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Written by: George Cooper
More and more firms are turning to business intelligence to help them make strategic decisions going forward.
This approach, of course, makes complete sense.
Instead of having unintegrated data in multiple tools, or siloed between teams, firms are using a management dashboard or business intelligence tool to bring together different data sets.
Once displayed in a clear way, this can be a powerful, indeed invaluable way to make better, data-driven decisions.
Typically, firms feed business intelligence tools by aggregating data from a range of different sources. Engagement data may come from a CRM and marketing and lead generation data (as well as broader metadata) may be based on how often advisors log into your CRM or what time to form submissions normally occur and the like etc… The data is then displayed using a tool like Google Data Studio or Power BI.
When we work with firms, we like to go deeper. We help them gather data and draw out insights about how clients feel, their concerns, their level of satisfaction - direct data on performance and insights to help with growth and strategy. All from the horses’ mouths, if you like, rather than broader data on engagement.
As we collect that data over time, we can see changes in client needs, sentiment, satisfaction and the like. Trends begin to emerge, in the aggregate, as do important markers about individual clients and families. This data is valuable on the individual client level to help advisors best serve them of course. But on the management and strategic level, something really interesting begins to happen.
We actually open the door to the world of predictive insights.
With client-level data that changes over time and larger and larger data sets sitting with individual firms - a world of predictive data becomes possible.
By using machine learning models to piece together correlations between data points, in time it will be possible to create a totally different breed of business intelligence, one that goes beyond dashboard and data aggregation. Instead we’ll access data that predicts (and notifies you about) relevant insights to help advisors and leaders make better decisions.
Imagine a world where a CRM integration can send a notification to warn an advisor of a client who’s likely to be thinking about leaving the business based on predictive triggers.
How about annual review meeting agendas that use predictive insights to suggest valuable potential changes to a clients financial plan in advance of a meeting?
Or hyper-personalization of client-facing content and communications, again, driven by data and prediction on topics of interest, client concerns or changing sentiment?
Not to mention instant recognition of anomalies in data sets. This will help keep data clean and ensure business intelligence and strategic decisions aren’t led astray by faulty data.
With these sorts of predictive models, the power and accuracy of the insights provided are proportional to the amount of data that are available. By aggregating multiple data sources, the model becomes smarter.
As data continues to be gathered, it also becomes more sophisticated over time. Every week, month, and year that goes by, more data is available and the model grows with it.
As we think about the possibility, we’re painting a picture of a world where advisors and leadership teams are armed with the tools to make better decisions at all levels. That includes decisions on the high-level direction for the company, as a whole, or specific decision about a clients’ plan or circumstances.
Business intelligence automation and predictive insights are some of our core long term focuses in the Absolute Engagement lab. The role of business intelligence in advisory firms is clearly growing and, as we start to collect more and more data, the strategic return on data aggregation is significant. Once ‘prediction’ comes into the fold, I suspect this will be a gamechanger.
The possibilities for firms to unlock growth and create a client experience that is second to none are enormous and the predictive element may be where business intelligence really begins to shine.
Related: Gamification: What Does it Really Mean for Advisors?