1. Taking data reporting to the next level
One of the most significant obstacles to defining an effective strategy is the lack of transparency into internal effectiveness and market opportunities. While most firms have a decent grasp of assets under management and revenues, this information is lagging and might not reflect the true capabilities of the salespeople or changing market needs.
For example, a financial representative in a fast-growing market might seem to be a superstar. However, it is possible that, while the growth of their portfolio might look impressive, it is lagging behind the overall increase in market and segment size. This means that — despite apparently strong performance — the firm could, in reality, be losing market share.
This problem is even more complicated for asset managers since they often lack details about individual customers and can only see the view at the partner level. For this reason, it’s crucial to strategically expand the KPIs that leadership tracks to include the external view and trends.
Several analytics specialists, such as Broadridge, WealthComplete, Albridge, and WalletShare, provide meaningful data about demographic changes within the market, which can be translated into a better understanding of the true performance of the salesforce. In addition, defining KPIs such as size and share of wallet will provide a more accurate view of the actual performance of the distribution group and enable them to focus on key growth markets.
In addition to deepening the insights within reporting, it’s important to note that the style in which these findings are presented also needs to be elevated. This may require a combination of technology and change management.
While most companies now have software such as Power BI, Einstein Analytics, or QlikView, these tools typically are used by mid-level managers to copy and paste relevant information into PowerPoint to present to senior management. This delays when decision-makers can access the information.
There are two possible ways to address this issue:
- Focus on technology and integrate reporting into pages that leadership views daily (for instance, an intranet homepage). This requires reporting to be set up effectively, highlighting KPIs and ensuring no latency. Also needed is better system integration, which would rely on strong data architecture to ensure information flows smoothly.
- Focus on change management, providing the firm’s leadership with dashboards tailored to how they individually view the company and training to drive self-service.
2. Using client segmentation as a strategy driver
While the level of maturity varies greatly between clients in terms of the sophistication of their segmentation, in general, both asset and wealth managers tend to lag in this area when compared with other financial institutions.
Most clients use a combination of simple metrics, such as age and net worth, but ignore the trove of data that they can freely access. During the financial planning process, advisors usually gather detailed information about individual clients and their specific situations. While that data is used for financial planning purposes, leadership could use it (in an anonymized form) to help better understand their overall market and identify any changing trends. This could help them to decide on which specific segment they want to focus.
Obviously, there is an even larger opportunity for a consolidated financial services organization that has more data about individual customers from their other accounts. For example, a couple choosing not to have children will have significantly more investible income than a couple with children. So while the former’s current net worth might not place them in a desirable segment right now, their future potential might be significantly higher than the couple with children.
Segmentation also should include behavioral indicators to assess risk tolerance. For example, knowing how various segments are likely to react to dramatic market shifts can help leadership adjust their training schedule to target specific advisors within defined regions.
Specific techniques that could be brought into play here include cluster analysis; predictive modeling to analyze relationships between characteristics and customer value; chi-square automatic interaction detection (CHAID); and linear regression, which identifies drivers of high customer value and then uses those drivers to determine client segments.
3. Optimizing client product match at a portfolio level
While the responsibility for developing new products typically lies with product groups, many companies miss the opportunity to employ a feedback loop with their distribution channels.
Having more nuanced behavioral-based segmentation can inform product teams about growth areas and help them prioritize future development efforts. This can help distribution leadership to optimize the product training regime for their salesforce. Using analytics, rather than just anecdotal evidence, can give them a stronger voice to drive the development of genuinely needed products in the market segment in which they compete or hope to compete.
This approach is especially important for asset managers since they are further removed from the end customer and are less likely to understand the market that they ultimately service fully. Machine learning (ML) algorithms can help by leveraging historical marketing and sales data to predict which products will have the highest chance of success with a certain customer.
4. Optimizing client channel preferences
Salespeople tend to be extroverted and are comfortable making regular follow-up calls. Some clients might not need or want this level of interaction, though, which can vary based on the specific phase of the sales cycle.
Understanding this nuance can help drive leadership decisions about investment in various communications channels. Annual customer surveys can help leadership understand the level of interaction a customer might want at each stage of the sales cycle. For example, a client might want to discuss a product with a financial representative right before the transaction but prefer to learn about new products on the company website.
In Europe and Asia, we have seen an increased use of mobile applications for processes such as onboarding. This has been true for both wealth and asset management. By automating the data-gathering process, leadership can ensure that the right information is conveyed to the right customers, in the right way, at the right time. This can also feed into the design of communication channels, such as individualized web portals and the tone of voice used for chatbots.
Another capability that can help optimize channel experience is using natural language processing (NLP) technology to understand the true reasons for customer outreach. While some CRM systems require financial representatives and wholesalers to input “reason for the call,” various studies show that this technology is not used as well or as widely as it could be.
EY teams have used NLP with several clients to analyze the volumes of call and chatbot transcripts, as well as web history. In this way, management can gain valuable insight into channel preferences for individual segments. These insights can then be extrapolated into a larger population and result in a tailored, individualized experience for the client.
5. Behavioral matching of salespeople and potential clients
Various firms within financial services use statistical analysis to optimize matching between individual sales representatives and target customers. This is not widely used in wealth and asset management, though, and offers an opportunity to increase the customer conversion rate. For example, most wealth managers have a significant portfolio of orphan accounts and, most of the time, these accounts are stagnant until they are closed during periodic clean-ups.
Most firms already have a significant amount of data about these accounts, based on the financial plans that have been created for them. At the same time, many financial advisors in the firm are looking to expand their overall portfolio, and the firm has data points about them.
The secret to success is to combine statistically derived segmentation of the customers with the segmentation of financial representatives to identify matches with the highest likelihood of success.
It’s important to point out that the data for the segmentation should include not only structured but also unstructured data. For example, using the NLP analysis of call transcripts, management can obtain sentiment signals that provide feedback regarding the quality of the interaction between the client and financial representative.
This analysis can also give management an overview of the health of the portfolio. This process, when implemented, can typically be executed overnight, giving management an opportunity for early intervention in case of negative interactions and addressing a potential issue before clients take radical action, such as closing the account. As a result, companies have an opportunity to turn something that has typically been an operational headache into a growth opportunity.
6. Optimizing territory assignment via behavioral matching
Behavioral matching can also be applied in asset management to optimize client assignment. Today, most of these assignments are based on proximity, tenure and success with the client, and overall market size. However, augmenting this with a nuanced, segmented view of both clients and wholesalers can ensure that the revised territories have a maximum chance of retention and future growth.
This is especially the case if a change is driven by an adverse event, such as the departure of the original wholesaler. Once again, the secret to success is statistically driven segmentation of both clients and wholesalers.
Genetic algorithms and minimum cost flow algorithms can be helpful tools here, as well as prior methods (based on theory) and posterior methods (based on observation).
7. Protecting your reputation
For a business with an extensive sales force, monitoring client interactions can be especially challenging. While supervisors can listen to individual calls, it’s impossible to listen to every single interaction.
This is another area where technology can be used effectively. One product, EY Voice, applies NLP technology to all phone-based and online client interactions. The EY Voice algorithm can detect, with a high degree of probability, information that is wrong, misleading, or incomplete — product disclosures, for instance.
So instead of listening to random snippets of conversation or examining random chatbot conversations, management can focus specifically on an identified text that might require remediation. This process saves management time and increases quality control.