Phase II: Call Performance Dashboards

The most accurate predictive model applied incorrectly will fail to create impact.

Deploying a predictive model effectively requires an iterative process. The goal is to provide your teams a helpful yet seamless experience with the prediction engine. This involves integrating the model output within your revenue technology to both encourage positive outcomes and collect team feedback to drive model iteration.

Continuous learning and iteration is a characteristic of an effective predictive model. To ensure continuously improving accuracy, we will implement an orchestrated data loop to provide the model with new outcomes, both positive and negative, to facilitate the incremental improvement of the output. Leveraging new ecosystem data and direct team feedback, Structure Digital will continue to evolve the model output to improve revenue impact over time.

Predictive Model Integration

Utilize and apply the newly developed predictive model where it will make the most impact for your team

Team Feedback Collection

Enable sales leaders and rep end-users to provide model training input

Continuous Monitoring

Evaluate and refine the model to ensure its accuracy and relevance as market conditions and customer behaviors change over time.

Impact Measurement

Create a clear and concise representation of your process for analysis and optimization

Predictive Model Integration

Outputs of the predictive model ultimately need to be available in the systems where they are most useful to internal teams. Most often, this means populating outputs into primary systems-of-truth where it benefits the teams the most.

Team Feedback Collection

Feedback loops enable sales leaders and rep end users to provide model training input through feedback about prediction accuracy. This human feedback can be used to illuminate differences between statistical analysis and personal experiences.

Continuous Monitoring

Using a predictive sales model can provide businesses with valuable insights and data-driven recommendations for optimizing their sales processes, improving sales performance, and driving revenue growth. However, it's important to continuously evaluate and refine the model to ensure its accuracy and relevance as market conditions and customer behaviors change over time.

The steps to deploy a predictive model include:

Data Collection: Collect and consolidate relevant data from various sources, such as historical sales data, customer data, prospect data, marketing data, and external data sources. This data should be accurate, complete, and reliable.
Data Preparation: Clean, validate, and preprocess the data to ensure its quality and consistency. This may involve data cleaning, data enrichment, data normalization, and data integration to create a unified dataset for analysis.
Feature Engineering: Identify and select relevant variables, or features, from the data that may influence sales outcomes. These features can include customer demographics, firmographics, past purchase behavior, website activity, marketing campaign data, and other relevant factors that may impact sales performance.
Model Building: Utilize machine learning algorithms, such as regression, decision trees, or neural networks, to build a predictive sales model using the prepared data. The model should be trained on historical data to learn patterns and relationships between the features and sales outcomes.
Model Validation: Validate the predictive sales model using appropriate techniques, such as cross-validation, to assess its accuracy, reliability, and performance. This step helps to ensure that the model is robust and can provide accurate predictions on new data.
Model Deployment: Once the model is validated, it can be deployed in a production environment to generate predictions on new sales data. This can be integrated into sales processes, CRM systems, or other relevant tools to provide sales teams with actionable insights and recommendations.
Model Monitoring and Iteration: Continuously monitor the performance of the predictive sales model and update it as needed. Monitor the model's predictions against actual sales outcomes and refine the model over time to improve its accuracy and effectiveness.
Decision-making and Optimization: Utilize the predictions and insights generated by the predictive sales model to inform sales strategies, resource allocation, and revenue optimization efforts. This can involve identifying high-potential leads, optimizing sales territories, prioritizing sales activities, and tailoring sales approaches to maximize revenue opportunities.

Impact Measurement

We view the relationship with you as a true partnership. Winning together will take time and effort so creating real measurable results is critical to sustaining the relationship. Revenue growth is a key success measurement therefore it’s important we develop a collaborative set of success criteria to drive toward.

We were getting ghosted in the sales cycle and wanted to know why. We asked Structure Digital to get involved to figure that shit out and they nailed it. The insights they gave us helped us figure out how to adjust our process and close more deals.