- Generate dynamic models: Incorporate data traditionally used for dynamic creative for up-to-the-moment predictions
- Scale holistic intelligence solutions: Go beyond point solutions to deliver what customers want
- Empower technical professionals: Democratize the intelligent recommendation system so the solution is king
- Drive smarter operating metrics: Connect assets across the enterprise with the Identity Applied Platform
Businesses routinely rely on traditional static models for a bird’s eye view of their industry and general market demand when setting prices for a new product or service. Though helpful in breaking down an expansive system quickly, all too often these outmoded model scores and values use little to no dynamic information about potential buyers.
In the digital era, legacy companies cannot afford to rely on out-of-date customer identity insights. Setting prices too high will alienate potential customers, but unnecessarily low prices won’t recoup the inventory-management cost or cover the overhead of potential returns. Either way, you’re leaving money on the table.
In these situations, everyone loses: the manufacturer, the retailer and the customer.
Time to shift gears
Businesses can tackle this issue of price optimization head-on by refactoring legacy models with dynamic identity – opting instead for enterprise models that have been enhanced with up-to-the-moment customer behaviors.
Whether updated quarterly or in real time, identifiable or pseudonymous, quality data strengthens enterprise models: far more accurate, far more effective.
Raymond Velez, a global chief technology officer for Publicis Sapient, said his team realized that they could make smarter price optimization predictions by incorporating data traditionally used for dynamic creative, i.e. personalized content, and altering inventory levels to that data.
“With experience data we are making predictions on each individual’s intent to buy as they visit a .com or ad experience. That gives us a testing frequency that’s far higher than traditional models with a far deeper understanding of customers and prospects,” Velez said.
Now you’re far more likely to offer customers and enterprises services and products they value. If there is excess inventory we can align offers knowing the cost of moving inventory around. If there is less inventory and our customer/prospect propensity models predict a dealer or store visit we can reduce offers. It works both ways in continuous optimization, which is tested all day, every day.
These data-driven insights laid the groundwork for Publicis Sapient’s Identity Applied Platform – a new framework for digital business transformation.