Olo’s app needs three different business cases
Olo announced its consumer delivery and carryout app on October 20. Its operator FAQ still describes a live beta initially for existing Olo restaurant customers, with commission-free orders and guest data shared after a first completed order. The materials do not establish a restaurant’s complete cost or marketing permissions. Launch · Operator FAQ
The commercially important question is the order’s alternative destination. There are three cases.
Additional demand: a customer chooses your restaurant who otherwise would not have purchased from it. Revenue increases, but contribution depends on food, packaging, fulfillment and any additional operating burden. A sale arriving during spare capacity can have different consequences from the same sale arriving during a bottleneck.
Channel substitution: a customer who would have ordered from your restaurant through another paid route moves to the new app. The benefit or loss is the change in total transaction contribution, not the full order value. That difference cannot be calculated from a commission headline alone.
Displaced direct demand: an existing customer moves from your own ordering channel. The restaurant may gain convenience or better retention, but it has not necessarily gained a meal sold. Any new costs or weakened customer relationship must be weighed against those benefits.
This distinction also changes the meaning of customer data. Having information after an order can improve visibility, but usable identity, permission to market, integration into an existing customer record and a repeat purchase are separate steps. Calling all of them “owning the customer” skips the difficult parts.
Our assessment: existing Olo customers with available capacity have the most direct reason to evaluate the channel. A restaurant outside that ecosystem should not treat the announcement as sufficient grounds to migrate its ordering stack. The business case needs evidence of demand or better economics that survives the full cost of the change.
A hundred app orders can describe very different outcomes
The following is an illustrative model, not Olo performance data, a forecast or an industry benchmark. It assumes the same 100 completed app orders in each case and assigns their hypothetical origin. No vendor prices, margins or savings are assumed.
Illustrative model - 100 completed app orders in each case
| Discovery-led example | |
|---|---|
| Additional restaurant orders | 60 |
| Moved from another paid channel | 25 |
| Moved from the restaurant’s own direct channel | 15 |
| Total app orders | 100 |
| Substitution-led example | |
|---|---|
| Additional restaurant orders | 10 |
| Moved from another paid channel | 70 |
| Moved from the restaurant’s own direct channel | 20 |
| Total app orders | 100 |
| Direct-displacement example | |
|---|---|
| Additional restaurant orders | 10 |
| Moved from another paid channel | 10 |
| Moved from the restaurant’s own direct channel | 80 |
| Total app orders | 100 |
The first example creates the largest increase in restaurant orders. The second creates a potentially meaningful cost comparison. The third needs a strong convenience, retention or cost advantage to justify the change. A dashboard reporting only 100 app orders would obscure all three stories. The largest order increase is not automatically the best contribution outcome; margin, channel cost and capacity also matter.
These categories are counterfactual: an operator cannot know with certainty where every customer would otherwise have ordered. New-to-channel customers are not necessarily new to the restaurant. A credible evaluation therefore combines total restaurant demand, channel mix and repeat behavior, while acknowledging that promotions, seasonality and local events can change the result.
Compare the restaurant’s contribution with and without the channel over the same period. Here, contribution means sales less food, packaging, channel fees and discounts, fulfillment, and incremental labor or service-recovery costs tied to those orders; it is not net profit and excludes fixed overhead, taxes, financing and owner pay. Include contribution from genuinely additional orders and the contribution difference when an existing order changes channels. Subtract only incremental costs not already included, counting each fee, discount, packaging cost, refund, remake and labor change once. Keep longer-term retention separate unless it is measured.
Refunds, remakes, overtime or reduced service to existing guests can consume an apparent gain. Conversely, an order fulfilled during unused capacity may be more attractive than its average-store cost allocation suggests. Neither effect is established by app sales alone.
If app orders rise while total restaurant orders stay flat, that alone does not show growth; the question is whether the channel change improved contribution. If ticket value rises while completed orders fall and intervention grows, the upsell may be making the constraint worse. Neither pattern establishes causation, but both are reasons to withhold a positive verdict.