WHAT DOES A DATA DRIVEN CONSUMER PACKAGED GOODS COMPANY LOOK LIKE?WHY IT MATTERS AND HOW TO GET THERE

2 What does a data driven consumer packaged goods company look like?

Contents

INTRODUCTION 05

PART 1: DELIVERING VALUE FROM DATA: FROM FIRST INSIGHTS TO SATISFIED CUSTOMERS 06

1. Consumer Insights 08

3. Manufacture and Distribution 10

2. Research and Development 09

4. Data Driven Digital Services 11

PART 2: HOW TO DELIVER DATA-DRIVEN VALUE IN YOUR ORGANISATION 13

Strategy and Planning 13

Deploy to the Enterprise 15

Iterative Improvement: Skills, Technologies and Processes 14

3

Data Driven Value: How Can Data Improve Customer Health?• Run marketing campaigns which build trust amongst

the health-conscious backed by quantitative assessments of health outcomes from R&D

• Design healthier products that still taste great by tweaking formulations in silico

• Reduce harmful emissions by redesigning products and supply chains

• Deploy digital services to offer diet and lifestyle advice around products, such as recommending healthier alternatives

4 What does a data driven consumer packaged goods company look like?

Introduction

Competition in consumer products is intense. CPG companies are fighting for new consumers in emerging markets whilst trying to fend off challengers in established ones. Their consumers are increasingly segmented, with each group wanting products tailored to their needs, customisable, and delivered to their schedule. Megatrends such as sustainability and health are reshaping what consumers buy.

This points to a need for CPG companies to get closer to consumers, and offer more personalisation. To be the first to market, they need to be agile in responding to changing consumer needs; identifying them quickly and accurately, rapidly developing new products, and manufacturing and distributing them to wherever the consumers are.

By putting the consumer at the centre and linking together data and models across their organisation, CPG firms can reduce their time to market and reap the benefits.

Companies are getting better at this – especially when it comes to using customer insights to develop products, renovating products with new ingredients, and flexing manufacturing.

This is good, and part of the journey to being a fully data driven organisation. But there are more opportunities to be realised. Much more can be achieved by breaking down data silos within and between departments and geographies, and maturing data processes across the organisation.

To deliver this vision, companies need good data, the ability to access it, and the right people to turn it into value. This whitepaper will discuss the many opportunities that data-driven approaches present across the modern CPG organisation, and draw on best practice from those with a track record of delivering value.

5

Part 1: Delivering value from data: From first insights to satisfied customers

A successful product starts with an idea based on a consumer insight. It is then developed by R&D teams, manufactured, and sold. Increasingly, firms also offer digital tools and services that accompany their products. These processes are individually complex and interconnected.

Each can benefit from predictive models that help streamline every bit of this process. The richer the data, the more powerful these can be.

In this first section we will look at examples of how data driven approaches can deliver value to these different stages, and also how breaking down silos can enrich these approaches further.

● Customer insights suggest adjustments to promote better behaviour: different portion sizes to reduce waste, changes to packaging to prevent spoiling

● R&D insights to renovate products with sustainable ingredients or build new products around sustainable goals

● Predictive maintenance reduces down time, inefficiency and waste in manufacturing

● Optimise energy efficiency, emissions and reduce waste through data driven manufacturing and supply chain decisions

● Digital services can recommend sustainable options based on customer requirements

Data driven value: How can data make a more sustainable organisation?

6 What does a data driven consumer packaged goods company look like?

CONSUMER

CENTRIC

MANUFACTURE

& DISTRIBUTION

GETTING

CLOSER TO

CONSUMERS

DIGITAL R&D

DATA DRIVEN

DIGITAL

SERVICES

7

1. Consumer Insights

Insights teams need to be able to find the signal that represents a new opportunity in noisy data.

Say that watermelon is an emerging trend – it could be a big opportunity for ice creams, lip balms, cleaning products, etc. How do you tell if it’s a real trend or a flash in the pan? How do you know the social media noise isn’t a minority of loud voices, masking a general apathy? Will the trend spread from one country to another, or will it prompt a backlash? What will different people pay?

This needs an ability to understand messy real world data from outside the organisation.

Consumer insights teams have ever more tools at their disposal, from social media, to consumer apps, to focus

groups and panel tests. But these also have potential to mislead. Really good insights come from models which reliably bring together these disparate data sources and combine them with data and understanding about products and how consumers interact with them. This is a complex task that requires adjustment for bias, with an appropriate weight applied to each signal.

