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.
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.
SAP today announced plans to acquire KXEN, a provider of predictive analytics technology. The terms of the deal are not known. This is an interesting development for both companies and highlights the focus on the democratization of predictive analytics, especially for marketers. The proposed deal puts the spotlight on two shifts in the analytics landscape:
Expert user to casual user. Our research shows that finding top analytics talent is a key inhibitor to greater customer analytics adoption. As a result, users expect analytical tools to cater to nontechnical, nonstatistician business and marketing users.
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?
Forrester is launching new research looking at how firms and companies can better use data and analytics. Please help us make this research better by taking our survey. We want to hear from you whether you use data extensively or not, and your responses will be extremely valuable. Plus you get a free Forrester report (not to mention the warm glow you'll get from helping out).
In addition, we appreciate any efforts to spread the word: Forward this to anyone who uses - or could use - data as part of their job.
On behalf of the Forrester team, thank you very much!
"Let's just say I'm not lost when it comes to data . . . but I could be more found . . ." – (eBusiness team member at a top 50 US bank)
Digital teams are surrounded by data and metrics — from KPIs to customer analytics. Yet I often hear from clients who wish they were just a little more comfortable knowing what the data is really saying, or which metrics are most important.
We just published a brand new report on The Mobile Banking Metrics That Matter which outlines how mobile strategists at banks can put the right metrics in place and work with their analytics teams to get data outputs that guide them toward smart business decisions.
Writing this report got me thinking about which books, blogs, and articles I’ve found most useful when it comes to really getting data and metrics. Here are five I think might help you too:
The Tiger That Isn’t. Probably my personal favorite book about stats and measurement. Written for a mainstream audience, the book works as a guide to thinking through what a given stat or data point really means — and when to trust or doubt such data. It’s also a great read, full of interesting nuggets and statistical oddities (like how the vast majority of people have an above-average number of legs). The book’s thesis is that people who consume data should be skeptical but not cynical about statistics. From there, it helps the reader more easily contemplate and act on the data and metrics they encounter.