“For predictive analytics, we need an infrastructure that’s much more responsive to human-scale interactivity: What’s happening today that may influence what happens tomorrow? The more real-time and granular we can get, the more responsive, and more competitive, we can be.” — Peter Levine, VC and General Partner at Andreessen Horowitz
There has never been a better time to build a flywheel business platform. The flywheel effect, sometimes called the virtuous cycle, happens when businesses build complementary initiatives that gain momentum as they reinforce each other. For example, as Netflix learns more about its subscribers, it’s able to produce more of the habit-forming, binge-worthy content that keeps its viewers engaged.
As Netflix customers consume more content, the company is able to produce even more relevant content and recommendations — which helps build its subscriber base and starts the virtuous cycle all over again. Likewise, Amazon is a flywheel platform business that learns from its subscribers’ behaviors and actions. Using this information, Amazon continually improves its selection, recommendations, and delivers exceptional customer experiences.
And the flywheels are turning faster and building more momentum, than ever before. At least they are in organizations that have mastered the dynamic duo of data analytics and AI. It’s easy to make the case that each of the world’s most valuable brands also is a leader in the use of big predictive data analytics and AI.
The world’s most 10 most valuable brands as of 2019:
- Amazon — $315.5 billion
- Apple — $309.5 billion
- Google — $309 billion
- Microsoft — $251.2 billion
- Visa — $177.9 billion
- Facebook — $159 billion
- Alibaba — $131.2 billion
- Tencent — $130.9 billion
- McDonald’s — $130.4 billion
- AT&T — $108.4 billion
A flywheel to spin up growth
Are your AI capabilities on par with data capabilities?
Assessing your AI and data analytics practices can help to prioritize your resources and identify imbalances that keep your flywheel from spinning smoothly. If your data analytics capabilities are mature but you have not begun to experiment with AI, there’s work to be done.
Big data, which provides massive data sets and user activity to greatly increase the quality of “education” AIs receive.
Machine learning is a subset of AI, and a method of data analysis that enables computers to learn without external instruction. Predictive analytics makes heavy use of machine learning and algorithms to perform tasks like identifying in-market prospects, improving customer recommendations, and flagging fraudulent online transactions.
Deep learning is a branch of machine learning that uses computer simulations called artificial neural networks.
Organizations that score high on both data and AI capabilities also tend to score high in the marketplace through more efficient operations and innovative products and services.
As your organization builds its own virtuous cycles, remember the goal is to generate your own datasets and build AI capabilities in a way that enables you to trust your own data, achieve more valuable insights, and make better decisions.
So, how are Artificial Intelligence (AI), machine learning, and predictive analytics actually working together to guide future growth? All the insights gained through the systematic collection of customer data can actually help organizations become more customer-centric. “Being more customer-centric” and creating “customer-centric cultures are stated goals for many organizations, but those goals are very difficult to achieve without a data-strategy. Consider this definition from HubSpot:
Customer-centricity is a way of doing business that fosters a positive customer experience at every stage of the customer journey. It builds customer loyalty and satisfaction which leads to referrals for more customers. Anytime a customer-centric business makes a decision, it deeply consider the effect the outcome will have on its customers.
Big data has become a key driver for enterprises to become more competitive and build future growth in increasingly competitive global markets. Those organizations that are able to collect, manage, and organize data, and to learn faster and improve customer insights will win. Predictive analytics is a powerful tool to accelerate this process of learning and applying customer insights. The use of AI and techniques like data mining and statistical modeling can take predictive analytics to the next level and enable our organizations to “see the future first” for a powerful competitive advantage.
Data Strategy and Predictive Analytics
As the world’s data — along with your organization’s — grows in size and complexity, you need a way to align that data with your organizational goals. A data strategy supports your overall strategy by mapping your progress and milestones, and how data will enable your organization to discover new insights and increase and competitive advantage.
In the last few years, every organization has gained access to more data, more storage, and more processing power than ever before, and at a lower cost. Organizations that strategically focus on building capabilities to leverage these trends will outpace those that don’t.
Perhaps the greatest opportunities afforded by these data trends is the ability to move toward better, faster decisions through the use of data analytics.
- As shown in the image below, less sophisticated organizations rely on descriptive analytics for insights as to what has happened. These insights are valuable, but relying on descriptive analytics exclusively is like driving your car by looking at the rearview mirror, since only historical data are involved.
- Further along the evolutionary cycle, diagnostic analytics are those that help us discover data patterns and relationships to identify why things happen, to better understand what to expect. Diagnostic analytics are useful in identifying outliers in data, discovering patterns, and identifying relationships.
Image Source: Singularity University Blog
- Moving further up the ladder of sophistication, predictive analytics employ a variety of statistical methods, including data mining and predictive modeling to analyze large data sets and make evidence-based predictions about the future. Modern predictive analytics tools and platforms that can help us generate more accurate future insights are becoming a huge market, estimated to reach more than $10 billion by 2022, according to Zion Market Research. Today’s more data-driven organizations use advanced analytics to improve marketing and sales campaigns and better understand customer behavior.
- Not so long ago, humans used cumbersome manual processes to analyze data and decide on a recommended course of action. The use of prescriptive analytics models goes beyond predicting to identifying potential outcomes and making recommendations about specific courses of action. Prescriptive analytics represent the most mature analytics models, and are concerned with answering questions like “What could happen?” and “What actions should we take?”
Predictive analytics and prescriptive analytics in the future
And though only about 11% of mid-to-large businesses used some form of predictive analytics in 2019, that percentage is expected to grow to 37% in 2022 as analytics tools and platforms become more affordable and business-user friendly. These businesses are trying hard to keep up with organizations including Amazon, Apple, Google, Facebook and Netflix that have already built a powerful competitive advantage using predictive analytics and prescriptive analytics. These organizations understand how to optimize services for current customers, and are rapidly learning how to forecast future subscriber and advertiser trends.
As organizations build more sophisticated analytics capabilities, predictive analytics will enable them to make better decisions more quickly than ever before, while more accurately forecasting future trends. And though sales and marketing departments are often the first adopters, prescriptive analytics has applications throughout the enterprise, including operations, supply chains, talent acquisition, logistics, and more.
Though none of us can predict the future, through predictive and prescriptive analytics, we are getting better at forecasting possible future scenarios and improving the actions we take to achieve better outcomes — a valuable skill in uncertain times. We can expect that within a few years, the use of prescriptive analytics will become as common as the use of business analytics today. The flywheel organizations that build their data analytics and AI capabilities today, and turn those capabilities into actionable insights, will be the ones to lead the way to future growth.
Learn more about predictive analytics and AI in marketing here!