After working for years on gut instinct and the industry average pricing of tax advisory services, our firm only recently ventured into data-driven pricing. This transformation paid off well in the company's profitability and allowed room for better relationships with clients. Here is our story of implementing FigsFlow's pricing software for tax advisers' journey and our realisation.

Sectional vs. Roller Garage Doors

Pricing Predicament

Like many tax advisory firms out there, we had inconsistent pricing. Some engagements were hugely profitable, while others barely hit breakeven. Customers were still being overcharged on projects where pricing was arrived at in a traditional manner: an arbitrary mix of hourly rates and rough estimates. And thus began some uncomfortable client conversations.

Data-Growth Solution

With the installation of specialised pricing software, our business changed in four main ways:

Historical Data Analysis

Through the analysis of three years' worth of project data, we were able to determine such things as the following:

1. Time spent on different service types;
2. Hidden complexity factors;
3. Profit margins per service category;
4. Client industries influence the actual service delivery.

How Long Does an MOT Take?

Value-Based Pricing Framework

A structured model was developed based on the following parameters:

• Project complexity scoring;
• Client industry types;
• Skills and expertise required;
• Value delivered to Clients;
• Firm industry standing.

Dynamic Pricing Components

Our new structure focuses on:

• - Market demand fluctuations
• - Client relationship value
• - Resource availability
• - Cross-selling opportunities
• - Lessons learned

Full Data Cleaning First

Historical quality data are critical in building price-accurate models.

Involve Team Efforts

Staff insights about project intricacies are invaluable in model generation.

Communicate with Clients

It helped them understand and accept the new model by explaining the value-based pricing approach to them.

Normal Calibration

These give quarterly reviews and adjustments to maintain the model in relation to market conditions.

Future Ramp Up

We are currently improving our pricing model by:

• Integrating Artificial Intelligence into Predictive Pricing
• Building richer value metrics
• Incorporating competitive analysis in automated processes
• Make adjustments to the market in real-time.

Effects on Relationships with Clients

Perhaps the most surprising results were the strengthening of relationships with clients. Clear data-backed pricing decreased negotiation friction and increased trust in our professional judgment.

The way forward

The shift to objective pricing has positioned us for sustainable growth. We're now able to:

• Confidently make our pricing decisions
• Easily scale our services
• Maintain profitability
• Provide transparent value propositions