CPG Data Analytics Solutions – Ultimate Business Guide 

Written By: Sudeshna Ghosh

Key Takeaways

• A multinational consumer good manufacturing company headquartered in USA recently saw a 30% reduction in ad hoc analysis time with Quantzig’s expert solutions.
• Quantzig’s design thinking dashboarding approach facilitated the identification of top three business questions for stakeholders, streamlining the decision-making process.
• This comprehensive solution resulted in a significant decrease in time, facilitating a near real-time reporting, and centralized portfolio views, significantly enhancing operational efficiency and decision-making processes.
• By leveraging CPG data analytics, brands can extract actionable insights, optimize decision-making processes, and enhance operational efficacy.
• Besides, from identifying out-of-stocks situations to optimizing visit scheduling and evaluating compliance impact on sales performance, this analytics tool empowers CPG brands to address critical inquiries and optimize sales outcomes.

Introduction

With the help of advanced analytics, CPG brands gain the ability to address critical inquiries such as identifying out-of-stock situations, optimizing visit scheduling, and assessing compliance impact on sales performance. This is the reason most of the leading CPG brands are now adopting a data-centric approach, extracting valuable insights and following trends to make strategic decision-making. This case study delves into how Quantzig’s CPG Data Analytics solutions enabled a multinational consumer good manufacturing company to achieve a 30% reduction in ad hoc analysis time, near real time reporting and centralized view of the portfolio.

Quantzig Success Story

Client Details

In the highly competitive landscape of consumer goods manufacturing, timely and data-driven decision-making is crucial for sustaining growth and competitiveness.

A leading multinational consumer goods manufacturer based in the USA, with annual revenues exceeding $1 billion, faced significant challenges due to data overload and static reporting processes. Recognizing the need for a more efficient analytics solution, the company partnered with Quantzig, a renowned analytics and advisory firm, to streamline its decision-making processes.

Challenges Faced by the Client

• Excessive Data Volume: The influx of data from the commercial team overwhelmed users, impeding their capacity to extract actionable insights effectively. This inundation hindered the identification of crucial information amidst the data deluge, hampering decision-making processes.
• Static Reports and Manual Analysis: Static reports necessitated manual effort for scenario-based analysis, causing delays in decision-making. The manual refresh process for these reports further prolonged turnaround times, inhibiting the company’s agility in responding to market dynamics.
• Disparate Insights and Lack of Cohesion: Insights derived from static reports were disparate and lacked a cohesive narrative, complicating the identification of actionable items. This disjointed information hindered the company’s ability to derive meaningful insights and make timely decisions in response to evolving market conditions.

Solutions offered by Quantzig

Quantzig approached the client’s challenges with a design-thinking approach, identifying the top three critical business questions for stakeholders.

• Using design thinking dashboarding approach: Leveraging intuitive dashboarding techniques, our experts streamlined the flow of information, enabling stakeholders to access key insights effortlessly. The implementation of granular data slicing capabilities provided stakeholders with a comprehensive view of market dynamics, expediting decision-making processes.
• Implementation of real-time reporting: As a result of these solutions, the client experienced a significant reduction in ad-hoc analysis time, with operational efficiency improving by 30%. Furthermore, the implementation of near real-time reporting capabilities provided stakeholders with centralized portfolio views, facilitating informed decision-making and enabling timely responses to market dynamics.

Impact Delivered

Through collaboration with Quantzig, the multinational consumer goods manufacturer successfully overcame its challenges related to data overload and static reporting processes.

• By leveraging intuitive dashboarding techniques and granular data slicing capabilities, the client achieved 30% reduction in ad hoc analysis time, near real time reporting and centralized view of the portfolio.
• These enhancements have positioned the company for continued success in the competitive consumer goods market, ensuring that it remains agile and responsive to evolving market trends and customer demands.

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Retail Measurement Data vs. Panel Data

Segmenting CPG retail analytics into distinct categories is essential for comprehensive insights. Two primary types of data crucial for brands to gather are retail measurement data and consumer behavior data, exemplified respectively by retail measurement data and panel data.

1. Retail Measurement Data:

This dataset encompasses information obtained each time a product undergoes scanning at a point of sale. It provides valuable insights into various metrics such as pricing dynamics, sales volume, promotional effectiveness, and competitive analysis. For instance, utilizing Byzzers Brand Ranking Report, a pet food manufacturer can delve into metrics like total sales revenue, sales per distribution point, and price fluctuations to refine their market positioning and strategy.

