As an ecommerce business, you likely hire a team of in-house data scientists to tackle a variety of critical tasks — building recommendation engines to increase sales, conducting market basket analysis to understand customer purchasing patterns, optimizing prices, predicting customer lifetime value, and more.
Having a dedicated data science team seems like a necessity.
However, what if there was a solution that didn't require a whole team of experts working on data analysis and model building to get the insights you need?
Before we discuss that, it's important to understand the challenges that come with building and maintaining an in-house data science team for your ecommerce business.
Recruiting the right people with the necessary expertise is challenging. Even if you find data scientists who are knowledgeable about the technical aspects of the job, ensuring they also have deep domain expertise in your specific industry can be quite tricky.
And once you have the team in place, keeping them motivated and up-to-date with the latest tools and techniques requires significant investment, and hiring them in the first place is an expensive affair considering the inflated tech market.
Additionally, relying on an in-house team can lead to issues like cognitive biases influencing decision-making and difficulties scaling the data science capabilities as your business grows.
All of these challenges can be solved with a single platform — Graas.
We will discuss how Graas can replace your need for an in-house data science team:
What Graas does that replaces your eCommerce data scientists? Why you don't need in-house data scientists with Graas
Let's dive right in!
What Graas does that replaces your eCommerce data scientists?
Graas is an end-to-end eCommerce analytics platform that covers all the bases to replace your in-house data science team. Here are some of the capabilities of the platform:
1. eCommerce dashboards
Graas provides comprehensive eCommerce dashboards that offer a holistic view of your business performance. This platform helps you eliminate siloed eCommerce data by consolidating data from multiple sources, including various sales channels, marketplaces, and marketing platforms, and presenting it in an easily digestible format.
The dashboards offer real-time updates, which allows business owners and managers to get an immediate view of their eCommerce performance without needing to consult a data scientist or wait for periodic reports.
Moreover, Graas's dashboards are interactive and customizable. Users can drill down into specific metrics, compare performance across different time periods, and adjust views to focus on the most relevant data for their business needs.
This level of flexibility and depth would typically require advanced coding skills from a data scientist, but Graas makes it accessible to all users regardless of their technical expertise.
2. eCommerce Data Warehousing
Graas makes eCommerce data management easier by automating the entire data warehousing process. It acts as a centralized repository, collecting and integrating data from multiple sources.
The platform handles complex data extraction, transformation, and loading (ETL) tasks without manual intervention, eliminating the need for specialized data engineers.
Graas processes data in real time, ensuring all analytics are based on the most current information. It standardizes data from different sources, a task that typically requires significant effort from data scientists.
By automating data warehousing, Graas provides a complete, scalable, and secure data storage solution, which acts as a strong foundation for advanced eCommerce analytics.
3. Deep dive analytics of business
While high-level dashboards provide a quick overview, Graas also offers deep-dive analytics capabilities that rival those of experienced data scientists.
One key feature is the ability to analyze performance across different sales channels and marketplaces. Graas allows businesses to compare how their products are performing on platforms like Amazon, Lazada, Shopee, and others. This granular view helps identify which channels are most effective for specific products or categories, allowing for more targeted resource allocation.
The platform also provides detailed product performance analytics. It can identify top-selling items, underperforming products, and potential opportunities for cross-selling or upselling.
Furthermore, Graas offers eCommerce predictive analytics capabilities. It can forecast future sales, predict inventory needs, and identify potential market opportunities by analyzing historical data and current trends.
These predictive insights, typically requiring advanced statistical modeling by data scientists, are generated by Graas, making them accessible to businesses of all sizes.
4. eCommerce marketing analytics
After getting your product right, marketing is the most essential aspect of getting sales. Graas provides robust marketing analytics capabilities that can replace the need for specialized marketing data scientists.
Graas integrates data from multiple marketing platforms, including social media, search engines, email marketing tools, and more. This integration allows a holistic view of marketing efforts, allowing businesses to understand which channels drive the most traffic, conversions, and revenue.
In the eCommerce Marketing Dashboard, Graas provides detailed metrics on campaign performance, including impressions, clicks, click-through rates, and conversion rates. It also calculates important metrics like ROAS, allowing businesses to quickly identify which campaigns are delivering the best results.
The platform goes beyond just tracking performance — it also provides insights into ad creative effectiveness. By analyzing metrics like click-through rates, conversion rates, and engagement rates, Graas can help identify the most effective ad creatives and optimize the ones that are not performing well.
