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October 31, 2019
Becoming a data-driven consumer goods company of the future
By: Alison Kennedy, Tony Gomes and Oliver Bittner

Your guide to reinvent and reach the core of your business: The Consumer

The consumer goods industry in Asia Pacific is at the precipice of an enormous market opportunity. At this tipping point, brands need to unlock value from consumer data to both defend territories and accelerate new market growth.


Figure 1: By 2022, APAC will account for almost two-thirds of the global digital commerce market. To take advantage of market developments, CPG companies need to understand the changes in consumer behaviour ushered in by the digital age.

According to Accenture’s “Insights to Digital Commerce – An APAC Perspective 2018”i , digital commerce in Asia Pacific is expected to reach more than $1 trillion by 2022, representing the largest digital market for consumer goods companies. The outlook is optimistic but evolving consumer expectations, and rising insurgency of digital-native businesses, are threatening traditional consumer goods players.

Brands are recognizing that consumers are no longer just the end users that can be reached by traditional linear approaches. Over 80% of C-level executives feel they must scale consumer-relevant initiatives and be more proactive in disrupting the industryii .

The opportunity

To improve consumer relevance, consumer goods companies must make decisions that are centered around the consumer. This can be distilled down to five questions that all consumer goods companies must ask – “Who is the consumer?”, “Where is the consumer?”, “When is the consumer buying?”, “What does the consumer want to buy?”, and “Why is the consumer buying?”.

These questions are challenging to answer in the traditional value chain paradigm. Sales and distribution partners might be reluctant to share data, and when they are willing, it would typically come at a high price tag and with some degree of time lag. Moreover, it is not feasible for consumer goods companies to deploy representatives across all consumer touchpoints – every shop, every aisle, every billboard – to observe, understand, and engage all existing and potential consumers. However, in the digital world, consumers generate data by the nanosecond in real-time, and a significant proportion of that data can yield potential value.

With strong data and analytics capabilities, a consumer goods company can generate insights by asking really pertinent and nuanced questions – “Who is the consumer similar to not just demographically but behaviorally?”, “Where is the consumer’s preferred point of sale/ channel of interaction?”, “When is the consumer most susceptible to buy or suggestible to promotional material?”, “What different products might the consumer buy?”, and “What is driving or blocking the purchasing decision?”.


Figure 2: CPG investments in predictive analytics capabilities are starting to pay off with 42% achieving some expected business value for expected costs.

For instance, data driven consumer micro-segmentation goes far beyond traditional segmentation (e.g. by age, gender, race, geography etc.) and complements behavioral data to better structure populations into meaningful and actionable groups (e.g. late-night shoppers, busy parents who value convenience over price, socially-conscious millennials who see consumption as a form of activism, etc.). Next, cross-sell and up-sell analytics discovers the types of products that consumers are more likely to add-to-cart or upgrade to. Price elasticity analytics can be applied to understand consumers’ sensitivities to price changes and discounts. All three are exceptionally powerful when combined because together, they enable personalized recommendations of products and promotions creating high likelihood of uptake.

Changing expectations

Advancements in digital technology have fundamentally shifted the way consumers in APAC live. This has a cascading effect on consumer’s expectations from consumer goods companies. Consumers are increasingly looking to spend less time on retail chores and more time on activities they value most. Digitally-inclined consumers in APAC not only want convenience and value but also seek right level of choice and information to make a purchasing decision. Their needs are evolving, and they will soon expect to get what they want, before they know they want it.

By 2022, 50% of the Asian population will be aged between 18 and 35 and will be moving into its prime spending years. This generational cohort is propelling the rapid growth of mobile and internet usage. It is expected that by 2022, APAC will have 3.5 billion mobile connections and 2.5 billion internet users. Consumption will be supported by GDP growth of over 4%, creating 388 million new consumer households during the same period. It is anticipated that one-third (approximately 700 billion) of global CPG growth by 2022 will come from APACiii .


Figure 3: By 2022, APAC will become the largest CPG digital commerce market (390 billion) twice as big as North America.

81% of companies believe they will grow faster than the competition.

Winning in the digital age is not just about getting consumer data and then applying analytics models, brands need to fundamentally rethink how they interact with consumers with value-adding digital propositions.

Getting the right data

There is no better way to get the right consumer data than to go direct-to-consumer. Going direct grants control of what data consumer goods companies gather, creates an unattenuated direct feedback loop between the company and the consumer, and most importantly, another channel to grow value.

Consumer goods companies must think beyond just directly selling, be it physically or digitally. Direct-to-consumer also entails providing value-adding propositions directly to the consumer. These not only drive faster go-to-market (as they leverage existing distribution channels when integrated), but also drive stronger consumer acquisition, engagement, retention and service levels.

