If you are like me, you probably spend a lot of time marveling at and studying the likes of Amazon, Google, and even Facebook. Each is only about 20 years old, yet they each occupy a spot in the world’s ten most valuable companies. Casting aside the varying forms of backlash they have experienced recently, there is much to admire about each firm’s growth and ubiquity.
Two other global firms that profile similarly in terms of age and size are Alibaba and Tencent. As Chinese counterparts to the aforementioned American companies, they are perhaps easier for those of us stateside to overlook. However, I recently read an article in the Harvard Business Review by Ming Zeng, chairman of the Academic Council of the Alibaba Group, that opened my eyes to how impressive Alibaba really is.
As we often write on the topic of financial advisor practice management, I wanted to share Mr. Zeng’s framework for creating a smart business—by his definition, “[a business that stewards an ecosystem] that is vastly more economically efficient and customer-centric than traditional industries.”
Objective: Automate All Operating Decisions
The key aspect of this undertaking is to automate as many operating decisions as possible, rather than relying on human action. Here we have taken Zeng’s four step process for doing so and added in some relevant applications for elite financial advisors:
Step 1: “Datafy” every customer exchange.
In this day and age, it is imperative that an advisor collects data on all interactions with prospects and clients—this includes how the client interacts with the company website, blog, social channels, and email. Even if you are a boutique advisor, you can still utilize a free tool like HubSpot’s CRM to accomplish much of this. Collecting this type of data provides real-time insights into your relationships and can improve your business operations.
Step 2: “Software” every activity.
Most would agree that sophisticated advisors aren’t relying on Microsoft Excel and a phonebook to grow their businesses anymore. Rather, they are using sophisticated financial planning software like MoneyGuidePro, eMoney Advisor, or Envestnet. But elite advisors shouldn’t stop there; we advocate the use of a marketing automation system for digital engagement, a corporate messaging tool like Slack, and virtually any other piece of software that can transition decision making from ad hoc to real-time, just as Zeng advocates for in his smart business framework. For advisors, this includes outsourcing the asset management function, which would provide a large portion of these efficiency gains.
Step 3: Get data flowing.
It is one thing to use a lot of software, but quite another to have each of the disparate pieces connected to one another. APIs, the way software systems talk to one another, are central to this. Whether you realize it or not, most of us have facilitated APIs when we use our Facebook login, for example, to connect with an Instagram account. On an enterprise level, this type of connectivity prevents data silos and allows data to be evaluated by all the firm’s stakeholders, irrespective of their job function. For example, one advisor that we partner with uses Zapier to connect all workflow-related apps, essentially automating the entire client onboarding process.
Step 4: Apply the algorithms.
If an advisor “datafys” their customer exchange, “softwares” every activity, and gets the data flowing, they will soon be accumulating a lot of data, and quickly. This is a double-edged sword. On one hand, your decision making will become more precise but on the other hand, it will be burdensome to sift through all the data without any algorithms or models to guide your eye to interesting patterns or the behaviors they reflect.
Large firms with terabytes of data might require a data scientist and engineer to account for this. Smaller firms, however, can almost certainly get by relying on their CRM or financial planning software to alert them of notable events like a client having a birthday, downloading something from your website, liking your post on LinkedIn, depositing or withdrawing an abnormal amount of money, etc., etc. We believe data is critical. To be efficient, that data must be synthesized into information upon which decisions can be made.
At Blueprint, we understand that financial advisors are under an immense amount of pressure to provide value to their clients and differentiate themselves from other advisors (including robots!). It is no easy task. Fortunately, in the digital age, it has never been easier to implement differentiated systems and processes or to find instructive examples, both inside and outside the advisory world.
Google, Amazon, or Facebook aren’t the only proxies for becoming the next generation of elite financial advisors. There are blueprints, so to speak, that exist, including this one from Alibaba.
You can read the HBR article in its entirety by clicking here.