The channel partner go-to-market model, while successful for many years in the high-tech industry, does come with its share of challenges. One of the most prominent and enduring is the difficulty technology providers have in learning more about their end customers.

Being a step or two removed from end customers, providers typically don’t have access to information on what end customers want and need, how they buy, and how they use a provider’s products. This makes it difficult for providers to tailor their offerings to specific end customers—whether that’s the product itself, the associated price, related services, and other dimensions of the buying experience.

This age-old challenge is becoming more critical today due to significant changes in end-customer demand and what customers expect from the channel partners they typically interact directly with.

These end customers—typically small to medium-sized business—are facing increasing pressure to keep up with the ever-quickening pace of technology innovation to remain competitive. With the steady stream of new technologies coming to market, end customers find they must interact more frequently with channel partners to stay current, and that they need channel partners to respond more quickly to their requests than ever before. At the same time, as end customers become more interested in solutions rather than basic products, their needs are becoming more complex—further stressing channel partners’ ability to respond.

In short, channel partners must become much more agile to effectively serve today’s end customers—and that’s a significant opportunity for high-tech providers to help.

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At the heart of this experience are two critical capabilities: analytics and machine learning.

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Delivering a winning channel partner experience

To succeed and grow in this evolving landscape, high-tech providers need to transform the experience they deliver to partners—one that will help partners be more responsive to and create stickiness among end customers and, ultimately, to expand that customer base and increase the pie for everyone. In other words, the more providers can make it easier for partners to sell what end customers need, the more both parties win and grow.

Here’s a simple example: A channel partner is looking for a solution for an end customer. Provider A’s marketing collaterals are hard to find, the price list isn’t up to date or is different across channels, and configuring the right solution requires the partner to call the provider and spend hours on the phone with a specialist to map out what the end customer needs. The whole process takes a week before the partner can present its quote to the end customer.

Conversely, Provider B has a robust partner portal that contains everything a partner needs to be self-sufficient. The partner can quickly and accurately spec out a solution without having to pick up the phone. All appropriate marketing materials are easily accessible, and the user interface is intuitive, easy to understand, and enables the partner to quickly find what it’s looking for. Additionally, a feature within the portal can present tailored pricing for each solution that reflects the type of end customer, its needs, and the specific channel the solution’s being sold through. The partner has what it needs to create its quote in a day, or maybe even hours.

Which provider will win the deal?

The importance of optimizing product, price, and speed

The second example above illustrates where today’s leading high-tech providers are headed. They’re making major strides toward delivering a great partner experience that’s based on how a provider optimally influences the three main levers that determine whether or not a sale is made and that establish and maintain partner loyalty: product, price, and speed. At the heart of this experience are two critical capabilities: analytics and machine learning.

Paired with the right combination of rich internal and external data, analytics and machine learning enable providers to give partners a competitive price for highly relevant products—and quickly—so they can stand out in a crowded and, for some sectors, increasingly commoditized technology products market. Here’s how:

  • Right Product: Providers can recommend the best products for specific market segments and customer profiles by bringing together a wide variety of data that’s available to them—including customer spend, competitive landscape and intent insights from third parties, and dark data—and applying machine learning to this data to help identify the most important and relevant product features.
  • Right Price: Today, leading high-tech providers are shifting from a one-price-fits-all approach to partners to deal-based pricing informed by machine learning. Machine learning enables providers to build a very detailed level of understanding of end customers’ intents, spending, growth, transformation initiatives, competitive landscape, and product features they value most. Such insights can be greatly enhanced by combining third-party and dark data with providers’ own internal transaction data. Using these insights, providers can then apply analytics to tailor the right pricing, including the most appropriate and effective partner discounts, by each deal for partners and help accelerate the deal transaction.
  • Right Time: A good price delivered quickly is better than a perfect price delivered late. While a powerful analytics engine can generate a good price, it has to be accompanied by clear decision rights (e.g., who gets differentiated prices), policies (e.g., which SLAs should each partner type have), and consistent sales force incentives to avoid delays in getting the price to partners. Fast turnaround time is a key route for providers to gain partner share of mind and loyalty.

With analytics and AI/ML, providers can take the next step toward becoming an intelligent enterprise, in which decisions are optimized and accelerated to improve responsiveness and overall business impact. When used in a partner setting, these tools can help providers not only quickly come up with the right product and price for a particular deal, but also continually learn the behaviors of and what’s important to both partners and end customers—and refine recommendations accordingly over time so they remain in tune with, and can help shape, demand.

Conclusion

The high-tech game today is changing rapidly, and everyone is scrambling to keep up. To win, high-tech providers need to be seen as the ones that, quickly and easily, give critical information partners need to respond to their customers. And increasingly, analytics and AI/ML capabilities are helping leading providers do this in a way that creates the best possible experience for channel partners.

In the next three installments of this blog series, we take a closer look at how providers are using these capabilities to positively influence the price, product, and speed levers that can win or lose a deal—and, subsequently, enhance the loyalty of the channel partners that are critical to their growth.

 

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Samir Mohan

Senior Manager – Accenture Strategy


Padampreet Singh

Senior Manager – Accenture Applied Intelligence

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