If you are using digital channels to reach out to your customers and you are not A/B testing across your campaigns, you are missing out on the best way to improve your sales.
What is A/B testing?
A/B testing is the process of comparing two variations of a campaign element, usually by testing users' response to variant A vs variant B, and concluding which of the two variants is more effective.
This article digs into why you should be A/B testing and how to run a successful test.
The Top reasons you should be A/B Testing
1) Clear insights into your customers
A/B testing highlights the elements of your campaign that are having the biggest effect on your customers. Or it can help you find new audiences that will boost your market size.
2) Stay ahead of your competitors
Constantly testing new campaign ideas can help you discover new markets or messages that your competitors, who are stuck with the same old methods, do not know about. The world is moving faster than ever before and if you stay still, you die.
3) Scale your sales quickly
It can take years for you to stumble across the perfect market or messaging. No matter how much effort you put into crafting the perfect message, there is a high chance it will fall flat on its face. Smart A/B testing takes the guesswork out of messaging as the data you gather will tell you if it works or not.
4) Better forecast your growth
With A/B testing you know exactly what element of your campaigns worked. So, you know performance wasn’t a fluke and you can ensure that element is in all your future campaigns. This purposeful methodology means you start to see more predictable results as you understand what elements really move the needle.
Now you see the value of A/B testing, we are going to lay out how to decide what to test and how to structure the test to get the most out of it.
How to Run an A/B Test
How to decide what to test
In a perfect world where time isn’t an issue and we don’t have to do three things at once, we would craft the perfect A/B test. But unfortunately that isn’t the world we live in. As with all things in life, we must understand the cost/benefit relationship. The core aim of A/B testing is to learn so you can scale those learnings. So, when you are deciding on what to test, ask yourself what would be the most impactful thing to know more about.
If you think expanding into new markets is critical for your business. You need to know which markets to move into. If you know your market and your aim is to grow market share then you need to know how to better engage potential customers in that market.
Another consideration is how much impact an element will have on the overall performance of the campaign. Will changing the first word of a message from “hello” to “hi” be a deal breaker for a potential buyer? Maybe, but common sense would say this will not be the case. How you motivate the buyer to act using offer messaging is more likely to have a big impact. For example you test two messages; one tries to motivate the buyer using urgency, e.g. “offer ends today”, and one using scarcity, e.g. “there are only three memberships at this price”. This is much more likely to have an impact on the buyer’s behavior, getting insight into what messaging gets users to act on an offer is very useful information.
Creating a strong(ish) A/B test
With A/B testing you run two campaigns during the same time period and change one element in the experimental version. Then you compare the results and see whether the control or the experimental version was more effective at driving your desired result (e.g. higher message response rate).
1) Identify the metric you want to effect
This will differ test to test, depending on your role or the needs of your business at the time. Take a look at your key targets; that may be sales, revenue, leads, etc and the KPIs you use to measure how well you are doing against your target.
This is a good place to start. What would really impact your business if you could improve that metric.
2) Create a hypothesis on what will affect the metric
Now, it is time to use your intuition. Come up with a best guess on what you could change about your campaigns that will affect your target metric. Write this in a cause and effect statement.
Here is an example; “I think adding urgency to our offer messaging will motivate buyers to act more quickly and increase sales for this quarter.”
3) Identify a campaign element to test
This should naturally come out of your hypothesis statement. Here it is important to remember that a good A/B test only changes one element. In our example we'll be testing offer messaging. So, we need to identify the campaign elements that already include offer messaging and only make changes to this element. Nothing else.
4) Create an experimental version of the campaign element
We are going to test the new offer messaging against our existing offer messaging (controlled version). Now it is time to craft the actual message or identify a new market if this is what you have identified as the thing that could have the biggest impact on your target metrics.
5) Set a duration and implement
Speed is important here, one of the biggest benefits of A/B testing is the speed at which you gain valuable insights and results. So, we need a time frame that will allow our test to gather enough results to allow us to make a decision on which version won or lost without having it run for months on end. A rough guide would be to have 100 results across both versions, run your campaign until you see this or you see a huge difference between the versions.
Like what you read? Check this out next: The Do's & Don'ts of Manually Prospecting on LinkedIn.
CoPilot AI is on a 10-year mission to fundamentally change the way businesses connect with customers. Three billion people now live on social media, and yet, businesses and their sales teams are still operating on outdated tools
like email and CRMs.
CoPilot AI automatically targets qualified people on social media, initiates one-to-one conversations and surfaces timely sales opportunities without requiring any cold calling, events, or expensive advertising. Talk to us to learn more.
Sign up for our newsletter
At CoPilot AI, we are more than just LinkedIn automation software. We partner with you to maximize authentic human connections for revenue growth. B2B sales and marketing is changing, finding new customers is getting harder. Make your LinkedIn automation as authentic as you are and start seeing results, tomorrow.
Speak to our industry-leading experts and learn how CoPilot AI delivers 1:1 relationships at scale