But all data isn’t created equal, and brands that fail to make the necessary distinctions between different types of data can end up hurting the customer relationship with counterintuitive data applications. There are three basic categories of data, and brands that hope to nurture customer relationships need to understand each type and know how to apply it. Breaking down the data, these three categories are as follows:
- Observed data: This information, or transactional data, can be found in purchase records and cash register receipts. Transactional data includes items like completed purchases and customer interaction records. It can also include purchased data that is used to shed light on demographic preferences for products, communication platforms, and a host of other specifics.
- Inferred data: This type of data comprises assumptions brands make from analyzing observed data. For example, Customer C buys cat food each week, so Customer C must own a cat; or, since Customer A contacted Company B by email, Customer A prefers email to other communication channels.
- Freely given data: The most valuable of the three types is freely given small data. Small data is information that customers voluntarily share with the brand. It can be acquired in a number of ways such as loyalty program applications, direct interaction with customers online or in a bricks and mortar location, contest forms, site registrations, and much more. As companies continue to engage in a two-way dialog with their customer, their capacity to collect small data grows correlatively.
Understanding how these data sources differ and the relative value of each is crucial for brand advocates who want to apply data more effectively and drive customer engagement. But the way some companies currently interact with prospects and customers are still negatively impacted by counterintuitive data applications – demonstrating that many haven’t yet grasped this important principle. Here are three examples of counterintuitive data application:
- Overriding small data with purchased information: Marketers love to get new data that sheds light on customer preferences, and purchased data can be incredibly useful – since it is typically drawn from larger datasets than companies could access on their own. But purchased data should never be prioritized over the small data customers freely provide to the company. Doing so not only prioritizes the general over the specific, it disrespects the customer contribution.
- Failing to understand the mobile lifestyle and the importance of real-time interactions: Many companies approach data analysis as a history lesson. They parse data to find out who their customers are, which is important, but too many don’t take the next step and put that information into context to identify where the customers are in their journey in real-time. The mobile lifestyle means consumers are always connected, and brand advocates who want to engage them effectively must quickly process and respond to new information.
- Missing opportunities to continuously learn from customers: Another mistake many brands make is a failure to keep adding to their dataset in meaningful ways. Customer preferences aren’t static – they change and evolve over time, and companies that don’t get ahead of the curve now will lose market share in the future. Surveying customers and prospects, and engaging in activities like A/B testing are crucial to success.
Data unquestionably has the potential to sharpen brands’ insights into their customer base. But, as the capacity for building great customer relationships emerges as a key competitive differentiator, it’s increasingly important for marketers, customer support personnel, developers, and other business leaders to truly understand data and apply it correctly.
This means knowing which data sources are most effective at driving engagement, and understanding the importance of the information customers choose to share. It means continuously generating more data and applying it effectively according to where the customer is – both in the sales cycle and in physical space. With the right context and knowledge, brands can avoid counterintuitive data application and thrive in the brave new data-driven world.