2. Panel Data:

While retail measurement data offers valuable sales metrics, it lacks specificity regarding the purchasers. Panel data fills this gap by providing detailed insights into consumer demographics, shopping preferences, and market trends. Many companies curate panel data, allowing brands to analyze consumer behaviors comprehensively. This data offers segmentation into vital categories including penetration rates across different markets, household spending patterns, purchase frequencies, and average basket sizes. Such insights empower brands to tailor their marketing strategies and product offerings to better resonate with target consumers.

In summary, by leveraging both retail measurement data and panel data, brands can gain a holistic understanding of their market performance and consumer behaviors, enabling informed decision-making and strategic planning.

What are CPG Data Analytics? 

CPG data analytics involves the systematic gathering and examination of data generated from sales and marketing activities conducted by a team in the field. Emphasizing data-driven approaches enables CPG brands to transform collected data into actionable insights by conducting thorough analysis and identifying emerging trends. In essence, data serves as the raw information points on a graph, while analytics comprise the valuable insights derived from analyzing and interpreting that data.

In the competitive landscape of the consumer-packaged goodsindustry, the strategic utilization of CPG analytics tools is paramount for businesses, particularly small to mid-sized manufacturers and up-and-coming brands operating on a start-up budget. These analytics solutions provide insights into product categories, purchase frequency, and purchase size, leveraging real-world data to drive continuous innovation and development. By analyzing retail data and market share, companies can optimize their operations and sales methods, identifying growth opportunities and minimizing losses and expenses.

Besides, data analytics in CPG industry is crucial for achieving peak performance and gaining a competitive edge. By analyzing competitor’s products, churned accounts, and action data, businesses can optimize pricing and promotion strategies to maximize profitability. SMB Byzzer provides valuable resources for small to mid-sized companies, offering insights into storefront management and territory development plans (TDP). Through effective utilization of CPG data analytics, businesses can navigate the complexities of the industry and drive sustainable growth.

Through smart pricing strategies and trade promotion effectiveness, businesses can increase their share of category and sell products more effectively, capturing both incremental and non-incremental volume. Moreover, Advanced CPG analytics solutions offer actionable insights into shopper loyalty and CPG trends, enabling companies to tailor their growth strategy and capitalize on emerging market trends. With access to analytics solutions like the Smart Pricing Actions Report and Smart Promotions Action Report, small businesses can make informed decisions, driving sustainable growth and profitability in the ever-evolving CPG landscape.

Different types of CPG Data

In CPG data analytics, three primary sources of data warrant attention from brands: observational data, activity data, and sales data.

1. Observational Data:

Observational data pertains to the in-store conditions observed and reported by representatives in the field. This includes an assessment of execution within the store environment and opportunities identified during visits. Metrics derived from observational data include stock levels, facings, competitive activities, promotional compliance, and more.

2. Activity Data:

Activity data refers to the specific actions undertaken by the team in the field. This includes insights into visit frequency, territory coverage effectiveness, and the most common actions performed within stores. Tracking and analysing activity data is crucial for understanding which actions have the greatest impact on sales performance.

3. Sales Data:

Lastly, sales data denotes each product volume sold over a specified period and at designated store locations. While this data may already be part of a brand’s tracking efforts, its significance is amplified when correlated with observational and activity data. This correlation provides valuable insights into the relationship between activities, store conditions, and sales outcomes.

To summarize, brands should leverage observational data to assess in-store conditions, analyze activity data to understand sales-driving actions, and utilize sales data to measure product movement off the shelf.

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How CPG Data Analytics Can Improve Efficiency in the Field?

In the dynamic landscape of the consumer packaged goods (CPG) industry, the importance of CPG data analytics tools cannot be overstated. These solutions provide invaluable insights into retail execution, pricing strategies, consumer behavior, and market trends, enabling companies to optimize their performance in specific markets. By leveraging retail analytics and consumer insights, businesses can identify key data points to inform strategic decision-making, from product promotions to store-level tactics.

Repsly’s retail execution solution empowers SMBs like Byzzer to navigate the complexities of the consumer goods analytics market, ensuring effective storefront management and product line optimization. With access to Retail Measurement Data and actionable data points, companies can refine their pricing and promotion strategies, capitalize on consumer preferences, and drive peak performance across retailers and products promoted. In essence, CPG marketing analytics serve as the cornerstone for success in the competitive retail arena, guiding businesses towards enhanced market penetration and sustained growth.

