Dashboard structure depends on business model.
E-commerce dashboard focuses on: traffic, conversion, average order value, return rate, repeat customer rate. Because revenue is function of these.
SaaS dashboard focuses on: churn, expansion revenue, new customer acquisition cost, customer lifetime value, feature adoption. Because recurring revenue is function of these.
Marketplace dashboard focuses on: buyer count, seller count, transaction volume, average transaction value, buyer repeat rate, seller retention. Because GMV is function of these.
Each business model has different revenue drivers. Dashboard should highlight drivers.
E-commerce dashboard
Revenue drivers
Traffic, conversion rate, average order value, repeat customer percentage.
Real example
Monthly revenue target: five hundred thousand.
Traffic: fifty thousand monthly visitors (target fifty thousand, green).
Conversion rate: two percent (target two point five percent, yellow).
Average order value: five hundred dollars (target five hundred dollars, green).
Repeat customer rate: thirty percent (target thirty-five percent, yellow).
Math: fifty thousand visitors times two percent conversion equals one thousand customers times five hundred AOV equals five hundred thousand revenue. Target would be fifty thousand times two point five percent times five hundred times one point zero equals six hundred twenty-five thousand. We are at eighty percent of target.
Problem is not traffic (we have enough). Problem is conversion (two vs two point five percent). Secondary problem is repeat rate (thirty vs thirty-five percent).
Actions: improve conversion rate (checkout friction test), improve repeat rate (email retention campaign).
SaaS dashboard
Revenue drivers
New customer acquisition, churn rate, expansion revenue, average contract value.
Real example
Annual recurring revenue target: five million.
New customers per month: twenty (target twenty-five, eighty percent, yellow).
Monthly churn: two percent (target one point five percent, red).
Expansion revenue per customer: one thousand annually (target one point two thousand, yellow).
Average contract value: forty thousand annually (target forty-five thousand, yellow).
Math: starting ARR four point five million. Add new customers: twenty per month times forty thousand times twelve months equals nine point six million. Subtract churn: four point five million times two percent times twelve months equals one point one million lost. Net: four point five plus nine point six minus one point one equals thirteen million. But actual is five million. Something is wrong.
Investigation: expansion revenue is below target (customers are not upgrading). Also starting number was wrong (actual starting ARR is three point five million not four point five). Recalculate: three point five plus nine point six minus one point one equals eleven point nine million. Still above five million.
More investigation: new customer number might be overstated. Or expansion revenue is much lower. Or churn is higher. Pull detailed data. Find churn is actually two point five percent not two percent. Recalculate with correct numbers. Find average contract value is thirty-five thousand not forty thousand.
After verification, numbers are corrected. New model with actual numbers produces five million which matches actual. Dashboard is now trustworthy.
Marketplace dashboard
Revenue drivers
Buyer count, seller count, average transaction value, transaction volume, buyer repeat rate, seller retention.
Real example
Monthly GMV target: ten million.
Buyers: one hundred thousand (target one hundred twenty-five thousand, eighty percent, yellow).
Sellers: five thousand (target six thousand, eighty-three percent, yellow).
Transactions per buyer per month: five (target six, eighty-three percent, yellow).
Average transaction value: four hundred dollars (target four hundred dollars, green).
Buyer repeat rate: forty percent (target fifty percent, yellow).
Seller retention: seventy percent monthly (target eighty percent, red).
Math: one hundred thousand buyers times five transactions equals five hundred thousand transactions times four hundred dollars equals two hundred million GMV. But target is ten million. Math does not match. Error in model or assumptions.
Investigation: transactions per buyer might be per year not per month. Or average transaction value is wrong. Pull actual data. Find transactions per buyer is actually zero point five per month. Recalculate: one hundred thousand times zero point five times four hundred equals twenty million.
Check GMV calculation. Find that GMV is calculated differently. Some transactions are through marketplace, some through seller directly. Dashboard only shows marketplace transactions (twenty million). Seller direct sales are ten million. Total company GMV is thirty million.
