Customer data: How to document and make sense of it (Part 2)

Welcome to part two of this three part series on customer data.

In part one, we discussed how to collect customer data through various methods. If you read this post, you might have noticed that it’s easy to get overwhelmed by the number of tools that can help you collect data. There’s a ton of them out there, and they all convince you that they are the magic bullet to uncover the best strategic insights for your business.

I’m here to tell you that most of these tools are exceptionally good at helping you gather juicy insights. But… no matter what tool you use and how much data you collect, it only becomes a meaningful stream of insights when you learn how to properly document this and make sense of it.

By doing this, you avoid failing to effectively use your data and diminishing the impact of gathering these valuable insights.

After all, you’re doing all this research so you can improve the way you’ve been doing things and improve your life and the life of your customers.

In this article, you’ll learn:

• How to document this data into a manageable and readable document.
• How to segment this data into categories to help you create strategies and content.
• How to prioritize a strategy that will help you maximize your return.

Document your data meaningfully

There are generally two types of data that you’ll collect, quantitative and qualitative data.

The quantitative data is mostly tracking data, such as your web analytics and social media performance reports. You don’t necessarily have to manually document this, since you can just download these reports. But if you’re not used to looking at an elaborate data dashboard, it could be useful to pick out a handful of metrics that are important to you, and bring that over into your own custom database to make a case for your team members.

The qualitative data is all the information you gather through interviews, surveys and reviews. The easiest way to document your qualitative customer data is by using your preferred documentation software. I recommend basic documentation software without a bunch of bells and whistles.

However, if you are a larger and more established business with a large and intricate database of customer insights, you’re going to need something much more robust than a simple documentation tool.

Since this article is more targeted towards smaller businesses who are relatively new to this, I’ll recommend a free tool called Notion.

It’s simple, but gives you everything you need to create neat and organized, and nothing more than that. Notion also makes it easy to share files and collaborate with your team members without having to store anything on your local machine.

What I’m mostly excited about with Notion is that they have a superb database feature that makes small scale data documentation a breeze.

Judgements and biases aside, use whatever you’re most comfortable with. As long as you can get a good overview of your data.

Listing the types of data

Listing the different kinds of customer insights you’re seeking to collect will allow you to create a clear data structure, and eventually group it into segments.

Basic customer data

This might seem pointless at first, but once you start growing this list of basic information about your customers, you end up with a strong foundation for customer segmentation. It reveals common customer traits and enables you to put together customer profiles. These will prove helpful later on.

• The language they use — note down specific keywords.
• Age group.
• Gender.
• Race and ethnicity.
• Job details.
• Industry.
• Income or annual revenue.
• Country and city.

Engagement data

If you want to measure your performance, you have to play the numbers game. How do your customers interact with your brand on your website, social media, paid advertisements and email? These data points allow you to analyze how you’re performing and set new targets. Without this, you won’t know what to prioritize and aim for.

• Website analytics, such as most popular pages, referral traffic, user flow, retention-, bounce- and conversion-rates.
• Ebook downloads or webinar sign-ups.
• Social shares, likes and replies.
• Email engagement rates like open rates, click-through rates and bounce rates.
• Paid advertisement rates like the number of impressions, click-through rates, conversion rates or cost-per-click.

Qualitative data

In my humble opinion, this kind of data is your most valuable asset. Actually listening to what your customers have to say, and using that to drive better product decisions and increase your customer’s lifetime value.

• The platforms and time of day they are most active on.
• The type of content and topics they consume the most.
• Their current challenges in relation to the solutions you offer.
• How they currently get around these challenges — this shows you who your direct and indirect competitors are.
• Product or service gaps captured through negative reviews, interview or survey responses.
• Customer service satisfaction.
• What customers currently love about your product or service.

This is quite an exhaustive list. If this is all new to you, no need to worry. You don’t have to gather everything at once. These different groups of data serve each their own purpose.

• Detailed customer profiles your business is serving.
• What marketing channels are performing well, and which ones are not.
• What challenges your customers face and how they are getting around it.

With this, you can craft valuable marketing documents, such as buyer profiles, buyer journeys, empathy maps and brand voice guides, that give you the power to transform your products or services and change the way you communicate with customers.

Analyzing the data

The goal of data collection is to create an easy-to-understand document that informs you and your team members of what marketing insights need to be prioritized and moved to the backlog.

Analyzing quantitative data

Extrapolating insights from qualitative data is fairly straightforward when you use the following data mining techniques.

When analyzing basic customer data, use the classification technique to categorize data into categories (classes). For example, you could group specific customers with the same income range to craft product offers they can afford but remain profitable for your business.

Clustering is similar to classification, but differs in the way in which data is classified into uniform categories based on a characteristic / feature. The intention here is to identify categories, rather than features inside of categories.

Outlier detection is well suited for the analysis of web or social media engagement analytics. This simply means you analyze certain patterns and identify anomalies between these patterns. One example is your Instagram conversion rates drop after posting new content. This helps identify what your customers find useful, interesting, irrelevant or triggering.

Sometimes you’re trying to identify relationships between certain data points to understand how a particular characteristic impacts another. This is called Regression Analysis.

Analyzing qualitative data

When we’re not dealing with numbers, but actual words coming from customer interviews or surveys, you approach this differently.

Through content analysis, you’re looking to keep track of the most relevant keywords and trends related to customer sentiment. How are your customers describing the problems they’re experiencing? Create a list of these trends and visualize them in word clouds. This type of data can then be used in your customer journey map.

Knowing how and why your customers feel the way they do, helps identify where in your customer’s journey opportunities for improvements exist.

Conclusion

We know now that gathering and analyzing these insights is more important than ever in an environment where your business is engaged with across multiple marketing channels.

Having an actionable strategy document that you and your team can work with to create new content is key to attracting new customers, increasing customer retention, and turning existing customers into brand advocates.

In the final part of this series, we’ll look at how to create content with these insights.