How Dodo Pizza replaced its contact center and increased a conversion rate up to 27%
Channels
Call center
Industry
Food tech
Dodo Pizza is a fast food restaurant chain specializing in pizza. The company has 739 branches in 15 countries, including the UK, the United States and Europe
Background
The Dodo Pizza branch office in Europe needed to find a way to help return customers profitably and efficiently during the COVID-19 pandemic.
The main goal was to reach customers who had stopped ordering and then find the way to bring "lost" customers back. By the "lost" customers they identified people, who had ordered Dodo's pizza at least once but had not made a single order within 6 months.
After summing up the data it turned out that there were 12,205 of them. Due to the high cost of call center services, franchisees could only afford to call 1,000–3,000 customers a month. Therefore, they needed the capacity of a contact center, but without having an actual one.

Why the contact center was “not it” for Dodo Pizza?
Expensive
A third-party call center costs a pretty penny to reach the whole audience. The team had tens of thousands of numbers on the CRM base but could only afford to call 1,000–3,000 customers a month.
Poor quality
Call center agents did not understand the principles of the industry. They called customers at their own convenient time, without taking into account important factors, such as holidays and salary schedules. They sent out batches of SMS with delays, moreover, they did not even know how to communicate with people.
Lack of analytics
The team had no choice but to blindly develop a methodology for processing and estimating the economic effect of calls, while a call center only provided poor survey results.
Goals
Increase the number of orders among “lost” customers
Reduce the costs of contacting clients
Automate the call process
Collect call analytics easily
Solution
Tanya 1.0
The Dodo Pizza team came up with an idea of designing a voice bot. As an NPS tools, the voice bot called clients who hadn't ordered anything in more than six months and asked for their feedback.
The increase in revenue after the first call was 10%
She asked customers why they stopped ordering from Dodo Pizza and offered to send a promo code with a discount for the next order.

How does the voice bot work
Tanya calls a “lost” customer
The customer tells her a reason why they didn’t order
Tanya thanks them and offers a promo code with a discount
10% is an increase in revenue
2% of customers made an order within the first week
The customer uses the promo code
After the voice bot came into play, the conversion rate held slightly lower than in the first case. However, the team spent $266 and
the voice bot had already brought $25,358 in revenue

Tanya 2.0
A week later, the voice bot started calling a new group of customers with a new scenario. There were people who regularly ordered from Dodo, but eventually stopped.
While the voice bot asked customers about the reasons for their long separation from Dodo Pizza, the team found that 43% of the missing customers simply had no reason to order pizza again.
Then the team scaled the project up to 30 pizzerias of the chain. Results in just two months:
150,000
People had a conversation with the voice bot
5,664
Customers eventually placed an order
$84,000
Dodo Pizza earned in total
The AI-powered robot calls clients and brings them back. The project started as a phone bot working for one pizza restaurant and made it to a large-scale business project powered by speech analytics for 50+ branches
Getting the most out of AI
Call new and missing clients on schedule
Send out text messages with promo codes
Repeate one’s question when the talking is indistinct
Analyse questions, prepares schedules and lists
Collect customer feedback
Convert speech to text and saves it to CRM
Understand the context
Results
up to 50%
Is a conversion to consent, as with live phone agents
27%
Is an average lead conversion rate (the script is passed all the way through)
$84,000
Tanya earned for Dodo pizza in just 2 months
by 3 times
The cost of contact with customers was reduced