IKASI moves beyond traditional segmentation

IKASI’s machine learning platform dives deep into customer responses to marketing campaigns and learns from them.

The system constantly strives for greater accuracy and deeper insight. So the more you use IKASI, the better your results.

Use the platform to maximize your ability to acquire, retain and up-sell.

Listen

 You’ve put a lot of time and energy into building customer profiles. Make it pay off. Upload customer transaction and interaction histories and give IKASI’s powerful AI software the chance to shine. Learn which offers to which customers generated the best conversion rates in the past. Know which offers—to specific customers—are likely to generate the best conversion rates today and tomorrow.

FOCUS

Get your team out of the business of translating data “insights” into marketing strategy. IKASI makes marketing strategy less of an art (and more of a science) by finding the right customers to target in a marketing campaign and predicting their responses. Now that you know who to target, you can concentrate on the message.

LEARN

As the marketing campaign winds down, IKASI’s platform absorbs and processes the resulting data seamlessly. It learns what worked and what didn’t, who responded and who didn’t—and why. And the more you use the platform, the smarter it gets. IKASI makes it easy.

Case Study: MLS Team

A Major League Soccer (MLS) team assigned roughly 1,600 season ticket accounts annually to each of its relationship representatives. The basic problem was that these reps only had time to focus on a limited number of accounts and were responsible for conducting their own risk assessments. The near-inevitable result? They missed large numbers of “at-risk” accounts.

With IKASI’s help, the reps were able to plug the gaps. By uploading account information and other sources of customer data, they gave IKASI’s predictive machine learning platform an opportunity to determine which accounts were truly at-risk. IKASI established three core risk profiles. Each rep was then assigned a small number of these high-priority accounts and given a simple mandate: keep those accounts on board.

At this point, the reps knew the right customers. Though they had 1,600 accounts, a focused list of 300 needed their immediate attention to reduce churn and maximize retention. They tried to maximize efficient use of time and energy as they worked to meet key touchpoint goals.

By being proactive, the relationship reps had boosted their chances of retaining these accounts. With increased sales, the potential also existed for these formerly at-risk accounts to emerge, at some point, as MVPs.

For IKASI, of course, the work is never done. Over the course of the season, as customer information is updated, the list of at-risk accounts is consistently refreshed as the system learns from past experience. Analysis improves and recommendations become more reliable. From this, the reps’ retention efforts become more successful. On this particular occasion, the team succeeded in retaining at least 40% of those accounts deemed by IKASI as “at risk”—a remarkable success rate and a testament to the power of AI to revolutionize sales and marketing.

Challenge
  • Identifying accounts most at-risk.
  • Instilling focus in relationship reps.
  • Optimizing investment of time and energy to
    achieve goals.
  • Current absence of data science or machine
    learning knowledge.
Solution
  • IKASI’s predictive technology creates three risk profiles to help identify at-risk accounts.
  • Relationship reps establish and meet touchpoint goals to promote retention.
  • System updates itself on monthly basis during season.
Results
  • 80% reduction in accounts deemed “at-risk.”
  • Doubling of renewal rate of “at-risk” accounts.
  • Much more efficient use of time, energy and
    financial resources.

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