Global data volumes are predicted to soar to 180 zettabytes by 2025, providing organizations with a goldmine of data. However, approximately 75% of this data remains untapped. For many organizations, even the 25% of data that is utilized often lacks application to generating fast, actionable insights, predictive analytics, or optimization.
By upskilling their revenue growth analytics capabilities, businesses can address the paradox of being "data rich but insights poor" and amplify their operating profits.
Below are some key areas where advanced commercial analytics capabilities will make a huge difference:
1. Pricing Strategy Optimization: A great example is dynamic pricing based on consumer behavior, market dynamics, inventory levels/capacity utilization, seasonality, etc. For most companies, this doesn't have to be complicated, and "dynamic" pricing can be replaced by "automated" pricing: a surgical, data-driven indexing approach that's still substantially better than ad-hoc, manual pricing efforts.
2. Sales and Marketing Enhancement: Integrating advanced commercial analytics transforms sales strategies and marketing efforts. Organizations can refine sales forecasts, tailor marketing initiatives, and enhance customer acquisition, retention, and upsell/cross-sell strategies by leveraging detailed transactional, customer behavior, and marketing performance metrics.
3. Operational Efficiency: Through advanced commercial analytics focused on inventory and supply chain data, businesses can pinpoint inefficiencies and optimize processes. This reduces operational costs, frees up liquidity, and enhances operating profits. For instance, predictive analytics in logistics has enabled significant companies like UPS to save millions in fuel costs by optimizing delivery routes. Automated markdown pricing for unproductive inventory has enabled a leading tire distributor to drive gross profits.
5. Downtime Reduction: Advanced analytics also play a crucial role in preemptive maintenance, especially in sectors like manufacturing, where equipment downtime can result in substantial revenue loss. ML models predict potential failures, allowing for interventions that minimize downtime and maintenance costs and drive operating profits.
To read more, here's a link to our LinkedIn article: https://lnkd.in/eyj3iWsp
As a fun side note, how big is 180 zettabytes? Assume your average 2-hour HD movie is about 3 GB. So 180 zettabytes can store ~ 60 trillion 2-hour HD movies. It would take roughly 7 billion years to watch these movies, assuming no rest.
#revenue_growth_analytics
Congrats on this impressive growth!