The high-tech industry is the epitome of innovation. Successful technology companies excel at bringing to market a steady stream of new products and features that are eagerly embraced by companies and consumers alike. In fact, ongoing rapid change and reinvention is in the DNA of all tech companies—they’re always looking for the next big development that can propel their business forward.
But it’s this very obsession with continual disruption that makes it all the more puzzling that technology providers still continue using an increasingly antiquated approach to how they price their products. And in doing so, they’re missing out on significant opportunities to strengthen their relationships with important channel partners and the chance to grow more profitably. This is especially critical for providers of commoditized products, for which every penny counts.
New pricing capabilities based on analytics and machine learning technologies offer a powerful alternative to traditional pricing approaches. With these capabilities, technology providers can help accelerate sales and optimize margins by moving from their traditional one-price-fits-all pricing approach to tailoring their pricing by each deal and each partner, and making it easier for partners to understand pricing and discounts.
From the old...
For the vast majority of technology providers, setting pricing and channel partner discounts is still largely a manual, intuitive, and subjective exercise, based primarily on anecdotal feedback from field sales people and partners, prevailing market and competitive trends, and the company’s overall go-to-market strategy. The problem is, this approach typically results in a standard set of prices and discounts that don’t consider the diversity of channels, partners, end customers, and specific deals—and, thus, may not be as effective as they could be in stimulating sales, protecting margins, and boosting partner loyalty. A discount that’s relevant in one situation may be inappropriate (and even wholly unnecessary) in another, but with the current approach, it’s difficult to know for sure.
Complicating matters is how hard channel partners have to work to make sense of providers’ pricing and discounts—a process that typically involves significant costs for partners in the form of market development funds and additional headcount. A provider regularly publishes (usually in the form of an Excel file) its standard list prices for all the products it sells and a schedule of channel partner discounts. Channel partners usually need to download this file from the provider’s partner portal and then begin the time-consuming process of sorting through potentially thousands of line items to determine how prices and discounts may have changed from the previous update, which discounts they are eligible for, and what the resulting impact will be on their sales and margins. Forcing partners to go through this exercise every time list prices and discounts are updated—which could be as frequently as once or twice a week—makes it harder for partners to sell the provider’s products and, in some cases, could even cause them to seek out another provider that’s easier to do business with.
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It’s time for technology providers to infuse greater agility and intelligence not only how they set prices and discounts, but how partners access them.
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To the new...
The fact is, in today’s competitive high-tech environment, the traditional approach to pricing is becoming an impediment to stronger growth. It’s time for technology providers to infuse greater agility and intelligence into not only how they set prices and discounts, but how partners access them. And it all starts with data.
Technology providers today have access to a wide range of internal and external data they can use to inform pricing and discount decisions. Basic historical transaction data—how many of which products were sold to which partners, when, and at what price—is something every provider has and is the foundation of intelligent pricing. This data can be integrated with other key attributes—such as region, partner type, end customer type, and past conversion rates—to paint a more complete picture of customer and partner behavior.
By stitching all of this internal data together with a wide range of available external data such as competitive information, seasonality factors, social media sentiments and other web data, and key market trends in end customers’ industries, a provider can begin to build predictive models using analytics and machine learning. These models help a provider understand what the future could look like and the optimal price/discount combination for every scenario that may most effectively influence customer and partner demand, at the right margin.
An intelligent approach to pricing can be used dynamically or statically, depending on a provider’s appetite. In a dynamic pricing model, a provider will allow an algorithm to automatically generate a price for a partner based on the data the partner provides on what it’s looking for and on what the algorithm has learned is the “right” price from similar past deals. While that’s powerful, some providers understandably may be hesitant to start by putting that much trust and power in an autonomous algorithm that interfaces directly with partners and customers. In this case, they can opt to begin with a static model, in which the algorithm acts as a recommendation engine for a sales person. The provider still gets the benefit of the algorithm’s intelligence, but a human is responsible for making the ultimate pricing decision.
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The algorithm automatically generates a price based on data provided by the partner and learnings from similar past deals.
The algorithm acts as a recommendation engine for a sales person but a human makes the pricing decision.
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In both models, the algorithm continually improves and refines its recommendations over time as it gains access to more data. Furthermore, partners get their pricing far more quickly without having to manually wade through an extensive file of list prices and discounts to determine what the products will ultimately cost.
With widespread access to more and richer data, as well as ongoing advancements in analytics and machine learning tools, there’s no reason for technology providers to continue using a one-size-fits-all, cumbersome pricing approach that can compromise margins, sub-optimally stimulate sales, and frustrate channel partners. Stronger, more profitable growth and competitive advantage await providers that embrace a more intelligent approach to pricing.
In our next installment in this series, we explore how analytics and machine learning can help technology providers optimize a second key lever that determines whether or not a sale is made: the right product. Read it here.