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In this episode, data scientist Aurélie shares her journey from one-off missions to successful commitments. We explore pricing approaches that capture the immense ROI it unlocks in the long term.
- Structure a monthly mandate focused on interpreting data, not just presenting it.
- Train internal analysts to self-service instead of constantly relying on your analysis.
- Present flat-rate pilot projects linked to ROI, not hourly adjustments. Measure against goals.
- Set high upfront prices for dedicated training and knowledge sharing. Recurring plans seem cheaper.
- Survey past ideal customers on pricing models they would welcome or reject
Presentation of Aurélie and her specialized profession
Aurélie runs a data consultancy after years of analyzing political 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 the company’s raw data into digestible dashboards and models optimized for non-technical managers. His skills make measurements accessible in all organizations.
She explains how, after 3 years of successful freelance work on Upwork, she now wants to diversify her clientele and capture more recurring income. We are exploring options beyond ad hoc data projects.
Finding Your Continuing Value
I asked Aurélie to break down the full lifecycle of a typical customer engagement. She explains:
Many leaders realize they need better data hygiene and visibility. They therefore hire her for an initial configuration of the dashboard during which she also trains internal staff.
The problem? These platforms then become self-service without needing regular contribution.
So, even though Aurélie brings immense initial value by making data digestible, she trains teams without needing her permanent objective. His immense expertise becomes commodified.
To capture recurring revenue, we suggest offering custom interpretations, not just dashboards. Position its goal by providing regular meaning from the metrics.
For example, Aurélie could share a monthly meta-analysis on what the data indicates about regional performance, emerging trends, optimization opportunities, etc.
Fixed-cost projects focus on repeated results
In addition, we advised to offer continuous data optimization checks within the framework of fixed-price contracts linked to concrete objectives of the client rather than hourly adjustments of indefinite duration.
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For example, quarterly engagements could focus on specific KPIs around increasing sales or customer retention. This frames the ongoing value of achieving business goals rather than simply turning data knobs.
High training fees Price anchoring Monthly plans
To make the monthly fees seem affordable, Aurélie could also charge extremely high personalized initial training.
For example, charging over $10,000 for dedicated knowledge transfer would anchor regular analysis to just $1,000 – $1,500/month as a basis for good business. This takes advantage of pricing psychology: an expensive setup makes recurring activities appear cheap.
And large initial investments justify higher costs. Continuous access to one’s learning only becomes cheaper after in-depth knowledge sharing.
Survey old customers about new pricing models
We encouraged Aurélie to interview 5 to 10 former clients corresponding to her ideal future clients about the pricing plans they would accept or reject.
More specifically, it could assess receptivity to:
- High fixed installation costs
- Recurring analysis/optimization at lower cost
- Packages combining the two according to needs
This intelligence allows new monetization models to be precisely targeted and tested with real market feedback.
Here’s the thing: Aurélie is already unlocking immense potential from previously neglected data through intelligent visualization. She should be richly rewarded for valuable work well after the initial dashboard setup.
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