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Build Custom Tools to Unlock the Full Power of Your Data
​Custom tooling empowers businesses to fully leverage their data by delivering a tailored, dashboard-like experience that seamlessly connects with other systems and APIs.
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Using a custom tool, users can run data science models on the fly and push results directly into other tools, driving real-time decision-making and automation. This approach provides flexibility, streamlines workflows, and democratises advanced data analysis.
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Examples of custom tools built by Datagator Solutions:
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​> Basket analysis by user group​
This tool allowed users to segment customers into various value tiers, enabling them to examine basket contents across these groups. By leveraging this data, the business was able to optimise product bundling and cross-selling strategies, boosting average order value and customer retention.
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> Look-alike audiences
This tool enabled users to identify customers with behaviours aligned to key segments of interest, facilitating the creation of highly targeted look-alike audiences. These audiences were automatically synced with Mailchimp, ensuring precise, data-driven marketing campaigns. This approach significantly boosted engagement and conversion rates by targeting customers who closely resemble high-value segments, driving more effective marketing strategies.
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> Natural language topic analysis (NLP)
This tool allowed users to analyse large volumes of forum messages to identify key topics and sentiments over time. By tracking how topics evolve over time, the business gained insights into shifting user priorities and emerging issues.
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> Marketing attribution model​
This tool allowed end users to adjust and experiment with different attribution models for their marketing data. By enabling users to change the attribution model, the business could better understand which touchpoints drove conversions and were able to refine their marketing strategies for more accurate budget allocation and improved ROI.
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> What-if analysis​
This tool enabled the business to run simulations that test different strategies - such as pricing adjustments, marketing spend, or product launches - and predict their impact on key metrics like sales, profitability, and customer engagement. By modeling various scenarios, the business was able to make more informed decisions, optimise resource allocation, and mitigate risks before implementing real-world changes.
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