Our perspective – and a roadmap for marketers – post Facebook’s revelations about customers’ personal data

Our work with leading brands to psychologically profile fans and customer preference groups is markedly distinct, and reassuringly isolated, from the personal data privacy issues raised by the Facebook and Cambridge Analytica revelations.

Notably our approach minimises, rather than maximises, use of PII data.

CrowdCat advocates a data responsibility approach whereby brands really only need to store, use, and secure information that is vital to engaging at a meaningful level with customers as individuals, and with their agreement. Increasingly this is not about storing spurious personal facts or demographic data, but about being able to group together like-minded customers using psychometrics – how they choose your brand. Brands can then improve the quality of personalised conversations based on what customers truly believe is relevant, and wish to hear about, from that brand.

Researchers such as CrowdCat prefer to use contextual psychometrics, essentially a non-PII route, that is, not using personally identifiable information. This means grouping audiences by their preferences in the context of that brand and that decision space, as distinct to specific facts – such as their income, age, postcode, or the websites they have visited – which in any case often prove of low relevancy and usefulness to marketers. Leading brands have found this new psychometric approach a highly valuable fit with their ethical and marketing engagement objectives.

This has the beneficial effect of only placing customers, anonymously, in a large group of many thousands of others – meaning the brand achieves both a personalised and a safe way of using data to talk to their customers.

 

Data Responsibility

Data responsibility is about precisely gathering information that can help the client serve the customer and using sophisticated processing techniques to minimise stored information to the smallest amount necessary.

The result of this approach means that the stored data is not only reduced, but also meets other important, positive criteria:

The data encodes only specific attitudinal information about that person and the product type

Even if a third party could obtain the information, it would not tell them anything useful outside of its designed purpose. For example, data showing a person prefers to read an insurance product summary without the use of “too many numbers in it” is not a likely target for identity theft, unlike their name, age, and address.

The data is not personal

In fact, it only identifies someone as being part of a group of customers, to aid a brand in personalising its service. These data groups are very large, often numbering in the thousands or tens of thousands, so it is impossible that this data could in any way be used to identify a single individual.

The data is transparent

The consumer can understand exactly what the brand knows about them and why it’s useful to let the brand hold that data. This allows the brand to have a transparent relationship with the consumer and simple technology allows best practice to be used to make sure the consumer is constantly able to view the data held at zero cost to the brand. This technology can also keep the consumer updated on any new data collected, along with its purpose.

The data is decentralised

The data is so compressed that it can be kept encrypted within the consumer’s cookie data. This not only keeps the data safe but means there is no central database to attract sophisticated attacks from cyber criminals.

This approach and philosophy is central to everything we do at CrowdCat and underpins our ethical approach to data gathering and use. We believe that enabling well-matched conversations between a customer the brands they prefer is a two-way street where everybody benefits.

If you have any questions or concerns, please do not hesitate to contact us.