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First Look – Aha


I got a chance to catch up with Aha! recently. Aha! is based in the Denver Tech Center and was founded back in 2006. Aha!’s premise is that it is now possible to build analytics into a platform and to focus on how to operationalize predictive models and deliver analytics within business processes. Initial customers are in healthcare, telecom, travel and transportation. Their aim is to deliver a complete analytics management system. The pain points of traditional BI solutions that they address are: limited use and access (big focus on self-service), long time to value (SaaS platform or rapid start up with minimal IT), rear-view focus (predictive analytics), piles of data (model-based analytics) and Excel/scattered data (single network). They focus on being “dynamic and aligned” and focused on business users.

Their market is the $2-$3Bn “business embedded analytics” that is part of the overall $26Bn global analytics market. In particular, they provide non power analytic users with access to analytics without having to obtain specialized skills. They see themselves helping the vast majority of business users who don’t use analytics today – the people that dominate the operations of a company like marketing managers, sales managers, customer care managers, product managers, marketers, engineers, and operations specialists. Financials matter to these folks but they don’t dominate the way they do with “traditional” financial department analytics users.

Aha! sells direct as a SaaS offering (setup fees and subscription), offers model development and data discovery services and licenses through OEM/SI partners. Partners are typically domain experts and vertically focused.

Some example customers include: a telecom company using analytics to handle the ROI of proactively building out a fiber network and to optimize sales and marketing to light up this fiber; a telecom handling customer retention, product segmentation and customer experience satisfaction; a healthcare company working on customer retention and acquisition.

Their offering (Axel) is a SaaS multi-tenant, multi-hosted system. It is designed to bring models into the business process – business process based models – make the analytics actionable and close the loop between analytics to actions. The whole thing is based on KPIs and designed to help companies actually act on their strategy, using a KPI model that runs from head office strategy to the front line. The platform has 5 core elements:

  • Language
    The Aha! Expressions analytic model definition language that allows business analysts to build the models
  • Dynamic services
    Secure, multi-tenant, forecasting, simulation and optimization
  • Visualization
    Self-service, near real-time and model driven
  • Data Engine
    Profiler, designer, ETL, Smart Pub/Sub
  • Extensions
    Support for third parties to extend and integrate the platform

The basic process looks like this (for a healthcare member retention example):

  1. Customer profile, billing, survey and claims data is used to create a model data file
  2. Predictive models are developed based on this data
  3. Customers are scored using these models
  4. Contact and campaign management define available actions based on these scores
  5. KPI-based models are developed using the same data
  6. Collaborative analytics link all this together to support decision making and drive ROI

The target for this customer was to reduce churn. They were up and running in 60 days, improved retention by 7.5% (v target of 3%), improved new member retention by 9%. NPV of $43M in a single enrollment period and an all-in ROI of 2447%. This was recognized at the World Health Congress as a top example of using predictive analytics to drive member retention and satisfaction. Users ranged from call center operations to VP level executives.

The model data was used to create retention or churn scores for each customer that were loaded into the operational system in batch. These scores can be updated regularly from the model data file and can be calculated live based on intra-day data or, in theory, even during a conversation (using a standard web-services interface). The use of this model is much the same as the use of any other predictive model except that the data is tightly coupled with the KPI hierarchy. Models can be built from and evaluated against the historical data that drives the KPIs, so that users start off with a valid historical base. Axel also provides a stochastic enrichment engine ( Monte Carlo simulation with category selection, probability, and triangular distributions) that supports PMML, allowing models built outside to be imported using PMML. Models can also be generated via an Microsoft Excel Template.

Aha! is driven by a KPI model hierarchy. In the case of this healthcare company it was Retention Campaign (Strategic), then the health plan a member was in (Tactical) then events within a member lifecycle (Operational). This drives how the data is viewed and KPIs – in this case customer retention measures of various kinds – are tracked against this hierarchy. So, for instance, each KPI could be viewed with respect to a specific member lifecycle step, a particular plan or a particular campaign.

Each KPI has a calculation defined for it and are calculated dynamically. In addition to mathematical calculations, the Expressions language also provides addition functionality that supports the calculation of KPIs based on Year to Date, Quarter to Date, Month to Date, Sum of values for a defined period, Average of values for a defined period, etc.

The interface allows different reference periods to be selected and the KPIs to be viewed within that period along with measures like averages, high/low values for the period, goals etc. For instance, this customer saw a lot of new members were signing up but then being lost. The prediction showed that the trend would clearly exceed their target for such losses and allowed them to see the impact on all their KPIs. This provoked a focus on the reasons for this and they found an external verification service that was needlessly disqualifying people. They had no expectation that this would be a problem and the tool allowed them both to spot it and see the impact on their KPIs quickly enough to take action before the open enrollment period was completed and the opportunity to fix it lost.

The most interesting thing about Aha! for me is the tie to a formal model of KPIs that drive from a high level to an operational level. This allows impact analysis and decision making to be clearly linked to the objectives set at different levels.

For more information on Aha!, you can visit their website at www.ahasoftware.com or download their paper on Business Embedded Analytics.


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