Visualize for Insights

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VISUALIZE FOR INSIGHTS

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Drawing insights from digital performance data is a complex affair. Analysts can infer same data completely differently with different contextual understanding. First step in establishing a framework, for drawing insights consistently, is to answer following key questions:-

Are we monitoring trends of critical KPIs by dimensions & segments?

Do we have dashboards for monitoring trends, exceptions & anomalies?

Do we have well structured framework for explaining trends, exceptions & anomalies?

Do we have data analytics infrastructure & capabilities for identifying growth opportunities?

Segmentation

We Segment every metrics without any exception. We strongly believe that insights from aggregate data is of now value, real value comes only if see data in smaller contexts (acquisition, behavior & outcomes).

Segmentation by Source

Identifying high priority traffic sources (brand search, direct, paid search generic, organic-brand, organic-non-brand, display, re-marketing, email etc ), creating segments & running them on reports specific to that traffic type.

Segmentation by User Behavior

Identifying key user behavior, segmenting it and understanding correlation of such user behavior with success.

Segmentation by Outcomes

Segmenting users by expected business outcomes (downloads, leads, subscription, purchase) and understanding cause & correlation with many metrics.

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Trend Modeling, Visualization & Monitoring

Monitoring performance trends by plotting trend graphs/visualization for performance metrics & segmentation. We prefer vast spread (combinations of metrics & dimensions) of visualizations instead of deep metrics trend with dimension as filter.

Knowing Digital Marketing Performance Trends

Digital marketing performance trends with special focus on paid campaigns, brand traffic, re-marketing & email marketing. Primary objective is to consolidate historical learning & map key tactical milestones.

Knowing Consumer Focused Product Updates

Mapping product changes on different relevant performance metrics (e.g Information architecture improving organic traffic, website migration hurting organic performance, in-system search improving page-views/visit etc)

Understanding Business Seasonality Trends

Nearly every business follows different seasonality, understanding this variation by month, location, product type becomes very critical in giving true context to performance.

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Recommendation Engines

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Collaborative Filtering Algos

Suggest products that customer are most likely to buy, based on his purchase history or on the product he is currently viewing. Models: Collaborative filtering and content-based recommendations. Recent techniques add a layer of reinforcement learning.

Affinity Analysis & Models

Affinity Analysis: Used to identify groups of products that are bought together.

Models: A Priori algorithm.

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Analysis of test scenarios

Creating hypothesis, doing root cause analysis & inferring test scenarios & growth initiatives are some of the most critical aspects of digital performance framework.

Root cause analysis

When performance changes occur under uncontrolled situations, and there are a myriad of different factors that could have impacted the change. RCA is done to determine causal factors of events and to identify appropriate response actions.

Decision Tree

Creating decision tree (we call it success/impact tree as well) by linking higher-order outcomes to lower-order causes. For performance, this can be represented as a hierarchy of KPIs and metrics.

Causation & Co-relation

Confusing Causation with Co-relation is one of the most biggest analysts do often. Hypothesizing high impact initiatives (campaigns, product changes etc) depends on our ability to differentiate between two & focusing more on cause then on co-relation.

Statistical confidence / confidence level for A/B testing

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Have a question for our team? Whether you need a expert opinion, interested in working with us, or just want to find out more about what we do, we’d love to hear from you !!

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