When Real Estate Analytics & Novel Advertising Helped a Leading Asian RE Giant Boost Apartment Sales

An established real-estate giant in Asia faced a conversion challenge. Despite being a home-grown brand that appreciated strong customer loyalty and consumer product interest with solid walk-in numbers, the same enthusiasm did not reflect in the property purchase numbers. After several months of witnessing good walk-ins to sales conversion rates, the brand saw a drop in conversion numbers. The brand, guided by intuition-driven judgment, attributed the drop to underperformance of the sales team. The real estate giant teamed up with TheMathCompany to test the hypothesis and see if real estate analytics could offer insights to bounce back in the housing market. Let’s see how this business analytics story unfolded.

Analysing existing data – clarifying biases and asking the right questions

Any analytics exercise begins with outlining a problem statement. Broadly, the challenge faced by the real-estate business was that walk-ins were not converting to sales. However, we needed to take a closer look at the situation to understand why there was a conversion problem – was the sales team the actual/sole cause or were there other contributing causes. Naturally, this called for breaking down the problem, asking the right questions and unearthing insights from data. Through first principles thinking, we arrived at critical questions, which we hoped data could answer; questions like but not limited to –

Is the sales team sufficiently trained?

Are the sales professionals experienced enough?

Is the product good enough?

Are the right marketing channels being used?

Are they selling the right way?

Is the product(supply) meeting the requirements of the target customer(demand)?

The next step was to use data to shed light on these questions. But know that this was a business that was still testing the waters to see if real estate data analytics potentially held any value, due to which, processes to collect, segment and analyze data were yet to be set in place. What followed was diligently digging through data – some useful, some irrelevant – some organized, mostly disorganized – to understand the customer landscape and the weakest links.

Exploratory data analyses revealed that the drop in sales conversion correlated to a point in time where all lower budget units were sold out. At the same time, customer walk-in numbers continued to flourish. But clearly, there was a gap that was causing these customers to walk-in but not purchase. On analyzing historical purchase data and customer profile data such as age, affluence, occupation and other demographics, we were able to segment customer walk-ins into ‘serious buyers’ and ‘just-looking’ brackets. We noticed that the customers who were walking in, displayed an interest in purchasing properties with the lower price tag – which had already been sold out. But they continued to walk in as a result of advertising messages geared at selling the lower-priced properties. Of course, when these customers walked in and noticed that the properties up on sale were priced higher, and did not meet their aspirations or budget, they simply walked out, thereby contributing to the low apartment purchase numbers despite clocking in a good number of walk-ins.

Three key insights offered by real estate analytics came to the surface:

A demand-supply gap was responsible for low purchase volumes despite the good walk-in numbers

The advertising vehicles were attracting the wrong customer group

The sales team was not the cause for low purchase numbers

While uncovering these insights offered the real estate player a far more realistic picture of the situation, the challenge of pushing premium property sales by attracting the relevant target audience persisted. And even if the advertising efforts were to be realigned to attract the right target audience with relatively greater purchase power, how do we effectively ensure that it converts to a purchase? It starts with understanding the customer.

Using real estate analytics to narrow in on the right target group – profiling key customer attributes

Based on business intuition, the brand identified its target customers as those who stayed within a 10km vicinity of the project (catchment area). The belief was that the aspirational value of their new project would drive customers seeking an uplift in living standards to purchase. While analyzing customers who had previously bought premium properties, we identified the target customers to be technology or banking professionals with plush salary packages. The question then became – Where are my target customers largely located at present? We leveraged geographic map visualizations to identify ‘heat zones’ or clusters of localities with high sales and high conversion rates. Contrary to our initial hypotheses, data showed that the brand was attracting “serious buyers” not from the catchment area but from affluent locations in the city centre. An odd choice, as the premium apartments up on sale were primarily located in pockets in the outskirts of the city, miles away from where these customers resided. It then grew evident that property owners were purchasing these apartments as an investment tool – with the intent of renting it out than moving in – which meant they were investors rather than residents, again contrary to the intuitive thinking of the real estate business.

On narrowing in on the right target group and validating it through data, the next step was to cue into specific traits of the investor audience. By means of real estate data analytics, we identified them as customers predisposed to affluent lifestyles and of 26-40 years of age.

Harnessing a novel advertising idea – mapping opportunity zones

We wanted to tap into BTL advertising techniques since the focus was a niche target group of investors. A critical piece of information we had at hand, was that our target investors audience was mostly located in the centre of the city. We decided to use this to strategically target and attract their attention through large hoardings.

