There’s a centuries old philosophical question that asks “if a tree falls in the woods and no one is around to hear it, does it make a sound?” We’re not here to discuss physics or quantum theories, but this does serve as an appropriate opening to discuss analytics in digital marketing. If digital marketers deploy ad campaigns without analytics, how will they know what worked and didn’t work? Modern, digital consumers leave a trail of digital breadcrumbs for marketers to follow and analyze along the path to purchase.
However, Forbes cites its own Insights Report, as well as work from ITSMA and Vision Edge Marketing, showing two startling statistics. First, only 22% of marketers have successful, data-driven initiatives delivering viable results. Second, 74% of marketers can’t even measure or report on the impact of their campaigns to the business’s bottom line.
As a digital marketer, if you’re going to make data-driven decisions you need analytics to deliver those data points. A/B testing will help you determine which channels are operating at peak efficiency.
30 Seconds for A/B Testing
Also known as split-testing, A/B testing is a method of experimentation involving your marketing campaigns. You develop two different types of campaigns or variations (Ad A, Ad B). Run those ads to see which one generates more interactions and conversions with your target consumers. The overarching goal is to determine which ad type/content language/marketing vertical is performing the best in your campaign. This alone gives you a strong starting point to make data-driven marketing decisions. Right away you have data to prove A is better than B, or vice versa. Unfortunately, only 44% of companies use any form of A/B testing software to test varying ad types or marketing channels.
Benefits of A/B Testing
A/B testing generates data that is used to make informed, data-driven decisions when proceeding with marketing campaigns. Here’s a list of just three benefits (there are more) to using A/B testing in digital marketing:
- Improve Content Engagement: Compare two versions of an ad, article, or landing page, you have the ability to see which one generates more engagement. Getting data on consumer preferences, enabling you to take A/B testing results and deliver a higher quality in the final version.
- Increased Conversion Rates: A/B testing offers two forms for consumers to view. The one that generates more conversions is the one you’ll eventually enhance further in other campaigns. Correct adjustments based upon initial A/B testing can help boost conversion rates up to 300%
- Bigger Conversion Values: Today A/B testing can be used on low-end products/services to refine lead pages or ad funnels. Tomorrow, that refined format can be A/B tested again for high-end products to increase conversions. This means even greater revenue increases.
A/B Testing is Great, and It’s Important For Phone Calls Too
If A/B testing is the engine in your digital marketing vehicle, analytics is the supercomputer in the dashboard. A/B testing will get you the data you need, but it’s up to you to implement it properly to improve marketing efforts. You can use those analytics to determine which of your marketing channels are working, and which ones are failing. This is arguably the primary benefit of data-driven marketing. Analytics improve your vision as a marketer and better understand the target consumers you’re hoping to reach. Analytics deliver the data you need to see across marketing channels and track consumers as they weave through desktop searches, mobile app interaction, and social media engagement.
A critical piece in the bridge between online and offline is phone calls. Analytics track a phone call back to the specific ad, campaign, or social media interaction that initiated the phone call. When those calls generate revenue for the business, you’ll know which campaigns are delivering the best ROI for your marketing spend. Retreaver understands the importance of A/B testing and the value of analytics. We make tracking and attribution of calls easier, so you can make more data-driven decisions.
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