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.
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.
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.
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.
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