I attended Huawei’s 2015 global analyst summit in Shenzhen last week and studied its latest strategy for big data innovation. In a change from its previous big data offerings around storage, Huawei has reorganized the data analytics department and focused on infrastructure software that enables big data applications from ISV partners. Mr. Zhu, General Manager of Huawei FusionInsight, talked about FusionInsight, which financial institutions like ICBC and China Merchants Bank use to enhance customer analytics capabilities like customer recognition, segmentation, and marketing automation. Basically, Huawei FusionInsight is a data analytics platform with two major components: 1) a distributed open “database” platform that includes Hadoop, Sparc, and Storm and 2) “middleware” with open APIs to enable multisource data management and analytics.
Chinese financial institutions have a huge amount of legacy transactional data as well as in-motion online and mobile banking data, but they are unable to deal with all of it. With the previous systems of record, financial institutions couldn’t analyze all of this structured and semi-structured data in a unified “data pool.” To solve this problem, they are using Huawei FusionInsight to consolidate multisource data and enable more efficient customer and marketing analytics. Huawei FusionInsight is creating new value in the customer journey for a leading Chinese commercial bank by allowing it to:
Retaining and delighting empowered customers requires continuous, technology-enabled innovation and improved customer insight (CI). The logic is simple in theory, but that doesn’t make it any easier to implement in practice.
In my recent report, entitled “Applying Customer Insight To Your Digital Strategy”, I highlight the top lessons learned from organizations in Asia Pacific (AP) that are successfully leveraging CI to fuel digital initiatives. It all starts by ensuring that data-driven decision-making is central to the digital strategy. With that in mind, I want to use this blog post to focus on two key lessons from the report:
Lesson One: Establish A Clear Mandate To Invest In Customer Analytics
Successful companies serve empowered customers in the way they want to be served, not the way the company wants to serve them. When building a mandate you should:
■ Expect natural tensions between various business stakeholders to arise. To secure buy-in from senior business decision-makers, start by illustrating the clear link between digital capabilities and data as a source of improved customer understanding. Identify measurable objectives and then link them to three to four scenarios that highlight where the biggest opportunities and risks exist. Continue to justify data-related investments by restating these scenarios at regular intervals.
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In my last blog post I outlined Forrester’s key customer insights (CI) predictions for 2015. Now I’d like to drill down into some of the key barriers to CI effectiveness we’re seeing among Asia Pacific-based organizations. This content was pulled from my recently published report, which Forrester clients can access here.
Core competencies of effective CI pros have typically centered on customer segmentation and campaign performance measurement. When extending these capabilities to digital marketing strategies, the goal is typically to enable more effective customer acquisition and onboarding by extending reach. In other words, digital innovation often simply means “better campaigns.”
But what happens once that process is complete? It’s not enough to have a world-class digital capability for acquiring new customers. Empowered customers expect the same type of seamless experience, improved efficiency, and heightened responsiveness in all subsequent interactions with your brand.
So why so many firms struggling to realize the full potential of customer analytics to effectively serve and retain their customers? I’ll give you four reasons:
Uber faces fierce competition in China from local taxi hailing service providers Didi and Kuaidi Taxi, which both launched Uber-style e-hailing services in 2014. Both providers use a costly subsidy model to entice taxi users to switch to e-hailing services. Kuaidi Taxi, which recently received $700 million in Series D funding to buy more self-owned e-hailing vehicles, has hired more drivers and continues to provide subsidies. Uber has a smaller user base than either Didi or Kuaidi and limited funds that it can leverage — so to win customers in China, Uber must engage customers differently. Uber can leverage its global organization’s existing customer analytics strategy and tools to better understand their (potential) customers and engage with them throughout the customer life cycle.
On New Year’s Eve 2014/2015, it was predicted that taxi service would be unobtainable as people concentrated on the New Year countdown. Uber analyzed historical customer data and was able to provide more appealing e-hailing options than Didi’s and Kuaidi’s cash coupons. Uber contacts customers in advance and asks them to confirm any rate increases due to its dynamic pricing model; this helps to set the correct expectations with customers about fares:
China has experienced a fast expansion of credit card usage in the past 10 years, accumulating more than 390 million credit cards by the end of 2013, around 16 times more than 2003. But Chinese banks suffered from low activation rates of credit cards. In my recent report, I found China CITIC Bank (CNCB) faced a similar challenge; their 21 million credit cards had less than 20% activation before 2012.
In 2012, to increase the number of active credit card users, CNCB decided to revamp its customer analytics capabilities to better understand customer profiles and manage customer relationships. As a first step, the bank used SAS Enterprise Miner to deeply analyze both active and inactive cardholders and their usage scenarios and to measure the effectiveness of its credit card campaigns and programs through cardholder analysis for customer segmentation and marketing program effectiveness analysis including:
Cardholder analysis for customer segmentation.CNCB first collected and classified basic information about its cardholders from past marketing campaigns and transactional data. It defined four basic types of cardholders: inactive users, moderate users, convenience users, and heavy users. The bank spent two months to build data marts from the summarized data. It decided to focus on two groups of inactive cardholders: those who could be swayed by marketing campaigns and those who were heavy users of other banks’ cards but not CNCB’s through the analytics engine.
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.
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.
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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.