Clarify definition. Dashboard shows marketplace GMV. Total company GMV is thirty million. Breakdown: marketplace twenty million, direct ten million. Now analysis makes sense.
Primary problems: buyer count below target (need marketing), seller count below target (need recruitment), buyer repeat rate below target (need product improvements), seller retention below target (red flag, investigate why sellers are leaving).
Actions: increase buyer marketing spend, launch seller recruitment program, improve product to increase repeat purchases, investigate seller churn root causes.
Agency dashboard
Revenue drivers
Number of clients, average monthly retainer, project revenue, utilization rate, project margin.
Real example
Monthly revenue target: two hundred thousand.
Active clients: forty (target fifty, eighty percent, yellow).
Average monthly retainer: three thousand dollars (target three thousand five hundred, eighty-six percent, yellow).
Project revenue: thirty thousand monthly (target forty thousand, seventy-five percent, red).
Utilization rate: seventy-five percent (target eighty percent, ninety-four percent, yellow).
Project margin: thirty percent (target forty percent, seventy-five percent, red).
Math: forty clients times three thousand plus thirty thousand projects equals one hundred twenty plus thirty equals one hundred fifty thousand. Target is two hundred thousand. We are at seventy-five percent of target.
Problems: too few clients (forty vs fifty target). Retainer too low (three thousand vs three point five thousand). Project revenue is low (thirty vs forty thousand). Project margin is low (thirty percent vs forty percent target).
Action: increase client count (sales and marketing), increase retainer on existing clients (value-based pricing), increase project volume (more project work per client), improve project margin (better scoping, automation, subcontracting).
Frequently asked questions
Should dashboard be identical across industries or customized per industry?
Customized. Different industries have different revenue drivers. E-commerce focuses on conversion. SaaS focuses on churn. Marketplace focuses on seller retention. Generic dashboard does not highlight what matters. But underlying principle is same: show metrics that drive revenue. Start with industry template, then customize to your specific business. With WEMASY's website builder, you can set this up directly on your website.
How do we define metrics that are specific to our business model?
Work backwards from revenue. Revenue equals driver one times driver two times driver three (simplified). For e-commerce: revenue equals traffic times conversion times AOV. For SaaS: revenue equals starting ARR plus (new customers times ACV) minus (churn times starting ARR). Write equation. Each component is metric to track. Equation helps validate metrics. If you cannot write revenue equation, you do not understand your business model.
What if we operate in multiple verticals (e.g., SaaS plus services)?
Create separate dashboards per vertical. SaaS dashboard focuses on churn and expansion. Services dashboard focuses on utilization and margin. Combine at executive level (total company revenue) but break out by vertical. Vertical-specific dashboards drive vertical-specific decisions. Company dashboard shows both contribute fifty percent each to total revenue. Sales dashboard for SaaS vertical shows pipeline by segment.
How do we know if our revenue model is wrong?
Compare dashboard math to actual results. Dashboard predicts revenue should be X. Actual revenue is Y. If X is much higher than Y, model is wrong. Investigate discrepancy. Model might be using wrong starting point, wrong drivers, wrong multipliers. Audit model. Example: SaaS model predicts fifteen million ARR. Actual is five million. Either new customer acquisition is lower, churn is higher, or expansion is lower. Pull data. Find which assumption is wrong.
Should we change dashboard structure if business model changes?
Yes. If company launches new product line, structure changes. If company goes from selling products to SaaS subscription, structure changes. Quarterly: review whether dashboard still reflects current business model. If not, redesign. Business model change should trigger dashboard redesign. Do not delay updating dashboard structure when business changes.
How do we handle seasonal metrics when business model is seasonal?
Show seasonal baseline. Marketplace might have high GMV in December (holiday shopping) and low GMV in September. December target is different from September target. Create seasonal targets. Or show year-over-year comparison to remove seasonality. Example: December revenue goal is five hundred thousand, September goal is three hundred thousand. Same quarter comparison year-over-year removes seasonal noise.