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Sena shows how farmers choose their inputs

Sena Decision AI shows agriculture teams which inputs farmers buy, where they buy them, and what would make them switch. Every figure traces to a farmer answer, a dealer photo, or the team's own files.

  • 190+ countries
  • 5M+ consumer network
  • 250+ integrations
Live readingsExample agriculture
Dealers displaying the product
167 / 214
Top reason farmers switch
Results
Named in 31% of switches
Dealers missing the product
47
Shipped, not on display
Monthly sales at stake
58,280
47 dealers missing
every figure opens a farmer answer or a dealer photo
AT THE DEALER COUNTER

Input sales are won at the dealer

Agricultural data analytics usually reads yield, weather, soil, and field data. Input sales turn on a different set of facts: which dealer a farmer trusts, what that dealer has on hand, and what a neighbor planted last season.

Sell-in ends at the dealer

Shipments show which agro-dealers took the product. The farmer makes the choice later, at the counter.

Sena:Sena confirms the products and signage farmers see at named agro-dealers.

Farmers weigh more than price

Price matters, and so do results on a neighbor's plot, credit terms, and the dealer's advice.

Sena:Sena captures which inputs farmers bought, why, and what would make them switch, by segment and market.

New regions need demand before stock

Expansion plans start from planted area and dealer counts. Farmer spend and preferences in that region sit outside those figures.

Sena:Sena captures what farmers in a target region grow, spend, and would pay.

The decisions

Four decisions agriculture teams make

Sena adds the farmer and the dealer to agricultural data analytics and settles these four decisions.

Decision 01

Confirm products reach the agro-dealer

Sena captures geo-verified photos at the agro-dealers a question names and confirms which products and promotions are on display against the coverage plan.

Decides:Which region or dealer group gets sales attention first.

See sales decisions →
Decision 02

Learn why farmers switch inputs

Farmers state which seed, fertilizer, or crop protection product they used, what decided it, and what would make them switch, by segment and market.

Decides:Which product claim, credit term, or dealer offer leads the next agribusiness marketing push.

See marketing decisions →
Decision 03

Size input demand in a new region

Farmers in a target region state the crops they plan to grow, what they spend on agricultural inputs, and where they buy.

Decides:Which regions and dealer networks to enter first.

See strategy decisions →
Decision 04

Test a new product or pack size

Farmers rate each pack size, price point, and product claim, and name the input it would replace.

Decides:Which products and pack sizes launch, at what price, and where.

See product decisions →
THE SOURCE OF EACH FIGURE

Figures trace to farmers and dealers

Each figure opens the photo or answer that produced it, beside the agriculture data in the team's own files.

A geo-verified photo per named dealer

Each dealer figure opens its dated, located photo. A dealer counts once its photo shows the product.

Consented input, checked before it counts

Each answer comes from a farmer who chose to share it. Every capture clears a multi-level QA flow. Sena declines a capture that misses the check, and the decline stays visible.

DEALER GAP

Count the dealers missing the product

On the commercial side, agriculture data analytics usually ends at sell-in. The dealer gap compares the agro-dealers on the coverage plan with the ones where a photo confirms the product, split by region and dealer type.

  • Large ag retail outlets and small rural dealers read separately
  • Each gap traced to a region and crop segment
  • Each gap priced from the team's per-dealer sales
Dealer gap calculatorExample figures
ConfirmedMissing
Dealers missing the product
47
Monthly sales at stake
58,280

The dealer gap calculator multiplies missing dealers by the team's monthly sales per dealer.

A walkthrough on one crop segment

The walkthrough follows one crop segment in one market, from dealer photo to the season plan.

By team

Which agriculture team owns the question?

Pick the closest fit, and the walkthrough starts from that team's question.

How it works

How Sena answers a market question

Step 01 · Capture

Capture

Sena captures geo-verified photos and consumer activity in the markets a question names, when the question needs it.

Step 02 · Read

Read

Sena reads those captures beside the files and systems already in use, across 250+ integrations.

Step 03 · Compare

Compare

Sena compares the market against the plan and shows each gap, with every figure traced to its source.

TWO WAYS TO READ A FARMER

Adding farmers to agricultural data analytics

AreaCommon methodSena
Dealer presenceSell-in and dealer ordersGeo-verified photos at each named agro-dealer
Farmer choiceAgriculture analytics on yield and field trialsFarmers state why they chose an input or switched
New regionsPlanted area and dealer countsWhat farmers in the region spend and would pay
Product testsTrial plots and agronomist inputFarmers rate each pack size, price, and claim
AuditingField reports reconciled by handEach count links to its dealer photo or farmer answer

One crop segment, one market, one decision

Talk to the Rwazi team about one crop segment, one market, and the decision on the table this season.

FAQ

Questions agriculture teams ask

01 What is agricultural data analytics?
Agricultural data analytics is the practice of reading field, yield, market, and farmer data to decide what to grow, sell, and stock. Sena, a Decision AI with access to real-world data, brings decision intelligence to agriculture teams by adding farmer answers and dealer photos to the team's own files, so each figure traces to the farmer answer or dealer photo behind it.
02 How is data analytics in agriculture used?
Data analytics in agriculture is used to plan planting, manage inputs, and price crops. Agricultural data analytics also covers the commercial side: which inputs farmers buy and why. Sena captures that from farmer answers, by segment and market, from seed choice to the dealer a farmer trusts.
03 What does an agricultural data analyst do?
An agricultural data analyst turns field, market, and sales data into planting, pricing, and sales choices. In agriculture data analytics, Sena adds farmer answers and dealer photos to show why sales move: which seed or fertilizer farmers switched to, and which named dealers display the product.
04 How is big data analytics in agriculture used?
Big data analytics in agriculture combines satellite, sensor, weather, and yield data at scale to guide planting and harvest. Sena adds agriculture analytics on the commercial side: what farmers buy, where, and why, from consented farmer answers and dated photos of agro-dealer displays.
05 Which agriculture industry trends shape input sales?
Agriculture industry trends play out in farmer choices: seed, fertilizer, credit terms, and which dealer to trust. Sena captures those choices from farmers in a named market, so a team reads each trend from the farm gate, before the season's input orders close.
06 How does the fertilizer market reach farmers?
The fertilizer market reaches farmers through dealers, cooperatives, and credit schemes. Sena confirms which products the agro-dealers a question names display and captures why farmers choose one fertilizer over another, and at what price, beside the team's own sell-in and dealer coverage plan.
07 Which systems does Sena connect to for agriculture teams?
Sena connects to the systems agriculture teams already run across 250+ integrations, including Salesforce, HubSpot, and Microsoft Dynamics, and reads uploaded Excel, CSV, and PDF files. Farmer answers and dealer photos join that data, so one answer covers the team's own sell-in files and farmer choices.