Conversion rate optimisation is very important, but it is also a very imprecise science. The biggest challenge is that customer behaviour is constantly evolving. The practices that customers responded to five years ago may not be effective today.
Marketers must use the latest tools and strategies to maintain a decent ROI. Predictive analytics models are highly effective, but they aren’t foolproof by any means. If you are planning on using predictive analytics to boost your online conversion rate, you will want to avoid making the following mistakes.
1. Be Wary of Your Ability to Understand Changes in Social Psychology at a Macro-Level
Some experts attempt to use predictive analytics models to identify future fads or predict changes in customer behaviour. You need to be cautious with these models because they are notoriously unreliable.
Even the most insightful predictive analytics model cannot account for unplanned variables. New events or product offerings can change customer perception overnight.
This doesn’t mean that predictive analytics tools are useless for conversion rate optimisation. However, there is a large margin of error, so you should only focus on variables that are easiest to analyse.
2. Don’t Extrapolate Data to Unrelated Campaigns
Many different variables affect your campaigns. These variables can include:
- Marketing mediums or online traffic sources
- Regions that you are promoting your offers in
- The demographics that you are targeting
- Marketing angles that you are using
All of these factors have a profound impact on the performance of your campaigns. The results from one campaign are not transferable to another.
Let’s assume that you are promoting your offer to 30 to 45-year-old women in the United Kingdom. You simply can’t use the findings of your campaign in a predictive analytics model to promote offers to college-aged to men in the United States.
This may sound obvious at face value, but it is amazing how many marketers fail to segment their campaigns properly. You need to set your campaigns up as granularly as possible to get predictive analytics data to optimise your conversion rate.
3. Setting Unattainable Conversion Goals while Collecting Initial Data
Marketers are often too ambitious with their conversion goals. They attempt to send cold traffic to their landing pages and entice their visitors to make a purchase.
Even if you were using search engine marketing with highly targeted keywords, it could be difficult to convert users on the first visit. Since your conversion rate will be very low, it can take months and thousands of advertising dollars to collect a statistically significant sample size for your predictive analytics model.
A better alternative is to set conversion goals that are easier to obtain. Instead of trying to pitch sales to new visitors, try building a lead generation funnel and get them to opt-in to a mailing list.
4. Being Overly Reliant on Poll Data
For decades, marketing professors have emphasised the importance of marketing research. Unfortunately, testing data often shows the jolting disconnect between poll results and actual customer behaviour.
What accounts for this discrepancy? Marketing surveys are composed of very direct questions. In practice, customers usually make decisions based on subconscious factors they were never aware of.
This doesn’t mean that you shouldn’t do any market research at all. Customers may help you identify some important variables to test.
However, you should never use data from market surveys in your predictive analytics model. You should only use testing data because that shows how customers actually respond to your content.
5. Testing With an Inadequate Sample Size
It takes a lot of data to draw relevant conclusions in marketing. I have found that 80% of marketers failed to collect enough data.
Most marketing automation and split testing tools have features that help you determine how much data you need. Pay close attention to them, because you don’t want to make predictions based on an inadequate sample size.
7. Neglecting to Factor for the Cost of Execution
Before you can use make any major changes based on your predictive analytics data, you must make sure that those changes will provide a decent ROI. You can’t ignore the reality that most changes have a price tag attached to them. The e-commerce website cost to make these changes may be too high to justify unless your data shows that the increase in your conversion rate is substantial.
Predictive Analytics Isn’t Foolproof for Conversion Rate Optimization
Predictive analytics tools have redefined marketing over the past few years. Despite the many benefits they provide, marketers are just beginning to understand their limitations. Predictive analytics is particularly imprecise when it comes to conversion rate optimisation.
This doesn’t mean that you should avoid using predictive analytics at all. However, you should restrict your predictive analytics models to testing very specific elements of your conversion funnel. Thousands of different variables come into play with conversion rate optimisation, so you should be wary about drawing any hard conclusions about your overall campaign.