So no need to hire a different data expert for your marketing analytics, Graas ensures that your non-technical marketer can also figure out insights from your data.
5. Insights and recommendations for real-time decision-making
Talking about insights, it’s perhaps one of the most valuable features of Graas, its ability to provide real-time insights and actionable recommendations.
While data scientists often need time to analyze data and formulate recommendations, Graas does this in real-time, allowing businesses to make quick, informed decisions.
The platform continuously monitors business performance and market trends, alerting users to potential issues or opportunities as they arise.
For example, if a particular product is selling faster than usual, Graas might recommend increasing inventory to avoid stock outs. Conversely, if a product's sales are slowing, it might suggest promotional strategies or inventory reduction.
Graas also provides recommendations for optimizing marketing spend. By analyzing the performance of different marketing channels and campaigns, it can suggest where to allocate the budget for maximum ROI.
The platform's AI-driven eCommerce insights extend to pricing strategies as well. By analyzing competitor pricing, market demand, and historical sales data, Graas can provide recommendations on optimal pricing strategies. This includes suggestions for dynamic pricing during peak periods or recommendations for bundle deals to increase average order value.
These real-time insights and recommendations are presented in a clear, actionable format, often accompanied by projected outcomes.
Why you don't need in-house data scientists with Graas
Here are some of the reasons why you don’t need an in-house data science team if you’re using Graas:
1. No technical expertise needed
One of the primary advantages of using Graas is that it eliminates the need for specialized technical expertise.
Traditional data analysis often requires proficiency in programming languages like Python or R, as well as knowledge of statistical modeling and machine learning algorithms. This level of expertise is typically found in trained data scientists, which can be costly to hire and retain.
Graas, on the other hand, is designed with user-friendliness in mind. Its intuitive interface allows users to access complex analytics and insights without needing to write a single line of code.
For example, tasks like data integration from multiple sources, which would typically require significant coding effort, are automated in Graas. Users can connect various data sources with just a few clicks, and the platform takes care of data normalization and integration automatically.
Similarly, advanced analytics functions like predictive modeling and attribution analysis, which would normally require expertise in statistical methods and ML, are built into the platform, which you can access through simple interfaces.
By eliminating the need for technical expertise, Graas democratizes data analysis within organizations. This means that business users, marketers, and decision-makers can directly access and interpret data insights without relying on a specialized data science team.
2. Translates data into actionable insights
While collecting and analyzing data is important, the true value lies in deriving actionable insights from this data. This is typically a key function of data scientists — interpreting complex data analysis and translating it into insights that can guide business decisions.
Graas automates this process, effectively replacing the need for in-house data scientists in this crucial role. It doesn't just present raw data or basic metrics — it provides context and interpretation.
For example, instead of just showing that sales have increased by a certain percentage, Graas might identify the specific factors contributing to this increase, such as a successful marketing campaign or a popular new product.
It translates complex statistical concepts into clear, business-relevant terms. For example, instead of presenting a correlation coefficient, Graas might explain that "customers who purchase Product A are 70% more likely to also purchase Product B."
The platform also prioritizes insights based on their potential impact on the business. This means that users aren't overwhelmed with data but are presented with the most relevant and actionable insights first. This prioritization, which would typically require the judgment of an experienced data scientist, is handled automatically by Graas's AI algorithms.
3. Gives actionable insights to optimize eCommerce business
Perhaps the most valuable aspect of Graas is its ability not just to provide insights but also to offer specific, actionable recommendations for improving various aspects of the business. This capability effectively replaces the strategic advisory role often played by your data science team.
In terms of marketing, Graas doesn't just show which campaigns are performing well — it provides specific recommendations for optimizing marketing strategies. For customer experience improvement, Graas analyzes the entire customer journey and identifies potential pain points. In product strategy, Graas leverages its comprehensive data analysis to provide actionable recommendations for product development and inventory management.
Importantly, these recommendations are not generic best practices but are tailored to the specific context of each business. It takes into account the unique characteristics of the business and its customer base.
Moreover, Graas doesn't just provide recommendations – it also estimates the potential impact of these actions. For example, it might predict how much a suggested change to the marketing mix could increase conversions or how a recommended pricing strategy could affect overall revenue. This allows businesses to prioritize actions based on their potential return on investment.
Long story short, Graas drastically increases the return on your data investments. Try for free today!
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