Data Analytics for Professional Services
A Full Overview
Since 2008 in IT solutions for professional services, ScienceSoft builds custom analytics software that allows companies gain real-time visibility into project management, personalize their offering, promptly adjust business decisions in line with market changes, and more.
Head of Data Analytics Department, ScienceSoft
Senior Business Analyst and BI Consultant, ScienceSoft
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Data analytics for professional services is a way to consolidate disparate data on project, customer, marketing, and talent management and drive insights on project delivery improvement, effort and resource allocation, customer attraction and retention, costs optimization, and more.
- Development time: 2—6 months for an MVP.
- Costs: $30,000—$800,000 depending on the solution's complexity. Use our free online calculator to get a ballpark estimate for your case.
- Core integrations: ERP, CRM.
- ROI: up to 220%
Key Analytics Features for Professional Services Companies
Project management analytics
- Monitoring operational KPIs, e.g., project margin variance, revenue write-off percentage, time capture completeness.
- Real-time project health monitoring with instant alerts on deviations in budget, progress, and other indicators.
- Benchmarking project performance against historical results and segmenting projects (e.g., by employee, department) to easily identify success and inefficiency drivers.
- Predictive analytics to forecast project resource requirements, timelines, and possible constraints.
- Suggestions on resource allocation optimization based on task type, employees' skills, current workload, performance, and more.
- Pinpointing projects with high returns potential to efficiently prioritize resource allocation.
- Multi-dimensional customer segmentation (e.g., by demographics and inquiry for B2C customers; by industry, company size for B2B customers).
- Monitoring customer-related KPIs, e.g., net promoter score (NPS), customer lifetime value (CLV), churn rate.
- Identifying customer preferences through the analysis of historical customer management data.
- RFM analysis.
- NLP-powered analysis of customer sentiment based on communication logs like surveys and call transcripts.
- Forecasting customer demand for certain services.
- Custom service quote options based on multi-factor analysis (e.g., historical project data, market trends, competitor activity, resource availability).
- ML/AI-powered recommendations for personalized service delivery (e.g., investment portfolio rebalancing in line with tax legislation changes).
Employee analytics
- Tracking the required employee-related KPIs, e.g., employee billable and productive utilization, human capital risk, revenue per billable employee.
- Insights into the performance of teams and individual employees (including contingent workers), e.g., to identify high-performing employees, detect root causes of low performance and skill gaps.
- Recruitment campaign analytics.
- Performance vs. compensation benchmarking.
- Employee engagement analytics based on the analysis of surveys, managed and unmanaged attrition percentage.
- ML/AI-powered recommendations on the required employee-specific training.
- Monitoring financial management metrics like revenue, operating cash flow, AP and AR turnover ratios, average billing rate.
- Financial performance benchmarking against industry peers and internal markers.
- Continuous market monitoring (e.g., macroeconomic indicators, regulatory and tax legislation changes, competitor activity) for timely risks identification.
- ML- and rule-based identification of financial management bottlenecks and optimization opportunities (e.g., for budget variance control).
- ML/AI-powered financial modeling and forecasting.
- Detecting financial reporting anomalies.
- Automated financial reporting to the required authorities.
Marketing analytics
- Monitoring the required KPIs, e.g., late-stage pipeline value, bid-to-win ratio, pipeline to QTR forecast ratio, cost of sales to revenue percentage.
- Analyzing customer interactions with the marketing content (e.g., website behavior, click-through rates).
- Marketing campaign performance evaluation.
- ML/AI-powered recommendations for marketing campaigns optimization (e.g., customer-specific communication channels or email timing).
- Generative AI capabilities (powered by solutions like ChatGPT) for automated marketing content creation (e.g., emails, newsletters, social media posts).
Visualization & reporting
- User-friendly, interactive dashboards that provide general and detailed data views.
- Standard and custom easy-to-interpret visuals.
- Scheduled and self-services reporting.
- UI tailored to specific user roles (e.g., industry consultants, accountants, customer management specialists).
Core Integrations for Analytics Solutions in Professional Services
ERP
- To enable data-driven project delivery improvement.
- To analyze financial management processes, get financial forecasts, and mitigate risks.
- To support accurate financial reporting.
- To optimize employees' productivity and increase talent retention.
- To efficiently manage customer demand and resource supply.
CRM
NB: Depending on your needs, we can integrate your solution with a customer portal.
- To personalize customer service and ensure accurate service quoting.
