Forrester Research, where I will continue the research and thinking on the CI services landscape (agencies, MSPs, CEAs, and data providers) previously covered by Fatemeh Khatibloo while she builds her research on personal identity management (PIDM).
Forrester defines CEAs as: “Agencies that focus on defining customer-oriented business strategies and mapping them to tactics and execution. CEAs help clients maximize customer profitability and optimize customer experiences by applying data and analytics to every interaction.” We believe that CEAs will not be the only type of business emerging from the MSP space. My research will focus on developing trends within the marketing services landscape, as well as studying emerging provider models. I will also support the CI team’s mission to support customer-obsessed business strategies. See our mantra here: CI’s Imperative In The Age Of The Customer: Drive Business Success.
There’s no doubt that, to consumer marketing professionals, data about the users of mobile network are highly valuable. But AT&T is finding that enterprise application designers, corporate security & risk professionals, corporate trainers and CFOs are very interested in this data as well - so much so that the US-based network operator is turning access to and collaboration on its data into a new business service.
Under the guidance of Laura Merling, VP of Ecosystem Development & Platform Services (and formerly of Mashery), AT&T Business Solutions is embarking on an ambitious plan for sharing its data in a secure programmatic fashion leveraging RESTful APIs. It had previously shared it data in a more informal fashion with selected partners and customers but found this approach difficult to standardize and repeat on a larger scale. It also has participated in data collaboration efforts such as the well-known hackathon with American Airlines at South by Southwest earlier this year.
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?
As an analyst on Forrester's Customer Insight's team, I spend a lot of time counseling clients on best-practice customer data usage strategies. And if there's one thing I've learned, it's that there is no such thing as a 360-degree view of the customer.
Here's the cold, hard truth: you can't possibly expect to know your customer, no matter how much data you have, if all of that data 1) is about her transactions with YOU and you 2) is hoarded away from your partners. And this isn't just about customer data either -- it's about product data, operational data, and even cultural-environmental data. As our customers become more sophisticated and collaborative with each other ("perpetually connected"), so organizations must do the same. That means sharing data, creating collaborative insight, and becoming willing participants in open data marketplaces.
Now, why should you care? Isn't it kind of risky to share your hard-won data? And isn't the data you have enough to delight your customers today? Sure, it might be. But I'd put money on the fact that it won't be for long, because digital disruptors are out there shaking up the foundations of insight and analytics, customer experience, and process improvement in big ways. Let me give you a couple of examples:
SaaS vendors must collect customer insights for innovation and compliance.
As of the end of last year, about 30% of companies from our Forrsights Software Survey, Q4 2011, were using some software-as-a-service (SaaS) solution; that number will grow to 45% by the end of 2012 and 60% by the end of 2013. The public cloud market for SaaS is the biggest and fastest-growing of all of the cloud markets ($33 billion in 2012, growing to $78 billion by the end of 2015).
However, most of this growth is based on the cannibalization of the on-premises software market; software companies need to build their cloud strategy or risk getting stuck in the much slower-growing traditional application market and falling behind the competition. This is no easy task, however. Implementing a cloud strategy involves a lot of changes for a software company in terms of products, processes, and people.
A successful SaaS strategy requires an open architecture (note: multitenancy is not a prerequisite for a SaaS solution from a definition point of view but is highly recommended for vendors for better scale) and a flexible business model that includes the appropriate sales incentive structure that will bring the momentum to the street. For the purposes of this post, I’d like to highlight the challenge that software vendors need to solve for sustainable growth in the SaaS market: maintaining and increasing customer insights.