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Diaspora Matters

How do Partnerships & Crowdfunding Projects Get Captured?

chu

This is an area you are not taught in auditing at colleges or professional studies. An area that leaves experienced risk management professionals with an egg on their faces. This also applies to those who enter the partnership route. It could be equated to The Art Of War and everyone should take an interest taking notes for future implementation.

The textbook highly recommends having constitutions in place to safeguard interests. However disputes abound where constitutions exist. The underlying cause will be greed.

Greed is in most cases hidden but resurfaces at critical periods such as winning a big tender or a big contract. And you don’t need to go far—just study the hunting styles of lions. Great team work and strategy in following prey—then the coordinated hunt and if lucky, dinner time. You may need to check out videos on Youtube for lessons.

Let’s share a case study;

Our first project: We pooled funds for a farming project with land secured, inputs secured under contract, soil tests done. With a week to go before tilling of land and planting—the following took place;

One member profusely protested about farming sesame and not maize? This took place despite a business plan developed and endorsed by everyone—he was part of it. Sudden change at the critical period of farming and changing crops to be planted.

The methodology involved an avalanche of complaints coming thick and fast—endless phone calls, and messages. This will tire everyone and the weak will pull out and this often results in leadership changes.

A sudden last minute change proposal followed by huge coordinated traffic often achieves changes. It’s a tiring game and often succeeds. Some in frustration can exit leaving their funds.

After achieving the change in stopping the original crop for farming— A change in crop followed, and also a change in place for operations. A distance of over 150 kilometres.

A series of changes took place resulting in a change from crops to livestock.

Financial & Risk Management Advice did not yield results—it never does. Those trying to bring light often end up leaving—which would be the objective anyway.

The new sheriff working in cahoots with a clique eventually took over promising great returns. This was before increasing share prices to eject those who could not afford and replacing them with new investors who paid huge sums.

The value of the project shot up by 500%. Later the same script was played to the remaining members till the project ended up as a family affair. Yes it ends up with these words; Iyi I company yemhuri yedu!

Where cash is involved-capture is extremely easy. The target is the treasurer—infiltrate the treasurer and it’s a done deal.

The project was not captured at inception, instead; the capturer waited for enough funds to be pooled, and  final decision to plant crops reached—then changed goal posts because if the planting had succeeded, it was going to be extremely difficult to convert it into own empire.

To you scholars and lawyers—there is a critical period at which a project is captured. Do revise your drafting of constitutions. (1) Funds Pooled + (2) Critical Action Point About to Take Place.

Stop Stage 2 by all means necessary—wreck havoc, increase the noise volume, leave everyone disoriented till some exit. Win by all means necessary. If there is a constitution—shred it into pieces!

Change amounts, change venues, change periods, change terms, change leadership, change everything to suit the needs of the capturer or a small clique of capturers. And the script is almost the same across the country including the diaspora.

Now did you study this at school? If an auditor—was this part of the risk management module?

Plan for this if planning to enter into a partnership or crowdfunding project. There is a stage 2 to navigate—succeed and there lies your success. You need to include this in project planning under risk management.

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Diaspora Matters

Betsero ye Data Analysis kuma bhizimusi madiki

balance sheet

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.

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