Personalizing Financial Services: Six Myths and How to Get Over Them

3 min read
Jul 7, 2022 2:07:42 PM

The challenge is the same in any sector: If each user thinks differently, has specific purchasing habits, and is at a different stage in life, how can we offer products and services that show them we know and empathize with their needs?

Personalization of products and services consists of using data to understand clients and create tailored solutions for them. In the case of the financial industry, its applications are numerous.

A clear example of how personalization can help financial institutions is portfolio collection. Each person with a default portfolio is a different world with specific motivations and conditions. Suppose a massive collection strategy is designed to reach a diverse universe of clients. In that case, it will not be easy to obtain the same level of success as a personalized approach based on the analysis of the available data of each user.

Regardless of whether there is a precedent with a similar initiative, data analytics makes it possible to predict which types of users will like certain products and allows companies to focus their marketing efforts better.

Below are six myths about personalizing financial services and how to dispel them:

1. Personalization has a clearcut ending

Personalization is an ongoing process. Implementing it once and waiting for it to work forever is not enough. It is due to something simple: People change, and their consumption behaviors constantly evolve.

Personalization must be a dynamic tool because its ability to adapt to changes in user expectations is the key to measuring its success. Behind a genuinely practical personalization experience is the ability to automate that update cycle.

2. Personalization is not possible without huge amounts of  data

All personalization strategies are indeed based on data to understand behavior patterns. It is also true that, before implementing a personalization strategy, the quality of the available data must be assessed, which is not to say that only organizations that have reached their analytical maturity can personalize services.

If you are looking to build robust personalization, you will want to have at least a year’s worth of data collected, but it is possible to start more modestly.

The results that can be expected in these cases are not the same as those that would be obtained through a more robust data repository but would allow delivering a better experience from day 1 than that be offered within a static model.

3. Personalization works the same way for every business

Amazon has 20 years of experience developing personalization tools now available to AWS users. Within that package of services, there are options such as Amazon Personalize, which does not require specialized machine learning or data science knowledge to create appropriate personalization strategies in various cases.

Another avenue for personalization can be Amazon SageMaker, a service designed for organizations with extensive experience leveraging data and interested in going beyond personalization.

This tool allows data scientists to create algorithms and models that take advantage of artificial intelligence to solve all kinds of needs.

4. The benefits of personalization are hard to mesure

If the personalization process is based on clear objectives, understanding the extent to which a return on investment should not be a problem.

If the personalization strategy aims to acquire new users, it must be clear how these results will be measured from the beginning. If you want current users to increase their number of transactions, you must define what amount of that increase, how often it should occur, and why.

The different areas involved in the personalization strategy must participate in the definition of these parameters.

5. It will only benefit one of your digital assets

Too often, organizations think of personalization as a strategy to position one of their digital assets or channels. However, this lack of focus can be a severe mistake if, along the way, they leave out what matters: improving the end-user experience.

Designing a recommendation model solely for a digital asset or channel prevents you from taking full advantage of a personalization process.

Personalization is often done based on transactional data when focusing solely on the channel. However, the best results of a personalization process come from understanding a wide range of user attributes that provide a complete view of their needs.

6. The cost of data storage makes personalization a very expensive strategy

A good personalization starts by defining how data is captured; the more data and its quality, the better the personalization and experience offered to end-users.

Today, cloud storage services allow the storage costs of this information to be reduced considerably, while the stored information is protected with the highest security standards.

The cloud frees organizations’ imaginations to create personalizable services and experiences with no storage constraints.

The personalization of services is no longer a complicated process; its costs make it increasingly affordable. The best way to know if an organization is ready to use this strategy and add value to its business is to test the available information on a small scale.

Once the need to implement personalization has been identified, keep in mind that there is no single path to achieve it and that various tools and methodologies exist to attain successful customization.

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