There’s a goldmine of raw data and rich information lying dormant in the majority of advice businesses.
Advisers can implement a simple data analytics strategy to gain a deeper understanding of their clients, lift client engagement and identify growth opportunities.
Giant retailers like Coles and Woolworths are among the most advanced at gathering, cleaning and analysing customer data to gain deeper insights into shopping trends then using that information to directly hit consumers with customised offers, relevant discounts and new products. The end result, is stronger engagement and loyalty, and of course higher sales.
But there’s nothing stopping financial advisers from employing a similar Data Analytics (DA) strategy in their own practices and achieving the same result. By leveraging cloud-based DA tools, advisers can uncover what’s really going on across their clientbase, gauge the effectiveness of their practice’s current advice proposition and identify any shortcomings.
It’s invaluable information that can help them retain clients; introduce new services like aged care advice, succession planning and savings, budgeting and cashflow management; and lift overall client engagement and satisfaction.
The key to a successful DA program is to start with clear objectives. Advisers should think carefully about what they want to learn and achieve.
To help structure a thought process around data, it’s useful to view DA via three distinct levels: client, system and business.
DA at the client level is simply about engaging your client base and asking for information.
Advisers should ensure that all client interaction, but especially the initial engagement, is easy, relevant and detailed. If it’s a ‘Tell us more about yourself’
form or client feedback survey, make sure the questions are targeted and don’t only ask yes or no questions but solicit in-depth responses.
Ultimately, the more accurate, complex and detailed the data, the more reliable, powerful and effective the conclusions will be.
Advisers may need to provide an incentive to clients to get them to participate. It doesn’t need to be a financial reward or prize. It could simply be the opportunity to find out more about themselves and their peers.
DA at the system level examines the data housed within a particular software program or system.
For example, some savings, budgeting and cashflow management tools, including Moneysoft, allow advisers to compare and rate a client’s financial performance relative to a similar population of peers. Clients’ performance can be benchmarked in a number of ways such as by age, occupation, geography and marital status.
Within these tools, client data is collected and processed in real-time, which provides the adviser with a highly accurate and up-to-date picture, which inturn is reflected through the relevance of the advice given to the client.
At the business, or enterprise level, DA involves looking at the various systems operating within a business and correlating information across those systems. For example, gathering and analysing the data across platforms such as Xplan integrated with Moneysoft, then combining it with information from a CRM such as Salesforce and an investment platform can lead to richer insights into a client’s total financial life, rather than looking at each system in isolation.
Once you have a combined picture it becomes even more powerful. For example, the prevailing expense profile of other similar families, may reveal that a client can achieve their goal of taking their family to Europe three years ahead of schedule. It may even show that, compared to people with similar income, they have unproductive surplus cashflow, which could be used to halve the term of their home loan, purchase an investment property, or top up superannuation – thus a further engagement opportunity is identified.
At the business level, DA is helping companies intelligently engage their clientbase.
It’s not about how much data they can collect. Intelligent engagement is about shifting from ‘data’ collection to ‘data’ farming and from ‘culling’ information to ‘cultivating’ information.
A small amount of quality data will generally be much more valuable than reams of outdated data because ultimately if it is being analysed and is found to be ‘dirty’ or unreliable, then the conclusions drawn will be inaccurate and ineffective. Going back to my earlier point around accuracy, collecting the right type of information and keeping it clean, is very important.
Once a business has a quality set of reliable data, it can start to use that information, and build from there.
By leveraging client, system and business analytics, advisers will be in a stronger position to refine and enhance their value proposition, deliver more innovative solutions and grow their business.
High quality, accurate data can reveal striking insights, ultimately helping you to know your client better and as a result, lead you toward highly-targeted advice strategies, that are more accurate and support longer-lasting adviser to client relationships.
Jon Shaw is Head of Operations at Moneysoft.
This article was also recently featured within Adviser Innovation and can be found here