Thanks to new technologies, brands have access to an extremely rich store of behavioral analytics data. They know how people browse, search, shop, and interact online--as well as when and how often. But most organizations face challenges when it comes to turning that data into actionable insights.

Using behavioral analytics data to shape future performance starts with the right foundation - how you approach your data and integrate it into your day-to-day processes matters just as much as the fidelity of the data itself. Here are some of the most common ways your analyses can go wrong, and how to avoid these mistakes.

1. Not having clear goals

The first step in any analytical research process is defining your goals. What questions are you trying to answer? What (specifically) do you want to learn about your customers? And what overall business objectives do you want to achieve? 

Map out the behaviors you’re curious about and what you’re trying to achieve. For example, you may be trying to:

• Encourage repeat purchases
• Reduce onboarding costs
• Lower time to resolution for customer experience issues
• Find and eliminate friction points along the customer journey
• Increase conversions in your sales funnel

From there, you can establish which metrics, or key performance indicators (KPIs) to track in pursuit of those goals. 

In the past, the research process was expensive, time-consuming, and rigid in its definition, making it hard to adjust or change the scope along the way. With advanced analytics tools like Scuba, however, you can create new queries on the fly, allowing you to answer new and evolving questions as the data unfold, and get answers in seconds. 

2. Using too small of a sample size

When it comes to getting statistically accurate (and therefore useful) results from your research, sample size matters. Yet determining the right sample size is a common challenge. In traditional market research, it’s impossible to get information from absolutely everyone, so you choose a random sample of individuals to represent the population as a whole. 

If your sample is too small, you may not be able to detect small disparities in metrics like churn rates, customer sentiment, or conversion rates. And you may inadvertently include a disproportionate number of anomalies and outliers, thus skewing your results and wasting valuable time. 

The general rule of thumb is to have no fewer than 100 people within your dataset for statistically accurate results (so long as your population doesn’t exceed 1000). So, if you have a population of 6000, 10% would be 600. 

3. Not visualizing data

If you’re familiar with Anscombe’s Quartet, then you know charts, graphs, and maps are more than just pretty pictures--they’re a path to deeper interpretation and discovery. Developed in 1973, F.J. Anscombe used four seemingly identical datasets to demonstrate the value of visualization. 

Each dataset produced the same summary statistics for mean, standard deviation, and correlation, which could lead one to believe they were quite similar. However, after using plot graphs to show the data in visual form, the datasets proved to be markedly different.

The human brain processes visual data 60,000 times faster than text, which is why trends, outliers, and patterns are much easier to pick out in a graph or chart than in a spreadsheet full of data. Scuba provides interactive and flexible visualizations for easy analysis of new behaviors and segments. You can switch between types of visualizations (bar, line, pie, Sankey diagrams, etc.) in a single click to elicit new discoveries.

4. Improperly formatted or “dirty” data

Answers to critical research questions are only as good as your data. If your data is improperly formatted (i.e. missing fields or improperly named columns) it can lead to lots of extra work or worse--inaccurate insight. One of the biggest challenges in analyzing massive amounts of behavioral data is the amount of effort that goes into formatting or “cleaning” the data to prepare it for analysis. 

Scuba takes much of this work off your plate. Our customer intelligence solution can work with semi-structured data, which makes it super easy to manipulate or edit data and significantly reduces data-prepping efforts. All your data needs is a timestamp, and a user ID, and you can get all the context and granularity you need to uncover what you’re looking for.

5. Incomplete or siloed data

Data collections within organizations tend to be siloed by internal departments, resulting in inaccurate or incomplete data (and preventing you from gaining the deep, actionable insights you need). The use of cloud-based data warehouses or data lakes can help to break down these silos--but only with the right analytics tool. In addition, scripting or ETL tools can help, but require lots of resources, plus knowledge of SQL, Python, or other programming languages. 

If your data analysis is limited to only one channel, you’re not getting the full picture. While other analytics tools on the market are often siloed in their capacity to cross-reference data, Scuba combines data from any and all sources, via any event stream (as long as it’s time-stamped). This means teams across your entire organization, including customer experience, sales, product management, and IT, can all use and benefit from real-time insights about user behaviors.

6. Not using data to make decisions

You’ve collected the data--now what? It’s time to find some takeaways from your analyzed data and put them to the test. Behavioral analysis should be an ongoing, iterative process that looks something like this: 

• Define your questions & metrics
• Collect data
• Interpret the results
• Test your theory or theories
• Measure the impact
• Repeat

All too often, companies aim to be data-informed, yet continue to rely on intuition or biases to make business decisions. According to a recent survey on business intelligence, 58% of respondents said their companies use gut feeling over data for at least half of their day-to-day business decisions.

Since Scuba is continually importing new data via text files as they are created, you’re always getting the most up-to-date answers to your questions. This means you can base decisions on facts, not guesses.

7. Not all tools are created equally

Your existing BI or analytics tools may not be cutting it. They may be strong in one area and weak in others--for example, you may be able to visualize your data any way you want to, but getting there is slow, or pivoting to new views is inflexible. Tools that require SQL or intensive processing in order to achieve deep analysis may result in slowed answers, especially if your data science team is stretched thin or nonexistent. 

Scuba combines the storage, analytics, and visualization layers in a single solution. And with streamlined data preparation, management, processing, and IT cost, you and your team can focus on moving the business forward.