A high degree of data expertise and a long-term data strategy is required to collect, store, analyze, and effectively activate a full range of consumer data. The pressure to build first-party (1P) data capabilities will only grow stronger with less access to third-party (3P) cookie information, increasing regulations, and the need to meet consumer expectations.
CPGs must make an effort to exchange enough value to merit consumer data sharing to create a sustainable competitive advantage. Those who do not will become price-pressured commodities.
The key sources of 1P data are purchase data from e-commerce and other DTC sales, customer service, CRM, and loyalty programs. Data from customer service interactions and product reviews provide rich, individualized data when linked to consumer records to enhance consumer “golden records.”
CPGs are increasingly selling DTC and engaging in personalized campaigns. These interactions help enhance consumer records. Some companies are employing online quizzes and monitoring location data. Additionally, net promoter score and other survey data is often integrated into customer data profiles.
Customer preferences and behavior can also be derived from analytics. This includes identifying look-alike prospects, content affinity, and propensity modeling, which can drive campaign content, timing, channel placement, and personalized product recommendations. Machine learning allows for improvement of these models.
CGT: Can consumer data strategies be built by consumer goods companies of any size?
Any size CPG can have a data strategy that is a right fit for them.
We’ve seen companies create segments of “0.” The intent is to set the vision and then gather behaviors and attributes to populate these important segments. With dynamic segmentation, people can fall in and out of specified segments.
A minimum of 10,000 profiles is needed for a good predictive customer scoring model. Platforms like Facebook don’t accept segment activations of less than 1,000 profiles. One hundred thousand profiles would be plenty to warrant investment into a customer data platform. Half of Treasure Data clients have CDPs with under 2 million profiles.
CGT: How do the strategies differ for SMBs vs. large CGs — and every company size in between?
CPGs acquire new customers and grow lifetime value by delivering exceptional consumer experiences. Large companies have more developed data strategies and see larger returns from customer data management initiatives.
Smaller businesses are closer to customers, making it easier to develop deep customer intimacy. Large companies must systematically employ tools, gather, and analyze data, and then effectively activate to develop and maintain the type of customer intimacy that grows a business.
As a company grows, more sophisticated tools and skills are required to acquire, manage, and activate against larger amounts of customer data. The return on investment is greater for larger companies, although companies of any size can benefit from a disciplined process for customer data management.
Companies with multiple brands and geographies can realize greater returns with a centralized approach that streamlines technology, de-dupes data with identity resolution, and leverages scale to get the most insights.
Centralized customer dataoffers greater efficiencies and more room for growth. Companies that use centralized data benefit from increased efficiency, more actionable insights, reduction in risk, lower costs, and less room for error in decision making.
Global enterprises have added complexity navigating complex privacy and security laws that differ from country to country. Good data governance thoughtfully outlines which employees, in which departments, in which countries, can access various first-party data.
CGT: How does a CDP help consumer goods companies make their glut of consumer data understandable and actionable?
CPGs can manage and utilize data with centralization, predictive analytics, and machine learning. It’s important to make insights discoverable with an easy-to-navigate interface to identify and understand strategically valuable targets.
Centralization: Break down silos and create a 360-view of the customer to surface previously buried insights, target media spend effectively, and personalize messaging at scale.
Insights: Get more value from data quickly with next-best action recommendations and DIY predictive models. Conquer foresight and optimization with analytics and reporting at a segment, campaign, and customer level. Set up custom reports and dashboards or use out-of-the-box options.
Customer Experience: Recognize specific customers across devices and channels and create personalized experiences.