Asset managers are increasingly turning to analytics to optimize how they improve fund distribution, strengthen advisor relationships, and provide investment opportunities. Using data to better understand and analyze the problems facing investors can significantly boost fund flows and performance.

Here’s how issuers are using asset management analytics to target distribution and connect with advisors.

1. Using data analytics to reach the right advisors

Data analytics are crucial for segmenting and targeting the right investors. Asset managers who can deftly use data analytics will better understand what an advisor prospect needs and the nature of their practice. 

Of course, there is no “one size fits all” investment solution. Every advisor is serving clients with different needs, risk tolerances, and goals. An advisor whose practice is mostly catered to older high-net-worth individuals might have very different product goals than an advisor who is trying to help young professionals save for retirement.

Advanced strategies for segmenting advisors

Asset managers traditionally segment advisors by AUM, channel, or territory, but advanced analytics makes segmenting more nuanced. 

Consider advanced analytics segmentations such as:

• Demographics and investment preferences. This includes but is not limited to age, risk tolerance, preferred asset classes, and investment strategies.
• Trading frequency and portfolio turnover patterns. Are they looking for niche strategic products, or do they tend to take a “set it and forget it” approach?
• Technology adoption and digital engagement levels. Are they using the latest technology and tools to gain an edge, or are they comfortable with older approaches?
• Cost-conscious behaviors. To what extent are they choosing the lowest-cost product available over more popular or successful products?

• Investment philosophy. How do they approach building portfolios? Are they skeptical about certain themes or particularly drawn toward a type of investment (such as a “gold bug” or “crypto bro”)?

Behavioral and business model segments

Asset managers can also learn a lot about an advisor by looking at their behavioral history and what their business model demands.  

For example:

• Client age. An advisor with younger clients might take a growth-focused approach, while an advisor who caters to retirees will be more preservation-focused in their advisory practice.
• Advisor practice size and focus. Is it someone running a small, self-directed shop, or are they part of a full-service wealth management team? The answer will impact the types of products these advisors are looking for.
• Niche specializations. Some advisors are purely focused on high-net-worth families, while others focus on retirement planning. Generalists exist as well as institutional specialists, but there are also advisors who cater to specific professions. Every profession has unique investment needs, and knowing who an advisor centers in their practice is essential for pitching them products.
• Fee structure. Finally, whether an advisor works on commission or fees can be revealing. Asset manager analytics can help issuers better identify the fee thresholds and fund types that advisors and investors are seeking when they make investment decisions.

2. Making marketing campaigns more personal

Once you’ve identified your target audience, use segmentation to reach them via targeted marketing campaigns. 

For instance, if you’re a commodities issuer, targeting advisors with a history of investing in commodities at specific times – or who are actively researching commodities – will likely yield better results than a generic marketing strategy. 

Targeting strategies that work

As market conditions and trends change, marketing your ETF to the right advisors is essential. 

These advanced strategies can help:

• Sector-specific messaging for particular industries such as tech, energy, or healthcare.
• Risk-level customization for conservative vs. aggressive advisor portfolios.
• Geographic targeting for specific regional markets or demographics.
• Market volatility-responsive campaigns that adjust messaging based on current economic conditions.

Customizing your messaging

Not all advisors are the same, so your content shouldn’t be, either. Growth-focused practices need different materials and collateral than preservation-focused advisors or niche specialists. 

Here’s how you can adapt your marketing strategy for different advisor segments:

• Growth-focused: Practices that are growth-focused will respond to performance, alpha generation potential, and more aggressive allocation strategies.
• Preservation-focused: These practices will be drawn to risk mitigation studies, downside protection features, and capital preservation case studies.
• Retirement-focused: Practices that center on retirement will be drawn to income generation capabilities, age-appropriate allocation models, and sequence-of-returns analysis.
• Wealth-focused: High net worth advisors will respond well to tax efficiency studies, estate planning benefits, and sophisticated portfolio construction tools.

Always keep your sales team in the loop about your targeted campaigns so they can reinforce those concepts when speaking with advisors.

