Predictive vs. Anticipatory Analytics and Your Business Goals

What’s the Best Analytic Approach for Your Business?

Thanks to predictive analytics, which leverages data mining, statistical algorithms, advanced modeling and machine learning techniques, businesses are able to identify the likelihood of future outcomes based on historical data.

While the practice of predictive analytics has become a fairly common practice among many companies, especially in the marketing space, it is not meant to tell you definitively what will happen in the future, particularly if you change any parameters associated with the customer set. Instead, predictive analytics can only tell you what might happen given the same set of circumstances – and it is often very good at doing just that. But at the end of the day, predictive analytics are still probabilistic in nature.

A common criticism of predictive analytics is that environments and people are always changing, so static, historical trends are too simplistic and sterile to say something will or will not happen with a great degree of certainty.

Enter Anticipatory Analytics. As technology has evolved, so has our ability to process data at an incredible rate, making it possible to perform what has become known as Anticipatory Analytics. While still a relatively new concept, anticipatory analytics is gaining prevalence as a methodology. It leapfrogs predictive analytics in that it enables companies to forecast future behaviors, quicker than traditional predictive analytics by identifying change, acceleration and deceleration of market dynamics. Anticipatory analytics addresses businesses’ more challenging issues and places the onus on the business to take an action to reach a defined outcome. While anticipatory analytics is born out of predictive, there are clear differences that must be defined.

What are Predictive Analytics?

Predictive analytics helps predict future behavior based on past data and behavior trends. The prediction is based on statistical models, which can range from a simple linear equation to more complex models as those in neural networks. Predictive models are very accurate when past trends continue in the future. It is not as accurate in identifying the inflexion points, or the real-time signals that may alter a future outcome.

What are Anticipatory Analytics?

Anticipatory analytics builds on the foundation of predictive analytics where we can identify and adjust predictions based on inflection points such as the acceleration and deceleration of certain business behaviors or sudden change in business direction. Anticipatory analytics helps to anticipate the future needs of a business before they show obvious signs in their respective opportunity/risk profile.

Advantages of Anticipatory Analytics

Gaining the ability to foresee and plan for risk and opportunity before they occur is incredibly powerful. With it comes many practical applications:

  • Expand prospect pool: Target businesses that may not have met earlier qualifications based on current selection criteria but have near-term potential
  • Optimize account management: Organize sales channel and account segmentation by considering current and future opportunity profiles
  • Identify up-sell opportunities: Pinpoint customers that are ripe for your product/service given their growth trajectory
  • Get ahead of competition: Increase market share by anticipating needs and getting an offer/message in market before your competition

Use Cases: Anticipatory Analytics in Action

Assume you are a marketer and you have built highly sophisticated predictive models about who’s most likely to respond to your offer. This model is relying on who has responded in the past and is looking at specific behaviors and characteristics of those customers to identify patterns that help you identify similar customers to target. This works great if you are not planning to change your message or offer. But what if you want to make slight variations to your product/service and need to identify a completely different audience? The traditional predictive response model is not going to help you there at all.

Whether you’re looking at new markets or new sets of customers to engage with, you can leverage anticipatory analytics to help better understand previously undefined targets. While it won’t necessarily tell you who’s going to respond to you based on who has acted in the past – everyone else is targeting them – it will tell you the companies that are likely to grow in the future, giving you the first-mover advantage.

Anticipatory analytics can act on real time signals, like:

  • Bankruptcy or any other derogatory event
  • Loan inquires
  • Change in linkage
  • Change in geography
  • Executive transitions
  • Inquiry for insurance
  • Purchase of new equipment

Now let’s look at how this might be applied in the financial sector. Financial institutions are well aware of who’s coming to them with a demand for borrowing money; predictive analytics helps them stay ahead of the most likely customers that will seek loans. But using anticipatory analytics, they can figure out whether this demand for borrowing is because that company’s trying to grow or if the company is borrowing to keep their head above the water. By looking at real-time signals in the data, they have a much greater context behind the borrower’s request. They already knew who’s coming to them and what their current status was based on our risk scores, but anticipatory now gives them a much deeper view so they can fine-tune their response and messaging for these different circumstances.

Selecting the Best Analytics Approach for Your Business

While anticipatory is certainly an exciting new development in the world of analytics, it is not meant to replace traditional predictive analytics. Both approaches are valuable and can even work side-by-side.

For companies that have a core strategy in place and continue work on a constant path, predictive analytics does a very good job to continually look at past trends and forecast opportunities that make sense for them. This is very good for meeting short-term goals. But for any company looking for new approaches or implementing new tactics that take their strategy in new directions, anticipatory analytics may be the better choice; this typically benefits more long-term planning.

Here are instances when either predictive or anticipatory analytics may be right for your business:

Use When: Use When:
-Past trends are robust and do not change frequently -You have new product or service
-The target variable is well defined -Expanding a current product set to a new customer base or a new market
-There are multiple samples available to build and test robust models -Your business case is dynamically changing, i.e., presence of fraud, geographical area going through major changes, etc.
Key Considerations: Key Considerations:
Very small count of target incidences, model could be over-fitted and not stable Continuously test and learn to have a good understanding of customer behaviours, needs and expectations
Examples: Examples:
Risk analytics Fraud analytics
Demand models Cross-sell/Up-sell opportunity analytics
Response Models Servicing Optimization


It is important to understand the different business situations where traditional predictive analytics can be best applied and where anticipatory analytics may be a more appropriate approach to solve the business problem. One is not necessarily superior to the other – it’s about which methodology is best utilized in solving the specific business problem.

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