Agile Analytics

Part 1: Positioning Statement

For farmers, industries and grain exporters who need to buy and sell their grains in the local market, AGRINESS

is a digital broker that offers different prices of different agricultural commodities through its online platform,

allowing its users to buy and sell grains with a single click. Unlike other classic and expensive brokers,

AGRINESS allows the realization of businesses online instantly with commissions below the market and without

administrative delays, allowing its clients an ecosystem of instant solutions for the realization of their

agricultural businesses.

Part 2: Sketch a Demand/Value Hypothesis

Problem Scenarios/Jobs-to-be-Done Current Alternatives Your Demand/Value Hypothesis

Lack of buying and selling of

soybean contracts when there is

market uncertainty.

Metric: Budget Prices

(purchase and sale price of our client

portfolios)

We wait for the market to rebound

or call our clients to find out their

positions and if they want to carry

out contract movements.

If we know the budget price of our

buyer and seller portfolio then we

can offer them pre-agreed contracts

so that they do not miss out on

business opportunities.

copyright Fashion Express

Part 3: Design a Testable Solution for Your Value/Demand Hypothesis

Epic User Stories

As Max the Sales Manager, I want to visualize the available stock of merchandise from our portfolio of buyers

and sellers so that I can offer them personalized deals.

Storyboard

copyright Fashion Express

Dependent Variable/Goal for Epic

The key questions here are:

1. Max uses information provided by BI?

2. Does this information allow Max to build personalized offers?

The specific dependent variables for this, observations we might instrument into code and Google Analytics are:

1. Stock dashboards available by clients + Price boards budgets by clients

2. Transacted personalized offers

Child Stories & Analytics

Child Stories Analytical Question(s) Analytics

As Max, the Sales Manager, I want to

visualize the remaining stock of our

soybean buyers and their respective

"budget prices" in order to offer them

business according to their interests.

How often are personalized offers made in relation to general market offers? How often does this personalized

offer lead to a new business deal?

Metrics: - Creation of personalized offers in relation to general offers. - Conversion of personalized offers

to agreed businesses

copyright Fashion Express


Agile Analytics Part 1 : Positioning Statement For farmers , industries and grain exporters who need to buy and sell their grains in the local market , AGRINESS is a digital broker that offers different prices of different agricultural commodities through its online platform , allowing its users to buy and sell grains with a single click . Unlike other classic and expensive brokers , AGRINESS allows the realization of businesses online instantly with commissions below the market and without administrative delays , allowing its clients an ecosystem of instant solutions for the realization of their agricultural businesses . Part 2 : Sketch a Demand / Value Hypothesis Problem Scenarios / Jobs - to - be - Done Current Alternatives Your Demand / Value Hypothesis Lack of buying and selling of soybean contracts when there is market uncertainty . Metric : Budget Prices ( purchase and sale price of our client portfolios ) We wait for the market to rebound or call our clients to find out their positions and if they want to carry out contract movements . If we know the budget price of our buyer and seller portfolio then we can offer them pre - agreed contracts so that they do not miss out on business opportunities . copyright Fashion Express Part 3 : Design a Testable Solution for Your Value / Demand Hypothesis Epic User Stories As Max the Sales Manager , I want to visualize the available stock of merchandise from our portfolio of buyers and sellers so that I can offer them personalized deals . Storyboard copyright Fashion Express Dependent Variable / Goal for Epic The key questions here are : 1 . Max uses information provided by BI ? 2 . Does this information allow Max to build personalized offers ? The specific dependent variables for this , observations we might instrument into code and Google Analytics are : 1 . Stock dashboards available by clients + Price boards budgets by clients 2 . Transacted personalized offers Child Stories & Analytics Child Stories Analytical Question(s ) Analytics As Max , the Sales Manager , I want to visualize the remaining stock of our soybean buyers and their respective " budget prices " in order to offer them business according to their interests . How often are personalized offers made in relation to general market offers ? How often does this personalized offer lead to a new business deal ? Metrics : - Creation of personalized offers in relation to general offers . - Conversion of personalized offers to agreed businesses copyright Fashion Express