Company

Verisure is part of the Hellman & Friedman investment portfolio. It is the second largest home alarm provider in the world, trading as Verisure in most of the countries they operate, and as Securitas Direct in Spain and Portugal.

The company has more than 4.7 million customers in 17 countries across Europe and Latin America. 

Business Model

Verisure’s business model integrates product development, design, and sales with installation, service, and a 24/7 professional monitoring solution.

It offers alarm systems for homes and businesses with a ratio of 80:20 and has introduced Senior Protection, a bracelet for the elderly promoted together with CaixaBank.

The contracting of the alarms follows a series of steps that are:

1. Online lead
2. Validation of lead data
3. Appointment to visit home or business
4. Installation and sale

Objective

Verisure is a company that increases its turnover year on year but has a strong focus on efficiency and profitability. For this reason, Verisure IT needed to focus on qualified leads that drive higher long-term profitability.

Solution

In a context in which the campaigns were highly optimized, the challenge required a careful review of the most sensitive areas in terms of efficiency. A consultancy was carried out, analyzing the available data. Making Science proposed they face this challenge through artificial intelligence (AI) technology developed in-house: Gauss Smart Advertising. Predictive models are capable of anticipating the future value of the leads registered on the web, making the whole process of acquiring new customers more efficient.

In order to implement this project, not only analyzed the available data but also the volume of this data. As AI is data-driven, this is a key factor.

The proposal was to make real-time predictions of the propensity to install, the last step in its business model, as we have seen above, and therefore the most closely linked to turnover.

The test was carried out in Italy, in Alarm campaigns in Search Ads. To evaluate the results objectively, an AB test was conducted creating a test group (with Gauss Smart Advertising) and a control group (on-going campaigns). 

The test group was asked to optimize to pROAS (predicted return of ads spend) and the control group was asked to optimize to CPA (cost per acquisition).

Results

The results show the excellent achievement of the objectives, as we managed to improve installations by 8.3%, reducing CPI by 27%

• +8.3% Installations (Test vs Control) 
• -27% CPI