Rurimi rwaamai rwunoita zvinhu zvizhinji zvakawoma zvireruke. Nekudero tinopembedza muzvinafundo wepa University of Zimbabwe VaLuckson Mugomo ava vanova mazvikokota muzvidzidzo zve Data Analysis uye kuva nhengo ye ZBIN. Vakabvuma gore rakapera kuita Premium Chat iyo vaitipakurira zvizere zvingatibatsire mumabhizimusi edu pakushandisa Data Analysis ne Science.

Chidimbu chezvavakatipakurira chiripo pasi;

Good evening ladies and gentlemen, it’s an honour to meet you guys. l really appreciate the time. As we tune ourselves to the time we were waiting for.

Informal marketers nemaSMEs vanowanzoshandisa nzira dzakapfava dzemabhizinesi, asi pane mhando dzedata analysis dzavanowanzoregeredza, uye kushaya hanya nadzo kunogona kuvapinza mumatambudziko akasiyana. Heano maitiro ekuti dzimwe dzedata analysis dzinosarirwa kunze uye matambudziko anogona kuvaitika:

1. Customer Segmentation Analysis (Kuongorora Kwevatengi Pakutenga):

Inoregeredzwa Nei: Informal marketers nemaSMEs vanowanzo pinda mumugwagwa wekubata vatengi vese sevanhu vane maitiro akafanana.

Matambudziko: Kugumbuka kwevatengi kana kushaya chokwadi kwevatengesi. Vanogona kushandisa mishandirapamwe isina kutarirwa, vachitambisa zviwanikwa pasina kuvandudza zvavari kupa kune vatengi vanoda chaizvo.

Zvinoita Kupiwa Njere: Segmenting vatengi zvinoenderana nezvinodiwa, maitiro ekutenga, uye nhoroondo kunogona kuvabatsira kugadzira mishandirapamwe inoshanda uye kuchengetedza vatengi.

2. Sales Trend Analysis (Kuongorora Mafambiro Ekutengesa):

Inoregeredzwa Nei: SMEs dzinowanzo tarisa pane chikuva chazvino chemari, pasina kuongorora zvizere mafambiro ekutengesa kwavo munguva refu.

Matambudziko: Kushaya kuchenjera pakuronga nguva dzepamusoro kana dzekuderera kwemitengo uye kutarisira inventory, zvichizounza kana kushomeka kana kutambisa zviwanikwa.

Zvinoita Kupiwa Njere: Kuongorora mafambiro kunobatsira kutarisa nguva dzepamusoro pekutengesa uye kugadzirira kushandisa zvizere zviwanikwa panguva dzakakodzera.

3. Inventory and Supply Chain Analysis (Kuongorora Inventory uye Cheni Yezvinhu):

Inoregeredzwa Nei: SMEs uye informal marketers vanowanzotadza kuongorora zvakanaka mashandiro echeni yezvinhu uye kuongorora masheya avo.

Matambudziko: Kuwanikwa kwezvinhu kunogona kupererwa kana kutakura zvinhu zvisina kudikanwa zvinodya mari. Dzimwe nguva izvi zvinokonzera kukundikana kubhadhara zvinhu zvakakwana kana kufa kwezvigadzirwa zviri mumaoko.

Zvinoita Kupiwa Njere: Kuongorora mashandiro ekuwana zvinhu, kutarisa kunzwisisa mashandiro ayo uye kuona kuti zvigadzirwa zviri mukuchenjera here zvinodzivirira kana kutora mikana pazvinodiwa.

4. Customer Lifetime Value (CLV) Analysis:

Inoregeredzwa Nei: Vatengesi vakawanda vari pamisika isina kurongeka vanowanzogadzirisa kutengesa kwavanosvika asi havakwanise kuverenga kukosha kwevatengi kwenguva refu.

Matambudziko: Kusaziva kuti vatengi vapi vane kukosha kwepamusoro kunokanganisa marongerwo emari, vasingagadzirise sarudzo dzebhizinesi kugutsa vatengi vane hukama hurefu.

