The Data Revolution in Indianapolis
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As a subscriber you can listen to articles at work, in the car, or while you work out. Subscribe NowWhen you hear the words “Artificial Intelligence” (AI), the first things that pop into most people’s brains are Amazon’s Alexa, Apple’s Siri or maybe Tesla’s autopilot feature. These tools have really changed the life of their users, making things easier and more efficient. But, what can AI do for businesses? It all starts with data.
The Data Revolution
Data is ruling the business world and everyone has been trying to grab as much data as possible about their customers and how they use their product or service. But it doesn’t end there; companies are even tracking and storing data about how their employees interact with their customers from simple things like how long does it take a support representative to resolve an issue or more complex scenarios of tracking which stage in the production of a complex product is responsible for the defects. The gathering of this data was the first stage of the Data Revolution.
Data Analytics – the Second Stage
After collecting all of this data for some time, companies got smart and asked what does this data tell me? This is where data analytics comes into play. Data analytics is really a retrospective look at your data to try and see trends and outcomes in it. You normally would pull all your related data from multiple sources together to try and figure out how the company could improve. Wall Street analysts have been doing this for years with the public filings of companies to try and predict how a company will perform in the next quarter or year.
The outcome of data analytics for too many executives has been fancy graphs and dashboards presented to them that show the trends of the business and where their employees are devoting time and resources. We are currently at the tail end of this second stage. It’s a relatively common practice now to review these graphs and dashboards.
The Third Stage – Intelligent Business Processes
The toughest thing about the outcomes is that data analytics is always looking back and reporting on stuff that has already happened. When an executive sees data that their sales reps are constantly going after those whales and failing to land them, wasting a ton of time and money doing it, it’s tough to not ask the question, “How can I change the behavior of my team to focus on the right things?”
For example, consider a sales rep situation. The most successful sales reps all know choosing the right deals to go after is the key to their success. They know by looking at the details of the deal if they are likely to have a chance or not. They might not know why, but their experience and knowledge can drive that decision. The less successful reps typically don’t have this experience and knowledge, so they go after the deals that the successful reps wouldn’t touch. These mistakes are the deals that cost companies in lost productivity and money.
What if we could embed into the sales process in near real-time a way to take the successful sales rep’s knowledge and experience to direct the less successful one to the appropriate deals? That should increase company revenue by closing more deals. On the flip side, by bringing up the less successful reps you have the potential to reduce your cost of sales and reduce attrition and the costs associated with hiring and training employees.
The way you do this is by embedding intelligent decisions into your business processes through the use of AI.
What is AI?
AI really is just a lot of mathematical formulas coded into software that makes a prediction based on your data. The way to build these intelligent decisions into the process first starts at understanding where things go bad. Is it that you don’t get enough leads into the funnel? Is it pricing? During this process, your data analytics is a key piece to help determine what factors drive success or not in the business process.
Once this is understood, then the magic of AI occurs. Using your data and those key factors a model is built and trained to predict the outcome of the process.
Embedding the Intelligence into the Process
While it is great to have a prediction, that prediction is just a mathematical number. Data scientists and analysts can help you better understand the meaning of the number. Once you understand what this number means, you can then use it to drive human behavior in your process. Going back to those less successful sales reps, maybe the issue is that they are attacking the wrong leads. In that case, maybe the decision is to hide the leads that have a poor score from the reps so they don’t know they are there.
The model, through analyzing your data and understanding what are the key variables to a deal closing or not, now has that same knowledge and experience as your successful reps and can give those recommendations in near real-time to all the reps embedded directly into your process. This scale and knowledge will then help everyone be a successful rep, helping your company grow and achieve your goals.
This stage of the data revolution is just getting started. Work is being done right here in Indiana to help companies through this revolution. With the presence of Salesforce in Indianapolis, and their $15 billion acquisition of Tableau, Indiana has an opportunity to be at the forefront of the AI revolution.
Doug Surfleet is the director of solution delivery for Indianapolis-based Atrium, a next-generation consulting services company.