Sentrana

The Science to Lead Markets™

MarketMover™ Technology


Our goal for MarketMover is to support every stage of the organization’s market-based pricing lifecycle. To this end, MarketMover supports the five organizational competencies required for scientific pricing, and the individual software modules support the more granular organizational actions comprising each of these:

1. Data Management

Sentrana provides a data warehouse to support complex, ad-hoc queries from the user base that allows them to see the interconnections between our pricing recommendations and all other data, and to support price modeling. This data warehouse automatically receives secure data messages from various data marts contained within the enterprise as well as other 3rd party external data providers. The seamless, secure transfer of transaction data from our clients to Sentrana, as well as the cleansing of this data, is vital to accurate scientific pricing and model validity. This also includes ensuring that the data warehouses are structured optimally for querying functions as well as computation needs. Accordingly, we identify the indexes and primary keys and caches necessary to optimize the performance of all critical queries that are posed to our data warehouse.

2. Scientific Price and Sales Optimization/Simulation

The price recommendations presented in MarketMover are the end result of continuously updated econometric and statistical models that balance the most recent transaction data with historical sales information. This balance is what enables us to build statistically precise demand profiles from historical sales without compromising our ability to detect emerging trends that will impact your revenues. The models that drive MarketMover are recalibrated on a nightly basis to incorporate the previous day’s volume and price information for each customer’s purchases as well as any changes in input costs. Both prices and the items that are recommended to a customer can be optimized to yield a maximum increase in a combination of top-line objectives, including:
  • Revenue
  • Gross Profit
  • Market Share
  • Gross Profit Margin

The optimization exploits the scientific models that are created from historical data and the subjective expertise from the market to pinpoint the best prices and cross-sell opportunities to present to customers. MarketMover also allows users to simulate how the optimal price and sales penetration opportunities change as the underlying variables that are used to create the model are altered. This gives the users a “laboratory” in which to perform what-if predictive simulations of the optimal answer.

3. Structured and Dynamic Price Management

A complete scientific pricing system must be able to seamlessly integrate business considerations with mathematical optimizations. There are many reasons that a price produced by the optimization engine should not be directly taken to the market. These might include customers’ pre-existing agreements with the seller that constricts prices to a certain range, or competitive pressures within certain product categories. The number of rules that an enterprise can specify that govern customer-item-price combinations can easily exceed tens of thousands. In fact, for a business with 100,000 SKUs and 100,000 customers, there are 10 Billion potential customer-item combinations, and each one could conceivably be assigned a price rule. The Price Management functionality in MarketMover gives the enterprise an easy way to create, track, and adjudicate all the rules. Any new rule created by a user is routed through a workflow hierarchy so that the appropriate user can approve it.

Similarly, while the organization needs to be able to track the cumulative effect of its price offers over time, acute changes in COGS, margins, or gross profits may necessitate analysis of key business drivers. MarketMover allows users to set triggers that will automatically override a scientific price recommendation and default to a pre-set price. Of course, a system that rests on such complex algorithms must be transparent if users are to trust its recommendations. MarketMover gives users the ability to see the entire data universe and construct SQL queries for use in management reports and ad hoc data analysis.

4. Price Execution

At each customer touch-point, wherever the customer touches the enterprise and receives prices and sales offers, the final results from the Price Optimization, Rules Management, and Price Management must be presented to the customer. This presentation directly occurs on a Sales Force support tool, and the sales force is allowed to organize the results per customer, or per item category, etc. On the same interface that users can see the pricing and sales recommendations, they are allowed to provide feedback on those recommendations so that the MarketMover’s Model Management system can benefit from the users’ market-facing insight.

The Price Execution stage is also where new sales penetration offers are presented to customers. Using sophisticated mathematical techniques, MarketMover can identify with high statistical precision the items that a customer is likely to purchase that they do not already purchase from you as well as the introductory price correlated with the highest odds of leading to sales conversion.

5. Performance Monitoring and Governance

The efficacy of the scientific pricing and sales offer capability must be continuously tracked, and the performance of this capability must be measured against a baseline of non-scientific pricing expectations. This area of MarketMover functionality gives managers the ability to measure the performance of the organization at an aggregate level, and drill down into any level of disaggregation they desire such as by geographic region, customer, product, sales representative, store location, or by any combination thereof.

To help managers see how much of their financial performance would be impacted if more sales reps accepted the scientific pricing and sales recommendations, or if each rep accepted more of the recommendations, a “benefit simulator” is provided. This helps management to set expectations for firm performance that can steer external guidance.

TECHNOLOGY