Automated scores in Speech

Use data from multiple feedback signals to score 100% of unstructured data at scale.

Automated scores combine multiple feedback signals to describe various aspects of an experience, such as how much customer effort an interaction required or an agent's adherence to quality objectives. Medallia Experience Cloud uses automated scores to monitor, evaluate, and score 100% of the unstructured data generated in an experience program.

Automated scores use both structured data (such as hold time) and unstructured data (such as survey comments).

Medallia Experience Cloud uses the concepts of scores and flags, both of which can use multiple signals. An automated score typically ranges from 0 to 100, while an automated flag is a simple "yes" or "no" binary metric.

For example, an automated score for call experience might include scores calculated by Speech for call duration and hold time. Other automated scores might combine Speech data with experience data from other sources to describe a customer's experience across all contact channels or to calculate a customer's brand interaction effort.

To learn more, see Automated scores in the Text Analytics section.

Automated score calculation components

Each of the automated score components below is assigned a weight indicating its value relative to other components. For example, a call experience automated score might weigh overtalk higher than hold time because the organization considers talking over a customer to be worse than having them on hold.

  1. Present topics — These items are desirable and increase the automated score.

    For example, agent greeting is a present topic in the default Agent QM score.

  2. Absent topics — These items are undesirable and decrease the automated score.

    For example, agent profanity is an absent topic in the default Agent QM score.

  3. Metadata — Metadata is structured data about the interaction, such as hold time or overtalk.

    Metadata availability may vary based on the types used by default automated scores. If an automated score uses metadata that is not available, that value's weight should be redistributed to other score components during customization.

    Metadata availability and priority tend to vary by customer, so automated scores can be customized and new automated scores can be created to suit specific implementation scenarios.

Preconfigured examples

Experience Cloud includes preconfigured automated scores that may be customized to use different topics, value weights, or metadata.

The preconfigured automated scores summarized below use data from Speech. For the exact topics, metadata, and value weights used in your implementation, contact your Medallia expert.

  1. Call/Chat Experience Score — Describes customer experience during a chat or call center interaction

    Present topics
    • agent greeting
    • verification of information
    • closing
    • ownership
    • empathy
    Absent topics
    • agent unprofessional language
    • uncertainty
    • repeat; customer confusion
    • frustration
    • either party asking the other for repetition
    Metadata
    • silence
    • hold time
    • call duration
    • overtalk

  2. Call/Chat Agent QM Score — Evaluates an agent's adherence to quality protocols during a chat or call center interaction with a customer

    Present topics
    • agent greeting
    • introduction
    • customer name request
    • additional assistance
    • pleasant close
    • use of caller's name
    • empathy
    • ownership
    • courtesy
    • acknowledgment of customer
    • bridging
    • self-service promotion
    • summary of customer's request
    Absent topics
    • agent tragic phrasing
    • unprofessional language
    • asking for customer repetition
    • not yielding to customer
    • uncertainty
    Metadata
    • overtalk

  3. Call/Chat Interaction Effort Score — Quantifies the overall effort required from the customer during a chat or call center interaction

    The interaction effort score uses two components. The first component uses topics and metadata that indicate customer difficulty. The second uses topics and metadata that indicate customer ease. These components have equal weight in the combined Interaction Effort Score.

    • Interaction Effort Score (Hard)
      Present topics
      • channel switching
      • self-service
      • repetition of information
      • generic treatment of customer
      • transfers
      • escalation
      • length of time
      • negative emotion
      • language describing perceived difficulty
      Metadata
      • transfers
      • overtalk
    • Interaction Effort Score (Easy)
      Present topics
      • ease of use
      • positive ending
      • agent confirmation of ease
      • positive emotion
      • first call resolution
      Metadata
      • overtalk
      • silence

  4. Call/Chat Multiple Contact Flag — Uses yes/no flags to indicate multiple contacts to enable analysis of records for first contact resolution

    Topic flags
    • customer made multiple attempts to call
    • customer made contact via other channels like chat or email
    • agent offer of additional help

Fields used

Automated scores that use Speech data use the following fields:

Agent QM Score

  • a_media_overtalk_percentage

  • k_bp_cc_call_opening_greeting_yn

  • k_bp_cc_call_opening_script_yn

  • k_bp_cc_call_asks_for_name_yn

  • k_bp_cc_call_additional_assistance_yn

  • k_bp_cc_call_close_call_pleasantly_yn

  • k_bp_cc_call_address_caller_yn

  • k_bp_cc_call_demonstrate_empathy_yn

  • k_bp_cc_call_ownership_yn

  • k_bp_cc_call_courtesy_language_yn

  • k_bp_cc_call_tragic_phrase_yn

  • k_bp_cc_call_unprofessional_language_yn

  • k_bp_cc_call_acknowledging_customer_yn

  • k_bp_cc_call_repeat_information_yn

  • k_bp_cc_call_not_yielding_yn

  • k_bp_cc_call_uncertainty_yn

  • k_bp_cc_call_bridging_yn

  • k_bp_cc_call_recap_summary_yn

  • k_bp_cc_call_promote_self_service_yn

  • k_bp_cc_call_agent_qm_score_frc

Call Experience Score

  • a_media_silence_percentage

  • a_media_duration

  • a_media_overtalk_percentage

  • e_bp_cc_hold_time_int

  • k_bp_cc_call_experience_closing_yn

  • k_bp_cc_call_experience_confusion_yn

  • k_bp_cc_call_experience_empathy_yn

  • k_bp_cc_call_experience_greeting_yn

  • k_bp_cc_call_experience_ownership_yn

  • k_bp_cc_call_experience_repeat_yn

  • k_bp_cc_call_experience_uncertainty_yn

  • k_bp_cc_call_experience_unprofessional_yn

  • k_bp_cc_call_experience_verification_yn

  • k_bp_cc_call_exp_score_frc

Call Interaction Effort Score

  • a_media_overtalk_percentage

  • a_media_duration

  • a_media_silence_percentage

  • e_bp_cc_number_transfers_int

  • e_bp_cc_hold_time_int

  • k_bp_cc_call_easy_effort_agent_confirming_ease_yn

  • k_bp_cc_call_easy_effort_ease_of_use_yn

  • k_bp_cc_call_easy_effort_emotions_yn

  • k_bp_cc_call_easy_effort_first_call_resolution_yn

  • k_bp_cc_call_easy_effort_positive_ending_yn

  • k_bp_cc_call_easy_effort_score_frc

  • k_bp_cc_call_hard_effort_channel_switching_yn

  • k_bp_cc_call_hard_effort_emotion_yn

  • k_bp_cc_call_hard_effort_escalation_yn

  • k_bp_cc_call_hard_effort_escalation_customer_yn

  • k_bp_cc_call_hard_effort_generic_service_yn

  • k_bp_cc_call_hard_effort_length_of_time_yn

  • k_bp_cc_call_hard_effort_perception_of_hard_effort_yn

  • k_bp_cc_call_hard_effort_repeat_interaction_callback_yn

  • k_bp_cc_call_hard_effort_repetition_agent_yn

  • k_bp_cc_call_hard_effort_repetition_customer_yn

  • k_bp_cc_call_hard_effort_repetition_of_information_transfer_yn

  • k_bp_cc_call_hard_effort_self_service_yn

  • k_bp_cc_call_hard_effort_transfers_yn

  • k_bp_cc_call_hard_effort_transfers_customer_yn

  • k_bp_cc_call_hard_effort_score_frc

  • k_bp_cc_call_interaction_effort_score_frc

  • k_bp_cc_interaction_effort_score_band