What are the most common mistakes to avoid when testing and experimenting in marketing?

Once, our marketing team tested a new feature on a client's website. It was anticipated that it would enhance user engagement. With no clear hypothesis, we decided to roll it and monitor user behaviour. A month passed and we saw changes in metrics, from time spent on the site to CTR. With no specific hypothesis, we couldn't determine the feature's actual impact. Did users find the feature engaging or confusing and spent more time on website? Is the change in CTR directly relate to this feature or not? Hypothesis provides direction and allows more precise understanding of the results, so efforts are not wasted.

In 2023 and beyond, it's important to avoid common mistakes when testing and experimenting in marketing. Common mistakes to avoid: unclear goals, inadequate sample size for testing, neglected target audience, failure to analyze data, failure to adapt strategies based on results, and not keeping up with industry trends. Thanks & Regards, Suresh.tech

Conducting an A/B test should be straightforward and measurable. It's not advisable to compare two entirely different designs because it becomes challenging to gauge what resonates with customers. The goal of an A/B test is to understand how minor changes like titles, images, or background colors can increase response or conversion rates by 15-25%. Once you grasp this concept, your design creativity will get better and better.

Having a well-defined hypothesis is a common mistake most people make. A hypothesis has to be specific, and not influenced by other uncontrollable variables. High-level vague hypotheses like - Increasing content post frequency would grow followers is an example of a high-level vague hypothesis. A clearer hypothesis should be something like this - shifting 2 of our weekday posts to weekends would get more average followers per post. This is better because it is clear that the experimented content would be the day of the post during the week and a consistent metric of average followers per post. It is also good to note that other factors should be kept consistent to reduce external factors' influence, e.g. quantity and format of posts.

Clearly Defined Objectives: It's crucial to have well-defined objectives before conducting tests or experiments. This ensures that you know what you're trying to achieve and can measure success accurately. I disagree with the notion that you should always avoid risky experiments. While it's essential to minimize unnecessary risks, calculated risks can lead to groundbreaking discoveries and innovations in marketing.

I remember a campaign, the primary goal was set to boost brand awareness among a new target demographic. We excitedly focused on the number of impressions and page views,primarily and the numbers looked extremely promising. However, soo we realized though page views increased but average session duration dropped, eventually bounce rate skyrocketed. If we had chosen engagement metrics like session duration and bounce rate, we would have immediately noticed the new audience is visiting but not engaging with the content. Reason: The creative content wasn't resonating with this demographic. Our oversight highlighted how dangerous it is to rely solely on vanity metrics or those metrics which do not align with the campaign's primary objectives.

It's important to use consistent language when defining a measurement framework which is a helpful tool in measuring the impact of anything for example: metrics are the things we keep track of at the most granular levels (views, impressions, shares etc.) KPIs: Should be a little higher level i.e. "raise awareness", "efficient use of dollars", "generate leads" Business Impact: should be the ultimate goal of what is being measured, ie revenue earned via sales or money saved via efficiencies Tying metrics, KPIs and business impact together is key and this is where things like attribution come in as well as having data tracked at all points along the customer journey

Not running the test long enough is in the same category as having a too small sample audience or dataset. If the data you collect is small, it always increases the chances of making false decisions and individual anomalies having too much effect on overall results. When testing different platforms with the same content, same media or same hashtags, marketeer often gets partially false results, as the platforms, the audience using a particular platform, the content types, content distribution algorithms, etc. all differ enough to give false results. This happens just about always when the platforms or media are considered being similar and similarly used by the audience. Tests need to be adjusted differently across platforms.

Ensure you are testing one thing at a time. E.g. if you are testing a new ad variation - identify one single aspect that you would like to test. Is a different background color? A different CTA? This will allow you to draw a conclusion that you can use moving forward. E.g., "We see a higher CTR when our CTAs are written in the first person."

We had one campaign where A/B test for an ad design dis not show any significant improvement in CTR for new design. We could have discarded it as a failed attempt, we dug deeper to understand reason behind. We started by gathering qualitative feedback which showed that though new design was visually appealing the messaging wasn't clear. We redesigned the ad and combined visual appeal with clearer messaging which subsequently improved engagement.

Ignoring External Factors: External events play a significant impact on metrics. For example, a drop in sales or website visits might be influenced by external factors such as a major news event, a public holiday, or even server downtime.