How to Use Pricing Intelligence Software for an Immediate Revenue Uplift
- Pricing optimization applies customer and market data to find the optimal product and service sale amounts that will maximize company revenues or profits. It models the factors that impact quantity sold and statistically measures how customers respond to price changes. It then compares alternative price scenarios, identifies the optimal sales figures and forecasts how price changes impact revenue and profit.
- Immediate and continuous revenue growth can be achieved with this strategy. Research shows that a price strategy program increases annual revenues by 2 to 8 percent, on average, and nearly all the increase falls straight to the bottom line.
- Pricing optimization software, often called pricing intelligence software, replaces guesswork and untested assumptions with dynamic, data driven, fact-based recommendations delivered in real-time.
Trying to manage complex pricing data in spreadsheets is laborious, inefficient, prone to error and unlikely to surface the optimal item or service sale amounts that maximize market share, revenue or profit. The number of price permutations and combinations make manual price setting impractical.
Pricing intelligence software is needed to harvest market, customer and competitor data. It analyzes a multitude of variables and data relationships. It creates elasticity models and applies algorithms to deliver fact-based optimal sale amount recommendations.
It's a big step up from rudimentary schemes such as static list or matrix prices. But the effort to implement precision pricing creates agility in dynamic markets and is proven to increase annual revenues by 2 to 8 percent, on average.
Pricing Intelligence Software
Once you have adopted a pricing optimization strategy it's time to apply technology for process automation, information reporting and scale.
Here's how we use pricing software to render real-time recommendations that quickly grow revenues.
We start by sourcing data that can be used to show how customers make purchase decisions and how they will respond to price changes. That data includes:
- market, customer and competitor data
- firmographic data (i.e., company size, location, industry)
- company transactional data (demand forecasts, product sales, customer purchases)
- customer behavioral data (digital footprints, online behaviors, customer sentiment), and
- customer financial performance data (i.e., customer lifetime value, customer tenure, cost to serve and customer profitability)
It's a lot of data but that's what is needed to achieve precision results. A data transformation pipeline can automate this process.
There are two issues that can slow this step. A data enrichment process may be needed for companies that are missing key data and a data cleansing process may be needed if company data is poor quality or lacks integrity. Each of these speed bumps can usually be remedied in short order.
Once the data is ingested it can be normalized, used to create or update a data model and calculate demand and price elasticity. That will immediately show you which products offer the biggest financial upside. For example, product renewals will have a lower elasticity than the original purchase and are therefore prime candidates for revenue uplift.
The process to calculate elasticity requires rigor. But once you have a model with demand elasticity and price sensitivity you have what you need to simulate price changes and see the impact to customer purchase decisions and company revenues and profits.
Calculate Optimal Sale Amounts
Analysts can interpret the more obvious relationships between sale amount and customer purchases. But AI algorithms can automate the process, decipher a larger number of factors with confidence levels, and prioritize results to get to the best answer quickly.
Sometimes there is not one best sale amount for an item so the system will render categorized or prioritized recommendations. This is most often done by customer segments and item clusters. Customer segments will group customers based on their buying behaviors and how they respond to price changes. Product clusters will group items or services based on their propensity to absorb price increases.
It's important that sale amount recommendations be understood in human terms and not just machine generated calculations. Avoid pricing intelligence software that uses black box deterministic results and instead validate the logic behind the recommendations.
The best price optimization software goes a step further and uses AI to surface upsell, cross-sell and bundle recommendations. This requires understanding sale amounts in the aggregate or by product combinations. For example, it may be worthwhile to sell one item below customer willingness to pay if that item attaches higher margin ancillary items.
Over the years we've discovered a number of tips and best practices when designing AI algorithms. When we build algorithms we create metadata to make the logic easier to understand and maintain. We generally discard outliers in item clusters and customer segments. We also calculate multiple prices for each item, such as a floor amount, target amount and highest amount. We also make price models interactive with goal setting.
There's no better way to align corporate and price strategies than to use solving algorithms which start with the company's target revenue or profit objectives and work backwards to price calculations.
Some of the best price optimization software applications use interactive dialogues for What-If analysis and pro forma forecasting. Analysts can perform revenue or profit goal seeking scenarios at a macro level or for different customer segments or item categories. The ability to project how different sale amounts impact revenue, margin and profit shifts the strategy from hindsight to foresight.
Automate Approval Processing
Once an analyst, product manager, category manager or other manager accepts the results from pricing software, any new sale amount adjustments should be routed to affected stakeholders for collaboration and approval processing.
Many times, the processing can be streamlined based on the impact of the change and by identifying the types of customers affected. Smaller or incremental sale amount changes may not require much approval processing or be automatically accepted.
In addition to approval processing to confirm new sale amounts, additional approvals should be setup to permit price alterations or discounts by salespeople. CRM software with its workflow tools is the ideal source to manage all these approval processes.
Enable the Salesforce
The final step is to aid sales productivity with sales enablement. This may include integrated Configure-Price-Quote (CPQ) software or guided selling such as alerts or next best action prompts. It can also include sales discounting policies to eliminate revenue leakage or ERP software to automate the entire quote to cash cycle.
We usually present the salesforce with ideas and get their feedback. For example, ideas may include CRM software prompts for item or promotion recommendations on the sale opportunity, quote or sale order.
Client specific recommendations may include ancillary items that increase margin or deal size, services such as support or maintenance that improve customer retention, or terms that increase sales win probability. Similarly, algorithms may display customer specific probability of increased price acceptance by applying factors such as customer engagement score, lead or opportunity score, customer satisfaction (CSAT) score and customer lifetime value.
As you may suspect, there's a learning curve and the process gets better with each iteration. Pro forma accuracy is based on the quality and quantity of the source data. In the beginning most companies lack empirical evidence and have few tested assumptions.
However, the data model is improved with each experiment and adjustment. If accompanied with AI the data model will become self-learning and deliver price recommendations that would otherwise not be easily discovered.
The Best Pricing Intelligence Software
There's no need to create this technology from scratch. There are several good price optimization software applications. In our experience, when searching for the best pricing software with a client we first evaluate the strength of the core capabilities. This includes things like data harvesting, APIs and AI. We then go deeper on the more advanced options such as white space analysis, the delivery of multi-component recommendations (i.e., for up-sell, cross-sell, kits or bundles) and packaged integration with CPQ and CRM software.
But here's the thing, the commercial software market is nascent, and the applications vary significantly. If you know what you are looking for you can acquire a high fit for purpose solution. If you don't know what you are looking for you are more likely to acquire a misfit.
Some of the more popular price software apps include Bubo.AI (www.bubo.ai), MarketRedesign (www.pricecypher.com), Pricefx (www.pricemoov.com), PROS (www.pros.com), Syncron (www.syncron.com), Vendavo (www.vendavo.com) and Zilliant (www.zilliant.com).