4 Ways to Make Your Analytics Strategy More Reliable

You've got the perfect analytics strategy, but it just doesn't seem to work. When you're dealing with a lot of data and a lot of moving parts, it's easy for things to get lost in translation or muddled by ambiguous messaging. So how do we ensure that our strategies are reliable?

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Get the right data.

Once you have the right data, you will want to ensure that it's reliable. Data quality is critical to any analytics strategy, as you can't trust your metrics if they're not accurate or relevant.

While some data quality issues may seem obvious (e.g., missing information), it's important to remember that even seemingly minor errors can impact your analysis. For example, incomplete values can lead to skewed results in statistical analyses; incorrect codes for products and customers might throw off segmentation studies, and useless demographic information about users might result in wasted efforts when trying to improve conversions on your website or app.

If you're having trouble with data quality, the first step is to evaluate your data sources. You may consider purchasing additional information from third-party providers or conducting more in-depth interviews with customers and vendors. If possible, try to collect data directly from end users (e.g., running A/B tests) instead of relying on proxies like surveys and questionnaires.

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Know what data to ignore.

Not all data is created equal. Some data may be more reliable than others, while some may be less accurate. Some data can be more actionable than others, and some of it will be more relevant to your company's needs. Some information might come in at a faster pace than other information—which could make it vital for you to have a plan for keeping track of all the new information coming in and making sure that it's processed quickly enough so that you'll have time to react before the next wave hits.

The point here is: just because something looks like an analytics problem doesn't mean it actually is one! Be sure to take a step back from your work and analyze whether there's something wrong with how well-oiled your analytical wheels are turning—or if maybe there just isn't anything wrong with them!

Analytics is a powerful tool, and it's easy to get excited about it. But it can be equally easy to forget those analytics are only as good as the data they're processing—and that means that any analysis you run on bad data will be bad itself! Don't let your enthusiasm for new ways of looking at your business blind you from what's really happening with your company. When in doubt, always remember: slow down and think before leaping into any new project.

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Run experiments for critical changes.

If you have a critical question about your business, you can run an experiment to test the answer. You could use a control group and a treatment group to know whether a new feature has increased engagement with your app or platform. The control group would not see anything new; the treatment group would see this new feature. You could select some users out of all of your users (random sample).

You'd then measure how much more engagement there was in each group before introducing the change and after introducing it. If it's statistically significant—meaning that there's only a tiny chance that this difference could have happened by chance—you should feel confident saying that your change affected engagement.

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Have your analytics audited annually.

You know those times when you walk into a restaurant and notice that the tables are a bit sticky or there's a faint smell of smoke in the air? This isn't necessarily because the owners or employees aren't conscientious about cleanliness; it's just that they're so busy running around trying to keep everything up to code that they don't have time for regular inspections.

It's the same with your analytics strategy. If you're not getting an external audit every year, there could be gaps in its implementation and basic maintenance that could catch on fire before they spread out of control.

An external audit can help identify gaps in your strategy, areas where improvements can be made, opportunities for growth and much more—all while helping to keep your business safe from fires (or losing money).

Making your analytics strategy more reliable will ensure you don't waste time and money on the wrong initiatives.

Analytics is a tool, not a solution. A good analytics strategy will help you make better decisions, reduce waste, improve your business and increase profits. The more reliable your strategy is, the more likely it is to be successful.

Conclusion

We hope these tips will help you make your analytics strategy more reliable. If you're looking for more guidance, we recommend checking out our blog on how to create a data-driven culture and some of our other related articles.

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