Business Intelligence in Accountancy

Artificial Intelligence / Machine Learning

It is then no longer just about bringing data together and visualizing it, but also about data storage, data management, data connectivity, big data, advanced data analytics and Artificial Intelligence / Machine Learning.

Data must be brought to the information layer in the broadest sense of the word in order to make better data-driven decisions for both the internal and external playing field. And of course, all of this must be done centrally, in a coordinated, safe and manageable manner within the applicable laws and regulations.

To achieve this, it is important to think about information strategy and provision. Who should have what information available at what time and for what purpose? This is of course accompanied by the right IT strategy and IT system choice and a solid data management strategy.

Interesting KPIs in the accountancy industry

Divided into different levels, such as financial, productivity and quality, relationship management, new business, personnel & organization, and marketing.

Financial

Number of customer groups (families, shareholders, managers / decision makers)
Percentage of customers with recurring revenues (hourly * rate) versus transactional revenues (subscription)
Customer value (expected duration of relationship with expected financial value)
Customer revenue (revenue versus costs incurred for a customer in a specific period, revenue by segment, revenue by department, etc.)
Turnover per period compared to the same period in previous years
Turnover per employee (productivity)
Outstanding items (parental sum analysis / periodicity / debtor assessment, etc.)
Profit & Loss analysis

Productivity

Employee-customer ratio
Burners & urners, billable / non-billable hours including accrual
Average number of consultancy hours or annual accounts per customer, employee
Average processing time for receipts and invoices
Turnover by function
Hours per work type and work types per hour category
Under Hands Work (WIP) age, mutations, etc.
Top customers based on invoiced turnover
Progress overview

Quality

Relationship production activities / consultancy activities
Ratio of automated entries versus manual entries

Relationship Management

Number of contact moments per year per type
Customer satisfaction (Net Promotor Score)
Cross selling / Client share
Churn

New Business

Number of new customers per period, relationship manager, etc.
Conversion rate from leads, quotation, etc.
Lead analysis

Staff and organization

Number of employees and distribution of gender, age, contract hours, years of service, position, etc.
Default
Employee mobility (new, retired, advanced employees)
Employee card

Marketing

Top branches divided by turnover and number of clients
Client mobility
Client and their network
Clients by office, province, city, etc.
Client size and priority
Product portfolio
Detailed geographic information
The number of newsletters that are opened, click-rate and click-through rate
Website: number of unique visitors, average time on the website and conversion rate
Penetration rate product line