How Innovative Marketers Address the CPG Data Gap

Traditionally reliant on retailers, the consumer packaged goods (CPG) industry has long sought creative ways to unlock buyer insights. COVID-19 accelerated direct-to-consumer (DTC) sales and rapid ecommerce growth through retail partners, but this remains a relatively low percentage of overall sales and a limited source of data. Explore innovative examples of how CPGs fill in data gaps to make better predictions about post-pandemic buyer behavior. 

1) Sophisticated Data Enrichment

90% of CPG executives identify data collection, activation, and scaling as the biggest obstacles to achieving their marketing goals. Many CPGs collect buyer information, but they have fewer direct B2C connections than other industries and expanding those connections can be cost prohibitive. On average, the largest CPGs’ buyer databases are only one-tenth the size of comparable retail businesses.¹  

For years, CPGs have leveraged data enrichment to address missing first-party data and create a 360-degree buyer view. Acquiring third-party data fills in missing demographic and behavioral insights to help marketers understand what’s driving purchases of their product. Given recent shifts in the data industry and the sunsetting of third-party cookies, many CPGs have started to collect more first-party data and written off third-party data, assuming it is no longer valuable. This dismissal is premature – not all data providers are reliant on cookies, and CPGs can still benefit from non-cookie-based third-party data. 

Solutions for CPGs

• Contextual Marketing: Most CPGs can’t count on buyer loyalty and instead focus on going after switchers, new-to-category groups, and other audiences they seek to win over. In this case, third-party data can provide broader contextual insights that inform these targeting tactics. Examples of accessible data categories that can provide context to CPG purchasing behavior include demographics, household size, app engagement, brand propensity, and commute distance.
• Identify Highest-Value Buyers: CPGs with repeat buyers often lack first-party insights about what’s driving buyer loyalty. Data enrichment can reveal the common factors that high-value audiences share. This information helps marketing teams create lookalike audiences, make decisions about where to grow B2C relationships and first-party data collection strategies, and identify other high-ROI opportunities.
  
  

2) Predictive Modeling and Machine Learning

While some CPGs lag in data compared to other types of businesses, many do have access to extensive data, whether it’s through first-party data collection, data exchanges with allied brands, or third-party data enrichment. Brands can end up data-rich, yet insight-poor. To address this gap, they turn to predictive modeling, a machine learning technique that cuts through the noise by making advanced data-driven predictions. A joint study between Google and BCG found that AI and advanced customer analytics techniques can allow CPGs to achieve 10% revenue growth or more.²

There are many valuable ways to embrace machine learning, but since many CPGs were rocked by pandemic-related changes in buying habits, predictive modeling is a major area of focus for brands that seek to update revenue growth management strategies for today’s changing market. 

Solutions for CPGs 

• Predictive modeling without historical data: Brands have traditionally relied on past trend analysis to make future predictions, but COVID-19 has rendered historical data much less useful. Instead, CPGs can leverage a combination of data enrichment and predictive modeling to make post-pandemic buyer predictions based on other data points, such as household size, daily commute, or relationship networks. These predictions can inform goals in location-based marketing, product development, store inventory, and more.
• Predefined data features: In-house data teams are challenged to pivot models based on changing buyer behavior, but often lack foundational data to train their new algorithms. Mobilewalla offers curated, highly predictive feature sets to streamline the feature engineering process and help data scientists make accurate predictions, faster.

3) Smarter Programmatic Marketing

CPGs spend more on advertising than any other sector.³ Limited first-party data may be partly to blame, as it’s impossible to create granular audiences without supporting insights. The data enrichment and predictive modeling tactics described above can address the core of the ad spend problem by helping to establish relevant, unique target groups and identifying their different needs and triggers. 

However, the digital ad ecosystem is becoming more and more platform-specific; ad budgets are spread thin and increasingly fragmented. As brands launch and iterate data and predictive analytics strategies, they also need to consider short-term ad buying improvements and other considerations for optimizing ad campaign ROI. 

Solutions for CPGs

• Innovative Audience Segments: Many CPGs rely on broad demographic or behavior-based audience segmentation in advertising campaigns, but these can create overly large, non-specific audiences. As an alternative, Mobilewalla offers campaign-specific, syndicated, and custom audiences based on customer intelligence and patterns observed and analyzed within our massive data repository. These precise, validated segments pave the way for targeted messaging and higher ROI.
• Answer the Attribution Question: To understand cross-channel ad attribution and curtail underperforming ad spend, marketers can create persistent consumer identities to observe how individual buyers engage with cross-channel touchpoints. From there, it’s possible to attribute post-impression behavior to ad campaigns to see what’s working.

Learn More: CPG Data Solutions

Download our CPG Overview to explore how Mobilewalla helps the CPG sector fine-tune buyer insights to identify opportunities and create targeted, high-ROI campaigns. Or, contact us to start a conversation with one of our CPG industry data experts today. 

Sources:
[1] https://www.bcg.com/publications/2020/how-cpg-marketers-can-maximize-value-of-data
[2] https://www.bcg.com/publications/2018/unlocking-growth-cpg-ai-advanced-analytics
[3] https://www.bcg.com/publications/2020/how-cpg-marketers-can-maximize-value-of-data