Nestlé took a different approach to its Chinese social command center than it did in other countries. Nestlé China used the firm’s global digital acceleration team (DAT) framework to create a centralized social command center in its Beijing headquarters.
The four Ps — purpose, people, process, and platform — are all important to establishing a successful social intelligence capability. My recent case study, entitled "Succeed With Social Intelligence In China", shows how Nestlé localized the four Ps to establish a successful social intelligence capability in China. The company:
Set measureable goals for social activities. One of the major challenges that Nestlé China faced was how to use social to manage precampaign customers and how to measure the effectiveness of social marketing campaigns. When Nestlé China built its social command center, it set a detailed goal to improve precampaign customer management, including A/B testing of customer usage hypotheses, customer feedback on marketing content, and spokesperson selection satisfaction rates.
Hired experienced employees with both global and local social marketing experience. The person that Nestlé China chose to head its social command center was involved in the creation of Nestlé's first social command center outside of China. The company allocated dedicated resources to the platform who had a keen noise filter and could determine which actions the business should take based on the data. It developed regular training sessions for its lines of business and assigned the social expert to share social intelligence findings with the rest of the business.
Discussing with Asia Pacific marketers, I often hear that they struggle to find and recruit the right social marketing skills, including data analysts. While staffing is important insofar as tactics go, having a proper team structure to execute on these tactics is, in my view, even more crucial.
In fact, they can mitigate some of these HR challenges with a properly structured social team. My report on building a usable social team structure addresses how organizational models will evolve as social marketing matures. These models include the a) Hub, b) Hub and spoke and c) distributed hub and spoke.
The Hub, for example, is meant to help firms that are starting out on social marketing. This could be a firm that is beginning to get more serious about how social is used strategically to drive business outcomes, or one that operates in highly regulated industries like banking and finance. The centralized hub model puts all of the responsibility (and money) for social marketing in the hands of one small team. This model provides training wheels for marketers for social marketing — especially in learning how to coordinate or test social marketing campaigns in the early phases of social maturity. A centralized hub acts as an incubator for social marketing experimentation and allows other teams to focus on their own objectives until the social program can be implemented at scale with minimal risk. Execution can be in-house, but some marketers partner with an external agency for additional dedicated resources.
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.
We listened to marketers of the world’s biggest brands when they asked, “What’s the impact of Facebook on my brand?” and we decided to take a look for ourselves. We proudly present our latest research, “The Facebook Factor.” In the report, we answer the pressing question, “How much more likely are Facebook fans to purchase, consider, and recommend brands, compared with non-fans?” We used logistic regression modeling to find out. The impact? We call it the “Facebook factor,” and I urge you to read the report to find out how you can leverage our methodology to assess the Facebook factor for your brand.
In the report, we use four major brands as case studies to assess the Facebook factor for Coca-Cola, Walmart, Best Buy, and BlackBerry(Research In Motion [RIM]). Guess what? Facebook fans are much more likely to purchase, consider, and recommend the brands that they engage with on Facebook than non-fans. As the graphic below shows, Facebook fans of Best Buy are about twice as likely to purchase from and recommend Best Buy as non-fans.
Would you classify your marketing organization as "highly accountable"? What I mean is, are you always able to accurately measure the true business value of your marketing efforts, and do your senior leaders trust the results? If you're like most marketers, the honest answer to that question is a resounding "no". Proving the business value of multichannel marketing is getting progressively harder—and more important—because:
Traditional marketing measurement practices are rooted in stable but inflexible tactics that leave marketers ill-equipped to keep pace with the real time nature of channel digitization.
CFOs wield ever-more influence over marketing budgets, which is driving your CMO to lean harder on you to measure business results with scientific rigor.
Your customers are in control; uncertainty and unpredictability are the norm; and marketers that can't adapt appropriately are doomed to fail.
This is where you come in. I believe that Customer Intelligence professionals are remarkably well positioned to address these challenges head on, and improve marketing accountability across the enterprise. Why? Because you sit at the cross-section of unfettered access to mountains of customer data from a dizzying array of online and offline sources. "Big data" as the recent article data, data, everywhere in The Economist puts it, is big business. CI professionals are right in the middle of it all helping firms capture customer data, analyze it, measure business results, and act upon the findings.