As big data analytics becomes more common practice for marketers today, new challenges and obstacles arise. To correctly make sense of all your data, it’s essential for you to have clean data. Dirty data not only causes a storage headache, it can also lead to inaccurate results. By not following best practices to their clean data, organizations may find themselves putting the carriage before the horse when analyzing historical and large data sets for marketing and sales.
Let’s examine the most common challenges of keeping data clean.
1 – Formatting
There are many solution providers in the marketplace who focus on cleaning your data. Before you go and sign a contract, first outline the data points and fields you’d like to analyze. Many times than not, dirty data is caused by basic formatting issues. As the formats differ and run through a series of imports and exports, the data issues exponentially grow.
2 – Duplicate and incomplete
Duplicate data can occur from having multiple databases and platforms that repeat the same metrics. While building and consolidating your data warehouse, always remove duplicates and fill in incomplete fields. As you filter through the databases, find which systems hold the best accurate data for usage. Common examples of data consolidation include using your ecommerce shopping cart for customer revenue and the email service provider for customer email metrics such as opens or clicks.
3 – Central location and repository
Data can come from any online or offline channel (phone, email, or shopping cart). Find a central location to store the data and standardize storage rules. Whether you’re a small or large business, utilize a CRM (customer relationship management) software or data warehouse to centralize.
4 – Inaccurate results
Take every result with a grain of salt. In eCommerce, we’ve seen how the color or size of a button by a few pixels can affect conversion rates drastically. We can include simple missteps such as focusing too narrowly on the data or insights that tell us what we want to hear. Use actual core metrics as a baseline to your business in measuring growth while including industry standard metrics for high level analysis. Data can be as accurate as you make it to be.
Now that we are aware of the challenges that arise in keeping data clean, here are some methods to reduce data tunnel vision.
1. Strategize and plan
Whether you are small/medium size business or large enterprise company, planning your data analysis strategy is the key for success. First, find what data is actually available. Second, determine what result the data provides (is it actionable?). Third, test your data and results for validity. Finally, repeat and analyze again (the cycle never stops!).
Warehousing and storing huge amounts of data can sometimes be useful. Just look at companies like Amazon and Netflix. They have years of customer data to recommend the next web store or launch a new film series based on behaviors of the consumer. The length of data is not useful in all cases though. A year’s data may not be as valuable when comparing metrics such as email open rate versus time spent browsing on a particular device. With how fast technology moves, other variables can affect the data’s effectiveness. Create guidelines for your data and always test the results in a sample or live environment.
3. Limit the data variables
Simply put, don’t try to do too much. Data is a great asset and can be applied in many different ways. We can use data to analyze customers behavior online and offline. Find a common actionable goal for data usage and eliminate as many variables as possible.
Retention Science aggregates and cleans your behavioral, transactional, and demographic data. Our marketing platform uses data science to profile and predict customer behavior and deliver automated targeted marketing campaigns that engage and convert.
To learn more about how you can transform your data into revenue and customer retention, please schedule a chat with us today.