Organizations are desperate for insights, but the reality is that they’re drowning in data. In the journey to transformation, data creates both opportunities and major challenges, and marketers now know that there can be too much of a good thing.
The flood of data isn’t just a matter of volume either. Data is becoming more complicated. This means that getting real value out of the data we collect is going to be the main hurdle for many businesses. According to Gartner’s predictions, only 20% of data analysis will actually deliver beneficial business outcomes in 2022.
Right now, the sea of data is overwhelming, and it’s only going to get bigger. In order to read between the lines and keep your head above water, it’s important to analyze and interpret data effectively – this starts with knowing that types of data to prioritize.
What is customer data?
Firstly, let’s understand a bit more about customer data and what it is. The definition of customer data is any information you collect from your customers in the first-party context. This includes all personal, demographic and behavioral data, and can come from a number of different sources.
These sources include your website, apps, physical stores, customer surveys or any other platform where customers share their information with you. The growing number of channels and devices is the reason why volume and complexity are increasingly problematic. And with more touchpoints than ever before comes an additional issue – format.
In order to improve data value, data standardization needs to be considered and must be consistent across key data types.
The 4 different types of customer data
Knowing what type of data to focus on matters. By prioritizing the data that’s most critical to the business, you can gain valuable insights into what makes your customers tick and how best to improve customer experiences.
Here are the types of data you should be focusing on:
1. Basic personal data
This data is the foundation of each customer relationship, and includes basic information such as names, email addresses, contact numbers, job titles and other personal data. It also includes demographic data such as gender, income or firmographic data in B2B.
While on its own this data doesn’t provide much value, when combined with behavioral data, it can help you spot trends and common attributes among specific customer groups.
2. Data on interaction or engagement
Engagement data is collected from the different touchpoints where customers interact with your brand. This could be on your website or on your social channels, and can be things like page views, time on site, downloads, contact form enquiries, likes or social shares.
Tapping into this type of data means being able to inform the buyer journey and having measures for campaign effectiveness.
3. Behavioral data
Gain insight into customer experience with behavioral data. Similar to engagement data, behavioral data looks at interactions, but focuses on how customers interact to a product or service directly. Things like purchase history, cart abandonments or subscription renewals are a good example of how customers behave in relation to what you’re offering.
4. Attitudinal data
Want to know what customers think about your brand, products or services? Attitudinal data can give you a better understanding of how you’re perceived through reviews, support ticket comments and customer surveys results. Used well, this type of data can be a great indicator of customer preferences across different segments.
But beware – this data can be hard to understand, as some customers won’t give much away when it comes to their personal thoughts and feelings. To make this data work, you have to be consistent and capture information across all segments and customer groups.
How to collect first-party customer data
According to a Merkle Report, over three quarters of customers would be willing to share their data in exchange for a better customer experience, such as personalized offers and content. But the way brands capture data is important. In a time where trust is the new currency, data privacy needs to be front of mind and first-party data is the answer.
Some of the best ways to collect this type of data include:
- Tracking on your website
- Transactional data such as invoices and payments
- Surveys that help you measure customer satisfaction
- Social media
- Marketing analytics
- Subscriptions or registrations
- Promotions, competitions and offers
Whatever tactics you engage in to capture customer information, remember that a privacy-first approach is key. This is the only way to ensure transparency and to develop stronger relationships with customers.
The art of analyzing customer data
In addition to getting data collection right, marketers also need to adopt cutting-edge analytics tools to turn raw data into actionable insights. Using customer data in the right way can improve your marketing efforts considerably, but without analytics in place, your data is practically worthless.
Through intelligent digital analytics, it’s possible to integrate everything from website data and ecommerce data to social data with your marketing strategy. This gives you a way to understand customer preferences and also helps you personalize their entire journey.
You can also use digital analytics to run A/B tests, inform your buyer personas, improve content marketing, find out more about lead sources and even conduct competitor research.
Data = dynamic, relevant experiences
While data is now considered the world’s most valuable resource, surpassing assets like oil and gold, data by itself is useless. The information we collect on our customers is only beneficial if it’s applied in the right way.
One of the ways that marketers can harness the power of data in 2022 and beyond is real-time personalization. As mentioned before, customers value their privacy but are willing to give up information for a meaningful experience – and in order to make interactions meaningful, they have to be relevant.
Personalizing content in real-time is a reactive approach that instantly delivers customized content based on each user’s behaviors. It works particularly well with localized content, industry-based content or account-based marketing activities, and the growth of AI will make it easier to create a deeper level of personalization in the coming years.
If you want to avoid drowning in a sea of data, have a strategy in place to make the most of the different types of data discussed above. Then use the data to drive dynamic, hyper-personalized experiences – the sort of experiences that customers love.