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You’ve spent countless hours crafting the perfect marketing strategy, using every trick in the book to attract and retain customers. You’ve implemented advanced algorithms and leveraged cutting-edge technologies to better understand your audience. But despite all your efforts, you just aren’t seeing the numbers go in the right direction.
If so, you might be a victim of the five common pitfalls in customer analytics. Customer analytics is a powerful tool in the marketing arsenal, assuming you know how to use it properly. It allows you to better understand customers on a behavioral level not merely on a demographic level. However, it’s not enough to simply gather data; there has to be a careful method to your madness in order to avoid making common mistakes that can lead to lopsided insights and poor marketing decisions.
And that’s what we’ll delve into right now: some of the most prevalent errors that can plague your marketing strategy when it comes to analytics. But first…
Your brand needs a strong understanding of your customers to trounce your competition. If you don’t know ‘em, you can’t engage ‘em. This is where customer analytics comes in.
Customer analytics is the process of collecting and analyzing data about your customers to gain insights into their behavior, preferences, and needs. This data can be used to create targeted marketing campaigns, improve products and services, and best of all, enhance the customer experience so it’s personalized, tailor-made for every customer.
Customer analytics is crucial for companies because it allows you to make data-driven decisions. Without customer analytics, you may be missing out on valuable insights that could help you grow your audience. In short, customer analytics is an essential tool in any tech stack and is the secret ingredient to dominating the market.
However, simply adding a customer analytics tool to your tech stack isn’t a guarantee of success. When it comes to analytics, there are a few common mistakes that many brands continue to make. Avoid these at all costs!
Every business collects data. No surprise there. But many of them lack a clear understanding of exactly what data they need to collect. You’ve got to collect data that relates to your own business objectives; otherwise, the data you gather will not be useful to you.
If the conversion rates for your streaming app rely on users adding a show to their favorites list, then you need behavioral analytics on how many and how often your users favorite a show. If the most critical event in your casino gaming app is how many times a player pulls the arm to the slot machine, then you’ll need data into how often your player has pulled that arm for the last three months at least! Know what data you need.
Data quality and completeness can affect the validity and reliability of your customer analytics results. Use data validation tools to find inconsistencies in your data. Then standardize your data. This involves identifying and correcting errors, removing duplicates, even filling in missing values, and fixing spelling or formatting errors. Make sure your data is consistently structured, so it’s easier to analyze, no matter where it comes from. Part of this process entails defining data quality standards and training your team to stick to them. Outline your processes for cleaning data and integrating data from sources.
The other pitfall to how easily we collect data these days is collecting too much of it. Like real-world, non-digital hoarding that Marie Kondo would abhor, collecting more data than necessary can become overwhelming and time-consuming to analyze. Have pity on your poor data science team (or the intern you hired to do the spreadsheets). Just focus on the data relevant to your business goals and forget the unnecessary information.
Customer analytics can raise ethical and legal concerns in several ways. Don’t forget that you’re collecting and analyzing a lot of personal and behavioral data about mobile app users — not just names and addresses, but also purchase histories and in-app clicks. Privacy and the protection of that personal data is a critical component of your work as a mobile marketer. Ensure you have their consent, and be transparent about how you use the data that’s being collected and used.
Then there’s bias and discrimination. Customer analytics can be influenced by bias, such as when data is collected from a non-representative sample or when certain variables are given more weight than others. Analytics can also be used to discriminate against certain groups of customers, such as those based on their race, gender, or age. Be aware of these problems so your analyses don’t end up with unfair outcomes.
The whole point of customer analytics is to gain actionable insights that you can use to improve your business. If you don’t act on the insights you gain, you’re wasting your time.
By acting we mean taking the time to review the data to uncover trends and customer behaviors that you can tap into and create campaigns around. It means developing plans and executing on these campaigns then regularly reviewing and updating your strategy. It means measuring the success of your marketing efforts with the right metrics and KPIs.
Customer analytics will always be an essential aspect of any successful marketing strategy. Just remember that it’s easy enough to avoid these five common pitfalls. Remember to always be clear about your goals, use data from multiple sources, and continually evaluate and adjust your strategy as needed. With these tips in mind, you can utilize customer analytics to better understand your target audience and make data-driven decisions that ultimately lead to greater success for your brand and your organization.
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