Zvinoita Kupiwa Njere: Kuongorora CLV kunobatsira mabhizinesi kushandisa zviwanikwa zvavo zvinobudirira pazvikamu zvevatengi zvine mikana huru yekusimudzira bhizinesi kwenguva refu.

5. Cost Analysis and Profitability Analysis (Kuongorora Mitengo neKuwana Purofiti):

Inoregeredzwa Nei: SMEs dzinogona kuverenga purofiti pasina kunyatsorondedzera mitengo yakazara, semuenzaniso mari dzekutakura, kutengesa, kana mitero.

Matambudziko: Izvi zvinoguma nekusaziva kuti ndedzipi zvinhu kana zvigadzirwa zviri kuita purofiti uye ndedzipi dziri kutambisa mari, zvichitadzisa kutora matanho anovandudza bhizinesi.

Zvinoita Kupiwa Njere: Kushandisa data kuongorora mitengo yakazara kunobatsira kuchengetedza purofiti uye kuvandudza masarudzo ekuti zviwanikwa zvirongedzerwe kupi.

6. Predictive Analytics (Kuverenga Zvinogona Kuitika):

Inoregeredzwa Nei: Informal markets uye SMEs vanowanzoita sarudzo dzebhizinesi dziri kutevedzera manzwiro emazuva ano kana zviporofita, kwete kushandisa data kuita fungidziro dzekuzivikanwa kwekambani munguva refu.

Matambudziko: Izvi zvinogona kuunza kukundikana kuronga ramangwana zvakanaka, kurasikirwa nezviwanikwa, uye kukanganisa kugadzirwa nekusimba kwemasheya.

Zvinoita Kupiwa Njere: Kushandisa predictive analytics kunovabatsira kufanotaura mikana uye matambudziko anouya, uye kuita sarudzo dzakanyatsobatana nedata kuti zvigadzirise kuwana purofiti uye kuchengetedza bhizinesi.

7. Competitive Analysis (Kuongorora Kukwikwidza):

Inoregeredzwa Nei: SMEs dzinozvitarisa dzoga uye dzinonzwa sekunge hakuna kukwikwidza kwakanyanya mukutengesa kwavo.

Matambudziko: Kusaita ongororo yemakwikwi kunoita kuti vakundikane kunzwisisa zvigadzirwa kana masevhisi avanofanira kuvandudza kuti vagare vachikwikwidza.

Zvinoita Kupiwa Njere: Kuongorora maitiro emakwikwi nevatengesi vakuru uye vakuru vemumunda kunogona kuvapa nzira dzekusimudzira bhizinesi ravo uye kushandisa misika yavanogona kuwana.

Kushaya hanya nedata analysis inodzika mushe mukugadzirisa bhizinesi kunogona kuunza SMEs nema informal marketers munataisireva nematambudziko anosanganisira kuderedza purofiti, kushomeka kwevatengi, uye kutadza kukwikwidza nemusika mukuru.

Kana managing yevashandi kumashop kuri kukunetsa, unogona kushandisa data analysis kuti uwane nzira dzekugadzirisa matambudziko aya.

 Heano maitiro data analysis ingakubatsira pakumanager basa rako:

