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.
This Forum will help you identify brand new software opportunities and run with them. It will hit on the must-have competencies that will empower application development and delivery leaders to execute on their company’s engagement strategies. This includes accelerating development processes, creating digital experiences, reaching mobile customers, and exploiting analytics and big data. Forrester analysts will deliver forward-thinking content while industry specialists – from companies such as McDonald’s, Mastercard, and GE Capital - will provide insight into some real and revolutionary new business approaches that are relevant to you right now.
I cut my teeth as a data analyst helping brands communicate more effectively—building segmentation and targeting models that differentiated contact frequency, offers, and messaging across a brand’s customer base. But in the face of today’s more empowered customers building static scoring models and relying on batch-based campaigns is insufficient to win, serve and retain customers. Today most enterprises rely heavily on technology to help them interact with customers across channels, and as we evaluated in the newly published Forrester Wave: Cross Channel Campaign Management, Q3 2014, brands have several compelling choices.
We identified, researched, and scored solutions from nine vendors: Adobe, IBM, Infor, Pitney Bowes, RedPoint Global, SAP, SAS Institute, SDL, and Teradata. Our approach consisted of a 41-criteria evaluation; reference calls and online surveys of 96 companies; executive briefings; and product demonstrations.
We identified four leaders in this mature, but evolving category. What sets leaders apart?
Depth of cross-channel capabilities. Leaders consistently received high scores in cross-channel data integration capabilities, which includes cross-channel customer identification and centralized response history management. But the purpose of collecting this data is so marketers can make smarter—more customer obsessed—decisions. Not surprisingly then, leaders also receive high marks in areas of interaction management such as cross-channel decision management and real-time analytics.
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.
Last year, we published The State of Customer Analytics 2012 (subscription required) based on the results of our annual customer analytics adoption survey where we uncovered key trends of how customer analytics practitioners use and adopt various advanced analytics across the customer lifecycle and highlighted challenges and drivers associated with customer analytics.
This year, I am teaming up with my colleague and attribution guru Tina Moffett to further explore measurement, attribution and customer analytics practices ranging from the type of attribution techniques in vogue to the adoption of advanced analytics methodologies. With this expanded survey we want to understand how you use and apply measurement and analytics in your organization to optimize both cross-channel marketing campaigns as well as customer programs.
In particular, we’re fielding questions to understand the goals and challenges associated with measurement and analytics, the adoption and application of measurement and advanced analytics methods, the use of several marketing and customer metrics, the customer insights process and workflow as well as the organizational aspects that support measurement and analytics. We encourage you to participate in this survey, as this information will help you benchmark your measurement and analytics adoption efforts.
Customer insights professionals have many customer analytics methods (sub's reqd) to choose from today to perform behavioral customer analysis, and new techniques emerge as the complexity of customer data increases. Analysis of customer data involves the use of data-mining and statistical methods that span descriptive and predictive analytics. But how do you decide which customer analysis methods are right for you? How do you plan your customer analytics capability with the right mix of methods that address specific questions and uncover customer insights?
Using our Forrester TechRadar™ methodology we are kicking off research that will address many of the questions above as well as explore:
The current state of each customer analysis method, its maturity, market momentum, ecosystem interest and investment levels.
The potential impact of each method on your ability to understand and predict customer behavior
The customer analytics methods to be included in this report range from behavioral customer segmentation to propensity models, social network analysis, next-best offer analysis, lifetime value analysis, customer churn analysis to name a few.
If you are interested in participating in this research as an end-user/client, expert or customer analytics technology or services vendor reach out to me directly at ssridharan [at] forrester [dot] com.
Thanks in advance for your participation! All research participants will receive a copy of the published report.