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Intelligence is defined by the Oxford dictionary as the ability to acquire and apply knowledge and skills. In the context of helping an advisory practice grow their business, Artificial Intelligence (AI) and Machine Learning (ML) are now being deployed as key tools to gain greater intelligence on advisor prospects and clients. With this expanded toolkit, advisors can acquire more knowledge about the individuals they are working with and strategically apply that information to design more engaging and personalized experiences to grow and retain their client base.
Despite the acknowledgement and need for this intelligence - Accenture reports that 99% of advisors believe AI plays a role in the future of financial advice such as using tech to power and enhance the client-advisor relationship - just 51% of advisors surveyed by Financial Planning say they are actively implementing AI and machine learning tools beyond the planning and investigation phase.
To that end, fintech platform TIFIN Wealth just launched TIFIN Intelligence, adding a layer of AI and machine learning-powered enrichment and prioritization to prospecting, nurturing, and client engagement. We reached out to Institute Founding member, Niharika Shah, EVP & Chief Growth Officer and Michael Lewis Director of Machine Learning & Artificial Intelligence, to dig into why simple analytics are no longer enough and how new technologies can be alchemically combined to help advisors to deeply know their customers, prioritize prospects to increase conversion, nurture more effectively, and strategically build growth campaigns.]
Hortz: What was TIFIN’s motivation in launching TIFIN Intelligence? What industry challenges does it address?
Shah: TIFIN Intelligence started with the premise that the standard client acquisition and growth process is inefficient and results in waste. A Kitces Research report states that the average cost of client acquisition is $3,119 per client. Of that, $2,600 is the cost of the advisor’s time and rest is discretionary marketing spend. We think that’s outrageous. Advisors should not be wasting time or money on prospects that are not their ideal prospects.
Lewis: At TIFIN, we have demonstrated success in matching people with investment products. We extended the same approach to match advisors with their “next best prospects.” As we began to build out that use case, it struck us that there was an opportunity to make growth more efficient and effective by also matching advisors with opportunities across their prospects and clients.
Hortz: What are the key components TIFIN built into this platform to address these challenges?
Shah: Essentially, the intelligence packages we created help identify opportunities across an advisor’s best prospects and book of business. For example, rather than wasting time when the likelihood of each prospect being ideal is low, the Intelligence module identifies prospects that look like an advisor’s ideal customer of today – and/or tomorrow. The use of AI in honing in on people’s wants and needs already has precedent in other industries, with Amazon’s recommendation platform, for example, being responsible for approximately 35% of sales.
Another area of growth the platform offers is ensuring that advisors are mindful of their clients’ financial life journeys. As they progress through different stages and goals, our Intelligence module can identify which clients are prime to have a conversation so that advisors can personalize their interactions accordingly and be there when people are ready for help or to explore other capabilities a firm may offer.
Lewis: We do this by combining machine learning methods with rich data from first-, second-, and third-party sources. The data provides a 360-degree view of a prospect and our algorithms optimize toward business outcomes. For example, an advisor could be looking at their book of business and ask and get answers to questions like, “Who are my best 20 clients where I should offer alternative investments or charitable giving solutions?” It becomes a superpower for growth – identifying and converting prospects at speed through intelligence and revealing new opportunities for additional services as clients go through different life stages
Hortz: How is this process different from what’s already out there?
Shah: The basic premise is that we use Artificial Intelligence and Data Science and couple them with very deep Financial Intelligence to create our algorithms. While there are other firms that deal with or have the capability to help you identify prospects or next best actions, we believe our offering is truly differentiated in how we integrate these processes.
Lewis: The true value of the TIFIN ecosystem is how we designed our technology and systems to do both, identify and activate engagement strategies. We can identify, activate, and train our algorithms across our entire platform that work together to provide precise and outcome-oriented activation of growth opportunities. It is a one-of-a-kind closed loop ecosystem.
Hortz: How did you design your platform and technology to be able to generate comprehensive, actionable insights on prospects and clients?
Shah: One company we experienced, for example, had a platform where they deployed one-time prioritization based on what they felt they had learned about clients through 20-30 years of big data. While that was a great start, we observed that nothing was closing the loop back in their algorithms and showing what did or did not work. Plus, nothing was happening from an optimization standpoint. When it comes to our Intelligence product, we capture data on prospects or clients that interact with the entire TIFIN ecosystem. That goes back to our Customer Data Platform (CDP) and optimizes algorithms.
Lewis: Our platform will continue to learn from intelligence gained from an advisor’s best clients. So, for example, if a client uses, say, our Clout digital marketing product, over time the Clout algorithms will optimize and the feedback intelligence models will also benefit from Clout’s data. Solutions like Clout can also help clients nurture prospects within the same TIFIN platform, helping advisors act on what they learn through the Intelligence module as they seek to convert prospects.
Hortz: Can you further discuss your prioritization models? What are the different modules you created and how does an advisor work with them?
Shah: One module is simply using the TIFIN data ecosystem to enrich aggregate data. Our BHAG – or “Big Hairy Audacious Goal" – is to be the largest first-party data resource in this industry. This is a straightforward enriching of data that would otherwise be disparate, fragmented, and not actionable. Email, for example, is not actionable, but email coupled with household income is. We then turn data into signals that are meaningful for a firm. Queue in prospect prioritization, client cross-sell and lifecycle opportunities, and now you are seeing growth. And then the advisor may use the prioritization modules for prospects and client lifecycle opportunities we discussed previously.
Another use case we often talk about is advisor recruiting. Which firms are your next best acquisition targets? We can model your advisor recruitment program based on data from past successful acquisitions.
Lewis: For multidisciplinary firms, cross selling through a firewall has always been a challenge. How do you evaluate mortgage clients, for example, to see if they make sense as clients for your emerging wealth business? Or for retirement advisors, how can they identify 401(k ) or 403(b) client opportunities through their wealth advisory business? By telling an advisor how a pool of prospects would make ideal clients, that advisor can save a world of time and effort by focusing attention on a narrower group.
Hortz: Can you share any general advice or recommendations on the mindset and technology needed for advisors to build more comprehensive insights on their prospects and clients?
Shah: To succeed in the future, all practices will need a data science solution, but firms do not need to build it themselves. It is also important to consider how high-impact activation of data science often requires teams working across organizational and functional boundaries. Oftentimes technology, data, marketing, and advisor engagement teams are organizational constructs within firms that may create siloes. Firms that can overcome organizational boundaries will reduce the friction and multiply impact across the organization. We believe the orientation towards growth can be the catalyst to align organizational resources towards a joint mission.
Lewis: Our advice would be that advisors should apply the best technology they can so that they can focus on what they are good at and trust an outsourced expert to do the rest. The way we cannot expect even the smartest rocket scientists to be proficient in building investment portfolios, wealth management firms that are really good at managing money are not built to have expertise in big data or data science initiatives. Our thought is if we can become their data science analytics partner, with measurable growth results, not only can we create better outcomes for the advisors, but for the end-clients. This supports TIFIN’s ultimate mission: improving financial wellbeing for more people.