Data education can be a silver bullet for the property industry

Discussing how to enable property companies to make better use of data science and AI to navigate the fast-changing economy, Natalie Cramp, CEO of Profusion, says property professionals don’t receive enough data education.

Related topics:  Blogs,  Data Education
Natalie Cramp | CEO of Profusion
18th August 2023
"If you understand your customers almost everything flows from there."
- Natalie Cramp, chief executive at Profusion

Growing inflation and interest rates, rent hikes, property price declines, changing working patterns and the cost-of-living crisis all combine to impact nearly every aspect of the property industry. Anticipating and reacting to these changes requires advanced analysis of a huge amount of information. This is also true of attempts to reduce costs and increase efficiency through tools such as AI driven automation. Reduced demand results in more competition, putting a premium on personalised and engaging customer experiences.

If we look at some of the UK’s biggest industries such as finance, insurance, retail and logistics, we can see how the corresponding startup sectors have outperformed proptech in terms of companies founded, growth, funding and partnerships. Last year, Olli Vigren reported management intransigence and a lack of investment in digital transformation were key reasons as to why the property sector was falling behind in embracing new tech. In a survey of people hired to drive innovation forward at major property companies, he found that 57% spend less than half of their time on innovation, while 71% of innovation teams were five people or fewer.   

This begs the question, if technology such as data analysis platforms and AI has the capacity to transform the property industry, why are so many companies reluctant to invest in innovation or partner with proptechs? Where does the management intransigence that Olli Virgen identified stem from? 

Part of the answer may be found in the level of data education within senior decision leaders. We tested 300 business leaders with at least a decade of experience in their respective fields about their level of understanding about data. The results were not good. According to our scale a score of 35% was deemed a basic level of understanding, 45% meant intermediate and so on. CEOs on average scored 30%, department directors 39% and senior managers 34%. When broken down by industry we discovered that professionals in the property market were among the worst with an average score of 29%. 

This is not to paint an overly negative picture of the property industry’s relationship with data and technology. On the contrary, it highlights the great opportunity for many companies to embrace innovation to survive and thrive in the current economic environment. However, there is a lot of room for improvement - particularly for small and medium sized enterprises. 

One of the biggest hurdles to starting this journey can be concerns over high up-front costs and potential disruption. These fears are often misplaced and can be mitigated by taking a few practical steps. 

First, as I’ve highlighted, a lack of understanding of data can hamper the adoption and use of new technology. It can also really impact day-to-day ability of staff to effectively understand and apply the information they already have at their disposal. Upskilling on a variety of data skills at every level and every department of a business can be one of the most efficient ways for a company of any size to start its data journey. Far too often organisations make the mistake of starting with technology; transformation should always start with people. 

I am not saying you need to teach your whole organisation to code, but they do need to understand what’s possible with data and AI. This includes pitfalls and things to avoid, how to ask the right questions, using data to effectively make decisions and complete day-to-day tasks efficiently. This should be connected to your overall business strategy. Done right and you will give your team foundational expertise; as they develop, your data ambitions can grow. 

But this isn’t a one and done situation. It’s important to constantly evaluate the knowledge you have within your team against changing commercial priorities and new technical developments, and then update your training accordingly. 

Next, ensure you have a clear vision of what you’re trying to achieve with data. This is not about what data is or becomes, it is about what you want data and AI to enable you to do. Engage stakeholders across the business and build a practical data strategy that you can deliver with the resources you have, and a roadmap that will guide your work. Be clear on your success criteria. 

Finally, determine your ‘quick wins’. Seeking to make every single business function and decision informed by data insights is the ideal destination but it is not the first step on the journey. Therefore, it is better to trial a small pilot project to assess the impact. Define reasonable KPIs for the project and then conduct regular reviews. You will learn a lot even at this small scale which you can then apply to larger initiatives. 

Likewise, ensure you lay core foundations that will support you in the future, such as an organisation wide dashboard to enable visibility and rapid decision making and bottom up customer segmentation. If you understand your customers almost everything flows from there.

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