AIM services enable you to measure, understand, take action on and improve your customer experience ecosystem that drives your business growth for the long term.

Customer Experience Insights

Providing actionable customer experience insights is our flagship service offering. Analyzing data from your entire customer experience ecosystem allows us to achieve strategic, specific and actionable insights for you that will propel your organization to delivering a better customer experience and improved business performance through leading edge analytics tools and techniques.

Read several examples of customer experience insights that can be illuminated with the right kind of data and analytic approach, including the use of causal modeling.

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  2. Direct and Total EffectsCausal modeling allows for estimation of the direct importance of each key driver, and the total impact of each key driver that may include its indirect impact through other key drivers.
  3. Prioritizing Key Drivers: IPMA – Using the total effect importance estimate along with the current performance level of each key driver, AIM can provide you with an Importance Performance Map Analysis (IPMA) that will help you prioritize which key drivers to act on first to have the biggest impact on your desired outcomes.
  4. Customer Experience Impact on Business Performance – In addition to key drivers of the customer experience, AIM can discover key drivers of mission critical business performance outcomes such as revenue, product units sold or customer attrition.
  5. Linking Entire Customer Experience Ecosystem to Business Performance – When we can link multiple customer experience ecosystem components to a common unit of analysis (e.g., location), the breadth and value of the causal models increases significantly. AIM has expertise in the measurement, data management and analytic techniques necessary to create these cross-data source causal models.
  6. Incorporating Customer Comments – In addition to quantitative ratings data, AIM can quantify and analyze customer comments data as part of describing what topics are on their minds, general comment sentiment, as well as incorporating comment themes into causal models in some circumstances.