Channel Visibility: A Channel Pulse Insight
The Q1 Channel Pulse report from 360insights asked senior channel professionals about their level of visibility to various channel activities. The majority rated channel visibility “Fair” (in the middle between good and bad). What does this imply about the state of channel analytics and insights?
When we asked the Channel Pulse audience what their priorities were for gaining better visibility, they rated “Demand Generation” and “Sales Execution” at the top. Effectively, they want to know how much forward demand exists for their products and services and how well the sales channel is executing against that demand. Therefore, it’s the first thing any channel management professional would want to know.
The fact that senior channel professionals don’t have great visibility into forward demand speaks volumes to the state of channel visibility. It’s clear that channel leaders are at the very beginning of their journey to leveraging their valuable data for better analytics, insights.
The Journey to Channel Visibility
What these professionals are trying to do to gain channel visibility isn’t unlike what all companies are trying to achieve with Business Intelligence (BI) software. The goal is not just visibility into what has already happened, but accurate forecasting of the future – predictive analytics.
There are several steps that an organization goes through to get to predictive analytics. It would appear from the latest Channel Pulse, most senior channel professionals want to take this journey, but many don’t know where to start.
Here’s a quick summary of what’s required to go from basic reporting (looking back at what happened) to predictive analytics (insights into what’s about to happen next).
Prior to using resources, people and software, it’s important to build a plan. Ask yourself, what data is available and where is it located? What do you hope to learn from that data? How will knowing this improve your ability to manage your channel or meet the needs of internal constituents?
It’s not unusual that the data required is in several different systems. For example, enablement data is usually in some form of Learning Management System (LMS), market demand information is in a marketing automation platform, revenue and inventory are in an enterprise ERP system, etc.
Most companies start their journey using business intelligence to create dashboards from these data silos. Dashboards are easy to consume visual representations of the most useful data. They are designed to provide a quick view of what recently happened or what’s happening now.
Modern BI software is designed to connect to disparate data sources and assimilate the data to create dashboards. By connecting data silos a single pane of glass view of critical channel data is enabled.
This sounds simple, but it often fails. Failure occurs for two reasons, first the data is inconsistent, missing, or simply not well maintained. Second, many of today’s channel platforms lack the necessary connection points to enable BI software.
One Step Forward – Two Steps Back
The first step might yield a channel visibility wish list, but also reveals the challenges that will need to be overcome to achieve it. Frequently, to achieve the desired goals the data will need to be cleansed, augmented and consolidated.
Data Cleansing is the process of detecting and correcting corrupt or inaccurate records. This process identifies incomplete, incorrect, inaccurate or irrelevant records. These records are then replaced, modified, or deleted. During cleansing, data is also normalized. This is where data values are changed to common descriptions (i.e., US, United States, USA all changed to USA).
Data Augmentation is the process of filling the holes in the data. This is often done using 3rd party data. A good example of this is adding addresses, phone numbers and/or email addresses to CRM data.
Data Consolidation is exactly as it sounds, it when all of the data is brought to a central location (data warehouse) where it can be more easily manipulated.
Cleansing and consolidating data often are time consuming and costly, but they are necessary steps to any meaningful channel analytics or insights.
Today’s Channel Dashboards
Most organizations get through cleansing and augmentation and get stuck when it comes to consolidation. Consolidation requires vendors provide access to the data within their platforms. Many platforms were not designed for this, and many of the vendors see this data as their value-add. Often, access also requires technical skills beyond the control of channel professionals.
Vendors are more than willing to work with customers to create dashboards within their platforms, but hesitant to give it up for outside use. This leaves channel professionals with program level dashboards that aren’t consolidated in any way to form a broader view of the channel. Said differently, we can see how a program is performing but can’t correlate the data into overall performance or into an understanding of why the performance exists. The lack of consolidation is also an inhibitor to gaining forward channel visibility.
The Digital Divide
One of the biggest barriers to moving to the next step in the channel visibility journey is access to the necessary resources that have the right blend of data science and BI know-how. These resources are often controlled by outside organizations like finance and IT. Their priorities are not the same as those of senior channel professionals, and channel visibility often falls low on their priority list.
Even if those skills are recruited into a channel organization, what comes next requires the ability to manage a highly sophisticated technical project. Most channel organizations simply aren’t equipped to do this, so they are stranded at this point. This is why we refer to this part of the journey as the Digital Divide.
Crossing the Digital Divide to Channel Visibility
What comes next is data modeling and the development of algorithms to achieve predictive channel analytics. If you’re not familiar with modeling, it is a process used to define and analyze the data required to support the business needs identified in step one. A data model is a conceptual representation of objects and the building of associations between objects. Data modeling defines not just data elements, but also their structures and relationships.
This is followed by developing algorithms to identify and compare patterns and trends with industry and cross industry indexes and benchmarks. Using AI and Machine Learning we can begin to forecast future results.
Our lead data scientist, Nataliya Portman, recently wrote an article titled “Leveraging Predictive Channel Insights to Improve Program ROI” which delves into achieving predictive channel analytics. In the article, she discusses applying mathematical models to key performance metrics to produce reliable forward-looking channel visibility.
As you can see, this takes an expertise generally not found in channel organizations today.
The Channel Pulse told us what we already knew, which is that most companies are stuck at the Digital Divide. They feel they have fair visibility but know there is still a lot to learn. They can’t forecast accurately, and they are left wondering why certain programs work for certain resellers while other don’t. They believe their answers lie in the data, and for the most part they’re right. However, if the data isn’t easily accessible or understandable, true program transparency and actionable business intelligence can never be achieved.
To leave this plateau and make the rest of the journey, data needs to be consolidated, models need to be built, and a blend of channel knowledge and data science is required. In today’s channel world these skills are rarely found. Moreover, the platforms to execute on this type of deep knowledge are still fairly rare. Realizing true channel incentive visibility today requires the combination of data science and automated platforms. To learn more about leveraging these solutions to your advantage in channel sales incentives visit 360insights.