How to Escape One-Time Projects & Get Repeat Business

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In this episode, data scientist Aurélie shares her journey shifting beyond ad-hoc gigs into retained engagements. We explore pricing approaches that capture the immense ROI she unlocks long-term.

Key Takeaways:

  • Structure a monthly retainer focused on interpreting data, not just presenting it.
  • Train in-house analysts on self-service instead of relying ongoing on your analysis.
  • Pitch fixed-fee pilots tied to ROI, not hourly tweaks. Measure against goals.
  • Set high upfront pricing for dedicated training and knowledge share. Recurring plans feel cheaper.
  • Survey ideal past clients on pricing models they would welcome vs. reject

Introducing Aurélie and Her Specialized Craft

Aurélie runs a data consultancy after years analyzing policy statistics for the United Nations. She helps clients like Wendy’s and Gaddison visualize business metrics to inform executive decisions.

In other words, Aurélie transforms raw company data into digestible dashboards and models optimized for non-technical leaders. Her skills make metrics accessible across organizations.

She shares how after 3 years freelancing successfully on Upwork, she now wants to diversify her client base and capture more recurring revenue. We explore options beyond ad hoc data projects.

Finding Your Ongoing Value

I asked Aurélie to elaborate on the full life cycle of a typical client engagement. She explains:

Many leaders realize they need better data hygiene and visibility. So they hire her for an initial dashboard configuration during which she also trains internal staff.

The problem? Those platforms then self-serve without needing her regular input.

So while Aurélie provides immense upfront value making data digestible, she trains teams out of needing her ongoing lens. Her immense expertise becomes commoditized.

To capture recurring revenue, we suggest packaging customized interpretations, not just dashboards. Position her lens providing regular meaning from the metrics.

For example, Aurélie could share monthly meta-analysis on what the data indicates about regional performance, emerging trends, optimization opportunities, etc. This positions her insight at a premium.

Fixed-Fee Projects Focus on Repeat Outcomes

Additionally, we advised proposing ongoing data optimization checkups under fixed-fee contracts connected to tangible client goals versus open-ended hourly tweaks.

For example, quarterly engagements could focus on specific KPIs around sales increases or customer retention. This frames continuous value in business objectives achieved versus merely turning data knobs.

High Training Fees Price Anchor Monthly Plans

To make monthly retainers feel affordable, Aurélie could also price initial custom training extremely high.

For example, charging $10K+ for dedicated knowledge transfer would anchor regular analysis at just $1000-$1500/month as bargain basement. This takes advantage of pricing psychology – expensive setup makes recurring feel cheap.

And extensive initial investments merit higher costs. Ongoing access to her learning comes cheaper only after deep knowledge sharing.

Survey Past Clients on New Pricing Models

We encouraged Aurélie to survey 5-10 past clients matching her ideal future customers on what pricing packages they would welcome versus reject.

Specifically, she could assess receptivity to:

  • High fixed setup fees
  • Lower cost recurring analysis/optimization
  • Packages blending both based on needs

This intelligence allows accurately targeting and testing new monetization models with real market feedback.

Here’s the thing: Aurélie already unlocks immense potential from previously neglected data through savvy visualization. She should be handsomely rewarded for such high-value work long after initial dashboard configuration.

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Written by Preston Lee

Editor at Millo.co

Preston Lee is the founder of Millo where he and his team have been helping freelancers thrive for over a decade. His advice has been featured by Entrepreneur, Inc, Forbes, Adobe, and many more.

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