The word 'customer' is etymologically derived from the old Dutch 'calant' (friend) and the French 'chalant' (being very interested). Its counterpart is the well-known 'nonchalant' (negligent). Treating your customers as friends, being genuinely interested, and offering unsolicited advice when necessary, is a beautiful ambition. Especially if you want to do that across the company in an active way. According to Parker& Hudson's culture ladder, we know various stages of how actively a company operates:

1. Pathological: We do nothing as long as we don't get caught (for example by the regulator). It is abnormal behavior that damages relationships.

2. Reactive: We respond to problems, putting out fires as they arise. It is mostly behavior based on an ad-hoc policy.

3. Calculative: We respond to incoming requests according to a fixed process. Bureaucratic behavior looms in this context.

4. Proactive: We try to prevent questions before they are asked. Clear (core) values are drivers for behavior, and there is a strong will to continuously improve.

5. Generative: We are progressive compared to others, we generate even new demand ourselves (hence generative). We do not give the customer what he asks for, but what he needs.

Legendary in this regard is Henry Ford's statement: "If I had only listened to what customers asked for, I should have given them faster horses." If you're brutally honest, at what level do you most see behavior within your office? And what is needed to take a step? On the Adfiz website (www.adfiz.nl/dossiers/actief-klantbeheer), you will find a practical step-by-step plan (see also pages 36 to 38 in this edition). When formulating an ambition (step 1 in the plan), the aim should be level 4 (proactive) or 5 (generative), with a minimum of level 3 (calculative). Everyone on the work floor can roughly estimate at what level the team operates. Discuss it with each other: what is going well, what can be more ambitious?

With most software packages, you can set up the calculative level for a large part. You can create segments and determine a contact frequency for each segment, on which the software provides a signal based on 'business rules' (if...then) to call the customer or pay a visit. For example, at least once a year for companies, once every two years for independent professionals, once every three years for better private individuals, etc. In some packages, you can set up a workflow that automatically sends an email based on the signal, asking, for example, if a customer wants a maintenance conversation. In practice, this does not work smoothly. The segments are too large (making the message and process too impersonal), and many customers approached are not interested. It is very labor-intensive, while the response (and conversion) is often low. This is not profitable and also demotivating for your employees.

Utilizing Smart Technology

The Adfiz step-by-step plan states that you should aim for a uniform and consistent customer approach that is profitable enough for your office, with automatic signals being generated from the database. If you want to do this without deploying extra advisors, for tens of thousands of customers simultaneously, who all have different, often still latent needs, you cannot avoid using artificial intelligence. The technology—essentially an algorithm—personalizes one-on-one, so you are always relevant. And you remain so, because it learns from every interaction. This is necessary, as customer needs can change daily. The goal is to approach the right customer, at the right time, through the right channel, with the right message. What concrete applications can we imagine?

'Anyone aiming for a uniform and consistent customer approach cannot avoid using artificial intelligence'

Informing or Advising?

The AFM also recommends formulating your "ambition for customer care in the management phase" and determining an approach. There is a plea to go beyond the minimum duty of care. Read through the AFM document 'Interpretation of informing and advising'. Advising in the sense of the Wft involves a personal recommendation to a (potential) customer about a new financial product to be concluded by a specific provider. Informing occurs when one of the mentioned criteria is not met (such as a recommendation on an existing product). Therefore, informing is well applicable on a scale during management, as it does not (yet) require a complete advisory process.

Prevent Valuable Customers from Leaving

This module predicts the future value (Customer Lifetime Value CLV) that a customer will deliver. Customers with a high CLV may currently have low turnover but hold much potential to become a good, loyal customer for your office. They evidently match well with your office's advice and product offerings, without realizing it themselves. The CLV can be combined with a module that predicts which customers have a high churn risk (churn=cancellation). The result is a list of customers with a high CLV and a high risk of canceling their package. You can proactively address this by having an advisor contact these customers. As the technology works with thousands of data points simultaneously, the conversion chance when you approach these customers sometimes increases by as much as 40 percent. That's naturally motivating for your employees. The customer is also happy because you can proactively help them with personal advice for a complete package.

Proactively Answer Frequently Asked Questions

In communication with the customer, there are various possibilities. A module called dynamic FAQ (Frequently Asked Questions) predicts which specific questions a particular customer will have within a certain period. As a result, the customer, for instance, within their portal or in a newsletter compiled with liquid content, only sees FAQs that are relevant to them at that moment. This is the beginning of personalization (see also the article in VVP 2, April 2022). Research shows that approximately three-quarters of customers expect such a personalized offer from their financial service provider. Worse, three-quarters get frustrated if you do not offer that. Consequently, customers lose trust in your company.

Recommend the Best Fitting Coverages or Services

This module can make a recommendation for a product or service per customer, which the customer currently has a need for based on data analysis, indicating which content the customer finds relevant to read and their preferred approach: mail, mobile, website, or perhaps an insert with the renewal. This may sound like future music, but it has been applied in retail for years. Technically, this is called contextual recommendations. What does this look like? People like you often choose a comprehensive legal assistance coverage. Click here if you want more information. Here you start with informing, and depending on whether it is an adjustment of the coverage or a new financial product, an advisory process follows.

Profitable

Until now, this complex technology was only available (and affordable) for large corporates. Therefore, several software companies, including InsuranceData, Building Blocks, Bug Business, WeGroup, have joined forces so that service providers (and their affiliated offices), underwriters, and the larger intermediaries can implement this profitably.

Finally: the goal of using artificial intelligence is not to replace humans but to use the advisor's valuable time as effectively as possible, so the right customer receives personal attention at the right moment.

Source: this article originally appeared in VVP, read here the online article.