By in-depth tracking the three categories of retail data highlighted earlier and leveraging them to drive a continuous improvement cycle, you and your team can adeptly address critical inquiries such as:

1. Identification of Out-of-Stock Situations:

With a robust data-driven approach, the ability to swiftly detect instances of out-of-stock occurrences becomes enhanced. This empowers proactive measures to replenish stock levels promptly, preventing potential sales loss and mitigating customer migration to competitor offerings. Moreover, by instituting a long-term analytics strategy, discernible patterns across various retailers can be discerned, facilitating more precise adjustments to order quantities and optimizing in-store strategies.

2. Analysis of the Relationship between Visit Frequency and Sales Performance:

Through data analysis, insights into the correlation between visit frequency and sales performance can be gleaned. This understanding enables informed decisions regarding resource allocation and visit scheduling to maximize sales outcomes.

3. Evaluation of Compliance and Its Impact on Sales Performance:

Utilizing data analytics facilitates the evaluation of compliance levels within retail environments and its subsequent influence on sales performance. By identifying instances of noncompliance, proactive measures can be implemented to rectify discrepancies and recapture potential lost sales opportunities.

4. Identification of Stores with Compliance Issues and Suboptimal Sales Performance:

Leveraging data analytics enables the identification of stores experiencing compliance issues and suboptimal sales performance. This insight empowers targeted interventions to address underlying issues and optimize sales outcomes.

5. Utilization of Predictive Analytics to Anticipate Consumer Activity:

Predictive analytics serve as a proactive tool to anticipate consumer activity trends, enabling timely stock replenishment and preemptive measures to avert out-of-stock scenarios. By establishing predictive models and leveraging historical data, brands can forecast stock depletion patterns and optimize inventory management strategies.

6. Implementation of Data-Driven Approaches to Ensure Display Compliance:

Data-driven approaches facilitate the identification of display compliance issues and enable corrective actions to be taken promptly. By closely monitoring sales trends and display effectiveness, brands can initiate targeted interventions to rectify compliance discrepancies and optimize promotional outcomes.

7. Utilization of Data Stories to Convey Insights to Retailers:

Employing data stories aids in effectively conveying insights derived from analytics to retailers in a comprehensible manner. By translating complex analytical findings into compelling narratives, brands can articulate their value proposition and secure favorable positioning on store shelves.

8. Tracking of Key Performance Indicators (KPIs) to Drive Goal Attainment:

Tracking KPIs provides quantifiable metrics to gauge progress towards organizational goals. By selecting SMART indicators and analyzing KPI data, actionable insights can be derived to inform strategic decision-making and drive continuous improvement initiatives.

9. Leveraging Predictive Tools for Optimal Performance:

By harnessing predictive tools, CPG companies can anticipate consumer behavior trends and tailor field activities accordingly. These tools enable proactive measures such as optimizing order sizes for high-volume stores and strategically planning rep visits to maximize sales lift. By integrating predictive analytics into retail execution strategies, companies can stay ahead of the competition and ensure efficient resource allocation for driving sustainable growth.

10. Enhancing Retail Execution Through Data-Driven Insights:

CPG data analytics provides valuable insights into retail execution, including secondary display effectiveness, display setup optimization, and shelf share analysis. By leveraging these insights, companies can enhance field activities, ensuring optimal product placement and visibility. This approach not only drives sales lift but also facilitates the acquisition of new accounts while minimizing churned accounts, thereby maximizing overall performance and profitability in the field.

In summary, by adopting a data-driven approach, brands can harness the power of analytics to address key challenges, optimize operational efficiency, and drive sustainable growth in the competitive CPG landscape.

Read more: How We Transformed Stock Out Prevention and Inventory Redistribution for a Thriving CPG Client 

What the Future holds for CPG Data Analytics?

The future of CPG data analytics promises a paradigm shift towards greater precision, agility, and strategic foresight. Leveraging predictive analytics, brands will anticipate consumer behaviour trends, enabling proactive stock replenishment and targeted interventions to optimize in-store strategies and ensure compliance. Data-driven approaches will illuminate the complex relationship between visit frequency, compliance, and sales performance, guiding resource allocation and field team deployment for maximal impact.

Moreover, the utilization of data stories will facilitate compelling communication of insights to retailers, fostering collaborative partnerships and securing advantageous shelf placements. Through continuous tracking of key performance indicators, brands will iteratively refine strategies, driving goal attainment and sustaining competitive advantage in the dynamic CPG landscape. As analytics capabilities evolve, CPG companies will unlock unprecedented opportunities for growth, innovation, and customer-centricity, positioning themselves at the forefront of industry advancement.