The document discusses Vistex's channel analytics capabilities. It outlines challenges clients face with enterprise data and describes Vistex's approach to assess lifetime customer value, benchmark performance, and monitor results. Key aspects include evaluating most valuable customers, churn risk, program effectiveness, and spend optimization. Methods covered include scoring customer value based on revenue, recency, frequency and purchasing. The presentation emphasizes using data and analytics to discover patterns, optimize outcomes, and provide actionable insights through benchmarking, scenario modeling, and automated scorecards.
These 4 (lifetime) value scores are useful for understanding the quality of customer revenue streams that accelerate business growth through new and existing customers. In our engagements these value scores were predictive of high performance for our clients:
MVS was the best predictor of incentives impact on general business performance. There are other techniques to assess the value of your customer.
RVS was the best predictor of future churn rate
AVS was a good predictor of new customer growth
PVS was the best predictor of overall revenue growth
I’m going to give you a demo make it clear ViZi is the tool we use.
Here’s what you ’re going to see in the demo. For us analytics is a process of data science that can be enabled with the use of integrated software, that does things a certain way.
Discover > Assess > Recommend > Optimize
Increasing complexity, decentralization of program management make it difficult to gain insight into which specific programs and partners are performing the best. Without such knowledge client was not able to target programs and partners to get the best ROI.
Measuring the true lift provides an unclouded view of these opportunities. Through data quality improvement, integrating critical datasources including sales-out data and then applying the appropriate analytics, we unraveled the complexity, broke down data silos and ultimately optimize spend.
For example a $1 billion company generates 80 percent of its revenue or $800 million through its indirect channel. Incentive spend tends to be ~5% of revenue or $40 million and 15% of that spend is wasted in some way. The identified $6 million in overspend by gaining a deeper understanding of its partner incentives. We are helping capture 35% of that opportunity by focusing on low-hanging fruit, saving $2 million every year that can be invested in other areas of the program to drive growth such as:
adding support for existing, emerging or strategic partners
extending partner coverage to new whitespace opportunities
adopting more robust analytics to continue to drive partner program effectiveness
We developed natural control groups that allowed us to compare similar groups of partners where the only difference was one group received an incentive and the control did not.
Marketers need to gain a unified view of partners, by program, value and geography to improve return on incentives.
Today this data lives in many different areas.
As presented in these 3 use cases, analytics empowers channel marketers by bringing together data from a wide variety of sources including spreadsheets and databases; marketing automation, training, social media, web, ERP and CRM systems
A single unified analytic view to set strategy, create alignment across programs and drive growth.