REALLY GOOD INSIGHTS COME FROM MODELS WHICH RELIABLY BRING TOGETHER THESE DISPARATE DATA SOURCES

Understanding Ice Cream Preference by Region

By combining loyalty card and point of sale data in the US, Tessella helped identify how spending in different demographics correlate to taste. This led us to identify an interest in fresh fruit ice cream flavours on the West Coast, which fed into product development.

Consumer Paint Pricing

Tessella helped a global consumer paints firm identify and clean datasets held across multiple silos, and external data, to create product demand and pricing models. This helped them optimally manufacture and price new paint products in different markets to meet cycles of demand.

Monitoring Consumer Concerns to Allow Real Time Adjustments

Capgemini’s People Data Centre analyses consumer data from sources including blogs, social media, ratings sites, and traditional market research, then uses this to generate near real time insights.

For example, if customers had problems with packaging quality or disliked the scent of a product, this could be highlighted to the part of the organisation that needs to make the change. This is offered as-a-service to help go to market strategies.

For more on using data to predict consumer trends, see: https://www.tessella.com/insights/predicting-consumer-behaviour-predictive-analysis

8 What does a data driven consumer packaged goods company look like?

In a fast moving, hyper personalised world, R&D needs to respond quickly to new demands identified from consumer insights.

This may mean more experiments in-silico to test formulations quickly, or recommending candidate formulations to deliver specific properties (eg taste, smell or shelf life). It may mean modelling the effects of swapping out ingredients for greener alternatives. It may mean analysing product safety to prove health outcomes.

Models can also be used to perform testing or other manual work such as measuring acidity. And they can automate time-consuming regulatory work, e.g. collecting and uploading data, or alerting researchers to issues.

These models can be made richer by incorporating data from beyond the department. Linking models to insights teams can help them understand what is possible. Linking models to manufacturing may identify roadblocks that require products to be redesigned – something best spotted early.

In large companies, access to other R&D data (e.g. re-examining historical data that was poorly captured, or looking at data from international counterparts) can help refine models and avoid duplication of work.

R&D DATA USED THROUGHOUT THE VALUE CHAIN DRIVES GROWTH AND AGILITY ACROSS THE BUSINESS

2. Research and Development

Insilico Model Development For decades Tessella has helped CPG firms develop insilico models for their R&D teams. Many have involved creating and combining models into chains to predict how substituting ingredients with alternatives impacts taste, smell, texture and shelf life. Others have involved modelling biodegradability of packaging to assess environmental impact, and predicting subjective sensory outputs from new formulations without the need for panel tests.

Automated Regulatory ApprovalTessella built a system to streamline the entire regulatory approvals process for new products. Product developers can specify a proposed new product and will immediately be provided with a short summary of regulatory conditions across markets, based on structured and unstructured reports from each. Later in the design process they can then validate whether a proposed product formulation will gain approval. This eliminates the need for complex communications processes across multiple departments, as all relevant information is stored in a single digital platform and kept up to date.

For a more detailed discussion of these issues, see our whitepaper A Roadmap for Data-Driven Transformation of R&D in Consumer Packaged Goods.

A Single Global Analytics Platform for R&DThe R&D division of a large CPG, with 5,000 scientists around the world, recognized that consolidating siloed analytics processes into a common platform would accelerate discovery and collaboration, while reducing risk of regulatory non-compliance.

Tessella designed an ‘analytics lab’ platform to do this. This is now used to support global product development, and to model process engineering for new products to ensure manufacturing scale up is fully considered in design. This helped break down silos in both data flow and human communication between R&D silos, and between R&D, marketing and manufacturing.

9

A consequence of personalisation is that smaller batches of products need to be produced more quickly, with more variety within product groups. This means more agile manufacturing capability. Data can help.

Supply and demand models can help understand who needs what when, and where ingredients are available, to make decisions about optimal use of facilities. Capacity models can help understand where flexibility exists and the consequences of making changes.

Models can be used to manage scheduling of batches to optimise use of facilities. Predictive maintenance models can reduce downtime and variability. Quality control models understand unintended variations and identify solutions.

As digital maturity progresses, further value can be realised by linking these models across multiple facilities, allowing interconnected simulations of how one change impacts manufacturing as a whole (building towards a ‘digital twin’). These insights can also link to R&D models, helping them understand how easy a formulation is to produce before throwing it over the wall.

