Data-driven companies are nine times more likely to achieve above-average profitability. Surprised? No, us neither! From getting feedback from customers to capturing user data, more and more businesses are using data to deliver actionable insights on customer behavior and trends. 

Using analytics to understand your customers’ needs and desires is not only key to informed decision-making — it’s becoming an essential part of a business’s success in today’s global marketplace. 

But getting your head around analytics can seem daunting for some, especially if you’re inexperienced or don’t have specialized insights staff. Some companies shy away from deep analysis of verbatim feedback because in the past it has been cumbersome and complicated. It can be tempting to look for an agency or outsourced team to do the work for you. These services will analyze your customer data based on what they believe is important, usually using a generic, industry based framework that is not at all specific to your business or customers. While farming out the work might be easier in the short term, outsourcing your data analysis will never be the best long term solution. 

Here’s why you can – and should – analyze your data in-house. 

1. You have the control over the framework 

Why is it important to use a framework when looking at your data? The short answer is that a framework allows you to move through data analysis in a way that’s systematic. Frameworks give you a methodology to follow as you work with your team to find a solution to a problem.

How do you build a framework? By identifying what you want to get out of your data, and creating a repeatable process to uncover it. As the market changes and a company launches new products, strategies or interventions, the framework needs to evolve to give the most accurate picture. But only you know what you want from your data. 

Chances are that if you’re not using a framework, different people will use a different approach to solve the same problem. That can be a problem especially with external providers. Often, the frameworks they use resemble a black box: it’s impossible to determine how or why they present you with a particular quantification from the data they gathered. This inability to “peek inside” the process can make it difficult to trust the data, or understand the context it was derived in. 

Another issue with letting external providers manipulate your data is that giving the control away can mean that when you need changes to the framework it could be slow, expensive, or simply impossible. That’s a risk you can mitigate by keeping the data analysis in-house. 

2. Your customers are the key to a better business strategy

An effective business strategy and tactics relies on high quality, relevant intelligence in order to work.

A lot of customers give feedback to tell you what they think – in all sorts of ways. Perhaps they’ll write a review or answer a survey. To go through all those comments and get answers, some companies still use a manual process to categorize and analyze verbatim feedback – a process that gets very tedious (and not to mention inaccurate!) after the 5,000th survey! 

However, advances in machine learning now allow every company to ‘hear’ every bit of their customer’s feedback no matter where they are saying it. Text-based responses like reviews or customer complaints can now be aggregated and quickly analyzed to identify common trends. 

If you analyze this data in-house, this resource is always “on-tap”. You can mine and leverage this asset at will to answer questions, validate assumptions and inform your overarching strategy. 

If you have to go to an external provider, your ability to effectively utilize this resource is impeded. Going through a third party will be slow, inflexible and ultimately, limit the ways you use your own data. 

For example, you can use all this data to measure the impact of a recent product update on your retention rate and your overall customer loyalty trend. Those deep-down insights about how customers truly feel about your business are much easier to gather from within your company, instead of having to explain to an outsider what you’re looking for. 

Developing the capability to analyze your data in-house will give your team access to market-leading customer understanding, whenever they need it. 

3. You know your business’ context best

The choice of how to structure your customer feedback data into themes and sub-themes is subjective. When making this judgment, you need to consider how the business is structured and how it will best receive the data. 

For example, if you are a product company, with different teams working on different parts of the product (or features), perhaps you want to structure your themes around those areas of the product. The things you spot in an analysis should confirm what you already know and build confidence in what you are seeing, as well as bubbling new themes and topics to the surface. 

Businesses will have very contextual information: words can mean very different things depending on the context. For example, consider a bank that offers multiple account types; Saver, High-Interest, and Daily Spender. Customer might be sending in feedback about high interest - do they mean the bank’s interest rate on a credit card, or the rate of interest in a current account. Only someone very familiar with the business context in which they operate will understand what exactly it’s meant. 

The most precise and insightful findings come when you bring together the domain expertise held inside the business with sophisticated machine learning and AI techniques and data analysis.

When data is analyzed outside of the business, it creates a disjoint between the core business expertise and what customers are trying to communicate. While agencies working for various companies might know some of the context relating to the industry, they won’t be able to understand your business context with the same level of precision as you.

Creating that synergy between data analysis and domain expertise confers both operational agility as well as a tighter learning loop. This way, your strategy will be continually tested and refined by data insights.

Conclusion

Using analytics to understand your customer's needs is key to developing a sound business strategy. But numbers and single data points alone aren’t enough – in order to make the right decisions, you need deep, actionable insight into your customers feedback and behavior. 

While it can seem tempting to outsource the data collection to obtain them, actionable insights only come through asking the right questions. And for that to happen, you need to both know the business context and control over the framework. 

This doesn’t mean turning to teams of analysts developing complex formulas with no clear return on investment. Using the knowledge you already have, and modern software to collect and analyze, you can extract important insights that will enable you understand the 'why' behind your customer feedback. Use that valuable knowledge to your advantage to get a deep understanding of your customers so you can make sound decisions for your business.

Analyst Insights Text Analytics