3. Using data to optimize distribution

Analytics provide essential data for improving operational efficiency. Use it to find patterns, correlations, and anomalies that indicate asset health, potential asset failure, distribution bottlenecks, or successful channel performance.

Where to look for data insights

Asset managers who track the right internal metrics can pinpoint precisely where their processes are breaking down, then fix them to save time and money. 

You can streamline your processes by monitoring the following:

• Sales cycle length analysis to identify stages where deals get stuck or delayed.
• Lead response time tracking to optimize follow-up processes and improve conversion rates.
• Resource allocation optimization to find which activities generate the highest ROI.
• Workflow automation to identify opportunities through repetitive task pattern analysis.
• Team productivity metrics to balance workloads and improve advisor service quality.

Using data insights to create real improvements

Various sources of data (your CRM, website analytics, and sales records) can be mined for valuable insights. With the right analytics tools, algorithms, and proper data analysis, you can identify opportunities to make your operations more efficient.

Here’s what you should be tracking:

• CRM data integrations to show complete advisor interaction history and preferences.
• Website behavior analysis to reveal which content types are driving advisor engagement.
• Sales pipeline predictive modeling to predict quarterly revenue and resource needs.

4. Predicting what advisors and investors will do next

Successful asset management firms analyze real-time data and historical data sets to forecast advisor adoption patterns and investor demand cycles, helping them improve their resource allocation. 

Historical and real-time data

Here’s how you can combine historical and real-time data for deeper insights:

• Season investment pattern analysis. Understanding patterns of when something will be in demand can help asset managers time product launches and marketing campaigns for maximum impact.
• Historic adoption curves. It takes time for a new ETF to gain traction. Understanding how much time it will take for a new product to gain investor attention and adoption is critical.
• Market volatility correlation. Advisor risk appetite and product demand will be, to some degree, dependent on how volatile the market is at any given moment.
• Economic indicator tracking. Being on top of economic indicators can help asset managers anticipate increases and decreases in allocations.
• Resource planning models. Your sales team will have a limited amount of time, energy, and attention. Understanding when you need to increase your bench size can help you meet demand at the right moments.

Predictive analytics

Predictive analytics can also help issuers anticipate investor behaviors to determine who is more likely to purchase or hold an ETF. For example, an investor who is an AI bull might be all in on disruptive tech, while an advisor with a stable of retiree clients might be more interested in products that generate income. 

Use these strategies to determine who is likely to invest where:

• Scoring models to analyze advisor portfolio composition and client demographics.
• Trading frequency analysis to identify advisors likely to embrace new products.
• Client age and risk profile patterns to indicate ETF suitability.
• Historic product adoption timelines to predict when advisors will move from trial to full allocation.
• Investment philosophy alignment scores to target advisors with compatible strategies.

Using these analytical approaches throughout the product lifecycle will help you better anticipate market cycles and roll out more effective risk management strategies.

Spotting red flags

Finally, predictive analysis can be a canary in the coal mine. It’s important to know when an advisor relationship is heading south.

Use these early warning systems to identify advisors at risk of reducing allocations or switching to a competitor’s products:

• Declining engagement. If you notice declining engagement metrics – such as fewer calls, reduced meeting requests, or less frequent email responses – that could indicate an advisor pulling away.
• Portfolio shifts. If an advisor has made some portfolio allocation shifts and those shifts are showing reduced commitment to your fund family, that’s another sign to be wary of.  
• Behavioral changes. Direct communication is also useful for data. If an advisor is complaining about the performance of your products or there is an uptick in support tickets from them, it's a surefire sign of growing discontentment.
• Sentiment analysis. Finally, client satisfaction score trends and advisor feedback sentiment analysis can provide you with additional insights as to the temperature of a given advisor. If the sentiment scores are going down over time, that’s a surefire sign they will start exploring other investment options.

5. Tracking performance to stay competitive

Advisor data isn’t the only type of data that can be used strategically. It’s also important to look at broader performance metrics and competitive positioning.

