A signal is a sign, an indicator, a communication. When presenting qualified leads to your sales team, assist them meaningfully by applying signals - those indicators of a lead qualified between MadKudu’s Customer Fit and Likelihood to Buy models - to the prioritization process in a way that makes reps faster and smarter in responding to MQLs. Enabling your teams to increase their efficiency and effectiveness by leveraging MadKudu Signals can be the difference between a healthy pipeline and otherwise a lot of wasted effort.
Described in another MadKudu University article here, signals enable the sales team to better understand the scoring of a lead. Signals are primarily based on the analysis of the model and detail which traits of a lead positively or negatively impact the score. While relative priority is flexible, these signals are usually based on the most relevant computations and made visible to your sales team for them to act on the data available to them. Highlighting these traits provide additional insights for the team behind why a score is given to a specific lead.
There is an opportunity, through tailoring the signals, to optimize the handoff to sales done by marketing at the point of every MQL. You have the ability to enhance your team’s understanding and communication by building out and monitoring the use of signals in the Data Science Studio. With MadKudu, you can increase or decrease the priority or importance of a signal as well as adding or deprecating signals. This provides a level of flexibility not previously afforded from the CRM or MAP.
In the Data Science Studio, the MadKudu admin or architect for your account can edit the order or priority of a given signal. The handful of signals visible for a lead are there to quickly provide information that can give an SDR, BDR, or Account Executive an edge in understanding and reaching out to a prospect. For this exact reason, Signals must be kept relevant and updated, aligned with the business and the focus of the sales organization. Signals can easily be updated by duplicating your current model in the Data Science Studio and following the support documentation for signals.
Computations in MadKudu are often simple, but are also potentially complex formula fields. They contain variables based on demographic, firmographic, or technographic enrichment or first party data from your CRM. They are beneficial to the sales team so as to increase or decrease specificity in your signals. New signals all start with initial information gathered in computations. In the Customer Profile section of the Data Science Studio, create new computations under the computations tab.
Relevant signals are imperative for your team, increasing their workflow efficiency. Using a feedback loop and structuring it in a simple way within the CRM so as to facilitate alignment between sales and marketing will also give you a clear path to the improvement of your signals and also potentially the underlying components with your model decision trees and overrides. Feedback can help to eliminate redundant or uninteresting signals or assist in prioritizing the signals most important to your SDRs. Well tended, MadKudu Signals are an organized, dynamic and in the end simple use of data to help your organization, and especially sales team drive more revenue.