The Pega® Sales Automation application uses built-in decisioning capabilities to provide managers with insights about how sales representatives perform during their 90-day probation period. During the first 90 days, the adaptive models analyze data about the sales representatives including their profession, experience, training performance, weekly pipeline data, and the pipeline delta.
The Pega Sales Automation application suggests actions to managers based on the performance of sales representatives. It also provides suggestions to sales representatives about how to improve their performance. The artificial intelligence model predicts the probability to reach the target, for example, reaching a target of 300k in 18 months. You can configure the definition of success (300k), T-Zero (3 months), and T-End (18 months).
Review the following sections:
Pega Sales Automation widgets overview
The Pega Sales Automation application provides the following widgets:
Sales manager coach widget
The Sales manager coach widget, which is available on the sales manager's dashboard, displays the effectiveness of sales representatives. This widget is dynamic and responds to actions that are performed by the sales representatives. The widget captures, for example, changes in the following:
- Number of created contacts
- Number of converted leads
- Opportunity pipeline
The adaptive models capture all of these actions to predict the effectiveness of the corresponding sales representatives. Radar charts provide precise insights about the adaptive models.
The coaching actions are based on the difference between the maximum propensity that sales representatives achieve per predictor and their current propensity. The coaching actions are prioritized based on the differences scale. For example, actions associated with the predictor where the sales representative has a maximum improvement scope are listed first.
Sales coach widget
The Sales coach widget presents all of the future actions of sales representatives. To see all of the reporting sales representative's actions, select a particular sales representative from the list. Click an action to view additional information and the list of Knowledge Management articles that are associated with it. For example, if a sales manager wants to view the pipeline, a list of opportunities filtered by the needs improvement stages and a list of Knowledge Management articles are presented. You can configure the Knowledge Management articles for each action by using the Knowledge Management category.
Coaching plan widget in Pega Pulse
The Coaching plan widget in Pega Pulse displays coaching recommendations for all the sales representatives reporting to a sales manager. Sales managers can develop personalized coaching plans for each coaching recommendation displayed in the Coaching plan widget. The sales coach tool then monitors progress on the coaching plans and provides progress updates to sales managers and sales representatives.
Coaching plan creation and closure flow
The following figure illustrates the coaching recommendation life cycle.
Sales manager’s actions
The sales manager receives a notification from the Sales coach at the start of the week. The manager then completes the following steps:
- Click the recommendation notification. The manager’s Pulse feed opens, displaying new coaching recommendations.
The Coaching plan widget displays in the panel on the right side of the Pulse feed. Coaching recommendations appear on the Suggested tab in the Coaching plan widget.
- Filter the recommendations by the sales representative and identify the coaching recommendations to pursue.
- Click the button for each recommendation and create a coaching plan.
The Recommendation dialog box contains details about the Recommended Value and Current Value for the selected category for the sales representative.
- Select the next touchpoint by choosing a Follow-up date.
- Click .
The recommendation status becomes Active and a notification is sent to the sales representative that a coaching plan has been assigned.
Sales representative’s actions
The sales representative receives a notification from the Sales coach indicating that a coaching plan has been assigned. The sales representative then completes the following steps:
- Click the notification to open the Coaching Plan dialog box.
- Communicate with the sales manager using the Pulse feed available on the Coaching Plan dialog box.
- Provide progress updates using the Pulse feed available on the Coaching Plan dialog box.
Only the sales manager and the sales representative can see these Pulse communications—they are not visible to anyone else in the organization.
Sales Coach Bot’s Actions
On the day that the sales representative follows up on coaching plan, the Sales coach bot posts a comment in the Pulse feed of the Coaching recommendation explaining the sales representative progress. The post contains details about the degree of improvement made by the sales representative. The sales manager then decides whether to follow up with the sales representative, complete the plan, or cancel the plan.
Historical data and adaptive learning
The Pega Sales Automation application uses self-learning, adaptive models to generate the effectiveness scores for each sales representative. This method is based on core Decision Strategy Manager capabilities. If needed, you can add or remove predictors.
The key predictors are configured in the adaptive model rule based on the analysis of data from real production environments. The adaptive model after the training period activates or deactivates predictors. Production data is also used to create weekly snapshots for sales representatives during their first 18 months in the organization. The system uses data from the Human Resources systems to improve the accuracy of the predictors.
