The value of data analytics tools for digital business enterprises such as Ecommerce companies can be estimated from its increased adoption along with the overall benefits such as better decision making, cost reduction, and revenue growth. Capital investments into analytics by Ecommerce companies only counts when they can drive improved decision making.
Ecommerce businesses face challenges primarily revolving around how to acquire new customers at a lower cost and how to retain them for maximizing product sales. This can often drive the following critical decisions on:
- WhichÂ marketing channelsÂ are bringing in the best customers?
- What percentage of our customers are signing up for the final product sale?
- How many users are returning to our online retail website on a daily or monthly basis?
- What is the bestÂ customer profileÂ for our products?
What is Product Analytics?
In simple terms, product analytics shows product management who their users are, what they need, and how to retain them. Product analytics drives better customer insights and data-based decision making by collecting and interpreting all product-related data.
In short, product analytics provides an automated method to analyse users and provide them with a better customer experience. Product analytics simplifies the tracking of users right from the time they take interest in your brand till they buy your products.
Why do companies use product analytics?
Ecommerce companies need to know how their users and customers are engaging with their retail products or services. Product analytics enables them to track the digital footprint of their users with their products to view and collect information of what they like (or dislike) along with the product line that they engage with or return.
Product analytics converts unstructured business data into valuable insights by integrating a variety of data sources into a single and organized window for decision makers.
Why Product Analytics is so important to companies
According to a McKinsey report, companies that deploy customer (or product) analytics earn twice as much profit as any competitor company that does not use these technologies. With the large volumes of business data being generated in this information age, only those companies that can derive business value and insights from this generated data (using analytics tools) can gain from the benefits of data-driven product management.
Along with influencing product decisions, product analytics can be used for:
- Understanding user and customer buying behaviour.
- Measure the progress of any product line.
- Prove the viability of any product idea.
- Inspire innovative product ideas.
How to use Product Analytics
Product analytics tools are mostly designed around the following 2 functionalities, namely:
- Data tracking, which analyses user visits and user actions.
- Data analysis, which analyses and visualizes data through user dashboards or generated reports.
Additionally, in order for product analytics to be successful in informing and driving corporate decision making, product analytics team must work seamlessly with the product management and other departments to:
- Create efficient processes that can build trust in product analytics and its insights.
- Develop a feedback loop that can help teams collaborate and coach with each other for better quality and output.
- Develop close partnerships with the product teams.
- Develop a common language among teams that can facilitate sharing of core metrics (example, product mission, customer behaviour).
- Educate your company personnel to take more informed decisions using data insights.
Common features of Product Analytics
Most product analytics tools offer the following features:
- User trackingÂ features: to track user activity within the website or app.
- User profiling and segmenting: to know the profile of visiting users or customers and classify them according to age, device used, place, and behaviour patterns.
- Create and analyse marketing funnels: to visually measure how any customer navigates through a series of events (example, product sign-up, profile completion, and onboarding).
- Cohort analysis: to analyse how customer behaviour has changed or evolved over time.
- Dashboards for data visualization
- Measure feature-wise user engagement.
The critical importance of generated data in modern product management has elevated the unique advantage that product analytics offers online retailers across the globe. Analytics has simplified the idea of deriving business insights from the huge volumes of unstructured business data and has transformed the efficiency of product management.
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