Chinese businesses have been in a state of digital transformation for the past two decades. Since the early 1990s, many enterprises owned by national or local governments have been privatized, and many of those realized that they could make information technology their key competency. However, traditional retail and manufacturing brands in China are very fragmented. The country lacks a local version of Wal-Mart or Macy’s — large organizations that dominate specific sectors.
The rise of Internet companies and their new business models is digitally disrupting already struggling traditional brands. Internet companies in China are using their strong capital resources to take center stage in many markets, creating new service delivery models, bringing online experiences offline, and making transactions through online marketplaces instead of in physical stores.
Most of the traditional brands that I spoke with in the course of the research for my most recent report were unable to react properly, as they were using immature digital intelligence to understand online users. But traditional brands have now realized the value of doing business online and intend to apply advanced digital analytics to understand customer behavior across the multitude of digital channels — web, social, and mobile. For instance, Chinese banks are starting to employ digital analytics to understand how people use Internet financing.One of the four largest Chinese banksis accustomed to analyzing transactional data but has limited experience in online user behavior analysis; to offset this, the bank recently announced a plan to implement web analytics tools to understand how customers interact with its website, search engine, and social platforms.
On November 5, Dell announced new data and analytics services at its annual Dell World customer event. Having integrated the analytics products it acquired — such as Kitenga (from Quest), Boomi, and StatSoft — Dell is now trying to build big data analytics capabilities based on a big data platform, visualization, and advanced analytics. These analytics technologies are appealing to technology management teams in end user organizations, but they may not meet the expectations of lines of business, especially for marketers facing increasing and rapidly changing demand for data and analytics.
As part of its effort to enhance its customer analytics offerings, Dell hired new leaders with marketing experience for its analytics business, such as its new GM of advanced analytics for marketing. And the company has started to move analytics offerings into the marketing arena:
Social media analytics is extending to the cloud. Dell hosts its social media analytics products on the Microsoft Azure platform and has started offering social listening products powered by Radian6. The cloud platform helps Dell serve a wider variety of client companies, from large organizations like the American Red Cross to small and medium-size businesses. The vendor is helping marketers optimize their customer segmentation, but it needs to do more to help marketers better recognize and engage with customers.
Master data management services are strongly integrated but are weak on analytics. Dell consolidates data (such as transactional, CRM, and supplier data) from disparate sources into a central repository and distributes it downstream. However, its customer data analytics capabilities are poor and make it difficult to evaluate, for example, average customer lifetime value.
Forrester recently published its 2015 Predictions for Asia Pacific. I wanted to highlight some specific trends around customer insights (CI) and big data, two very hot topics for many AP-based organizations.
We strongly believe that success for many organizations hinges on your ability to close the gap between available data and actionable insight. Marketing is taking the lead here, as CI pros seek to use data to fuel customer engagement improvements. Hence 2015 will be a year of increased fragmentation as reliance on analytics spreads across organizations.
What will this mean for you? More cloud-based and mobile analytics, more demand for interactive and responsive analytics, and more use of specialist and niche BI and analytics service providers. Given this backdrop, Forrester believes that:
Analytics spending will increase by at least 10% across the region. Yes analytics spending will increase, but less of it will be visible in the CIO's budget. Marketing and other business departments will drive analytics investments to address specific challenges and opportunities. The technology management (TM) organization will have little control over the implementation and deployment of niche and specialist BI and analytics services.
Apologies to those purists who recognize this post’s title as a misquote of Mark Twain. In this case I’m not referring to myself, Samuel Langhorne Clemens, or indeed any human being, so I’ve gone with the more popular expression. Instead, I’m talking about campaigns – you know, those marketing tactics declared dead by many, but which brands continue to leverage for cross-channel communications. Back in February, Forrester’s Tracy Stokes used a similar analogy in her excellent post “Digital Marketing is Dead; Long Live Post-Digital Marketing: What It Means for CMOs.”
I’m resurrecting the theme because campaign management is alive and well. That being said, Customer Insights (CI) professionals now approach campaigns much differently than in the past. Smart marketers know they must engage their customers with contextually relevant content that sparks an interaction cycle and provides utility while creating a value exchange. Orchestrated appropriately, campaign management can be a key enabler for post-digital marketing.
Blogged in collaboration with Rebecca McAdams, Research Associate, serving Customer Insights professionals.
Consumers are connected, constantly influenced by marketing messages, their friend’s social posts, blog posts, reviews, mobile messages, and Twitter posts. In fact, US Adults have an average of three connected devices. Consumers are leaving breadcrumbs of information behind, across multiple channels and devices. Marketers are jumping at the chance to connect with their customers through proactive marketing campaigns and even through non-marketing interactions. But which interactions actually drive impact? What interactions are responsible for sales conversions, and which interactions merely "assist" conversions? CI Pros and marketers are stumped; they must measure these complex interactions to help drive future marketing and media investments and to actually measure their marketing efforts.
