Common mistakes when applying AI to sales
A sales director can optimise their team's efforts by applying AI to answer questions about their own data, such as what products are most likely to be purchased together, or which customers are at risk of churn, then adjust their strategy to target the most promising opportunities.
A sales director at a manufacturing company is deciding this week how to allocate their team's efforts to meet the quarterly targets, and they are currently basing it on intuition and past experiences. However, this approach can be flawed and may not yield the best results. By applying AI to sales, the director can make more informed decisions, but only if they avoid common mistakes that can hinder the effectiveness of the technology.
Automating a process nobody mapped
One of the most significant mistakes companies make when applying AI to sales is automating a process that has not been properly mapped. This can lead to inefficient and ineffective use of the technology, as it is not tailored to the specific needs of the sales team. For instance, a shift supervisor at a manufacturing plant checks the maintenance log to ensure that all equipment is functioning properly, but if the process of maintaining the equipment has not been mapped, the AI system will not be able to optimize it. This can result in wasted resources and decreased productivity.
A thorough understanding of the sales process is essential before implementing AI. This involves identifying the key steps involved in the process, from lead generation to conversion, and determining how AI can be used to enhance each step. By taking the time to map the process, companies can ensure that the AI system is used effectively and efficiently.
Treating historical sales at planning grain as complete when it is not
Another common mistake is treating historical sales data as complete and accurate when it is not. Historical sales data is often incomplete, inconsistent, or inaccurate, which can lead to flawed insights and decisions. For example, a category manager compares two suppliers based on historical sales data, but if the data is incomplete or inaccurate, the comparison will be misleading. This can result in poor decision-making and decreased sales performance.
Companies must ensure that their historical sales data is accurate, complete, and consistent before using it to inform their sales strategies. This involves cleaning and preprocessing the data, as well as identifying and addressing any gaps or inconsistencies. By doing so, companies can ensure that their AI system is providing accurate and reliable insights.
Giving the capability only to the analyst who already had it
Some companies make the mistake of giving the AI capability only to the analyst who already had it, rather than making it available to the entire sales team. This can limit the effectiveness of the technology, as it is not being used by the people who need it most. For instance, a sales team may have an analyst who is skilled at data analysis, but if the AI capability is only given to that analyst, the rest of the team will not be able to benefit from it.
Companies should consider making the AI capability available to the entire sales team, including those who may not have a background in data analysis. This can be done by providing training and support to ensure that all team members are able to use the technology effectively. By doing so, companies can ensure that the AI system is being used to its full potential.
For more information on how to effectively implement AI in sales teams, visit our sales teams page.
Declaring success against a metric with no baseline
Companies often declare success with their AI implementation without establishing a baseline metric to measure against. This can make it difficult to determine whether the AI system is actually having a positive impact on sales performance. For example, a company may implement an AI system and see an increase in sales, but if there is no baseline metric, it is impossible to determine whether the increase is due to the AI system or other factors.
Companies should establish a baseline metric before implementing an AI system, and then use that metric to measure the effectiveness of the technology. This can involve tracking key performance indicators, such as sales revenue or conversion rates, and comparing them to the baseline metric. By doing so, companies can ensure that they are accurately measuring the impact of the AI system.
When AI is not the right choice
While AI can be a powerful tool for sales teams, there are times when it is not the right choice. For instance, if a company has a very small sales team or limited sales data, AI may not be the most effective solution. In these cases, other technologies or strategies may be more suitable. Companies should carefully consider their specific needs and circumstances before deciding whether to implement an AI system.
If you are considering implementing an AI system for your sales team and want to learn more about how it can be used to improve sales performance, contact us to speak with one of our experts.
Frequently asked questions
What are the most common mistakes companies make when applying AI to sales?
Companies often automate processes that have not been properly mapped and treat historical sales data as complete and accurate when it is not.
How can companies ensure their AI system is providing accurate and reliable insights?
Companies must clean and preprocess their historical sales data, identify and address gaps or inconsistencies, and establish a baseline metric to measure the effectiveness of the AI system.
Why is it important to make AI capabilities available to the entire sales team?
Making AI capabilities available to the entire sales team ensures that all team members can benefit from the technology, not just the analysts who already have the capability.
How can companies determine whether their AI implementation is having a positive impact on sales performance?
Companies should establish a baseline metric before implementing an AI system and track key performance indicators to compare against the baseline and measure the effectiveness of the technology.