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.
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.
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.
Chinese people are hypersocial in their lifestyle and daily work, and Forrester forecasts that 681 million of them will be using social media by 2019. Online Chinese are actively engaging with brands and companies on social media: 29 brands or companies on Sina Weibo and 32 brands or companies on WeChat on average. Chinese businesses have realized the importance of social for customer life-cycle management. While they’ve started using social to increase brand awareness — such as broadcasting on Sina Weibo — they can’t recognize potential customers in this one-way communication. They use public WeChat accounts to shorten response times to client service requests — but they can’t predict these requests in advance. To address these challenges, businesses in China are starting to use enterprise-class analytics tools for Chinese social platforms.
Forrester surveys in China show that business data and analytics are increasing as the No. 1 technology priority for Chinese businesses; 55% of technology decision-makers in the country plan to use data and analytics to improve business decisions and outcomes in 2014, up from 43% in 2013. Chinese digital marketers are looking for powerful tools to better understand customer behavior, especially regarding customer acquisition. Businesses in industries like banking and financial services, telecommunications, and retail understand that data and analytics are critical for enabling business transformation — but they have struggled with a lack of data and analytics tools in the market.
The supply side of the Chinese customer analytics market is fragmented and includes a confusing mix of global and local providers. Most customer analytics solution providers started doing business in China in the early 2000s, when the country became much more open to foreign capital. Companies like Procter & Gamble and Coca-Cola introduced new marketing concepts and became the first to use customer analytics solutions in China. To serve these global companies, leading analytics vendors like FICO, IBM, Oracle, and SAS successfully built up their analytics businesses and extended them into local Chinese markets. Increasing demand for analytics also compelled local vendors like Alibaba and Baidu to start providing customer analytics solutions in China. My latest report, “The Customer Analytics Market in China,” profiles these customer analytics providers in four categories to ease your decision-making on vendor selection.
China faces a growing air pollution problem — one of the consequences of its significant economic growth over the past two decades. Surrounded by a large number of coal-burning factories in Hebei province, Beijing faces ever-worsening smog. To tackle this problem, city government has implemented new policies and laws, such as the Beijing Air Pollution Control Regulations, that provide guidance to technology vendors developing smog control solutions.
Optimized Energy Management Is The Key To Reducing Air Pollution
Beijing’s government is focusing on air quality monitoring and has invited tech vendors like Baidu, IZP Technologies, and Yonyou to develop solutions. The city wants to show the source of pollutants and how they will disperse across Beijing a couple of days in advance — but that doesn’t do anything to reduce the smog itself. Rather, the key to reducing air pollution is changing how China consumes energy. For example, the government could use big data analytics to:
Optimize factories’ energy consumption. Asset-intensive industries like steel, cement, and chemicals face challenges in analyzing the vast amounts of data generated by energy-monitoring sensors and devices. Tech vendors like Cisco and IBM could leverage their Internet of Things data analysis technology to help customers turn this data into actionable insights. For example, one steel factory in Hebei province is considering technology that identifies when an oxygen furnace is wasting energy because the temperature of the output smoke is too high.
Tencent’s news portal is one of the largest online news portals in China, with more than 25 channels covering all types of news. Tencent faces fierce competition, which it intends to combat by building its analytics competency. With the eyes of millions of Chinese soccer fans on the World Cup, Tencent has a chance to better target its news and reports by using social analytics — which the news portal did by launching a mini-site of World Cup 2014 coverage. More than 50 advertisers showed interest in the World Cup site, thinking that it would differentiate Tencent’s news offerings and draw more traffic. And they were right: The site got more than 3 million hits in the first week of the Cup.
Tencent now has the first social analytics website for sports in China. Supported by IBM’s Social Analytics engine and hosted in its SoftLayer data center in Hong Kong, the site aggregates data from most leading Chinese social platforms including Qzone, Renren, Sina Weibo, and Tencent Weibo. Full coverage of these social platforms can help Chinese businesses get a fuller picture of customers to better personalize and target offers. Tencent’s news editors also have a separate social analytics tool to find buzzwords or popular terms on social platforms and highlight these attention-getting phrases in their titles and articles.
This investment is delivering two major benefits to Tencent:
I was invited to speak at the Big Data and Business Analytics Forum in Hong Kong last week, and introduced our latest research on big data in Asia Pacific for both marketing and technology management professionals in the age of the customer. Listening to other speakers at the event who discussed Hadoop and explained the 4Vs of big data — volume, velocity, variety, and value — it dawned on me that there may be a significant gap in big data development between mainland China and Hong Kong. While Hong Kong is perceived as more technologically advanced, these terms were already buzzwords on the mainland 18 months ago. There are several constraints could have hindered big data adoption in Hong Kong:
Demographic limitations. With a total population of 7 million, Hong Kong doesn’t generate data volumes as gigantic as mainland China’s. This raises the unit cost of big data for Hong Kong businesses.
Budget to invest in new technologies. Hong Kong businesses are still struggling to recover from the 2008 financial crisis and maintain hiring freezes. It’s difficult for tech management to convince business leaders to invest over HK$1 million in a big data project and hire data scientists.
There are few local practices in unstructured data like social, location, and mobile. Hong Kong is open to global social platforms like Facebook or Twitter, meaning that multinationals can use global big data solutions to cover social in Hong Kong and keeping local adoption of big data technology for SoLoMo low.
Contributed by Bryan Wang, Di Jin, and Vanessa Zeng
JD.com, the second largest online retailer in China, went public on May 22, listing itself on Nasdaq after merely 11 years of existence. At the time of IPO, JD had a market value of nearly $30 billion. Despite its size however, JD still managed to increase its customer base by 62% in 2013. How did JD manage to continually achieve business growth? I believe this is due to three key factors that differentiate JD:
■ Comprehensive logistics network for online retail in China. JD.com invested heavily in a last-mile strategy to ensure that customers receive products as quickly as possible, establishing 82 local warehouses with 1,620 delivery and 214 pickup stations across nearly 500 cities in China. This has made same-day delivery available in 43 cities — far ahead of the capabilities of Google Shopping Express in San Francisco. To better reach customers in lower-tier cities, JD is also collaborating with local convenience store chains in provinces like Shanxi and Guangdong to further strengthen its last-mile delivery capability.