Data strategies powering omnichannel customer experience in the Chinese CPG industry

New GTM strategies aim to increase control

In recent years there’s been a visible trend that consumer brands revamp their go-to-market strategy to take market development into their own hands. Whilst certain startup brands have succeeded with a pure DTC (direct to consumer) strategy driven by digital commerce, most established multinational brands still rely on the traditional B2B2C distribution model where wholesale and brick & mortar retail account for the great majority of sales. Nevertheless, most traditional players have adopted DTC as a critical component of their omnichannel and O2O expansion strategy long before COVID-19. Not only is DTC an important growth engine, it allows brands to receive timely feedback directly from the users of their products and services.

Data as a critical component of DTC strategy

Informatica customers tend to be market leaders and many have undertaken similar strategies. As part of its new GTM strategy to improve consumer experience, a leading international beauty & cosmetics powerhouse wanted to launch a supply chain transformation by acquiring and leveraging direct consumer and related transactions data. E-commerce channels like Tmall have opened the opportunity for them to close a critical gap which long existed under the traditional distribution model – data scattered in various distributor systems and not easily available or accessible to the brands. In this case, our beauty customer soon discovered that answers to basic questions such as “who has bought what at what price” could not be easily answered due to the lack of a common data structure across Tmall's data platform for merchants, 3rd party e-commerce operators’ systems and internal systems such as SAP. As a result, critical analyses including demand planning and stock allocation could not be effectively carried out. Hence, the customer embarked on a data governance initiative focusing on business-critical data to introduce a common business language across its extended value chain.

Another customer is a leading global sports footwear and apparel brand which wanted to gain independence from its e-commerce operators’ data platforms to ensure accurate and consistent market communication across its many B2B and B2C channels. Globally the group places heavy emphasis on what and how the brand communicates to the market via its products and channels and enforces stringent global standards on product information, marketing copy, digital assets and translations. For instance, words that might overpromise or might lead to confusion or misinterpretation are carefully avoided to protect the brands and customer loyalty. To bring together the many internal and external stakeholders for efficient product content collaboration the customer leverages Informatica solutions to develop a product information hub which fuels their omnichannel commerce. In today's new economy, an authoritative source of product information has become a pre-requisite for highly engaging customer journeys which may entail tailored marketing messages, personalized offers, customized products and endless possibilities of in-store digital experience. 

The business case for an intimate customer understanding

With the view that new consumer behaviors accelerated by COVID-19 are here to stay, many multinational enterprises in China are doubling down on their investments into customer experience to gain a competitive edge. In partnership with ITPartner, Informatica recently hosted a virtual roundtable with nearly two dozen CIOs and business leaders from multinational consumer brands and retailers operating in China. The focus of discussion was rightly about how these global giants formulate and execute a holistic data strategy to elevate the experience of its consumers as well as enterprise customers.

One of our guest speakers from a luxury brand spoke about the trend of remote selling which has been rising due to advancement in technology (e.g. Wechat mini-programs, chatbots) and the proliferation of social selling i.e. KOLs (key opinion leaders) and celebrities. COVID-19 has precipitated this trend with store closures and social distancing. Our customer who follows a DTC strategy (for this particular brand) faces the challenge of how to consolidate and derive insights from the new consumer and interactions data in order to feed the increasing number of remote sales agents with tools that help them “see” who they are speaking to, what are their preferences for products and services, how to best relate to them, and what will be the next-best-action for effective engagement.

Alternative approaches to a Customer Data Platform

Similar business scenarios can be easily found elsewhere where brands have the means to acquire the relevant data and the vision to leverage this data to enhance customer engagement and increase culoyalty. Several CIOs at our roundtable informed us that they have either started or have completed standing up a Customer Data Platform (CDP) for this very purpose. While the ultimate goals of such platforms are similar - increase prospect conversion, enhance cross-selling to existing customers, improve overall experience and increase customer lifetime value, the implementation approaches vary from company to company. Some have embarked on multi-year journeys using primarily open source technologies and extensive development resources; others have supplemented their custom development with a number of specific-purpose tools such as tagging, segmentation and predictive modeling solutions. A few have gone on the market for an enterprise solution that could potentially address most of the critical requirements.