PREDICTIVE MAINTENANCE MODELS CAN REDUCE DOWNTIME AND VARIABILITY

Removing Variability in Manufacturing

A major chocolate manufacturer made significant investment in heat resistant materials. After three full scale manufacturing trials, they found unpredictable variance in hardness from one batch to another. Working iteratively with many sets of data from different facilities, Tessella eliminated variables and identified the two key factors causing 83% of variation. This enabled our client to release their product into a £100 million market.

Optimal Batch Scheduling for Personalisation

Tessella helped a major food manufacturer develop an algorithm to optimise the order of batches on their manufacturing lines. This looked at the level of cleaning and setup required between different product runs, to calculate a schedule that ensured all batches were completed in the shortest possible time.

Knowledge Management: Lessons From Other Industries

Tessella helped oil and gas company, Equinor, use natural language processing (NLP) to analyse decades of written maintenance incident reports and capture their insights. This was then turned into a system for maintenance engineers, so that when they were called to a job, their tablet immediately called up information on how that problem had been solved in the past.

For a more detailed discussion of supply and demand models, see our article: www.tessella.com/insights/how-to-develop-agile-and-resilient-supply-chains-during-covid-19

3. Manufacture and Distribution

10 What does a data driven consumer packaged goods company look like?

Nearly every product now comes with an app or web portal to provide ‘added value’ through recommendations and subscription services. Digital newcomers offer consumers compelling services that look hard to compete against. Luckily, CPG companies can go one better and draw on data and models from years of specialised R&D, to offer highly personalised insights.

For example, a skincare product could create an app which draws on their vast R&D data on absorption, scent, viscosity etc, and how each of these performs on a wide variety of skin types and environments. These specialised models and data can be used to offer highly personalised skin care recommendations which consider the confluence of skin tone, dryness, age, diet, local air conditions, time of year, and so on. They can also help consumers understand the product’s ethical and environmental footprint and make ethical choices.

This is not measuring clicks and spotting correlations. It is modelling fundamental chemistry and biology, using complex R&D data combined with real world environmental data, to offer scientific insight into how different skin types respond to different products in different environments. Not only does this create market advantage, it allows users to share data on how they use products which could provide valuable insights to R&D teams.

This represents a huge new opportunity, but will require an effective approach to identifying and using R&D data in consumer facing environments.

LUCKILY, CPG COMPANIES CAN GO ONE BETTER AND DRAW ON DATA AND MODELS FROM YEARS OF SPECIALISED R&D

4. Data Driven Digital Services

Using DNA to Improve Pet’s DietsMars’s Pet Care division has developed DNA profiling for pets, built around models of genetics, bioinformatics of gut biome, and food chemistry. This allows them to make personalised recommendations of diets aligned to optimal pet health, and so sell more pet food. But more significantly, it allows them to move from selling tins of food, to selling pet health plans that include a custom diet.

Hull Coatings for Increased Shipping EfficiencyAkzoNobel’s marine coatings division developed Intertrac Vision, a tool which shows how different hull coatings affect fuel efficiency. The underlying models were based on biofouling (accumulation of microorganisms) risk data they had collected from oceans around the world, as well as experimental data about the performance of their products. This allows their customers to make a more informed coating decision based on how and where their vessel will be used.

For a discussion on how to use rich R&D data to develop new insightful digital services for customers see: https://www.tessella.com/insights/how-rd-data-can-create-the-next-generation-of-customer-centric-digital-services

Accurate Colour Visualization for DesignersAkzoNobel has an app that allows their customers to visualise colour in a highly accurate way, allowing them to make decisions without the need for product samples. Underpinning this are models, built with Tessella, around the science of colour and light, allowing the app to visualise exactly the right tones, taking into account the lighting conditions and even the screen of their phone, to show them what colour will really look like.

11

12 What does a data driven consumer packaged goods company look like?

Part 2: How to deliver data-driven value in your organisation

Strategy and Planning

Start with a vision for what value you hope to get from data, aligned to your organisational circumstances. The challenge is treading a path between massive IT projects and endless Proof of Concepts.

A middle ground involves agreeing what good looks like for you; identifying achievable use cases that quickly deliver those goals; then using those to advance the organisation’s digital maturity.

In this final section we discuss how to ensure data projects succeed and deliver value, whilst moving you towards becoming a data-driven organisation.