Many clients have asked me this question, following it up by asking how to extend their web analytics capabilities to mobile in China. It’s not always an easy job. Marketers in China are becoming more familiar with web analytics tools to leverage internal customer data and external data from sources like social media to understand online customer behavior. Most use local web analytics tools like Baidu Statistics or Alibaba’s cnzz.com and are starting to engage with global vendors like IBM (ExperienceOne) and Adobe’s Omniture, but don’t know if effective mobile analytics solutions exist. Marketers in China face challenges in recognizing customers on mobile and analyzing, managing, and monetizing mobile data:
Marketers have difficulty identifying customers on mobile before they log in. Campaign management tools help marketers collect mobile device IDs or even sensitive information like mobile phone numbers. These tools are still web-like in that they can’t identify users until they log in. The marketing manager of one CPG company has made the reorganization of mobile users one of its mobile analytics priorities for 2015.
Mobile data rarely supports marketing decisions. Marketers in China can’t find integrated marketing campaign management solutions that include both web and mobile. The credit card department of a leading Chinese commercial bank found that customer response rates to its mobile campaigns were ineffective due to the gaps between its mobile and online marketing campaigns.
Brands have no idea how to monetize mobile data. Marketers know how to monetize data on mobile traffic and user profiles, but not how to add value from the mobile data itself. Exchanging mobile data with third parties will be a good idea if the data platform can solve data ownership, privacy, and regulatory issues.
Blogged in collaboration with Samantha Ngo, Senior Research Associate, serving Customer Insights professionals.
Even if you have a clear idea of where you want to end up, the route you take to customer loyalty isn't always straightforward. Outlining a strategic plan helps you understand what you need to do, but a roadmap identifies how, when and with what resources you should tackle each step. Forrester believes there are six components to designing an effective loyalty roadmap:
Time frame: The expected completion of tasks and delivery of results.
Desired outcomes: Key performance indicators (KPIs)that help you benchmark the performance of your advancing strategy based on your maturity.
Strategic themes: A summary of the objectives an organization needs to advance its strategy.
Key steps: The specific tasks — pulled straight from the strategic plan — which an organization must complete to graduate to the next maturity level.
Dependencies: The people, process, and technology required to execute the key steps. Changes to the current approach may require acquiring new team members, implementing formal processes, or buying loyalty technology.
Investment level: Where and when the allocated loyalty budget will be spent.
On October 14, I attended Big Data & Business Insights 2014 in Bangkok — the first public big data event in Thailand. I spoke about how to use big data to increase customer value in the age of the customer — a topic that seemed a bit distant from the audience’s daily reality. Most of them use traditional data warehouse and business intelligence tools and are new to big data solutions like Hadoop platforms, big data visualization, and predictive solutions. Here’s what I came away with:
Big data is still new to Thai businesses. Most big data projects in Thailand are still at the testing stages, and these trials are taking place in university labs rather than commercial environments. Dr. Putchong Uthayopas of the Department of Computer Engineering at Kasetsart University noted that big data projects in Thailand are now moving from pilot projects to actual usage.
Organizations need more details of real big data solutions. Thai businesses have held off investing in big data solutions because they felt uncertainty about the outcomes of big data projects. Attendees showed a lot of interest when I talked about big data usage in traditional industries, such as John Deere’s “Farm Forward” use case, which helped farmers make better decisions on what, when, and how to plant.
As I wrote in my recently published report, customer insights (CI) are an increasingly critical source of competitive differentiation in the age of the customer. Forward-thinking business and technology management leaders in Asia Pacific (AP) are actively looking to better leverage customer data and advanced analytics to increase marketing effectiveness and improve the customer experience (CX).
Unfortunately this isn’t the case everywhere. Many AP firms still lag in their understanding of customer analytics. They also lack the skills and ability to execute.
A collection of internal and external factors will affect customer analytics success. How can you improve your ability to transform available data into insight? Start by taking Forrester’s self-assessment to help determine where your organization falls in Forrester’s customer analytics maturity model and use that to identify specific areas of focus for future improvement.
But CI pros can also minimise risk by taking the following concrete steps:
Link customer analytics to broader CX and digital initiatives. Effective digital transformation fueled by CI requires an outside-in approach to customer understanding. For most AP organizations, this is only possible with direct CEO support. In the absence of executive sponsorship, successful customer analytics will likely be limited to improving and/or extending existing marketing approaches – important, but nowhere near sufficient.