Tracking ETF performance

By analyzing asset management analytics, asset managers can uncover patterns and trends about how ETFs perform. 

Understanding asset performance patterns reveals information about fund health and portfolio management effectiveness, enabling better-informed decisions. Data-driven decision-making reduces risk and maximizes the value of funds and products. 

Here’s what you should pay attention to:

• Daily tracking to understand expense ratios, tracking errors, and bid-ask spreads versus category averages.
• Flow analysis to uncover seasonal patterns and investor behavior triggers.
• Performance attribution analysis to identify which holdings are driving returns.
• Volatility and risk metrics monitoring to anticipate rebalancing needs.
• Correlation analysis with market indices and peer funds to optimize positioning.

Real-time monitoring

Real-time performance monitoring against benchmarks and competitor products provides advisors with data-driven insights they can share with their clients.

Automated daily performance dashboards can help advisors compare returns against a fund’s benchmark or top competitors. Risk-adjusted return metrics such as Sharpe ratio, alpha, or beta can be updated in real-time to provide critical information.

Performance attribution breakdowns are also useful for showing sector and geographic contributions to returns. Drawdown analysis and recovery time comparisons with your product’s peers can give your product an edge if your recovery time is faster. 

In general, big data can help you create better marketing strategies based on outperformance periods and consistency metrics. 

Competitive analysis

Competitive analysis is critical for understanding your positioning in the market. It’s important to know how your product differs from the rest of the field and where you have opportunities to improve or stand out further. 

Some useful vectors of competitive analysis include:

• Fee comparison analysis. If you are charging higher fees than the competition, you need to justify that premium pricing.
• Asset flow tracking can identify which competitor funds are gaining or losing market share.
• Factor exposure analysis can be leveraged to highlight the unique positioning of your ETFs against their competitors.
• Gap analysis should be used to identify underserved market segments or missing product features.
• Market share trends and advisor adoption rates across competitor products will also reveal useful data.

6. Measuring and improving advisor relationships

At the end of the day, business is all about relationships. Knowing how to measure those relationships can help you keep your current advisor client list while growing your AUM with new prospects.

Tracking satisfaction and engagement

Issuers need to track advisor satisfaction and use engagement metrics to identify relationship strengths as well as areas for improvement. This can be done through survey scores and Net Promoter Scores (NPS) from quarterly advisor feedback.

Direct feedback is obviously useful, but there are other data points that can be revealing as well. Email open rates, webinar attendance, and content download frequencies can all tell the story of an asset manager/advisor relationship. 

Looking at your team’s response times to advisor inquiries and support ticket resolution rates is also highly useful. Meeting frequency, quality scores from relationship managers, product adoption rates, and wallet share growth over time should all be taken into account.

However, the effectiveness of these metrics depends heavily on data quality. Accurate, timely, and comprehensive data collection is essential for meaningful insights.

Measuring relationship-building ROI

One way to optimize resource allocation is to accurately measure the ROI of relationship-building activities, events, and support programs. 

Some things to consider include:

• Cost per advisor acquired through conferences vs. digital campaigns vs. referrals.
• Revenue generated from advisors who attended training programs versus those who did not attend training programs.
• Conversion rates from different marketing channels such as events, webinars, and one-on-one meetings.
• Long-term value of advisors acquired through different channels.
• Budget allocation effectiveness across relationship-building initiatives.

Continuously monitoring the advisor experience

The price of maintaining strong advisor relationships is paying constant attention to the details. Keep your eye on critical touchpoints like onboarding completion rates, time-to-first-trade metrics, platform usability scores, and how often advisors interact with technical support.

Your communication effectiveness metrics are just as important. Track which marketing materials and educational content resonate with your audience, monitor response quality and speed, and measure the success of your outreach efforts. Continuous monitoring helps you spot potential problems before they can damage relationships.

Get the right distribution partners

Partnering with distributors who excel at data and analytics can make all the difference in connecting your ETF with the right advisors and investors. 

VettaFi offers a full suite of behavioral analytics and digital distribution tools to help asset managers grow their products and their AUM.