The Pega Sales Automation application has the following predictors in the adaptive model:
|Predictors||Human Resources predictors|
|Pipeline current (a number of the opportunities in a stage)||Number of days after joining|
|Delta between two snapshots (a difference between the current and the previous snapshot)||Experience in years|
|Customer Interactions created||Business card title|
|F2F meetings||Job title|
|Other meetings||Source of hiring: referred, sourced, agency, direct, applied|
|Contacts added by sales representative||Sub-source for referrals: referred by, referral functional area|
|Inbound emails||Previous company details: company name, size, revenue|
|Outbound emails||Predictive indices, for example: A, B, C, D, PI, A minus B.|
|Number of leads||HR ratings: green, blue, yellow|
|Leads converted to opportunities||Other information, for example, number of days for onboarding, completed courses, grades.|
An agent runs daily and executes a data flow that calls a decision strategy. A decision strategy contains the effectiveness adaptive model for all of the sales representatives who have not completed their first 18 months of employment period to capture the data that is required by the models. A decision strategy uses the standard Delayed Learning cache. When a sales representative reaches 18 months of employment, the application triggers a response strategy from a data flow to retrieve the data for relevant decisions and to train the applicable model.
Effectiveness widgets architecture
The Pega Sales Automation application uses predictors ranging between Sales Automation and Human Resources that are listed in the PredictEffectiveness model, for example, sales representative pipeline, delta of pipeline, predictive indices, previous company details, and human resource statistics. You can configure the outcomes in the adaptive model. After a sales representative completes the 18-month probation period and achieves the target you specified, the system sets the outcome to Achieved. If the sales representative fails to achieve the specified target, or the employment contract is terminated before the 18-month probation period ends, the system sends failed and terminated outcomes to the models.
Review the following sections:
Sales manager coach widget architecture
The sales manager dashboard widget retrieves the effectiveness scores of all sales representatives who report to the sales manager from the D_SalesRepEffectiveness data page. The widget presents a list of the sales representatives, their scores, number of employment days, and the optimal score for the present date.
To view the radar chart, select a score. The D_SalesRepEffectiveness data page calls the PredictSalesRepEffectiveness activity, which calls the GetEffectiveness activity to present current predictor values for each sales representative. The D_SalesRepEffectiveness data page also executes the PredictEffectiveness data flow. The system calls the SalesManagerCoachingActions data flow to suggest actions for each sales representative.
Coaching actions architecture
Coaching actions are evaluated in the SalesManagerCoachingActions strategy, which calls the CoachingActionsPredictors sub-strategy. The SalesManagerCoachingActions strategy runs through each predictor with the representative's propensity and maximum propensity for predictor, then suggests actions based on that information.
The Pega Sales Automation application uses the following strategies:
- EvaluateEffectiveness strategy
The Pega Sales Automation application uses the EvaluateEffectiveness strategy, which is used to train the PredictEffectiveness model as well as to predict the effectiveness score of each sales representative. To reuse this strategy, to train the model, and predict the effectiveness score, complete the following steps:
- Open the data flow.
- Under Strategy properties, change the mode from Make decision and store data for later response capture to Make decision.
The strategy sets the context for the model and calls the adaptive model. The system presents the results individually or as a set on the primary page, which you can configure in the strategy properties in the data flow.
The EvaluateEffectiveness strategy overview
The Pega Sales Automation application uses the EndProbation strategy, which is called from the SalesRepProbationCompleted data flow when the initial 18-month of employment period (T-End) is completed. The strategy retrieves the daily snapshots of the sales representative and sets the outcome based on whether the sales representative achieved their target. The system returns the results to the data flow, which sources them to the pxAdaptiveAnalytics data set. The adaptive models learn and advance by using this data.
The EndProbation strategy overview
The Pega Sales Automation application uses the following data flows:
- PredictEffectiveness data flow
The PredictEffectiveness data flow compares the sales representative object (Data-Admin-Operator-ID) with the previous snapshot (8 weeks earlier) to calculate the delta. The system retrieves the earlier snapshot for calculating delta by using the PegaCRM-Data-SFA-SalesRepSnapshot data set. The PredictEffectiveness data flow calls the EvaluateEffectiveness strategy with the mode set as Make Decision and updates the results, including the propensity, in the operator record. The propensity shows the effectiveness score for the particular sales representative.
The PredictEffectiveness data flow
- SalesRepDailySnapshots data flow
Every 24 hours, an agent calls the SalesRepDailySnapshots data flow, which captures snapshots of the predictors for each sales representative who has not completed the initial 18-month of employment period. To reuse this strategy, to train the model, and to predict the effectiveness score, the EvaluateEffectiveness strategy mode is set to Make decision and store data for later response capture. The application also stores the effectiveness propensity for each of the sales representative every day. The system calculates the optimal for each day by taking the average of the effectiveness score of all of the successful sales representatives on that day.
SalesRepDailySnapshots data flow
- SalesRepProbationCompleted data flow
After a sales representative completes the initial 18-month employment period (T-end), the system updates the outcome for all of the previously captured snapshots. The EndProbation strategy sets outcomes based on the total amount that the sales representatives achieved. To reuse this strategy, to train the model, and to predict the effectiveness score, the EndProbation strategy mode is set to Capture response for previous decision in the past period. The results from the strategy are sourced to pxAdaptiveAnalytics.
The SalesRepProbationCompleted data flow