It's everywhere! What are we talking about? The new customer offer. Stuff like this:
New customer offers are important. Acquisition is hard but is the main driver of growth for retailers.
From discounts and coupons to free shipping and bonus rewards, incentives for first-time buyers can differentiate a brand in a crowded market.
Their prevalence is a testament to their effectiveness in fostering customer acquisition and driving initial sales. They tap into the consumer psychology of getting a 'good deal,' which can significantly enhance perceived value.
So, what's the problem? Retailers have been taking a "set and forget" approach. They set up this offer and leave it when it's the perfect place to learn more about visitors and optimize performance. Could a different offer get better conversions? Could a different offer achieve the same results but give less value away?
This is where the art and science of offer testing come into play.
Use a structured testing framework
Here's an example:
- Establish some goals
Set some clear, realistic, measurable objectives. - Generate ideas for what to test
Brainstorm a variety of offers to include in your tests. Ensure you have clear parameters about what you can offer. - Identify your KPIs
There are the obvious things like click and conversion rates. But can you also track segments' ongoing performance that converted from each offer? Are they more or less likely to repeat purchase? What is their LTV? What's the AOV for each different segment? Was the value of the incremental conversions higher than the cost of the offer? - Create your variants
Put together copy and images to support your offers. Remember, if you also change the copy on variants, you must allow that in your test results. - Calculate required data and segments
Determine how much data you'll need for the results to be statistically significant. This will help you estimate the test length based on your website's traffic. If your volumes are low, you will need to test for longer. You should also have a control test with the original offer to compare your new offer performance. - Run the test
Launch your test and begin collecting the data. - Track your metrics
Have regular review periods, but also check in regularly to see if there are any anomalies or errors in the segments. - Analyze the results
When you have enough data, do the full analysis and see what insights you can find. Those insights will be valuable outside of your new customer offer, too. - Repeat and optimize tests
You should repeat tests to validate the findings as conditions change over time. You can also tweak and refine your active tests and add new ideas to improve results.
What could you test in your new customer offers?
The key to successful offer testing in eCommerce lies in multivariate testing. This means testing multiple incentives to see which performs best. Some example incentives could include:
- Percentage discounts (5% off, 10% off, 15% off)
- Monetary discounts (£5 off, $10 off, £15 off)
- Odd amounts (£7 off, 13% off, $11.11 off)
- Free gifts
- Free services
- Free delivery
By testing these different incentives, you can identify the most attractive offers to your potential customers.
Identifying the right metrics for promotion testing
You want as full an understanding of the performance of your tests as possible. The key metrics could be:
- Impressions
The number of people that saw the offer. - Clicks and click rate
The number and percentage of people who clicked on the offer were therefore interested. - Conversions and conversion rate
The number and percentage of people who used the offer when they purchased. - AOV
The average value of the transactions from people who used the offer. - Revenue vs. cost
What was the revenue for all the transactions that converted with an offer, and what was that revenue when you remove the total cost of the offer? - LTV
For each segment that used a different offer, what was the value of every transaction they made?
And you want to compare each of these for each different offer tested. Based on the volumes, you can calculate how much confidence you should have in the results.
Offer testing case study: US Polo Assn
A prime example of offer testing in action is our experience with US Polo Assn. We tested whether a dollar-off ($ off) or percentage-off (% off) promotion converted better. Interestingly, both offers resulted in similar conversion rates.
However, the dollar-off promotion gave away less value, making it the more cost-effective choice. This case study highlights the importance of looking beyond surface-level metrics to understand the true impact of your offers.
Analyzing test results
When analyzing the results of your tests, it's essential to look beyond the obvious. While larger discounts might drive higher conversion rates, they may not necessarily be the most profitable in the long run. Instead, focus on finding the sweet spot where the offer is enticing enough to convert and doesn't significantly erode your margins.
In the case of US Polo Assn, although the $ off and % off offers performed similarly in terms of conversion rates, the $ off offer was more cost-effective. This insight allowed them to choose an offer that attracted customers without giving away too much value.
