<\/div>\n <\/a>\n <\/div>\n\n \t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t\t

\n\t\t

\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t $\"Red-Dot\"$ \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t $\"red$ \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t

\n\t\t\t\t\t

Afiniti as a fully managed service <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
Historical data is used to build interaction algorithms, which predict the outcome of all possible caller\/agent pairings based on previous similar interactions. Data is refreshed nightly, so the algorithms continue to learn as caller and agent behavior evolves.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t $\"Afiniti$ \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
Precise attribution through unbiased benchmarking<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
Afiniti is the only company that uses built-in benchmarking to measure the exact incremental value our technology delivers. By turning our AI \u201cON\u201d and \u201cOFF\u201d throughout the day, we are able to prove the real impact on your key business metrics.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
<\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
Factors That Affect Performance<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
Data<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
Model performance is directly correlated with the amount of data available for each caller and agent<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
Telephony<\/li>
Outcomes<\/li>
Contextual<\/li>
Operational<\/li>
Third-party vendor sourced data<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
Variability <\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
Variation in possible matches is the basis for optimization<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
Caller tenure<\/li>
Caller history<\/li>
Caller profile<\/li>
Caller intent<\/li>
Agent experience<\/li>
Agent work shift<\/li>
Offers available<\/li>
Product mix<\/li>
Competitor<\/li>
information<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t
\n\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
Scale and Choice<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
As free agents or calls in queue accumulate, our opportunity to pair increases<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
AI Management Framework<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
\n
\n $\"Input$ \n\n $\"Modeling$ \n\n $\"Outputs$ \n <\/div>\n\n
\n
\n
\n
\n
\n
01<\/div>\n
\n
Input data<\/h3>\n \n \n \n <\/svg>\n <\/span>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n Input data, vetted and approved by the client, goes through rigorous\n selection process to ensure merit and deter bias\n <\/p>\n
Data used<\/h6>\n
\n
Telephony<\/li>\n
Outcomes<\/li>\n
Contextual<\/li>\n
Operational<\/li>\n
Third-party vendor sourced data (if client approves)<\/li>\n <\/ul>\n
Data not used<\/h6>\n
\n
Agent surveys<\/li>\n
Protected class data<\/li>\n
Web scraped data<\/li>\n <\/ul>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n
\n
02<\/div>\n
\n
Modeling & testing<\/h3>\n \n \n \n <\/svg>\n <\/span>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n AI modeling and testing, subject to robust AI governance process and\n controls, ensures operational stability and successful outcomes\n <\/p>\n
Data not used<\/h6>\n
\n Controls ensure run time algorithms don\u2019t affect operational\n performance outside of approved SLAs. Should the Afiniti service be\n impacted, the routing will revert to the client\u2019s legacy routing.\n <\/p>\n
Interaction models<\/h6>\n
\n Controls ensure run time algorithms don\u2019t affect operational\n performance outside of approved SLAs. Should the Afiniti service be\n impacted, the routing will revert to the client\u2019s legacy routing.\n <\/p>\n
Validation<\/h6>\n
\n Multipronged model validation and testing of comparative advantage\n and pairing strategies to confirm operational performance.\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n
\n
\n
03<\/div>\n
\n
Outputs & analysis<\/h3>\n \n \n \n <\/svg>\n <\/span>\n <\/div>\n <\/div>\n <\/div>\n
\n
\n Client specific dashboards ensure transparent outputs across\n performance and operational metrics\n <\/p>\n
Sample dashboards<\/h6>\n
\n
Telephony data<\/li>\n
Outcomes data<\/li>\n
Operational data<\/li>\n
Operational data<\/li>\n <\/ul>\n
Periodic bias testing<\/h6>\n
\n Periodic testing conducted to ensure models and data used don\u2019t\n introduce bias (in conjunction with our clients, since Afiniti does\n not access\/process customer protected data).\n <\/p>\n
Client compliance analysis<\/h6>\n
\n Afiniti provides which caller was paired with which agent and\n outcomes data allowing the client to conduct independent analysis to\n ensure risk and compliance adherence.\n <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n\n