<prompt> Theha mokhoa oa ho arola lipotso tsa bareki ka mefuta.nan  <response>: Instruction in English: Construct a system for categorizing customer inquiries by type. . Response in English: To categorize customer inquiries by type, a system can be designed that follows the steps below:1. Data Collection: Collect data from various sources such as customer service emails, support tickets, chat conversations, phone calls, and social media. This data will form the basis of inquiry categorization.2. Data Preprocessing: Clean up the data by removing irrelevant information, correcting spelling errors, and standardizing text formatting. Data preprocessing is important to ensure that the system can accurately identify and categorize inquiries.3. Inquiry Identification: Use natural language processing NLP techniques to identify customer inquiries from the collected data. This step involves analyzing the content, context, and tone of the inquiries to understand the customers needs and intent.4. Inquiry Categorization: Once the customer inquiries have been identified, they need to be sorted into different categories. Some common categories for customer inquiries may include product information, technical support, billing, and account management. Machine learning algorithms can be used to accurately categorize the inquiries.5. Inquiry Tagging: Assign tags to customer inquiries based on their categories. These tags will help to quickly and easily identify the type of inquiry and route it to the appropriate customer support agent.6. System Maintenance: Regularly update the categorization system to reflect changes in customer behavior, new product features, and company policies. This will ensure that the system remains accurate and effective in categorizing customer inquiries.Overall, an effective system for categorizing customer inquiries by type will help to streamline customer support operations, leading to faster response times, improved customer satisfaction and more efficient use of resources. . Response in Sesotho: Ho arola lipotso tsa bareki ka mefuta, tsamaiso e ka etsoa e latelang mehato e ka tlase: 1. Pokello ea Lintlha: Bokella lintlha ho tsoa mehloling e fapaneng joalo ka li-imeile tsa litšebeletso tsa bareki, litekete tsa tšehetso, lipuisano tsa moqoqo, mehala, le mecha ea litaba ea sechaba. . Lintlha tsena li tla theha motheo oa ho arola lipatlisiso. 2. Tlhahiso-pele ea Boitsebiso: Hloekisa datha ka ho tlosa tlhahisoleseding e sa hlokahaleng, ho lokisa liphoso tsa mopeleto, le ho beakanya fomete ea mongolo. Tsebetso ea data preprocessing ke ea bohlokoa ho netefatsa hore sistimi e khona ho khetholla le ho arola lipotso ka mekhahlelo. 3. Boitsebiso ba Lipatlisiso: Sebelisa mekhoa ea tlhaho ea puo ea tlhaho NLP ho tseba lipotso tsa bareki ho tsoa boitsebisong bo bokeletsoeng. Mohato ona o kenyelletsa ho sekaseka litaba, maemo, le molumo oa lipotso ho utloisisa litlhoko le sepheo sa moreki. 4. Karohano ea Lipatlisiso: Hang ha lipotso tsa bareki li se li tsejoa, li lokela ho hlophisoa ka mekhahlelo e fapaneng. Mekhahlelo e meng e tloaelehileng ea lipotso tsa bareki e ka kenyelletsa tlhahisoleseling ea sehlahisoa, tšehetso ea tekheniki, litefiso le taolo ea li-account. Li-algorithms tsa ho ithuta ka mochini li ka sebelisoa ho arola lipotso ka nepo. 5. Inquiry Tagging: Abela li-tag ho lipotso tsa bareki ho latela lihlopha tsa bona. Li-tag tsena li tla thusa ho tseba ka potlako le ha bonolo mofuta oa potso le ho e fetisetsa ho moemeli ea loketseng oa tšehetso ea bareki. 6. Tlhokomelo ea Tsamaiso: Kamehla ntlafatsa mokhoa oa ho arola lihlopha ho bonahatsa liphetoho boitšoarong ba bareki, likarolo tse ncha tsa sehlahisoa, le maano a khampani. Sena se tla netefatsa hore sistimi e lula e nepahetse ebile e sebetsa hantle ho aroleng lipotso tsa bareki. Ka kakaretso, mokhoa o atlehang oa ho arola lipotso tsa bareki ka mefuta e tla thusa ho nolofatsa tsebetso ea tsehetso ea bareki, e lebisang linakong tse potlakileng tsa karabelo, khotsofalo e ntlafetseng ea bareki le tšebeliso e ntle ea lisebelisoa. 