How customer segmentation could be used within Real Estate

Real Estate companies have customers with differences in the aspects of expectations and behaviour. You as a landlord have the possibility to communicate and treat your customers differently, depending on who they are and what they do. It is only a matter of segmentation.

Segmentation of customers is the process of dividing your customers into sub-groups based on shared characteristics. The segmentation could e.g. be based upon geography, demographics, the customers’ behaviour and satisfaction levels. The idea when creating segments is to find unique information that is common within one group of customers and at the same time different to other groups of customers.

The strategy for your customer sub-groups should be based upon the knowledge you have of your different groups. By understanding who your customers are and their drivers, you could better serve and develop each segment in the most feasible way.

 

Understanding your tenants, using internal and external data

To gather insights, you need to be data-driven. In the process of segmentation, you need to evaluate and test different types of data that makes sense in describing your customers. You could, and should, use both internal and external data that is available.

Geography such as residential areas could be one a good starting point for segmentation. The fact that people tend to live near other people that are like themselves, makes it possible to create relevant segments based upon geographical areas. And if you follow the residential areas over time, you will know if the trend is positive or negative and adjust your strategy accordingly.

It is advised to start with your internal information, because you know quite a lot from the information in your database. You probably know the age of the tenant; how long they have stayed in the apartment; movement patterns, payment behaviour and what add-on products/services they already use. If you are doing customer surveys, you could add their response to the mix of data. And do not forget to add information of in which channels the customers usually contact you. All this information combined makes a good start for customer segmentation.

External data that could be added on the residential area is statistical information regarding the family life stage, level of education, employment level, purchase power and degree of urbanization. What type of external data you can use is depending on which market you operate in. In Sweden you can e.g. buy reliable information from official databases (e.g. Skatteverket and SCB).

Note that the statistical information provided is true for a representative group, but not necessarily true for the individual customer, which means that you do not have to take GDPR into account.

 

Lifestyle segments models

To create a good segmentation model, you need to use and combine different sets of data. This is not something that is done in a blink of an eye, it needs preparation and handling of data as well as evaluation of the model itself. And the model needs to be updated on regular basis. You could do it yourself, but it could be advised to use ready-made lifestyle segment models offered in the market since it will save time and the models are quite good.

The ready-made lifestyle segments are created from external data mentioned above in combination with information about personal drivers, consumption patterns and interests (based upon large surveys). In Sweden you have many sources for lifestyle models, with Mosaic and Conzoom as two examples. The base in the lifestyle segmentation is the geocode, which means the geographic belonging (i.e. the person’s address). And the address itself is where the apartment is located, which means that the geocode for the Real Estate company is the same over time. Of course, the description of the lifestyle linked to the geocode will be updated regularly (sometimes as often as yearly), but the overall development is slow, which means that you could use the added-on lifestyle data over a long period of time and not necessarily need to update this information regularly.

A tip is to make sure to understand the size of the different lifestyle segments in your universe, to be able to determine where you have a high penetration. Even if the particular segment group is not the largest of your database, it may be overrepresented compared to share of that segment in your geographic area and this could play an important role in your strategy.

Personas

In your segmentation work, you often end up with a lot of different segments that define customers on a detailed level. It is valuable to know the details, but sometimes you would like to compromise, cut all the details to a minimum and instead use a few well-defined fictional characters that represent different customers, so called “personas”. The use of personas could be used internally, or in dialogue with external stakeholders, to make it easier to understand some typical customers and their behaviour.

You could use information from internal data and lifestyle models in combination to create your personas. It is advised to create a maximum of 3-5 personas, to make the personas both easy to understand and use within the organization. The personas could be used in addition to the more detailed segments.

At the end, a small advice: Think big and start small! The segmentation work will give you a better understanding of your customer database and if you start with the data you have, you can always move on and add more data on the way.

 

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Pernilla Klein
Industry Lead Real Estate
pernilla.klein@stratiteq.com
+46 73 661 21 35

 

 

 

About the author:
Pernilla Klein  is a Business Consultant and Industry Lead within Real Estate, working at Stratiteq where she is working with facilitating workshops and helping the clients with their data-driven journeys. She is passionate about using data within Real Estate for e.g. customer segmentation. Extracurricular; she takes ice-cold baths regularly during winter and is often seen attending live concerts with some obscure post-punk band (that no one has heard of).