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A Case for Price Segmentation and Value over Volume

Vendavo< Vendavo May 22, 2017

Not all customers value your products the same way. Segment-specific target prices enable your Sales team to negotiate with more confidence and capture more value from differences in your customers’ willingness to pay.

Automated segmentation solutions let you test and validate your price segmentation model. For the deployment of such models, pricing projects can create a better chance for early and smooth adoption by Sales. They can put a system in place where the fear of losing volume can be outweighed by the increased profitability of each deal. No longer are deals made based on “gut feel,” but rather they use the “right price” defined by each segment.

Advanced segmentation solutions can slice and dice your customer base far beyond the accepted limits of legacy data warehouse cubes. Leaving well-trotted customer value quadrants behind, they combine statistical data mining algorithms with processing power to pinpoint retention and value drivers in customer groupings. They can propose updates or overwrites to your existing views of the market, customer base, and deal pricing potential. Often they unearth microsegments previously hidden in massive transaction data or deemed unmanageable in size and number.

Adopting such new models needs a sustainable change management process.

Adopting a New Price Segmentation Approach

Sales reps need time to adjust their tactics in order to better prepare and accept segment-specific guidance. So, segmentation and target prices should not become a pure science project that imposes its target prices from the get-go. Your reps know the most about your customers and their behavior patterns.

There must therefore be iterations of theory-based modeling that propose the best course of action for a segment, then gradually include results of their review by Sales to converge towards enforceable segment-specific target prices. Sales will be skeptical at first, but the piecemeal inclusion of segmentation data will build trust that a model can represent the power to reach a customer’s willingness to pay, balanced by the risk of negative impact reviewed with Sales. 

Iterative review with the front-line sales reps may sound like an obvious best practice. Yet, a dependency on availability might be seen as a risk to slow down results. Therefore one project’s sponsor requested that, instead of Sales, expert external pricing scientists work with experienced back office analysts to simulate and create a new segmentation. When handed out, the Sales team could agree that this model indeed gave theoretically sound guidance.

Most of the higher target prices in all sections of the new and improved segmentation tree were best future behavior. Yet analysis had focused on statistically correct best efforts to improve on past pricing as understood from pure data. Sales now had to compare each and every current and proposed price to find where major differences might result in a non-defendable overnight price hike—an unrealistic request to overhaul the old segment and customer deal approach. Consequently, it took some time to adjust the model and allow a more realistic, fitting and gradual change.

Quicker delivery came to those projects that linked Sales into a team of pricing scientists and analysts to collaborate in an iterative creation, testing, and validation of the segmentation model. Segment reports were used to uncover and analyze segment issues or historical pricing issues through segment quality reports. Over time, the results were socialized with key opinion leaders and changes were accepted into branches in the model.

Their detailed knowledge of market, region, and customer pricing resulted in some manual tuning to the data-recommended models based on back office expertise. Further simulations converged to an optimized model that could fit the real business—and still have sufficient data science to create new business insights.

Include Sales in the process of creating the models of segment-specific target prices. This way, the reps will be less inclined to let “gut feel” take over and undermine a proposed data-based target price. They will be more confident to land better deals with segment-specific target prices that improve their performance—let alone the overall performance of your business.