How data transformation drives margin growth realization

Today, data transformation is crucial for CPG manufacturers in retail and foodservice to boost margins and grow revenue. Why is it especially important right now? With rising costs, households are tightening their budgets, and manufacturers need to adapt to these changing shopper behaviors. This shift in focus is what we at TELUS call "margin growth realization," and it underscores the importance of data transformation in navigating these challenges.

At a recent TELUS event, David Ganiear, a partner at PwC Strategy& shared, “Since exiting the pandemic, the CPG industry has faced unprecedented inflation. Manufacturers discovered that while prices increased, sales volume dropped. Now, they are focusing on building resilience for future challenges.” He added that in recent years, companies adopted a "field of dreams" strategy, building data warehouses and lakes with the hope that insights would follow. While this laid a foundation, it didn't drive the expected value. CFOs, CMOs, CIOs and CTOs experienced strained relationships due to significant tech investments without clear returns, highlighting the need for a more strategic approach to technology and data management.

In this blog, we will explore how effective data transformation and artificial intelligence can help drive margin growth realization, turning data investments into tangible business value.

CPG industry trends and challenges

The CPG industry is up against several challenges, like rising costs, shifting consumer behavior and the need for efficient supply chain management. Data transformation helps tackle these issues by harmonizing data to offer a unified view of data sets, enabling businesses to make better informed decisions more quickly.

Consumer behavior in retail

Household budgets are getting tighter, and consumers have less spending power. Meanwhile, the cost of goods keeps rising. This is changing how shoppers behave, with more value orientation and hunting for the best deals. They're finding ways to save by:

• Looking for promotions and deals more often¹

• Postponing purchases until items go on sale¹

• Focusing only on essential purchases²

• Skipping non-essential buys²

• Switching from premium brands to private labels³

• Upsizing to get better value packs³

Because of these factors, brand and retailer loyalty has dropped. Consumers are now frequently switching brands and shopping at different retailers to find the best prices.

Consumer behavior in foodservice

Customers are seeking value in foodservice, especially when dining out. They're:

• Noticing price increases on everyday menu items³

• Taking advantage of promotions to dine at off-peak times³

• Feeling that portion sizes have gotten smaller³

• Using restaurant loyalty apps for savings and rewards³

• Abandoning delivery apps once they see the fees³

• Looking for restaurants without credit card fees or surcharges³

With these consumer trends happening globally, it's more important than ever to start with data transformation on your journey to achieving margin growth realization.

Understanding data transformation

Simply put, data transformation is the process of harmonizing raw data from multiple sources and turning it into a format that's easier to understand and analyze. This process involves cleaning, structuring and integrating data to ensure it's high quality and ready for use. In the CPG industry, data transformation is key to understanding consumer behavior, optimizing supply chains and driving profitable growth.

Types of data transformation

There are several types of data transformation that data scientists can use to convert raw data into meaningful insights:

1. Data cleaning: Removes errors, duplicates and inconsistencies from the data set to ensure accuracy
2. Data integration: Combines data from different sources to provide a unified view
3. Data harmonization: Ensures that the integrated data is consistent and comparable. This involves standardizing data formats, units of measure and terminologies across different data sources to ensure that the data can be accurately compared and analyzed
4. Data aggregation: Summarizes data to provide a higher-level view, such as total sales by region or retailer
5. Data normalization: Adjusts data to a common scale without changing the differences between the values
6. Data enrichment: Enhances data by adding additional information from external sources

Benefits of data transformation: speed to insights and efficiency

Turning raw data into high-quality, actionable insights allows manufacturers to improve decision making, enhance operational efficiency, increase reporting accuracy and unlock advanced analytics. With full visibility into your data, you can understand how to alter a specific lever, like pricing or promotions, to align with consumer behavior.

Improve decision making

One of the main benefits of data transformation is the ability to make more informed decisions. With data sources harmonized, businesses can analyze trends, spot opportunities and use data to aid in decision making on the fly. Understanding consumer behavior helps you create targeted marketing strategies for each retailer or foodservice account. You can adjust prices and promotions based on what shoppers prefer.

Enhance operational efficiency

Data transformation boosts operational efficiency by streamlining business processes. Centralizing data reduces the time and effort needed to collect, harmonize and analyze information. This allows teams to focus on more strategic tasks, like developing new product launch plans. Additionally, integrated data helps optimize supply chains by identifying inefficiencies and areas for cost savings.

Increase reporting accuracy

Centralizing data helps enable teams within an organization to work with the same information, increasing reporting accuracy and reducing the risk of errors and discrepancies. For example, having a single source of truth for sales and inventory data can help prevent out of stocks and overstocks, leading to better inventory management.

Unlock advanced analytics

Data transformation unlocks advanced analytics and insights by providing a comprehensive view of data sets. This allows businesses to perform more sophisticated analyses, such as predictive modeling and trend forecasting. For instance, integrating sales data with market trends can help companies forecast demand and adjust their production schedules accordingly.

Implementing data transformation into business processes

Implementing data transformation in business processes involves several steps, from identifying data analysis sources to harmonizing and visualizing data. The result of data transformation is alignment between your technology and your business objectives. This sets the stage for achieving the ultimate goal of margin growth realization.

1. Identify and assess data sources

The first step in data transformation is to identify and assess all available data sources. This involves inventorying data sets and evaluating their current use and value to the business. By understanding what data is available and how it is used, manufacturers can find areas for improvement.

There are several types of data to consider:

• Internal data: Generated by the manufacturer and includes proprietary information such as profit margins

• Syndicated or vendor-aggregated data: Provided by third parties or brokers and includes retailer sales data for both your brand and competitor brands

• Panel data: Shared by third parties and offers insights into consumer behavior, such as household penetration

• Retail direct data: Distributed by the retailer and includes point-of-sale scan data

• Distributor data: Provided by the distributor and contains shipment information from the distributor to the retailer

2. Implement data ingestion tools

Once you've identified your data sources, the next step is to implement data ingestion tools to collect and centralize the data. These tools handle various data formats and ensure data quality. It's important to plan ahead for the data intake process to accommodate new data sources and changes in data needs.

3. Regularly check for data ingestion errors

Regularly checking for data ingestion errors is important to ensure data accuracy and completeness. Consider spot checking to validate data as it comes in and addressing any issues immediately. Maintaining data quality makes your analyses and insights reliable, building trust within your team.

4. Integrate and harmonize data

To integrate and harmonize data, standardize definitions and terms to establish a consistent view across data mapping sources. This step makes data meaningful and useful within your organization. Clearly define harmonization standards, such as aligning with an enterprise resource planning tool or a shopper decision tree. Additionally, define the products, periods, markets and metrics that matter to your business.

5. Visualize data

Visualizing data allows you to understand and interpret your data quickly and efficiently. This involves creating high-quality reports and dashboards that provide actionable insights. To begin, align with stakeholders on a small number of reports, refine them and ingrain them in the business processes. Integrating data visualization with decision-making tools, such as TELUS Trade Promotion Management, can further enhance its value.

6. Iterative improvement

Unfortunately, this isn’t a set-it-and-forget-it task. Data transformation requires continuous refinement and improvement. Treat data centralization as an iterative process, regularly reviewing and updating your data transformation strategies to meet evolving business needs. Stay up to date with new data sources, technologies and best practices.

7. Maximize margins

You’ve completed your data transformation and built in steps for iterative improvement–so, what’s next? To help drive success, focus on margin growth realization. This involves integrating sales planning and execution across various tools.

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