Woonbedrijf improves tenants’ quality of living

To read a Dutch version of this case study, click here.

Woonbedrijf manages the rental of approximately 31,000 residences in the greater Eindhoven area, accommodating approximately 70,000 people. Other activities of the housing corporation include maintenance, renovation and new building.

Woonbedrijf offers various types of residences in different neighbourhoods, each with its own specific character and target group. This means a targeted approach must be used for each one. The housing corporation’s vision is that good living involves more than just having a roof over one’s head. Together with its tenants, Woonbedrijf actively invests in the improvement of neighbourhoods. “Our aim is to create pleasant living conditions for our tenants and to involve them in this process. Their needs contribute to steering our policy,” explains Hamid Dardour, customer analyst at Woonbedrijf.

Better understanding of the needs and behaviour of customers
To enable this kind of customer-driven approach, Woonbedrijf requires in-depth and reliable information on the wishes and opinions of various different types of tenants and house-hunters. It uses IBM SPSS software to uncover this data. Comments Dardour: “We have been using IBM SPSS solutions for several years to quickly and efficiently collect and analyse information from our own systems as well as research data. These analyses provide us with transparent insight into the needs and behaviour of our customers. For instance, we use the software to segment properties by area and target group, so that we can align our approach and communication. This is how we know, for example, that written communication will not work in certain neighbourhoods. In these cases we will have to visit each residence in person or organise a tenant meeting.”

Segmentation is used in a much broader context than just tenants and house-hunters. Dardour: “To give an example, we reduce the tens of thousands of service requests we receive each year to a limited number of categories, such as neighbourhood, house type or year of construction. This allows us to quickly identify the problems that occur with greatest frequency, and deal with these problems collectively and therefore much more effectively.”

A wide range of research
Woonbedrijf uses the IBM SPSS software to carry out all sorts of research to collect customer data. For example, it continuously measures customer satisfaction levels using surveys. Dardour comments: “If people leave or rent a residence, we always ask them to fill out a questionnaire by default. The same applies when they submit a technical complaint or request individual improvements. Where tenants of blocks of flats are concerned, we measure their level of satisfaction against our service provision. The results of these satisfaction surveys are reported on a quarterly basis.”

Woonbedrijf also carries out ad hoc research. “Among other things, we analyse the wishes of tenants with regard to new neighbourhood facilities and investigate frequently occurring complaints,” explains Dardour.

Valuable text analysis
Woonbedrijf does not limit itself to quantitative customer research, as Dardour explains: “To gain effective insight into the needs and opinions of tenants, it is important to present them with open questions and to offer them the opportunity to provide feedback. This is extremely valuable for a customer-driven approach.”

Woonbedrijf previously used customer panels to gain feedback, but wanted to set up a larger-scale qualitative research framework to ensure that the opinions of a larger proportion of customers were fully represented. With this in mind, Woonbedrijf decided to purchase an additional text analysis solution from IBM.

“If you want to analyse a report on a panel discussion with eight participants, it is still possible to do so manually,” argues Dardour. “It becomes a different story if you conduct a survey based on open questions among thousands of people. This requires a powerful tool that allows users to quickly analyse large amounts of text and connect the dots.”

Translating customer feedback into improvements
The value of text analysis has already been amply demonstrated in practice. Dardour offers an example: “The Kwaliteitscentrum Woningcorporaties Huursector (Housing Corporation Quality Center for the Rental Sector), of which we bear the quality mark, provides us with their study results on an annual basis. These reports typically contain a large body of text, including responses to open questions and additional comments. Text analysis revealed that customers thought that our approach to communication and adherence to maintenance promises left something to be desired.”

Based on these findings, Woonbedrijf was able to implement the required changes in a targeted way. “Our communications on maintenance schedules are now much more transparent, giving tenants the opportunity to prepare for potential disruption,” comments Dardour.

Another example is the large-scale qualitative analysis of information provided by users of the organisation’s website, which has resulted in various important findings.

“We identified a need to provide more information on neighbourhoods and the floorplans of houses. They also wanted a ‘call me back’ feature and the ability to forward information to others. We have implemented all these features and have thus improved our service provision,” Dardour comments.

Risk mitigation through analysis of online discussions

The analysis of feedback on forum discussions and statements published on the Woonbedrijf website also provides the housing corporation with interesting insights. Dardour explains: “Before we launch a new initiative, we gauge the opinions of our customers by starting an online forum discussion. We also validate aspects such as our business strategy based on feedback to the statements we publish on our website. Text analysis allows us to reduce this feedback to a number of core arguments, gain clear insight into attitudes and emotions, and identify potential resistance.”

In this way, Woonbedrijf can better cater to the wishes and requirements of its customers, while also mitigating risk. This approach also minimises the cost and effort involved in the analysis process.

Predicting customer behaviour through data mining
According to Dardour, the text analysis solution from IBM allows Woonbedrijf to considerably expand the scope of its qualitative analyses and pick the right instruments for each new challenge. “For example, we are now ready to leverage information published on social media. It allows us to gain even greater insight into improving our organisation and integrating our approach.”

The next step will be to deploy the data mining capabilities of IBM SPSS software. “This will allow us to deepen our data analysis,” Dardour concludes. “We will be able to create models that allow us to explore patterns and even predict the behaviour of customers and house-hunters. In this way we will be able to cater for the needs of our customers even more effectively.”

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