- To understand customer sentiment toward the provided services.
- To increase marketing campaign performance.
See How Professional Services Companies Benefit from Analytics Solutions Built by ScienceSoft
4 "Hows" to Consider When Developing Analytics Software for Professional Services
Development of analytics software for professional services is aimed at delivering a solution that drives business value through tailored capabilities, a cost-optimized architecture, case-specific UX/UI design, and other factors. Below, you can explore major points of attention for developing a successful analytics solution for a professional services company.
1.
How to meet business goals
During the stage of business analysis, it is essential to conduct interviews with business executives, domain specialists, finance teams, and other stakeholders to elicit their needs and expectations, translate them into requirements for the solution, and prioritize them throughout the development process.
ScienceSoft often uses Agile approach in analytics solutions development. This helps us adjust the software according to early user feedback and avoid costly and time-consuming redevelopment (compared to introducing changes to the final software version).
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2.
How to optimize development costs
There are many ways to optimize development costs without sacrificing the solution's functionality or quality. E.g., ML/AI-powered capabilities do not always require building custom ML models. For cases like sentiment analysis and customer service chatbots, open-source models can be used with little to zero customization.
ScienceSoft's cost optimization practices include the reuse of proven third-party components and microservices, QA automation, and implementation of CI/CD pipelines. All this allows us to achieve up to a 78% decrease in costs.
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3.
How to optimize the TCO
A tailored approach to solution architecture design can help achieve the optimal cost-to-performance ratio of the analytics software. E.g., it can be feasible to use cashing mechanisms for storing frequently accessed data (e.g., on real-time project management) or enable workload-dependent scaling of resources to optimize cloud costs.
Software TCO also largely depends on the pricing of technology vendors. Being technology agnostic, ScienceSoft can choose among multiple providers and go for the ones that offer an optimal combination of performance, scalability, and pricing in each particular case.
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4.
How to ensure smooth user adoption
It's essential to give users instruments that facilitate the execution of their role-specific tasks. E.g., capabilities for granular representation of financial data can help financial teams identify cost-saving opportunities and revenue trends, while static dashboards that provide a 360-degree view of operations can give C-levels a quick and clear understanding of the company's health and progress.
One of our UX/UI design practices includes auditing the software that the company has in use and implementing similar workflows in the new analytics solution.
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Costs & ROI of Developing Analytics Software for Professional Services
Costs of professional services analytics software development can vary from $30,000 to $800,000+. The exact figure largely depends on the organization's size (e.g., companies with several service lines and geographical locations will require more data sources for integration) and the complexity of the analytics features (e.g., rule-based or ML/AI-powered analytics).
Analytics in professional services can bring an average ROI of 220% that is driven by capabilities for service personalization, high-value customer attraction, marketing campaign improvement, and informed resource planning.
|
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A basic solution |
A solution of medium complexity |
An advanced solution |
|---|---|---|---|
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Average organization size
?
The more employees, service lines, and geographical locations a company has, the more complex the solution will be in terms of the required features and the number of data sources to be integrated. |
<200 employees |
200-1000 employees |
>1000 employees |
|
Data complexity
|
Structured (e.g., relational databases, XML, Parquet files). |
Structured and semi-structured (e.g., JSON, ORC files). |
Structured, semi-structured, and unstructured (e.g., DOCX, JPG, MP3, MP4, HTML files). |
|
Data processing frequency
|
Batch (e.g., every 24 hours) |
Batch and real-time |
Batch and real-time |
|
Analytics scope
|
Basic reporting: core financial KPIs, resource utilization, billable hours, performance by employee/unit. |
Specialized analytics for different departments, service lines, and customer segments. In-depth financial analytics (e.g., insights into cost allocation, planned vs. actual KPIs). |
A 360-degree view of operations and assets across locally and internationally distributed enterprise systems (e.g., PMS, CRM). M&A analytics. |
|
AI and machine learning
|
|
Simple, ready-made AI models (e.g., for customer sentiment analysis). |
Custom or fine-tuned AI models (e.g., multi-factor what-if models, forecasting based on historical and real-time data). |
|
Reporting and visualization
|
Via market-available tools like Power BI, Tableau, Looker. |
Via market-available tools like Power BI, Tableau, Looker. |
Via market-available tools like Power BI, Tableau, Looker, and custom data visualization modules. |
|
Cost
|
$30,000—$150,000 |
$150,000—$300,000 |
$300,000—$800,000+ |
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