Customers of all kinds generally like to have choices—lots of options make it easier to get exactly what they need. But at some point, being spoiled for choice can lead to confusion. How many of us have found ourselves staring at the toothpaste aisle in the grocery store trying to figure out which one of the 60 varieties we really want?
Buyers of high-tech products can find themselves in a similar situation, especially those looking for sophisticated products such as network equipment or servers. These large enterprise customers typically find potentially hundreds of elements they can mix and match to build the product that fits their use case. Even small or midsize businesses and consumers seeking a simpler product such as a laptop have the freedom to choose from a range of features to tailor the machine to their liking.
All of this choice, while a boon for end customers, can create complications for channel partners. Customers demand choices, which means channel partners have to translate customers’ needs and desired outcomes into the right mix of features and functions offered by the products’ provider. Providers, in turn, must provide a rich yet simplified experience that allows partners to quickly work through a plethora of choices. But many providers aren’t doing enough in that regard.
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The fact is, when it comes to finding and selecting the right products, most technology providers have a big opportunity to improve the experience they’re delivering to channel partners. And that’s where machine learning intelligence comes in.
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Sorting through the options
High-tech providers generally publish a product catalog for partners that includes core products and the various technical features and functions that can be added or changed to customize each product. However, this puts the onus on partners to sort through all the options to determine the right product configuration that will solve each customer’s unique problem—and do that for every sale. In the case of some highly complex products, a partner might have to actually call the provider to talk with a technical expert to work out what will best meet a customer’s needs. That can be frustrating and time-consuming, and could jeopardize the sale if it takes too long. Furthermore, the supply chain impact generated by highly customized products is significant.
A robust product configurator available in a partner-facing portal could help. But many providers’ configurators are neither adequate to fulfill customers’ needs nor user friendly. Most are mainly a series of simple drop-down menus of available options that partners still must sort through to spec out the right solution. It’s really not much better than having to deal with long lists of items in a large product catalog. Partners still have to understand which combination of features and functions will deliver each end customer’s desired outcomes, and make hundreds of selections every day to configure what customers need.
Surfacing the right product with machine learning
The fact is, when it comes to finding and selecting the right products, most technology providers have a big opportunity to improve the experience they’re delivering to channel partners. And that’s where machine learning intelligence comes in. Some leading providers are beginning to use machine learning to do much of the product configuration legwork for their partners. Machine learning enables a provider to simplify its product portfolio for partners and eliminate the need for partners to wade through lists or drop-down menus for all but the most complex, custom configurations for highly unique needs.
Here’s a simple example: A certain partner is looking for a product for a particular market with specific desired outcomes. Drawing on a variety of internal and external data sources, a machine learning engine can determine and suggest to the partner the typical configurations the provider has sold in similar situations in the past. So instead of having to start from scratch to spec out a solution every time, a partner gets a recommendation, in real time, for a configuration that has a high chance of meeting the end customer’s needs while reducing supply chain complexity. In other words, the partner gets the right product—based on the most relevant configurations previous customers have chosen—through a greatly simplified experience.
Bundling services and incorporating as-a-service
Importantly, this approach isn’t only focused on products. Machine learning can also help technology providers more effectively identify the services they can bundle with specific hardware, in which sales situations. In some cases, certain basic services that previous customers have valued could be automatically included in a suggested configuration, while in others the services could be listed as options. While such upselling is now routine in an Amazon world, it hasn’t been commonplace among high-tech providers. But as more providers embrace it, they’re seeing a significant lift in services revenue in addition to product sales.
Furthermore, as hardware providers increasingly look to incorporate an as-a-service model into their business, machine learning becomes even more important. By analyzing each customer’s specific situation—for instance, their intent and price sensitivity—machine learning can help providers determine the customers for which an as-a-service approach might be a better value proposition than an outright purchase. For some customers, shifting what would be a capital expense to an operating expense could be much more attractive, and could make a technology-driven transformation more feasible.
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As hardware providers increasingly look to incorporate an as-a-service model into their business, machine learning becomes even more important.
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Informing the next wave of products
In addition to suggesting to partners the right combination of product features, functions, and services, machine learning can have a major impact on future products a provider brings to market by informing product development. Most providers do pass along feedback to product development teams on how specific products are doing in the marketplace, which features and functions are proving to be popular (or not), and what kinds of performance or technical issues customers are encountering. But in many cases, this is done informally based on anecdotal information, resulting in missed opportunities to gain a more comprehensive picture that could greatly enhance future products.
Machine learning can help bring structure and intelligence to the process. It can aggregate input from customers and partners, call center data on product issues, product performance logs, and a host of other data to identify meaningful patterns that can be fed back to the product development team. Is there a feature that’s not working properly? Are customers using some features more frequently than others? Are certain customers getting frustrated or confused using a particular product or feature? New machine learning methodologies and technologies make it increasingly easier to generate meaningful insights from structured and unstructured data that can be used to fuel product development.
Long live choice—and simplicity
While customers value choice in most buying situations, there’s a lot to be said for simplicity. When it comes to technology products, machine learning can help providers strike the right balance between offering a wide range of possible options and configurations to meet customers’ needs and a streamlined, intuitive, and simplified experience that presents to partners the right combination of these for each sale.
Now could we just do something about the toothpaste aisle?