Houza.com: attracting customers in real estate aggregator in UAE using PPC

About Houza.com

private and commercial real estate aggregator in UAE

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• NicheB2B, B2C
• GeographyUAE

At the beginning of our cooperation, the project was about to launch. SEO optimization was not generating traffic yet. To get the results faster, a decision was made to launch PPC advertising. Our task was to quickly commercialize the product, get quality target traffic and test the business model.

Project aims

• 2500 unique quality users a day and 30 000 users a month  
• achieving MAU and DAU indicators by means of paid-for advertisement as soon as possible

The Client also wanted to identify:

• market volume by traffic
• approximate user cost (not cost per click) from the paid-for advertisement
• potential cost per lead which has performed a useful action (call the agency, leave contacts, file an application for real estate) 

Having discussed the aims, we got down to business.

Step 1. Preparing the semantic core

Before we started our cooperation, the Client had conducted thorough research on competitors: created a list and parsed the semantics with the help of Semrush, prioritized the necessary semantics and split it into clusters.

We added data from Google and Serpstat, created the semantic core of the project, which was split into campaigns later on, and made a mindmap of the future advertising account. 

It’s rare for the Client to provide so much data at the beginning of the project. As a rule, we collect it ourselves. Here are the very basics of our methodology:

Step 2. Customizing the dashboard in Google Data Studio

To create the general dashboard of the project we used Google Data Studio. We made a report with the following data, tables and graphs:

• key traffic indicators for the date and period: number of sessions, number of users, advertising cost, conversion rate, conversion cost 
• dynamics of indicators over time. All project participants see how the CPA / CPC / ROI of the project changes, how the ratio of costs to turnover or to profit grows 
• in a separate table — detailed data for each paid traffic source, indicators of campaigns of various types and their dynamics

Step 3. Checking and updating Google Analytics and Google Ads account settings

At this stage our team

• checked the accuracy in data collection processes
• set up correct accounting of conversions and micro conversions 
• issued technical requirement for site tagging for product dynamic remarketing campaigns
• issued technical requirement for creating feeds for DSA campaigns 
• formed the structure of negative keyword lists for different types of campaigns 
• added work scripts to the Google Ads account: tracking 404 errors and errors in word match types, tracking negative keyword conflicts, scripts for working with DSA campaigns

Stage results: a starting point was fixed, an advertising campaign plan and launch priorities were formulated. 

Step 4. Launching advertising campaigns and setting up optimization

Analyzing competitors’ indicators showed the following statistics on requests:

• the total search volume in the niche is about 512,000 queries per month 
• the amount of paid traffic that competitors collect for priority queries is about 65,000 queries per month (data taken from open statistics Semrush, Serpstat, Similarweb) 
• the main traffic is generated by low-frequency queries from 2+ parts in the search query 

With this in mind, we have developed a strategy for the first 2 months of work. 

1. First, we run dynamic search campaigns (DSA). They will serve as semantic donors for the rest of the campaigns. In the process of regular optimization, we will take away the semantics that are significant in terms of traffic volumes from these campaigns and add them to the main search campaigns. In DSA campaigns, we will add them as negative words. As a result, after some time (with a large amount of traffic, usually up to six months), DSA campaigns should almost completely stop generating traffic. 
2. We launch regular search campaigns according to the Client’s priorities. 

We used the following account structure:

At the launching stage, every detail was thought through, since the system of negative keyword lists and cross-group negatives between campaigns play a very important role in the implementation of an advertising campaign. They make the system manageable.

Step 5. Analyzing the first results

In the first 2 months, advertising campaigns reached the following indicators:

• from 1,185 clicks per week with an average cost per click of $1.14 to 17,300 clicks per week with a cost per click of $0.38 at the end of the second month of running campaigns
• increase in the number of leads by 50% monthly. 

Step 6. Further work with the account

In the process of work, we regularly optimize campaigns according to our checklist, ungroup them to create an even more granular account structure. 

Our main task was to get an answer to the question — what is the price of a user and a lead for each type (query topic)? This allows you to invest not in the entire advertising channel, but in that part of it that is most profitable. 

What we did next?

We divided DSA campaigns by page type to remove budget competition between queries within campaigns. 

We supplemented traditional search campaigns with additional semantics:

• by area types
• by business center names
• by names of residential areas and estates
• advertisements focusing on commercial real estate

At the time of this writing, we continue to optimize advertising, reducing the cost of a lead.

Results

• The Client reached the desired indicators for DAU (number of unique users per day) and MAU (number of unique users per month) by the 2nd month of work. We constantly hear — go ahead spending more, there’s no need to save our budget
• The traffic grows from month to month while maintaining CPC
• Leads quadrupled in 5 months with only a 40% increase in CPC (from 32 cents to 53 cents) 
• The Client is satisfied with the quality of leads from Google Ads.

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