Is Data the New Oil?
Channel data is becoming deeper and richer, helping to drive channel performance management. In today’s world, channel data and predictive channel insights are now considered the “new oil”. The more access to clean, enriched and consolidated data, the better enabled your organization will be to driving revenue growth and reseller engagement.
Yet, many channel leaders are unaware of how to effectively utilize their data and turn channel insights into action. According to a recent Forrester report Winning in the Channel Requires Data-Driven Program Innovation published by Forrester, Dave Geoghegan, CEO of ChannelEyes, says, “Depending on the industry, upward of 80% of channel leaders don’t know who ended up with their products.”
The Forrester report written by @Jay McBain goes on to state, “channel data is a competitive differentiator”, it can be used to create great partner experiences particularly in industries where resellers have a broad choice of brands to represent. This also potentially correlates with increased growth. For channel professionals to design effective incentive programs, they should use channel data in a broader context that links to previous sales, customer feedback, pricing history, inventory behavioral data and other systems. The Forrester report continues with, Jason Atkins, CEO of 360insights stating, “Matching the customer journey with the partner journey through incentives and motivation techniques is a best practice.”
Leveraging Predictive Channel Insights to Improve Program ROI
Are you aware of the differences between data analytics and predictive channel insights, and do you fully understand the impact incentives have on your channel? What are you doing to get beyond channel analytics and move down the path to predictive channel insights? And finally, how do you turn this vast wealth of data richness into actionable insights? How can you build more effective incentive plans and realize greater returns with predictive channel insights? This article will provide some of these answers and outline a few different ways that you can leverage channel incentive data to make better business decisions.
Channel Analytics versus Predictive Channel
Channel analytics refers to descriptive statistics providing a summary of historic channel performance (e.g., total sales volume which can be used as a measure of incentive program success). It is also composed of associated charts and graphs which help management visualize and comprehend performance metrics. Channel analytics essentially transforms vast amounts of data into information meaningful to channel managers.
Insights are answers to business questions obtained from channel analytics that provide management guidance in decision making. For example, the distribution of SPIFF rebate amounts across product lines can reveal sales gaps (i.e. the analytics), helping management understand where brands have not gained popularity among buyers (i.e. the insight).
The Impact of Channel Incentives
An effective channel incentives program offers an award to motivate and engage resellers in marketing and sales activities. It also helps to build long-term relationships that drive higher revenue.
Choosing the right channel incentive will depend upon the business goal. For example, if the goal of a product launch is aimed at achieving a sales volume increase, then a volume rebate program might be a good choice. Once the target is reached, the rebate will increase incrementally, in accordance with growth of sales volume. This will keep resellers focused on the product over the long run.
Applying Predictive Channel Insights for Better Business Decisions
A common practice leading to channel intelligence, is the exploration of channel incentive data in order to better understand its structure and extract business value through SPIFFs rebate amounts, marginal profit, and dealer geographic location. Predictive channel insights are made possible due to the development of monthly forecasting models at the dealer or regional level of granularity. By applying mathematical models of key performance metrics, a formula can be obtained to produce a reliable look forward.
For example, a model can be created to predict rebate spending in the next month based on a linear function of rebate program choices, categorical dependent variables (e.g., season, quarter) and rebate spending in the current month. The function in this example is a combination of factors driving rebate spending in the next month and the unknown multiplying of variables or coefficients. These coefficients are learned from historic data via optimization algorithms that minimize the error between the actual and predicted amounts. Once a formula is learned, it can be validated through testing.
Other ways to
derive deeper insights from incentives data include:
- Segmenting resellers into high, low and neutral profit types and reporting the incentive spending per reseller segment
- Forecasting incentive program performance at the distributor/customer level and/or per product line
These micro insights allow clients to continuously
monitor incentive performance and manage their incentive portfolio more
effectively based on predictive forecasts.
The ultimate goal of predictive channel insights is to recommend optimal programs that will maximize ROI. By using mathematically advanced software you can construct and visualize graphical models to predict future results. As a result, predictive channel insights lead to a deeper understanding of the effect of programs and their relationship to ROI.