B2B Marketing and The Power of Small Incremental Changes
In data-driven marketing, it’s essential to experiment and test the criteria and individual elements that underlie your campaigns and strategies so you can fine tune and make the necessary improvements to ensure you get the results you want. Marketing teams that continuously bake experimentation into their strategies tend to be the most successful. Not only can they quickly identify what’s going right or wrong with a particular campaign, but they can also make small incremental changes, which accumulate over time to drive greater effectiveness. Even though small adjustments may seem inconsequential, they can lead to large effects when taken together. The whole of many small gains can amount to real growth in knowledge and help move the needle in achieving desired business outcomes. This holds especially true in B2B marketing, where it is more of a marathon than a sprint, characterized by incremental progress through different stages of the buyer journey.
Experimenting with small incremental changes carries many advantages for B2B marketing teams. First, small changes tend to have less risk. With small adjustments to your marketing strategy, you can experiment, learn, and refine what you're doing in a way that maintains the core of your brand and messaging than making larger changes. Secondly, small changes can offer more potential for learning, leading to more confidence in decision-making; there are more of them, and they happen with more frequency, providing continuous feedback and insights that can be acted upon. Proven insights from small experiments can also be scaled and applied at large across bigger campaigns, leading to a more effective use of marketing resources and spend.
Two of the most popular testing methods used in B2B marketing today are A/B and multivariate testing, both of which enable the marketing team to compare different versions side-by-side to determine which one works best. A/B testing or "split testing,” as it’s sometimes known, is simply a way to compare two different versions of a marketing element like an email or an ad. Testing allows you to figure out which of the two versions performs better. In other words, compared to the other version, does this one get more opens, more click-throughs, more conversions, or more impressions?
Testing multiple elements at the same time is called multivariate testing. This testing method gives marketing teams a way to examine multiple variables at once and also understand the ways they might interact with each other. The upshot is that you get insight as to which combinations of elements work best to achieve your desired outcome.
When using A/B testing, variables are changed one at a time, and the results provide direct evidence of which version performed better. Most B2B marketers will be familiar with A/B testing from running email campaigns. The most commonly tested parts of an email are the subject line, the call to action, and the overall layout. For example, if you’re running an email campaign aimed at enticing people to invest in a new financial services product, you could test two different versions with different subject lines to see which one generates the most engagement.
Multivariate testing, on the other hand, is ideal for testing multiple combinations of elements at the same time. For instance, with landing pages, you might test different combinations of hero images, headings and call-to-actions (CTAs). As part of your testing, you might keep the image the same and change the headline and CTA or vice versa and then see which combination resonates with users to achieve a higher conversion rate.
Testing can be extended beyond emails and used to compare ad copy and visuals to figure out which message resonates best with your target audience. For example, if you’re advertising a whitepaper on financial forecasting, you could create two versions of an ad for LinkedIn: one with a stat about market trends and a basic, generic graphic, and another with a testimonial quote and an interesting, eye-catching infographic. By checking the number of click-throughs and downloads, you can figure out which version is the most engaging for your target audience, which you can then use to create even better ads next time around. You could also extend testing to social media, trying out various posting times, content forms (e.g., video, image, text), and messaging tones to see which ones produce more engaged audience responses.
The number of options for testing are endless and can extend beyond marketing into sales and business development where different call scripts, email templates, and sales cadences can be tested to find out which versions perform best. The important thing is to embed the practice of testing your campaigns and channels as much as possible.
There are several things to consider when it comes to testing. The first is to ensure the length of your testing periods are long enough and the sample size is large enough to deliver meaningful results, considering market and customer behavior – for example, testing during periods when clients are more likely to be engaged than not, such as holidays. Maintaining consistency during the complete length of the test is also important to ensure none of the underlying parameters are changed while the test is ongoing. Each test needs to be considered as part of a continuous improvement where learnings can be applied to the next set of steps.
As the B2B buyer journey becomes much more fragmented, testing is becoming more of a challenge for many marketing teams. Continuously testing across multiple channels, content assets, and different audiences can take up a lot of time and resources. The good news is that there are exciting new AI-driven technology solutions that do a lot of the heavy lifting associated with testing by automating many of the tasks involved. Using AI, marketing teams can automate the setup and running of tests, dynamically testing different content assets across various channels. With the ability to process large volumes of data in real time, AI can immediately make the necessary adjustments to individual campaigns based on test results, all without the need for manual intervention. This AI-driven testing not only speeds up the process of optimizing campaigns but does so in a more accurate manner, reducing human error and bias.
At PredictiveB2B, we work with businesses in helping them build and optimize their testing strategies. Using advanced AI and predictive analytic tools, we help businesses embed continuous testing as part of their marketing strategies, enabling them to experiment with different content assets, marketing channels, and audience segments. Contact us to learn more.
