AI for BI: Your crystal ball dashboard

Why AI for predictive insights?
A key signal of AI maturity within any organization is the ability to gather AI-powered predictive intelligence that drives business decisions to shape the future.

Why is this high on the list? Because true AI transformation is marked by the org's outputs for its members, its partners, and the industry or mission it serves. Operational excellence is just a step to get there. Pairing intelligence with strategic objectives (whether education, advocacy, research, etc.) directly advances mission and/or member delivery. That's the peak of AI maturity. 

In other words, success is marked when Board and leaders are able to future proof and directly attribute AI to tangible improvements in member value, advocacy initiatives, legislative wins, learning outcomes, etc.

So, what's the prognostic powder that makes this all happen? Let's take a look. 

Tackling large datasets from disparate places
One of the biggest barriers to AI garnering true predictive analytics comes down to its ability to consume all of your data sets. Not just the native ones, or the ones easiest to access. AI should be tapping into every soundbite of data that interacts with your brand (and even before) over an extended period of time. Data history and data centricity are key to AI seeing the full data picture. That means, bringing your entire data feast to the table for AI is key:

• Demographic: Key audience descriptors at the contact level. Think of what could go on a business card: job function, geography, etc.

• Firmographic: Key audience descriptors at the company level. Think of what could go into an Annual Report: # employees, revenue, etc.

• Behavioral: Any interactions leading up to and with your brand, including viewing an ad, downloading a handbook, watching a webinar, etc.

• Transactional: Any interactions with a price tag, such as purchasing a publication, investing in membership, attending a conference, etc.

• Psychographic: Often referred to as the ‘squishier’ data, this is typically used for persona- and profile-building (challenges, aspirations, etc.)

• Zero party: Often the runt of the litter in terms of data volume, this could include survey data, email or content preference center data, etc.

Putting AI at the table with this spread, gives companies the broad strokes to identify patterns, fuel predictive modeling, and ultimately, synthesize information at light speed so that AI’s human counterparts can make the most intelligent and rounded business decisions.

The ‘right time’ is the key ingredient. When it comes to AI and business intelligence, it literally has no stop button. It’s constantly consuming, compartmentalizing, and assessing what’s salient so that you’re capitalizing on timing.

Getting you there quicker 

AI is always going to win when it comes to getting you from A to Z quicker, with higher accuracy. Think of it as your Waze app to the future. You'll always have a compass and the most precise map regardless of the barricades, traffic, and roads to get there. 

For instance, think of losing your keys somewhere in your house just before you have to drive to the airport for a flight. You have multiple rooms, closets, and cupboards. Getting your keys in time depends on your ability to trace where you were before. This means scouring all rooms in 10 minutes. You could get lucky by hitting the right room first, or you could run out of time.

Now think of an AI-powered BI system taking over your search. The system would be capable of hitting all rooms simultaneously, looking at the entire floorplan in an instant. It would quickly scrape and integrate all of your activities leading up to the loss of your keys. The combination of reach, accuracy, and synthetization would get you to your flight with time to spare.

That’s exactly how AI-powered business intelligence systems work. AI does the dirty work so that leaders, rev, and ops teams can make the right decisions quicker, based on more reliable inputs, faster synthesis, and deeper analysis of data.

Now, let’s take that same scenario and apply it in future tense.

Lost keys. 10 minutes to find them. Flight impending. Let’s say this happens more than once over a two-to- three-year span (and yes, this would absolutely apply to me). AI–once again, constantly working in the background–now has the ability to forecast when this could happen again, and provide a solution to prevent it from happening entirely.

That’s the sheer power of AI–working around the clock to uncover insights about the past, in the now, and yes, in the future.

Predictive org signals

By eliminating manual extraction, minimizing error, and consuming your entire data ecosystem, AI steps in on the front-end so that you get actionable insights across all business functions, including: 

• Marketing: Identify new markets or market shifts, profile future targets, lean on predictive lead scoring, surface campaign successes for future planning.

• Sales: Deliver accurate sales predictions, forecast buyer intent, flag successful sales tactics and coaching opportunities for future planning.

• Member Services: Influence member and customer engagement, identify members who are at risk of churn, and increase program upsell metrics.

• Programs: Synthesize interest and consumption sentiment, offer up new ideas on content, format, modeling, and delivery mechanisms. 

• Operations: Bridge gaps to process inefficiencies, identify points of failure before they happen, detect anomalies, etc.

• HR: Surface patterns in employee satisfaction, performance, churn, and retention.

• Finance: Forecast revenue, reallocate budgets, monitor risk, optimize cash flow, etc.
  

Powering predictions through HubSpot + Spark
With all this said, I'd be remiss if I didn't mention that delivering predictive insights automagically is where HubSpot, combined with Spark data, slays in the biggest way possible.

Remember that HubSpot is actually an AI-powered CRM. That means it's designed to centralize all data interactions so that analytical capabilities are top notch. Consolidating data from multiple vehicles, sources, and channels into a single interface is an excellent predictive primer for associations and nonprofits.

Now let's take it a step further. HighRoad Spark combines all that amalgamated data deliciousness captured in HubSpot with your AMS (and other association tech) data. This integration combo ensures that all data points are synchronized and up-to-date, fertilizing the richest soil for predictive intel.

Dashboards that tell a story

In the end, legacy dashboards - a collection of reports and graphs bundled in a way that report out what’s already happened - are changing at scale.

The new AI-powered dashboards are catalyzing change and action based on a timeless reel. That is, real-time intel that influences dynamic business decisions. And even more so, timeless, contextual, conversational intel that delivers predictions at both the aggregate and individual level.

All of this is enabled by seamless data and integration of AI into everyday business operations.