Robo Advice – Engaging the consumer

Robo Advice – Engaging the consumer
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Key takeaways

1

The real challenge does not lie solely in providing the means by which cost-effective advice can be delivered, instead it lies in being able to wholly engage consumers with their financial situation

2

Even for simpler cases such as providing advice on ISA investments, the number of questions required to be answered may potentially put consumers off in engaging with robo advice processes

3

It may be that the perceived value placed on a robo advice offering will be increased if consumers feel that they have had to put some effort into the process so that the recommendation is personal

Financial planning can often be a very stressful and confusing experience. Exploring and deciphering numerous different investment options and strategies in order to target specific future financial goals is not a process which many people tackle with relish, if at all. Nevertheless, an ageing population, escalating student debt, rising house prices, not to mention the introduction of pension freedoms, mean that many more consumers will now face important and irreversible decisions about their finances, in some cases, for the first time in their lives.

A growing need, therefore, exists for robust financial advice to be made more readily accessible to all. Robo advice has the potential to form the cornerstone of any solution by helping to deliver advice in an affordable, compliant and user friendly manner to everyone, at all stages of their lives. But, the real challenge does not lie solely in providing the means by which cost-effective advice can be delivered to the mass market, instead it lies in being able to wholly engage consumers with their financial situation and goals and in encouraging and compelling them into taking action.

Suitability Standards

It is clear that, in the UK, robo advice needs to meet the same suitability standards as traditional advice and this severely limits the extent for streamlining the advice process. Although there is some scope to cut back on fact finding, the following areas will still need to be covered, just as in the full advice process:

  • Attitude to risk with a statistically tested psychometric risk questionnaire which can reliably determine a consumer’s risk profile;
  • A quantitative test of capacity for loss (or risk of ruin in the case of income drawdown) in place of the more subjective assessment used by advisers in the traditional advice model;
  • Allowance for access to emergency funds or account taken of short term cash requirements;
  • Debts and other liabilities ascertained and taken into account; and
  • The affordability of any recommendation determined.

In order to ensure that these areas are properly addressed, a lot of information will need to be sought. Even for simpler cases such as providing advice on ISA investments, the number of questions required to be answered to maintain regulatory robustness may potentially put consumers off in engaging with this process.

How can this be overcome?

Engaging the consumer

EValue has carried out some consumer research using three different existing UK robo advice offerings. This study, although brief, consistently revealed that consumers engaging in a pure robo advice system (where advice is given based solely on computer algorithms) recognised the benefits of a lengthier, more comprehensive and personalised fact-finding process.

What this research shows is that engagement in a more extensive process can be achieved by clearly signalling to users that a greater level of input from them will result in a more personalised, and hence more appropriate, recommendation. In addition, employing behavioural finance and subtle gamification methods, offering feedback and encouragement at important milestones and presenting users with a hub page illustrating their progress through completed sections can help maintain consumer engagement.

Monitoring of the operation of the robo advice system will also help to identify parts of the process where consumers are struggling and drop-our rates are highest. A continuous feedback loop should be established to translate MI into system improvements in order to increase usability and improve engagement.

However, for consumers to truly engage with robo advice, establishing trust is essential. Not only must consumers be able to trust the advice given but must also be able to trust the robo advice proposition itself with their sensitive financial and personal information. Honest feedback and reliable endorsements from individuals in analogous financial situations are vital to building consumer confidence in an online robo advice process and its outputs.

Final thoughts

There is an obvious paradox here. On the one hand, consumers do not like and will often fail to complete lengthy online fact-find processes, especially when they are required to provide information which they do not have readily to hand. Conversely, they value the advice they receive and trust it more when they have been through a relatively full, more detailed and more personal process.

There is clearly a balance to be struck, raising the interesting possibility of a “win-win” scenario for consumers and financial services firms alike. Instead of avoiding giving online advice and choosing to operate in the dangerous grey area between impersonal guidance and advice, the financial services industry could deliver greater value to consumers by giving fully compliant online advice. For many consumers, it may be that the perceived value placed on this service will be increased if they feel that they have had to put some effort into the process so that the resulting recommendation can reflect their personal circumstances and objectives.

In the end, the success of any robo advice proposition will not only be measured by the credible, innovative technological solutions it can provide but also by the way in which that technology is harnessed to engage consumers with their finances and encourages and compels them into taking appropriate and timely action.

More information on EValue's Robo Adviec solution


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