Why you should continuously A/B test your scoring algorithms:

In the world of marketing automation, the use of scoring algorithms is an essential part of lead management. Lead scoring is a process of assigning a value to each lead based on their level of interest in your product or service. With the right scoring algorithm, you can identify the most qualified leads, prioritize your sales efforts and focus on those with the highest potential to convert into customers. However, creating the right scoring model is not a one-time job, and it needs to be continuously tested and refined to ensure that it’s still relevant to your business goals. In this blog post, we will explain why you should continuously A/B test your scoring algorithms and how to do it.

What is A/B testing?

Before we dive into the importance of A/B testing, let’s briefly explore what A/B testing is. A/B testing is a process for comparing two versions of a campaign or a process, such as a scoring algorithm. This typically involves randomly assigning leads to either one of the two versions, to measure which version performs better in terms of your desired outcome. For instance, if your goal is to increase lead conversion, you can measure which version of the scoring algorithm generates more qualified leads that ultimately convert into customers.

In terms of lead scoring, this can come in several forms:

• Randomly assigning leads to different scoring models. However, this approach can often make it difficult to align scores after you selected a winner.

• Setting up two scoring models and randomly using one or the other scoring model with different sales people. Or displaying both scores to sales and
seeing which score is more reflective of sales readiness. However, this approach can cause confusion to sales teams who are not aware of what makes the scores different.

• Maintaining only one scoring model, but making frequent changes to test your hypothesis. In this way, you would look back at the past results. Which scoring elements really differentiate the leads who went on to buy versus those who were not interested. The downside to this approach is that it can often take time to see results and compare, especially with long sales cycles. This third approach is the one used with several Chapman Bright customers, and you can read more about a practical example in our Sungevity case study “Improving
lead quality by implementing a consumer-centric approach to lead scoring and lead nurturing programs.

Why should you A/B test your scoring algorithms?

There are several reasons why you should continuously review and test your scoring algorithms:

1. Stay relevant: Your business goals, target audience, and product or service offering may change over time.A scoring algorithm that worked well in the past may not be relevant anymore. By continuously testing and refining your scoring algorithm, you can ensure that it stays aligned with your business goals and remains relevant to your target audience.

2. Improve lead quality: By A/B testing your scoring algorithms, you can identify which version generates more qualified leads. Over time, you can refine your scoring model to ensure that only the most qualified leads are passed on to your sales team, which increases your chances of closing deals.

3. Optimize your marketing efforts: By measuring the impact of different scoring algorithms, you can identify which marketing activities generate the most qualified leads. This allows you to focus your marketing efforts on activities that generate the most value for your business.

How to A/B test your scoring algorithms?

Now that we’ve covered the importance of A/B testing your scoring algorithms, let’s briefly discuss how to do it:

1. Define your hypothesis: Identify the problem you want to solve or the goal you want to achieve by testing a new scoring algorithm.

2. Create a test plan: Define the scope of your test, including the size of your sample, the duration of your test, and the metrics you will use to measure success.

3. Create your variants: As mentioned above, you can take multiple approaches here. Do you want multiple scoring programs? Or would you like to analyze and update your existing scoring program? Either way, ensure that your variants are significantly different from each other to have a fair test.

4. Run your test: And be sure to measure the outcome based on your desired metric. For example, if your goal is to increase lead conversion, measure the number of leads that converted into customers for each variant. You will often need several months for a test due to how long B2B sales cycles are. Nothing in business stays the same for too long, which is why we recommend reviewing your lead scoring programs at least once a year. Your buyers might be changing, your market might be changing, or your sales approach might be changing. Any of these can require an overhaul to your lead scoring processes.