How To Overcome Common Mistakes In The World Of Big Data
By Andy Nauman
Big data is no longer only for big companies. Many small and midsize businesses have jumped on the bandwagon and are investing in learning more about their customers.
Yet, many employees charged with gathering and analyzing this data are not necessarily qualified to do so and may lack the skill set needed for a profitable return on this investment. Often, these same companies lack the budget to bring a full-time data scientist onto their team.
An interim solution is to know the common mistakes others have made and avoid potential pitfalls that can hurt the business. This article will review four key areas when approaching big data: collection, analysis, action and results.
I’ve found that some of the costliest mistakes can happen before the project really begins, when the rules behind data collection and usage are misunderstood. As our world becomes more data-driven, privacy and security have come to the forefront, with many countries now passing consumer protection regulations.
Companies based in the European Union (or serving customers there) must follow stricter guidelines than their U.S. counterparts after the passage of the General Data Protection Regulation (GDPR) and may need to update their websites and data collection methods immediately to avoid fines and penalties. Whether using passive or active data collection methods, staying up to date on state and national regulations (which can be contradicting) is critical.
Data accuracy is also important if any type of analysis or action is to take place. As the adage goes, “garbage in, garbage out.” This includes scrubbing data for duplicates, standardizing formatting and appending data.
There are agencies that specialize in ensuring the customer information you have is accurate and includes all available public information. This may involve a simple cross reference of a name, email address and phone number to obtain the physical address or social media handles. Before choosing an agency, research its credibility, and speak with some of its customers; this can help ensure your data remains secure when sharing with a third party. Also, review the agency’s contract to guarantee your data is not shared with any of its other clients or external entities.
Too often, a business will invest in collecting as much data as possible about its customers and then either do nothing with it, or worse, blindly blast sales messages to everyone as if they were exactly the same. Thorough data analysis lets businesses find customer patterns and groups and use them to focus marketing campaigns and obtain results.
For example, if website usage data is combined with demographics, a company may find that males between 18 and 25 have a higher chance of buying product X after 11 p.m. This insight can be used for scheduled email blasts, website pop-ups, flash sales and more. Skipping the analysis step leads to missed sales and can even cost a business customers.
When developing an action plan to target each identified group, combine the data analysis with relevant third-party market research and online trends. The world of social media moves quickly, and tying a promotion to a trending topic or theme can lead to better results. I’ve found that knowing which platforms or communication methods appeal to each group can increase calls, click-through rates and conversions.
If email or text marketing is included in the plan, be sure to read up on the CAN-SPAM Act and the Telephone Consumer Protection Act (TCPA), which outline regulations regarding text messages. What may seem like a small violation can lead to significant fines.
Companies often make the mistake of thinking they’re automatically allowed to email and text their customers simply because a purchase was previously made; however, this is not the case. Most email marketing services require a double opt-in by the customer, and permission should always be granted before sending texts.
Speaking of email marketing, be certain that everyone in the company knows to not send mass emails via the company server and IP address. This can lead to the business domain being blacklisted by the email service providers, which prevents future emails from reaching those recipients until the domain (or IP) is removed from the blacklist.
In my opinion, an email marketing service should always be used to contact mass groups of customers, as these services help guarantee compliance is maintained. These services can also automatically remove people who have unsubscribed and assist in handling spam complaints.
Getting (And Measuring) Results
Begin with the end in mind by having clear and measurable goals in place at the onset. These goals should match the action plan. For example, are you looking to increase traffic to a specific landing page? Create and test appropriate Google Analytics goals a few days prior to launching the campaign. Also, be certain to create a separate view within Google Analytics based on the goal’s key performance indicators (KPIs). This can include removing internal traffic, establishing exclusion filters (e.g., for bot traffic) and setting the appropriate currency.
The same goes for pay-per-click campaigns via Google Ads (formerly AdWords) or Microsoft Advertising (formerly Bing Ads). Both platforms provide conversion-tracking codes for installation on websites. Ensure that these codes are working properly and attuned to the goals, and that they provide accurate measurement data.
Once the goals and tracking are established, regularly review the results data, and adjust the marketing plan accordingly. Often, regular small adjustments based on new data analysis can lead to incredible increases. When reviewing and comparing the goal results, look for patterns developing at 30-, 60-, and 90-day increments. Sometimes a campaign takes several weeks to ramp up, and it can be a mistake to stop the campaign before it has had sufficient time to work.
Following the four steps outlined here can go a long way toward getting a handle on data analytics and hitting company goals. Once new revenues begin coming into the company, investing in an in-house data scientist can help take the business to the next level.
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