Time to use self-identified data for an intact data strategy
Data is one of the most valuable assets a company has. We covered different data types for marketing in our previous post. Although behavioral data is a great source, using behavioral data alone for personalization is far from enough. This post will explain why leveraging self-identified (declared) data in addition to behavioral or transactional can help your business like no other.
Data is revolutionizing the world of business. According to PwC’s recent Global CEO survey, 64% of CEOs believe the way companies manage data will be a differentiating factor in the future. How does your company manage data? What type of data are you using for your business? Before you answer that, let’s dive into the role of data in the context of growth strategy.
Growth is an essential factor in a company’s business decisions. In the fourth quarter of 2018, CEOs consistently ranked predictable growth as the most critical business priority for 2020.
To achieve growth, you can either:
• Acquire new customers, or
• Extend the value of your existing ones.
You should do both! Yet, acquiring a new customer is 5 times as expensive as retaining an existing customer.
Given that customer retention is key to sustainable growth in the long-term, along with the fact that it is more cost-effective, you should focus on extending the value of your existing customer base.
The most common way to increase customer retention is through delivering personalized experiences and that is where the role of data comes into play. You can leverage segmentation through data to create personalized offers and build relationships with your customers. In fact, 91% of consumers said they would be more likely to shop with brands who recognize and provide relevant offers and recommendations, and 83% are willing to share their data to make this work. So, personalization is key to maintaining a loyal customer base and hence for business growth.
Today, most companies create segments with behavioral or transactional data which is useful but often limited. For instance, an e-commerce site selling sneakers can’t measure if a user owns a car with behavioral data.
Behavioral data can also be misleading. Let’s say that you bought a gift for your baby nephew from Amazon. It is likely that Amazon will send you offers about baby products with a wrong assumption that you have a baby.
On the other hand, self-identified data comes directly from your customers; providing you with definite rather than assumed answers. You can ask and learn about anything and receive the most relevant and accurate insights.
Examples of self-identified data you can link back to individual user profiles:
• Favorite type of music, artist, food, color…
• Shopping preferences
• Age, gender, marital status, occupation…
• Physical characteristics
• Most liked or disliked brands
• Relationship with competition
• Ownership information
• Adaptation levels
• Knowledge level on any topic based on test performance
If you need to be more familiar about the different data types for marketing, you can read our simple guide by clicking here.
The opportunity cost of not excelling at personalization is significantly high. Relying only on behavioral data can turn into a negative as wrong personalization hurts the customer’s brand affinity or trust. The inference based solely on what someone has purchased or their interactions with an ad is a strong indicator of future customer behavior but it is nowhere near enough. Companies should add self-identified data into the mix in order to create a more accurate customer experience.
There are many brands that are adopting a direct engagement and self-identified data strategy to better understand and segment their users:
Eventbrite is engaging its users with interactive tests during special dates and make event recommendations based on user profile and answers.
Spotify is enhancing its “discover weekly” playlist recommendations with user input. Users are able to share dislikes on song and artists, based on which next week’s list is modified.
A common example for collecting self-identified data is getting user feedback. Often, customer feedback is collected through classic survey tools which work for insights but lack key parts for creating personalized segments; it is an external experience with limited reach and answers are not linked backed to the user profile.
Self-identified data is hard to scale since it is difficult to create engaging content. Brands need to figure out how to engage in explicit and transparent conversations that are mutually beneficial.
Poltio provides a platform for first-party self-identified data collection through engaging interactive content. To learn more about how it works, visit Poltio.