The client, a CPG product company with an immature digital capability, wanted to become more data driven so it could use data to develop healthier alternatives to its products.

We worked with them to create a Data and Digitalisation Roadmap to build on their early-stage digital capability. In this case, the most important area was data storage and data management.

We identified a series of use cases that would deliver tangible business value whilst also allowing them to improve their data management infrastructure and governance.

This combination of project with tangible value, and roadmapping, allowed us to create support for the transformation. Our client now has a benchmark for substantiating future product claims and the foundation of their data management systems and processes.

Case study: Developing Healthier Products

13

Digital maturity is not an end goal, but an evolving journey. Keep running lots of modelling and simulation projects aligned to your goals, and trying new things. Keep working out what you need - skills, data capture, data management, collaborative platforms and so on - to deliver ever more with data.

Iterative Improvement: Skills, Technologies and Processes

As you progress, approaches that once seemed innovative become institutionalised, data becomes easy to access and understand, and building intelligent systems becomes the norm.

The client, a major drinks and snacks manufacturer, had lots of data and lots of ideas they wanted to investigate. But they were struggling to build an in-house team that could deliver these as they didn’t know what skills they needed now, or how this would change as the organisation matured.

Through our Data Science Partnership, we provided flexible access to a wide range of data science skills, allowing them prioritise use cases, and address lots of interesting problems using many different skills and techniques.

Example included analysing sources of sugar content variability to produce more consistent snacks; identifying sources of moisture uptake in manufacturing which affected product quality, and analyses of new bottle designs.

Through these use cases, they gradually came to understand the capabilities they most needed to establish in-house.

Case study: Building New Data Capabilities

14 What does a data driven consumer packaged goods company look like?

As you get better at delivering value from data, establish the infrastructure that allows data insights to be quickly delivered into your organisation, or to customers, where they can be acted upon.

This means setting up data stores, selecting tools, and building integrators that make data accessible to anyone who needs it. It means agreeing standard

Deploy to the Enterprise

processes for capturing and labelling data, so it is understood when people come to use it.

And it means processes, software and infrastructure that mean new models easily integrate into IT or operational systems, considering cloud architecture, AI DevOps, and user interfaces.

The customer, a major FMCG player, wanted to use data to create more ethical and sustainable products, for example greener alternative ingredients. This was a mature organisation with lots of data and expertise.

We deployed Data Engineers, Software Engineers, Data Scientists and Scientists to create a single resource to bring together all data and models from across the entire organisation – including R&D, supply chain, packaging, etc – into a single hub. This supported easy data discovery, in silico modelling, and chaining together of models to allow cross functional insights.

As we built it, we ran use cases within it to test it and learn how to improve it, whilst simultaneously delivering business value.

Case study: Making Data-Driven Innovation Easy

15

About Capgemini

Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. The Group is guided everyday by its purpose of unleashing human energy through technology for an inclusive and sustainable future. It is a responsible and diverse organization of 270,000 team members in nearly 50 countries. With its strong 50 year heritage and deep industry expertise, Capgemini is trusted by its clients to address the entire breadth of their business needs, from strategy and design to operations, fuelled by the fast evolving and innovative world of cloud, data, AI, connectivity, software, digital engineering and platforms. The Group reported in 2020 global revenues of €16 billion.

Get the Future You Want | www.capgemini.com

Team

Nam

e_D

ate_

Des

ign

er

Copyright © 2021 Capgemini. All rights reserved.

About Capgemini

Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. The Group is guided everyday by its purpose of unleashing human energy through technology for an inclusive and sustainable future. It is a responsible and diverse organization of 270,000 team members in nearly 50 countries. With its strong 50 year heritage and deep industry expertise, Capgemini is trusted by its clients to address the entire breadth of their business needs, from strategy and design to operations, fuelled by the fast evolving and innovative world of cloud, data, AI, connectivity, software, digital engineering and platforms. The Group reported in 2020 global revenues of €16 billion.

Get the Future You Want | www.capgemini.com

MA

CS_

GC

S_20

210

812

_MK

Copyright © 2021 Capgemini. All rights reserved.

For more details contact:cprd@capgemini.com

AUTHORS

Danica GreethamAnalytics Consultant

James DowningConsumer Products Strategy Lead

James HutchinsonLead Consultant: Consumer Products R&D Digitalisation

Mark KnightDirector of Consumer Products

John GodfreeHead of Analytics