The Heat maps helped us recognize areas with high-density bookings and areas with high conversion rates were grouped with a 7 km radius as ‘Opportunity Zones’. We wanted to identify pinpoint locations for billboards that would garner the most visibility amongst the target audience. Traffic congestion junctions certainly helped the cause, as they served the perfect opportunity for advertising – towering billboards with eye-catching advertising in the face of looming traffic.

Smart traffic analysis using Google Maps helped in identifying major routes that investors use while commuting to-and-from work, linking areas where the customers resided to multinational corporations and industry zones, workplaces that they frequented. We carried out a comparative study to determine how heavy the traffic flow was on different days and at different times. Surveys helped in comparing peak times traffic during weekdays as well as weekends. This gave the crew useful insights and helped in charting out routes and pinpoints that remained busy during rush hours, which would serve as optimal pinpoints for billboard advertising.

Using real estate analytics to hyper-target prospective buyers – it’s all about the location

It’s no news that real estate is all about the location; the same holds true of advertising too. We put together prime locales that would garner the most eyeballs, among the affluent. We tapped into the consumer aspiration lifecycle to target luxury car owners, typically a property purchase is the next evident step on their life cycle. Luxury cardholders were targeted by partnering with the premium credit card companies. We tried to reach investors where they were already at i.e. luxury or real-estate specific newspapers, online publications etc., and crafted advertising messages to highlight lucrative property ownership benefits such as rent appreciation, future property neighbourhood plans etc., to nudge their interest towards buying the property.

Sustainable analytics solutions – the way forward

The hyper-targeted advertising exercise helped the real-estate business attract more relevant prospective customers and convert them to buyers. But this was more than just a one-time effort geared at bolstering advertising-led conversions. We realized the business needed a strong analytics foundation to appreciate results in the long run – which meant transforming the business, its people and processes.

We revamped the pre and post walk-in experience to gain better customer empathy, engage customers and sell more effectively. We leveraged drone shots and VR views of the property to boost pre-walk-in appeal. The walk-in experience itself was revamped to improve customer engagement, and to gather key information about customers. Streamlining the data collection process was critical to driving change at a grassroot level. The sales team was trained through workshops, where they were educated on property features as well as gathering prospective buyer information during walk-ins, so they could sell better. There are key indicators that lead up to any property purchase, often influenced by family size, number of earning members, earning capacity, proximity of schools, offices to the property etc., to name a few. We digitized the walk-in process to capture critical buyer intelligence across 25+ metrics. Armed with stronger data, a well-trained sales team, and a more appealing customer experience, it was only time before the real-estate player began appreciating better conversions, and continues to do so, all courtesy a sustainable analytical solution.

Key takeaways – what really counts

Data and analytics helped greatly transform the real-estate business, which was skeptical about the analytics adding any value. Here’s a look at some key takeaways for businesses from this real estate analytics exercise:

Assumptions won’t take you far. Data has the right answers. But that does not mean you write off hunches or label them as irrelevant, it helps to test and validate hunches through data. What good is data alone, if humans cannot use their well-honed cognitive reasoning abilities? The key is to strike a balance between business knowhow and data nights, intuition-driven thinking and insight-driven thinking.

Real estate purchases are almost always emotional decisions, which is why every interaction with likely buyers must be driven by empathy, keeping their expectations in mind. It boils down to how well you know your customer and empathise with them. It pays to know when and where to reach your target audience. The best marketing campaigns can slip through the cracks, if you use mismatched mediums. Meet your customers where they are already at. Know the customer lifecycle. In this case, we knew that our customers predominantly had their aspirations and investments pegged in the order – hatchback > sedan > property purchase. So, it became easy to map out customers and where to reach out to them.

Each touchpoint in the customer journey can be an opportunity to gather valuable insights or create a valuable experience for customers. Leverage these touchpoints to understand and influence your customers. Varied perspectives, industry knowledge, first principles thinking, are all essential to gain a holistic view of your customers and a better chance of realistically solving the problem. Sometimes, the data at hand may not suffice, to solve the problem. You’ll have to look at innovative ways to gather data other than what’s already available – as we did with utilising the Google traffic analyses tool.

Lastly, expect internal resistance to real estate data analytics adoption, especially if you are in an industry that uses little to no data analytics tools; but make sure you overcome adoption barriers by gradually easing the team in to familiarising themselves with analytics tools, until it becomes a habit. Even the best analytics models are of little use without consumption.

Real estate analytics is yet to carve a place for itself in the industry. But if this exercise goes to show anything, it is that real estate players don’t just stand to benefit from standalone problem solutions but can also derive sustainable value with the right analytics adoption approach; an idea that is beginning to take root among first-movers in the real-estate industry.

Partner, TheMathCompany

Raghuveer TT