One of the challenges in outbound prospecting is testing messages. The reality is that organizations, teams, or individual representatives often struggle with knowing when to pivot their messaging, whether it's an email, a phone script, or a social media message. Pivoting too quickly can create confusion about what works and what doesn't. On the other hand, pivoting too slowly can waste valuable time, and as salespeople, we are always trying to convert time into money. This guide is essentially my thesis on how to rationalize testing messages in outbound prospecting.
The first thing to understand is that outbound prospecting involves using mediums like email, phone, and social channels to deliver a value-based message to a prospect. I explain to reps that they are like an Advil pill. Ibuprofen, the actual medicine, is bitter and hard to swallow. Someone at Advil decided to coat the pill in candy to make it easier for people to take this beneficial medicine. This is similar to outbound prospecting. The salesperson is the candy coating, making the message more appealing and easier to absorb. The ultimate success is when the prospect benefits from the product or service, which is the goal of outbound.
When thinking about messaging and effective prospect absorption—my term for how well the market takes in your message with enough frequency to create a successful and financially productive outbound effort—I refer to the laws of statistics. Identifying a representative sample size is crucial before deciding that the current messaging or target audience won't lead to a viable and financially productive outbound motion.
The Role of Statistics in Outbound Prospecting
To ensure that your outbound messaging tests are statistically significant, you need to determine an appropriate sample size. Here’s how to apply statistical principles to your outbound prospecting efforts:
1. Define the Population: In outbound prospecting, your population is the total number of potential prospects you can reach. This could be your email list, the number of phone contacts, or your social media audience. For example, let's assume you have a list of 5,000 potential email prospects.
2. Choose a Confidence Level: This is the probability that your sample accurately reflects the population. A 95% confidence level is commonly used, meaning you can be 95% certain your results are accurate within the margin of error.
3. Set the Margin of Error: This represents the range within which you expect the true population parameter to lie. A common margin of error is 5%. This means you are willing to accept that your results could vary by 5% above or below the true population value.
4. Estimate the Population Proportion: This is the expected success rate of your messaging. If you don’t have historical data, use 50% (0.5) as it maximizes the sample size needed and ensures sufficient testing.
Using these parameters, the sample size formula for proportions is:
Practical Application in Outbound Prospecting
In practical terms, this means that out of your 5,000 potential contacts, you need to ensure that approximately 357 prospects have consumed your message. This can be through email, a phone call, or social media interaction however we would define consumed in the following ways...
- A complete value narrative has been delivered verbally over the phone with most interactions being recorded for QAQC.
- An actual reply from a prospect indicating they are not a fit through email or social.
By verifying that these 357 people have seen and engaged with your message, you can confidently assess its effectiveness.
If your message does not yield the desired results within this verified sample, it may indicate a need for change. Conversely, if it performs well, you can confidently scale it knowing you have statistically significant data.
Leveraging statistical principles ensures that your decisions are data-driven, minimizing wasted time and maximizing the efficiency of your outbound prospecting efforts. By methodically determining when to pivot your messaging, you can optimize your approach and achieve better results in your sales processes.
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