The enterprise solution approach offers a number of advantages:

·      Lower project risks: Building a CDP is often a complex undertaking especially when the data volume is extremely large, the data variety is great, and the business requirements are fluid. Compared to a custom-build approach, a mature enterprise solution like Informatica’s Customer 360 suite can substantially lower project risks with its built-in critical functionalities in the areas of data processing, discovery, cleansing, modeling, matching and governance, as well as proven implementation methodology and project success from very large-scale deployments. Unless the enterprise has a sizable pool of experienced technical specialists, project managers, domain experts and an army of developers, going with a proven vendor is a much safer choice. In fact, the lack of skilled talents has been cited as the number one concern for project success for the CIOs at our roundtable.

·      Future-proof technologies: As new types of data continue to arrive (social media posts, IoT, chatbots, digital commerce, 3rd party sources, data from acquired companies and new markets) and use cases rapidly evolving, most custom-build solutions will struggle to keep up with the pace of changes due to limitations in architecture scalability, solution flexibility, configurability of important features and reusability of custom codes. For instance, Informatica’s identity matching engine is built upon years of research, tens of millions in investment and thousands of implementation projects. Still, new features and capabilities are constantly added to support the new use cases. In recent years a lot of investments have gone into AI, machine learning and NLP to support the situations where humans can no longer perform data stewardship at the required scale. In addition, when it comes to complying with current and future consumer privacy regulations like the GDPR, an enterprise solution with built-in functionalities like consent management and rights requests can provide huge advantages.

·      Faster time to value: As a result of missing data management capabilities and the excessive efforts trying to develop them, we have witnessed many custom-build CDP projects that were plagued by repeated project delays and ultimately failed to deliver promised business outcomes. With the backing of a strong technology vendor and a smart project approach including a roadmap of clear and realistic milestones, a number of our customers were able to achieve significant ROI in as little as 3 months. This is remarkable considering the sheer business scale and complex technical landscape at these organizations. Most organizations which run a CDP have a data science team dedicated to uncovering insights to support business initiatives. In most companies where custom-build solutions are prevalent the so-called “80/20 rule” applies – i.e. 80 percent of a data scientist’s time is spent simply finding, cleansing, and organizing data, leaving only 20 percent time to perform analysis. On the other hand, in a CDP which was built on a solid data foundation, a data scientist has the potential of significantly increasing productivity to deliver value much more timely.

·      Lower TCO (Total Cost of Ownership): Many enterprises underestimate the costs of running a CDP platform during the initial analysis of alternative solution approaches. Remember that in a typical enterprise the majority of IT budget (~65%) goes into Operation & Maintenance. Hence, the project team in charge of solution evaluation has the duty to thoroughly dissect any hidden costs to operate and maintain the platform in the longer run to have an accurate assessment of alternative approaches. Often a packaged solution provides greater cost transparency and predictability than a custom-build solution.

Consumers reward brands who invest in them

In the years leading to 2020 many research firms have predicted that CX will become the single most important competitive differentiator, ahead of product and price. Now that we are well into 2020, and in the midst of a global economic crisis, this prediction is proven more accurate than ever. According to a Gartner study, 89% of companies want to primarily compete on CX. This statistic shall not surprise anyone as the potential reward is huge!

In a well-cited McKinsey study which tracked the stock market performance of CX Leaders and CX Laggards in the ten years following the 2008 global financial crisis, organizations that had a CX-focused strategy were able to rebound faster and achieved 3 times the shareholder return than organizations that didn’t – an outcome we will likely see in the wake of this recent crisis. 

We welcome your comments and insights in our ongoing discussion of this important subject. Join us at our next CX webinar here in APAC titled Digital Consumer Engagement in the New Economy featuring Kenneth Shek, Head of Beta Labs at the Lane Crawford Joyce Group, to hear how they uncovered business-critical insights from data and AI.