GOAL

• Evaluate the feeding habits, expectations and perceptions of modern dog owners
• Identify requirements for future pet food and new business opportunities
• Analyze the public perception of Mars Petcare and their key competitors
• Gain actionable insights for marketing and product development

CHALLENGE

• Finding the best tool for qualitative and quantitative social media analysis and defining relevant pet food related search queries for all markets
• Researching relevant online communities, blogs, review sites and social news sites for cat and dog owners in UK, US and China
• Update and identifying consumer needs, feeding relevant trends and best cases
• Collecting valuable insights relevant for all three key markets; finding similarities and main differences between the markets
• Derivation of opportunities for customer-centric innovations
• Engaging the multinational project team, with team members from TD Reply’s Berlin and Beijing office
• Transferring the outcome to the relevant stakeholders the study belongs to (from different departments and country markets of Mars Petcare)

SOLUTION

The 3-step process of the netnography enables us to gain emphatic insights from pet owners, to identify new product developments and derive inspiration for the marketing and innovation pipeline of Mars Petcare.

• Set-up: First, we started with the project set-up, at which we defined relevant social media sources and pet food related search queries, initiated the automated data collection and a manual quality control of the data set.
• Buzz Analysis: Secondly, we did a quantitative analysis of relevant buzzwords and brands, followed by a qualitative content analysis, the interpretation of consumers’ postings (related to pet owner’s needs, lifestyles and the related purchasing behavior) and the clustering of all insights.
• Opportunity Mapping: In a third step we developed an opportunity map for Mars Petcare, with 3 main feeding trends, 9 opportunity fields and 20 rising product categories, which we enhanced with detailed descriptions, trend related personas, best cases, country deep dives and brand insights. 

GOAL

• Drive data-driven decision making across the business
• Learn, adapt and improve marketing based on data-driven insights
• Grow marketing efficiency and performance across all markets while ensuring one consistent way to measure performance over the 47 markets
• Create synergies among markets and learn from each other

CHALLENGE

• Be relevant. How to deal with such huge amount of data and select the ones that will help to answer real business questions?
• Trust in Data. How to cultivate a new habit of trusting and using data as a basis for decisions?
• Embrace change. How to engage the 47 markets of Miele to use the dashboard as a regular routine to support them in their daily work?
• Be actionable. How to turn the data-driven insights into real actions to create impact?

SOLUTION

In order to understand the challenges and business questions faced by the diverse stakeholders, numerous expert interviews were conducted. Having the most relevant business questions for Miele and for the industry in mind, TD Reply was able to connect the dots by developing a holistic KPIs set common to all 47 markets. In doing so, TD Reply could fill in the blanks between the data sources and drill down the amount of necessary data to the most important ones, facilitating the whole data collection.

Using Miele´s internal data and leveraging the powerful alliance of social listening, web analytics, search engines and data science via direct source connections, TD Reply could give context to the smart data to derive BIG insights. The Miele Dashboard Rollout was released in several waves. It went first live with 12 pilot markets, followed by 15 additional ones and finally 20 more markets joined the data-driven journey. Along the way, many new dashboard views tailored to answer specific business questions were developed, running advanced analytics within the Miele Dashboard.

Workshops all over the world were conducted in order to introduce the data-driven marketing culture and the Miele Dashboard in each market. In doing so, we were able to understand better the local specifics and tailor the Dashboard to their specific needs while understanding the global relevance.

RESULTS

• 12 pilot markets were already integrated in the Dashboard within the first 6 months
• A data-driven marketing dashboard available in 47 subsidiaries from Miele
• 7 views available answering various business questions and creating synergies among markets
• 234 users driving decisions based on data and allowing data democracy throughout the company
• Providing actionable insights to local management while allowing global transparency for global decision makers

GOAL

FrieslandCampina’s Global Digital Team embarked on a mission to use digital data to guide their business instead of relying on siloed digital initiatives. The goals were to drive data-driven decision making across business units as well as to adapt the right approach for implementing pilot-based programs. As a result, growth of revenue was expected.

CHALLENGE

• While there is a wealth of information and data available, how to derive clear business decisions from these numerous sources and how to create links between them?
• How to ensure that deliverables will be not just another insight, but create real business value and drive positive change in how the company’s business is run?
• How to engage not just the leadership team, but the local teams who are working on the pilot programs on a day-to-day basis?

SOLUTION

From the initial insights, the Pulse dashboard allows TD Reply to track and measure performance of pilot approaches, foresee market developments and better prepare the market to adapt to these changes. The dashboard is also helping to replace expensive market data by better utilizing Friesland Campina’s owned data.

TD Reply successfully established a purpose-driven, data-focused and agile business consulting process, working from local “real-world” business questions:

• Gathering data from both, global and local existing sources,
• Building and running statistical data models and advanced analytics
• Providing actionable results to local senior management and global decision makers
• Estimating the “size of the prize” business opportunities when changing investments
• Initiating local pilot projects or “micro battles” based on model results
• Measuring pilot effectiveness and efficiency on key business outcomes (revenue, sales volume, market shares)
• Preparing roll-out within the market and globally

Within the first 18-months, TD Reply successfully run-through the above process in 6 key markets and 4 product categories.

GOAL

The project is driven by three main objectives set by Coca-Cola and CCEP Germany: 

1. Segmentation: Better and fully scalable segmentation of the Away From Home (AFH) market
2. Optimization: Providing guidance on how to increase outlet performance
3. Prediction: Create a data-driven model to predict and optimize sell-out

CHALLENGE

Creating a novel data-driven approach with datapoints, models, and tools that are fully scalable to different markets across the globe proved to be the main challenge.

SOLUTION

In the first step, by identifying and sourcing relevant publicly available online data, TD Reply discovered potential new outlets and helped Coca-Cola to grow the customer database. An intelligent model was created to pinpoint the most fitting potential new outlets from the sourced data, in view of brand fit and popularity. The model allowed Coca-Cola to analyse over- and underperforming outlets and identify the main business drivers.

Second, for each outlet, TD Reply creates a custom optimization plan, which identifies and prioritizes levers to actively influence outlet performance through in-store activations, promotions, and the optimal assortment mix.

In the final step, TD Reply developed a prediction model that analyzes what causes sell-out deviations over time to anticipate peaks and troughs in daily sales. The model can predict next weekly/monthly sell-out based on external factors, including seasonality, weather conditions, or local events, thereby reducing the out-of-stock risk. This also allows to improve the visit planning based on information on future events, optimizing the sell-in frequency and reducing unsystematic deliveries.

As a result, Coca-Cola and its bottling partners were able to:

• Reduce out of stock
• Significantly increase the quality and size of their outlet database
• Focus on outlets that really drive business success
• Defined key variables impacting outlet performance
• Drive brand building and innovation by knowing more about their outlet
• Optimize resource allocation for underperforming outlets with high sales potential
• Optimize sell-in frequency
• Maximize marketing impact