Quarterly sales reporting for advisers is getting more scientific as predictive analytics technology is now being used for pipeline reviews.
Aviso Inc., a cloud-based tech firm that uses automated machine-learning algorithms and
portfolio management frameworks to better predict outcomes, has launched Aviso Insights, a tool designed to help sales forecasters identify weak spots early in a quarter and make course corrections.
“We are bringing data science techniques that worked in investment banking to guide organizations on what deals to work on,” said startup entrepreneur K.V. Rao, a former director of technology at WebEx Communications who built Aviso with Andrew Abrahams, former global head of quantitative research in JPMorgan Chase & Co.'s investment bank.
“As a wealth manager, you don't want to spend time with a high-net-worth prospect who may not buy,” Mr. Rao said. “Aviso uses pattern matching amongst new prospects and matches them with existing customers in a portfolio. We try to make scientific judgments on how the new prospect will behave.”
Adviser industry participants say large broker-dealers and national registered investment advisory firms may find value in Aviso's big-data product, using it to avoid missing sales and profits targets by mining sales, marketing and customer service data.
“This seems to be more targeted to very large firms selling products such as a Merrill Lynch or a big brokerage firm or insurance company … trying to move product ,” said Greg Friedman, president and chief executive of both the Junxure customer relationship management software firm and Private Ocean Wealth Management.
But, Mr. Friedman added, smaller firms are less likely to get value out of automated pattern matching and other big-data features.
"I don't know much about the technology, but I don't see how this is applicable to small firms,” he said. “I think about how advisers develop relationships, and it's through word of mouth and business connections, which doesn't match up to mass marketing.”
Tim Welsh, president of wealth management consulting firm Nexus Strategy, agreed, saying that the big-data approach to mining customer information may have great value for large organizations, but it's not workable at the smaller firm level because the average adviser may bring on only five or so new leads in a year.
“You need tons of data points to be able to make it worthwhile automating the process of analyzing lead flows and opportunity pipelines,” Mr. Welsh said.
Aviso in April received $8 million of Series A funding in a venture round led by Shasta Ventures, First Round Capital and Bloomberg Beta, among others.
Before predictive analytics was available, sales forecasting was largely a matter of guesswork, based on gut instincts and emotion from individual sales representatives up to senior executives that often lead to unrealistic sales projections, Mr. Rao said.
In a hypothetical case, Mr. Rao pointed to “Matthew Dockery,” a rep who estimates in a pipeline forecast to his sales manager that he will close clients worth $500,000 in business for that quarter. But Aviso's data science predicts $503,000 for the quarter because Matthew tends to be conservative.
“Aviso has two numbers, a judgment number, or Matthew's personal number, of $500,000, and the $503,000 objective reality that's driven by data science,” Mr. Rao said.
Conversely, Aviso can also pinpoint reps who may be targeting $1 million in sales but are behaving in a manner that will result in only $900,000, he added.
“We provide objective performance metrics that a firm can use to coach the underperformers,” Mr. Rao said.
Aviso is not the first data-science company targeting the advisory industry. For example, CRM giant Salesforce.com Inc.
announced in July the $390 million acquisition of RelateIQ, an intelligent computing startup that automates the capture of data from e-mail, calendars and smartphone calls to provide predictive-analytics insights in real time.
Phillip Goldfeder, a mathematician who teaches in the master's degree program for predictive analytics at Northwestern University, said he is aware of Aviso, which is part of a trend toward companies' using real-time sentiment analysis to make financial decisions.
“It's the human touch versus the detached Vulcan touch,” Mr. Goldfeder said. “One is purely emotional and the other is purely based on raw data. It makes for an interesting dichotomy.”
However, he added, time will tell just how effective analytics will be, because machines are only as smart as the people who program them.
“Over history, we've seen that people who can get ahead of the curve have done so,” Mr. Goldfeder said. “If there's a way to game the system, people will try to find an edge by gaming the system. That's one of the limitations of data science. It's only as good as the data.”
