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Data is one of the most valuable assets a company has. We covered different data types for marketing in our previous post. Although behavioural data is a great source, using behavioural data alone for personalisation is far from enough. This post will explain why leveraging self-identified (declared) data in addition to behavioural or transactional can help your business like no other.
Data is revolutionising the world of business. According to PwC’s recent Global CEO survey, 64% of CEOs believe how 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 personalised experiences; that is where the role of data comes into play. You can leverage segmentation through data to create personalised offers and build customer relationships. In fact, 91% of consumers said they would be more likely to shop with brands that recognise and provide relevant offers and recommendations, and 83% are willing to share their data to make this work. So, personalisation is key to maintaining a loyal customer base and business growth.
Today, most companies create segments with valuable but often limited behavioural or transactional data. For instance, an e-commerce site selling sneakers can’t measure if a user owns a car with behavioural data.
Behavioural data can also be misleading. Let’s say you bought your baby nephew a gift from Amazon. Amazon will likely send you offers about baby products with the 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, colour…
• 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 with marketing data types, you can read our simple guide by clicking here.
The opportunity cost of not excelling at personalisation is significantly high. Relying only on behavioural data can turn into a negative as wrong personalisation 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 behaviour, but it is nowhere near enough. Companies should add self-identified data to create a more accurate customer experience.
Many brands are adopting a direct engagement and self-identified data strategy to better understand and segment their users:
Eventbrite engages users with interactive tests during special dates and makes event recommendations based on user profiles and answers.
Spotify is enhancing its “discover weekly” playlist recommendations with user input. Users can share dislikes on songs and artists, based on which next week’s list is modified.
A common example of collecting self-identified data is getting user feedback. Often, customer feedback is collected through classic survey tools, which work for insights but lack essential parts for creating personalised segments; it is an external experience with limited reach and answers are not linked to the user profile.
Self-identified data is hard to scale since it is challenging 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.
E-mail us at info@poltio.com or login try it for free to join the data revolution!
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