# Market Share Graph

### **Overview**

The Market Share Graph provides a dynamic view of how demand is distributed across a defined competitive set over time. TickerTrends constructs this graph using high-frequency alternative data sources such as **web traffic** and **Amazon search volume** to estimate relative share in near real time.

By aggregating signals that reflect where users are actively browsing and what they are searching for, the model captures how demand shifts between competitors as consumer behavior evolves. Rather than measuring absolute usage, the graph shows each entity’s **share of total demand within a category**, offering a more responsive view of competitive positioning.

Rising share typically indicates increasing consumer interest, stronger discovery, or improved conversion across channels, often ahead of reported financial results.

**Customization**\
Market Share graphs can be customized to reflect a user-defined competitive set.

Users can:

* Select or remove specific companies or brands
* Build custom groupings based on their coverage universe
* Adjust the set at any time to reflect changing market dynamics

These controls allow for more relevant analysis by defining the market based on the user’s specific use case rather than fixed groupings.

### Historical Length

Coverage typically extends several years back, depending on data availability across web traffic and search datasets. This allows users to analyze long-term competitive dynamics, including share shifts, category expansion, and sustained momentum.

### **Granularity**

Data is modeled at daily granularity, enabling users to track short-term changes in demand driven by product launches, marketing campaigns, or competitive events.

### **Update Frequency**

Updated daily with a short delay of less than a week, depending on the underlying data sources.

### **Methodology**

TickerTrends constructs Market Share using a blended model of alternative data inputs, primarily **web traffic** and **Amazon search volume**. These inputs serve as proxies for consumer intent and engagement.

The model aggregates and normalizes these signals across a defined peer set, then calculates each entity’s proportional share of total activity within the category. The output is designed to reflect **directional changes in demand and competitive positioning**, rather than precise market share percentages.

### **Example Visualization**

<figure><img src="/files/kerPrfWDSTwDzwVCIHX6" alt=""><figcaption></figcaption></figure>

### **Use Cases**

Detect early share gains or losses before they appear in reported metrics.\
Monitor competitive positioning across a defined set of companies or products.\
Identify inflection points where challengers begin to gain traction.\
Validate demand trends by combining with other datasets such as Discussion Share or Search Interest.


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