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Top five pricing analytics 'I wish I would haves' to avoid
Pricing optimization, profitability, and cost management
Discover how businesses can avoid these five "I wish I would haves" when it comes to pricing and revenue management.
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Five “I wish I would haves” to avoid
As a consumer packaged goods executive, we understand what you're up against. Consumers are increasingly fragmented, sales needs autonomy but is limited in its ability to develop data-driven insights, and leadership struggles to protect margins and profitability. The underlying shifts behind some of these challenges are demanding new levels of agility and responsiveness at all levels of the business.
So when it comes to pricing and revenue management, don't get caught saying any of these things one year from now:
I wish I would have invested in understanding what really drives demand in my business at a granular level
If you haven't spent the time developing the capability to manage granular data in your organization and leverage it to make decisions, chances are you're lacking visibility into the true drivers of your business. Many leading companies are using detailed sets of data to understand the attributes of products and attitudes of shoppers that drive demand, and then architecting pricing, promotion, and merchandising strategies that address very specific demand generation opportunities and consumer occasions.
I wish I would have spent more time on proactive "shelf-back" strategies and less time on reactive "cost-forward" analysis
Pricing has evolved from an internal discipline to a cross-value chain discipline where right-to-left, shelf-back thinking is an imperative. Leading companies are thinking about the end consumer behavior they're trying to drive at the point of purchase and are then backing into how to create those opportunities for consumers while preserving profitable retailer relationships.
I wish I would have gained a competitive edge in strategic pricing analytics against my peers when we had the chance
Increasingly cost-conscious customers, more volatility in commodities, reduced effectiveness of promotions and decreasing brand loyalty all drive the need for major brands to be more strategic with pricing and use analytics at all commercial decision points. This is not only providing a competitive edge to companies who understand customers and their price sensitivities better but is also creating a differentiated perception for their brands with customers.
I wish I would have taken steps to build a next-generation commercial capability versus just conducting some pilots and tests
Companies looking for quick results often "dabble" in commercial analytics, conducting POCs, running tests, and responding episodically to vendors with promising new datasets or tools. Those that are truly "doing" next generation commercial analytics are focused on building scaled capabilities to deliver insights to Sales based on systematic opportunity identification, granular demand modeling, and detailed customer and consumer profitability modeling and profiling.
I wish I would have known the true cost of running my pricing analytics capabilities in-house and on-premise
The pace at which technology is transforming commercial capabilities creates a huge potential for answering business questions we couldn't before, in a faster, granular, and more connected fashion. And doing so at a fraction of the cost by leveraging cloud infrastructure at scale. Companies not leveraging the power of in-memory analytics and cloud computing are at risk to lose precious time looking at the spinning wheel in a dated application or end up spending millions on multiyear implementations only to find out the tools they installed are already obsolete as soon as they go live.
Five "I wish I would haves" to avoid in pricing and revenue management
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Interested in expanding margins and improving ROI? Polaris™ can help .
Polaris™ can help you unlock value potential and achieve an advantage in revenue management through a combination of: Deloitte’s top-ranked pricing methodologies; descriptive and predictive analytics with scenario planning; and simple, user-driven visualizations with guided analytics and decision making.
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