Data-driven distribution — leveraging data, technology, analytics and processes to drive client success and improve distribution productivity — should be a growth driver for all asset management firms. Four years ago, we surveyed asset managers to gauge their data-driven distribution plans, and we recently followed up to measure their progress.
We surveyed 10 leading asset managers to assess their accomplishments, barriers and aspirations relating to data-driven distribution. Respondents included heads of distribution, leaders in marketing and leaders of analytic organizations. We found that while the industry has made measurable progress, it has moved slowly.
The chart above summarizes what we heard from survey respondents: Many organizations now have at least some data-driven distribution solutions in market, though most often those solutions are being operated in a manual, episodic way by the analytic teams who developed and piloted them. Marketing slightly lags sales in deployed solutions.
No one in our survey reported a large, measurable impact from data-driven distribution. To date, 80% reported small improvements in either lowering the cost of sales (generating more engagements per salesperson) or improving selling effectiveness (driving more assets under management (AUM) or cash flow per engagement). Fewer organizations have seen impact from their marketing efforts, with only 50% noting small improvements in cost of marketing (more engagements per dollar of spend) or marketing effectiveness (driving more AUM or cash flow per engagement).
The lofty aspirations on improvements in sales and marketing from data-driven approaches remain. Organizations have made progress and delivered tangible results, but progress has been slow, with no high-impact outcomes to show for it. As one survey responded mused, “How many more years will it take to fully achieve [our] effectiveness goals?”
Data-driven distribution has had a bigger impact in other industries in terms of revenue lift, lower cost of sales and better customer experiences. So why hasn’t this been true for asset management? Overall, 80% of respondents said data-driven distribution would be a source of long-term sustainable advantage for their distribution organization, while 20% said it would be the source of advantage. None saw it as a short-term advantage, simple value-add or a cost of doing business. Additionally, none of the respondents cited leadership support as a barrier.
Distribution organizations face an important decision: Should they continue with their current measured approach? And if not, what should they do? Here are three important considerations:
- Buy the decision engine, if it is more appropriate. If you can build a decision engine that leverages data and AI because you have best-in-class in-house capabilities, proprietary data assets and an innovation mindset, then leverage this strength. This is a difficult game to play—to win, the organization must always stay one step ahead—but the very best will do so, and some in our survey may already be on their way. For most, strengths are in their people, processes and culture. These organizations know how to take a plan of action, execute and impact clients. These organizations do not need to build the decision engine, but instead should look to buy it. In doing so, they can focus on their strength: the execution of these strategies. Many organizations find that the secret sauce is not in the algorithms, but rather in the execution. It can be tempting to develop a solution that reflects all of the nuances of the distribution organization. But this takes time and money and creates algorithm management and governance issues downstream. A solution that can be deployed next quarter may be more valuable than a custom and nuanced one deployed a year later.
- Don’t let data be an excuse. Most respondents (60%) cited data availability and quality as the primary barrier to advancing their data-driven distribution efforts. Data has been a long-standing perceived barrier—not just for AI applications, but for sales performance management and incentives, distribution planning and a host of other initiatives. From our experience working with industry data packs and home-grown systems, there is ample evidence that existing data can enable better decisions and actions, if not perfect ones. Organizations shouldn’t let perfect be the enemy of good as it relates to their data-driven distribution efforts—data issues should be managed more as a speed bump than a barrier. There are many ways an organization can move around a data speed bump: If data is missing, consider techniques to impute missing values or consider leveraging sales reps to collect customer data more purposefully; if estimates are needed, consider structured Delphi methods to quantify responses. And new techniques that leverage ontologies and knowledge graphs can be used to stitch together data sets purposefully.
- Be clear and inclusive in setting goals. Have a clear and committed focus on the strategy behind data-driven distribution and apply that strategy across marketing, sales, finance and operational silos. Distribution organizations can embed system-wide thinking right from the business planning and forecasting process. Cash flow growth and asset retention come not only from established momentum, but also from sales and marketing tactics executed effectively. Even simple tactical planning—defining client objectives and how many touchpoints are needed achieve those objectives—can help to align resources across silos and help the organization meet clear and measurable near-term goals. We’re not speaking here about attribution, a complex task for any asset manager to perfect on their own. Instead, we’re talking about agreeing in principle on what actions drive value, in what way, with what customers, and then translating that planning into a promotional journey that provides clear guidance to all distribution entities that engage with the end client and enables quarterly measurement. Data-driven distribution is seen as a growth driver, but the approach to it needs more urgency and purpose to achieve impact. Most companies in our study (70%) are looking to data-driven distribution as a driver of incremental revenue. While no company surveyed has yet achieved their desired level of revenue impact, many have made meaningful progress. To reach the next level, asset managers and insurers should take stock of current progress and capabilities and then chart a course that plays to their strengths, is comprehensive and moves around perceived data roadblocks. The ones who do so will realize the gains the industry has been working toward for the past four years.
