Speed and Depth of Insights

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When it comes to insights, there is no one-size-fits-all solution.

 

We measure and inform today's omni-channel marketing process and customer journey with analytical insights and support tools. Each project passes through three main stages of analytics:

  • 1

    Discovery

    This phase focuses on confirming the correct data and informing the scope of analysis given the opportunities or limitations in the data. It is an extension of data visualization and is informed by interactive data workshops. 

  • 2

    Understanding

    Preliminary analysis and results shared in an interactive workshop allows the team to challenge and gain comfort in the results. Recognizing that modeling is not always an exact science and that data is not always perfect, this phase of analytics is transparent and iterative.

  • 3

    Confirmation Analytics (Descriptive, Diagnostic or Predictive)

    Depending on the specific project, the final analytics provides a descriptive, diagnostic or predictive solution. This final set of results is held up against the original learning agenda. In cases where learning’s have not been achieved or are not of significance, a test & learn agenda is set-up to pursue better data for the next cycle.

We work with clients to tailor the model structure to best fit their needs. All of our modeling platforms are designed to be flexible, enabling the user to customize as needed.

The trade-off however, is that these tools require expert, well-trained users to operate successfully. Achieving the optimal balance of speed and depth of insights requires not just the best use of analytics technology but also an appropriate level of skill and expertise while using it.

Below are challenges to any given analytics project that we find ourselves wrestling with in order to be confident that the final result presented is both accurate and insightful:

  • Endogeneity

    Do we believe we have identified true causation and not over-or-under inflated results?

  • Parsimony

    Is the analysis as simple as possible while still generating a robust insight? Have we minimized false positives?

  • Validation

    Can we verify results through hold-out samples and forecasts test?

  • Holistic

    Does the modeling approach control for all influences on the consumer decision analyses?

  • Realistic

    Does the modeling approach fully represent the consumer’s decision being made?

  • Appropriate

    Given the type and structure of available data, does the approach generate a usable result?

When settling on the optimal model approach, one should think through how the results will be used and how the recommendations will be implemented. For example, it might be better to recommend validating the results through a test & learn program before full implementation if there is any lack of confidence (either statistical or managerial) in the results. Providing a fuller context including benchmarks and case work to support results is helpful in socializing and encouraging adoption.