• Kutevedzera mashandiro evashandi: Ungashandisa data analysis kuona kuti vashandi vari kushanda sei kana kugadzira metrics dzekuita kwavo. Zvinhu zvakaita sekugadzirisa nguva dzavanosvika kubasa, maitiro ekuvashanda, uye huwandu hwebasa ravanoita zvinogona kukubatsira kuvandudza kutarisirwa kwavo.
• Kuita kwemasheya uye kushandiswa kwezvipo: Paunoramba uchishandisa data kuongorora mashandiro ezvipo nemasheya, unogona kuona kuti zvigadzirwa zvipi zvinotengwa zvakanyanya, zvinotora nguva yakareba kudarika kugadzirwa here, uye ndedzipi nzira dzingaitwa kuti kuderedza marara nemari yekuchengetedza.
• Kushandisa predictive analytics: Ungashandisa maalgorithms kuongorora mamodheru ezvinotarisirwa kutenga zvigadzirwa, zvichiita kuti ugadzire maoko ekutenga izvo zvigadzirwa zvinodiwa kazhinji, zvichideredza kurasikirwa uye kukwidza purofiti.
• Kuongorora reviews kana mafeedback: Kukanganisa kunowanzo kuitika mukubata nevashandi kunogona kuoneka mukufunga kwavo kana mafeedback evatengi. Data analysis inokubatsira kugadzira nzira dzekukoshesa mafeedback uye kugadzirisa nzvimbo dzinosangana nematambudziko.
• Kutarisira mari: Kubatsira ne data analysis, unogona kuve nemakakatanwa ekudzora mari uye kugadzira zvigadzirwa zvinonyanya kubatsira, zvichiita kuti uchengetedze purofiti.

Munzvimbo dzisingaputike (informal markets), unogona kushandisa mhando dzakasiyana dze informal data collection methods:

Surveys neGoogle Forms kana WhatsApp kugadzirisa ruzivo nezve vatengi nevashandi.

Dashboards dzePower BI kana Tableau kuti unzwisise mashandiro emakambani evatengi uye maitiro ekutenga.

Sales analysis uchishandisa Excel kuti uwane nzvimbo dzine simba uye nzira dzekuvandudza.

Data analysis inowedzera kujekesa pamashandiro evashandi uye inobatsira kugadzirisa basa rako se manager.

Dumisani Dube | Harare | Zimbabwe.

The recent Reserve Bank of Zimbabwe (RBZ) monetary policy decision announced on 6 February 2025 to reduce the foreign currency retention threshold for exporters from 75% to 70% has sent shockwaves through the agricultural sector, particularly among export farmers. This move, while aimed at bolstering the local currency, threatens to undermine the viability of export-oriented agricultural businesses, especially in an already volatile economic climate.

Zimbabwean export farmers, facing high input costs largely denominated in US dollars, have become heavily reliant on exporting their produce to secure foreign currency. This strategy allows them to hedge against the inherent risks of the local currency and ensure access to essential inputs for future production cycles.

The reduction in forex retention will directly impact farmers’ profitability. With a smaller portion of their export earnings retained in foreign currency, they will face increased difficulties in:

• Purchasing critical inputs: Importing fertilizers, seeds, and other agricultural inputs, which are often priced in US dollars, will become more expensive as they may have to source the extra foreign currency elsewhere.
• Servicing foreign currency-denominated debts: Many farmers have incurred debts in foreign currency for equipment and machinery. Reduced forex retention will make it harder to meet these obligations.
• Investing in farm improvements: Expanding operations, upgrading equipment, and implementing improved farming practices often require significant foreign currency investments.

Furthermore, the reduction in forex retention erodes the confidence of credit providers in lending to export-oriented farmers. Credit facilities often prefer to work with exporters to mitigate the risks associated with the unstable Zimbabwean dollar. The decreased incentive for exporters to retain foreign currency may discourage lenders from extending credit, further limiting farmers’ access to crucial financing.

This policy change comes at a time when Zimbabwean farmers are already grappling with numerous challenges, including unpredictable weather patterns, rising input costs, and limited access to markets. Reducing forex retention risks undermining the resilience of the agricultural sector and hindering its contribution to economic growth.

The government must carefully consider the potential negative impacts of this policy change on the agricultural sector. It is crucial to find a balance between supporting the local currency and ensuring the sustainability of the export-oriented agricultural sector, which plays a vital role in Zimbabwe’s economy and should be promoted and protected by all means.

Dumisani is an agricultural, compliance expert and lead consultant at fresh solutions Africa. He can be reached via email at freshsolutionsafrica@gmail.com. Follow us on x @fresh_solzim