The final keynote presentation of the NICSA General Membership Meeting
in Boston was devoted to mutual fund sales, and Big Data.
In short, fund firms need to find ways to use Big Data to promote success in the tried and true face-to-face strategies of fund sales, according to Bob Cunha
, managing director for marketing and distribution strategy at Eaton Vance
To make his point, Cunha, who was previously with sales data provider Market Metrics
, outlined for the audience the well-tested and timeless daily schedule for most mutual fund wholesalers.
Behold. The wholesaler wakes up in the morning, gets into the car and drives to an advisor office. He or she says hello to the receptionist and asks about the kids. Then into the advisor's to talk about [Input name of relevant local sports team] for five minutes. Then some general conversations about clients and what they've been wanting.
Finally, the advisor asks, "What can you do for me?", and then the sales begin.
Afterwards, vague plans about dinner or drinks or maybe a ballgame, and then, perhaps, the advisor maybe drops some tickets in a week, or a month, or maybe never.
This model is tried and true, according to Cunha, in the mutual fund industry, which he described as "basically an old-fashioned business." It is viable, it is important, and more than that, it is fundamental to any kind of sucess in mutual fund sales, he said.
It is also under threat, Cunha warned.
First off, it is expensive. Cunha estimated that on average, one wholesaler costs a fund firm $1 million. That includes compensation and bonus, internal sales support, infrastructure and other resources.
"Ultimately the threat is that those folk are expensive. Very expensive. And the loyalty of advisors is declining. Advisors are more analytical in nature," he told the audience.
Moreover, Cunha said, "the effectiveness of that old-fashioned business model has declined." Folks who walk into offices and talk about client interests and make plans about ballgames "need to be smarter about what they do."
Being smarter about sales involves finding better ways to use the wealth of data available about advisors to help sales people target the right people with whom to talk, and say the right things while having those conversations.
To illustrate, Cunha broke down the how the regional hierarchy works for his firm. Eaton breaks down the U.S. into 70 regions with roughly 3,000 advisors each. The firm probably has data for roughly half of these advisors, or 1,500. Maybe half of these, or 750, have bought products from the firm at some time.
At best, the wholesaler for that region has time in the year to meet with 300 advisors.
"The challenge is, if you can only see a tenth [of the people in your territory], let's make sure it is the right tenth," Cunha said.
Targeting the right tenth involves three areas of data:
1. Advisor profiling and targeting. In short, getting all the info possible on the advisor and what he or she needs.
2. Activity tracking. In short, getting all the info possible on how and where they do business.
3. Evaluation. In short, how the heck do you use this information to decide whether this advisor is a good target?
The problem though, Cunha warned, is that there is a lot of data to work with. There is total sales opportunity info you can get from a variety of data vendors. There are financial publication sources like the Barron's top 1,000 list. There are a variety of other third-party lists, including those from broker-dealers and advisor networks.
There is also the personal sales history contained in the brain of your sales person, who has been gathering and analyzing this qualitative data for years.
If you hit the sales person with too much raw data, Cunha said, your sales person's head is just going to explode in the parking lot before he or she ever gets a chance to get into the advisor's office. [He actually had a little cartoon video graphic of an exploding wholesaler noggin explosion. Very dramatic.] Or just as bad, he or she is just going to ignore the data.
Cunha outlined for the audience his firm's four general steps for using big data to help sales people, without leading to noggin destruction.
STEP 1: Establish Clear Goals.
Most firms, Cunha said, have a very simple goal with regards to sales: sell more stuff. Having something a little more precise can make a big difference, he said.
When his firm conducted research on advisor needs, they noticed that advisors wanted for their clients products that provided income and yield; helped them manage volatility, and address increasing tax issues.
In response, Cunha said, his firm came up with two clear, nuanced goals: increase market share in each of these three client need areas and seize upon related cross-selling opportunities.
For each of those goals, Eaton then figures out how each individual sales person, and the department as a whole, addresses these sales goals.
STEP 2: Gather Relevant Data, and Develop Algorithms That Make This Data Useful
Of course, the firm has to gather all the data available, from sales activity to email and website traffic, and so on — but that data doesn't help unless the firm can figure out algorithms to score this data to come up with actionable wisdom, Cunha said.
Developing these algorithms to scour this ocean of data is as much a factor of business strategy decisions as it is a factor of technical wizardry, Cunha said.
STEP 3: Get the Actionable Wisdom to the Sales People
At the end of the day, all of this wizardry must produce very straightforward sales wisdom, who to talk to and what to talk about, Cunha said.
Some sales people might want to drill down on the technical details. Fine, Cunha said, give them the opportunity to do so. But if they don't want it, don't drown them in it, he said.
Also let them access the data however they want to access it: via iPad or tablet, via cellphone, or via old-fashioned paper print outs.
STEP 4: Set Up Contact Objectives
Now you have the data, you have to decide how to follow up with it. How may times do you see a particular target face-to-face? How often by phone or email, and so on. You set up precise strategies and objectives for making this data count.
None of this is easy, Cunha warned, and will likely be a multi-year effort.
"This is a life's work, it doesn't happen overnight," he said.
The challenges are legion, he said: dealing with data integrity, developing and refining the algorithms and building the infrastructure to deliver the data. Changing the behavior of your sales people to seize upon these insights may be the hardest challenge of all, he said.
"Changing human behavior may be one of the biggest challenges of them all," he said.
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