The more common strategy is to launch a value-adding companion offering to the core brand/product. For example, L’Oreal’s Makeup Genius app allows consumers to try on cosmetics virtually. Companies with offerings like these, that allow consumers to try-on products virtually in the comfort of their home, would be able to collect data on who consumers are, what products they try and buy (or not buy), where and when do they engage, and provides in-app or off-app personalized recommendations. Furthermore, this try-at-home proposition enables such companies to collect data from segments who may not be comfortable trying on makeup at the store, e.g. first-time consumers seeking to experiment in private.

Some brands are much bolder with their direct-to-consumer offerings. Take Unilever India for example, with Humarashop, a hyper-local grocery marketplace platform for neighborhood retailers to sell Unilever and non-Unilever products online. The unique benefit of such a proposition is that it allows not just the collection of data on their own brands, but also on other brands, enabling the delivery of proactive and reactive competitive interventions, such as dynamic competitive discounting, and competitive site product placement. Furthermore, this proposition improves the relationship between Unilever and neighborhood retailers by driving value exchange.

It is imperative that consumer goods companies remember– a new proposition yields no value for the company if it yields no value for the consumer. The more relevant the proposition is to consumers, the more they will engage with the proposition. This generates more data for consumer goods companies to collect, which then yields more valuable insights.

Architecting end-to-end correctly

It’s certainly not enough to have the perfect direct-to-consumer channels that collect consumer data, if there is no means to store that data; if there are no ways for data scientists to access and mine that data for new value; if there is no end-to-end data governance; if there is no speedy processing and analysis of data; and if insights are not published to the right business users and applications.

For illustration, any cosmetics company that wishes to develop a genius app like L’Oreal’s, would need the ability to store vast amounts of pictures of users trying out makeup. Data lakes are needed to store that unstructured data, over and above traditional enterprise data warehouses. Furthermore, companies cannot just let that data sit within the data lake. Brands would need data labs to enable data scientists to explore these images, merge them with other datasets, and generate game-changing insights. Additionally, when dealing with private user data, it is crucial to have the proper security policies and guarantees in place to comply with regulations. Next, companies who want to do dynamic and real-time pricing through third parties, like Unilever’s Humarashop, would need the real-time ingestion of competitors’ prices, real-time predictive analytics to recommend the right discounts to be applied to Unilever’s products, and an API layer to publish these promotions to other downstream sales channels. Companies may also tailor-fit prices and discounts to the consumer, and push the offer to the consumer’s preferred channel, at the zero-moment-of-truth.

Consumer goods companies must not rush to procure and deploy data and analytics solutions without a plan. They first need to design a high-level architecture blueprint which comprises all the capabilities at the right maturity levels, which are needed to enable the target state analytics across the enterprise.

Adapting your organization

Incorporation of data driven insights and analytics can be challenging for traditional consumer goods companies. Although analytics is a supporting capability, consumer goods companies should not treat analytics like a traditional support function. Analytics hubs or centers of excellence are great ways to consolidate resources, develop and institutionalize best practices, and drive quality delivery of capabilities. However, there are benefits for certain analytics roles and niche capabilities to sit closer to business units (be it brand, product groups or geographies). This is also known as the hub-and-spoke model. By embedding analytics resources within business units, consumer goods companies can drive the following benefits: greater utilization of analytics, customized analytics capabilities that drive stronger results, and bi-directional knowledge sharing between analytics functions and business functions.

An analytics hub-and-spoke model is only half of the picture. Data and insights must be incorporated into the organizational culture. Insights must be embedded within business processes such that data becomes an undeniable part of the operational DNA. KPIs must be set up to encourage and reward data-driven decision making and actions. Training programs must be developed to upskill employees with data fluency and core analytics skillsets.

Creating the data-driven consumer goods company of the future

Digital propositions are irreversibly altering the way consumer goods companies have been doing business in APAC. Consumers are increasingly involved in each stage of the value chain and expect deeper, intelligent and more seamless engagement. For consumer goods companies the implications are profound.

Despite pressures to accelerate the data-driven transformation, consumer goods companies must move cautiously to optimize investments, capture growth while balancing the pace of transformation.

Many consumer goods companies have several product categories and brands and are spread around the globe. Couple that with legacy IT, and organizational inertia for change, and one would shiver at the idea of driving a full-scale data-driven transformation.

A well-considered and measured approach is required to kick-start the transformation, achieve quick-wins, while laying the foundations for scaled-out transformation. It starts with evaluating all parts of the business to identify pockets of value that can be unlocked from data, and then prioritize quick-win areas. Next, identify the data, architecture, and organization requirements to support the prioritized use cases. Then, continue to measure and refine each step of the way in the transformation to ensure success and sustained momentum.


i Accenture “Insights to Digital Commerce – An APAC Perspective 2018
iiAccenture “Insights to Digital Commerce – An APAC Perspective 2018
iiiAccenture “Insights to Digital Commerce – An APAC Perspective 2018
Accenture “High Performance IT Transforming IT to Enable a Digital Consumer Packaged Goods Enterprise”

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