Machine Learning…Imagine having the business intelligence to know which channel offers you design are going to actually deliver the ROI your company needs. What if every quarter that goes by your channel offers become more effective and your market share goes up in conjunction?
You might not have to imagine or wonder much longer. These outcomes are possible with the tools now being developed and deployed to take advantage of machine learning.
It’s in vogue these days to talk about the promises of machine learning (and they are many). But the name and concepts it represents have been around since the 1950s. With over a half-century of development, fading in and out of popularity along the way, it is only now, with modern computing power and the availability of robust data sets, that we can realize the full potential of these techniques.
Machine learning at its core allows for the creation of a mathematical structure that, through iterative optimization creates a mapping between a given input and a desired output. In contrast to traditional programing techniques, the relationships within the data need not be known in advance. The patterns that emerge from machine learning techniques emerge from the optimization of a quality metric (how good is my model at predicting the desired output?). These emergent patterns can be quite revealing, non-obvious and at times downright counter-intuitive. This though is the strength of these techniques, they are data dependent and do not rely on any preconceived notions of the problem at hand.
Machine learning output then can provide business insights that would not have been obvious at the outset. Our pre-conceived notions about what inputs drive which outputs in a business may not be accurate. Many companies launch programs into their channels based on what has been done in the past. If last fiscal year a program was run to drive sales of a new product line, then this year’s new product line might well be put into a similar type of offer.
This will typically be done in the absence of real data that shows whether last year’s effort worked or not. It’s just the way the company works its channel programs.
New SaaS platforms are evolving such that they can learn from real-time sales data and use that data to predict future propensity to purchase. As a result, vendors will know with a high degree of confidence which market segment or geographical region to target with particular offers. Once an offer is launched, data collection begins. That data, in turn, will inform business decisions about the nature of future offers.
Today though, most companies are still not taking advantage of current technology that could give them the real-time sales data needed to power machine learning. In fact, most companies rely on sales-out data generated at the end of a financial quarter. This is all after the fact information, and therefore doesn’t provide the business insights that machine learning (taking advantage of real-time sales data) could have provided.
Imagine the competitive advantage for vendors with new and disruptive insights into their channel partners’ propensity to buy. For the first time, companies will have the ability to make targeted channel offers based on real-time data on pricing, products, geographical coverage and other factors.
Channel Mechanics is at the forefront of providing tech vendors with the ability to utilize machine learning to drive channel business decisions. Their SaaS allows vendors to target products, special offers, discounts, SPIFFs, etc specifically at the partners that are most likely to take advantage of them. Purchasing behavior is made available directly to the vendor and that information is then in turn used to drive future business decisions. Channel Mechanics has been instrumental in deploying this type of capability with several vendors who are now fully up and running with related programs.
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