The sales effectiveness tool is currently actively used by around 10.000 sales consultants spread over 6 countries. The hit rate has increased from the historical 25% to 70%, meaning that today the sales consultants using the tool are spending 70% of their time with the right clients - clients who have actual and feasible potential for Randstad.
As one of the leading multinational human resource consulting firms, Randstad has 39,530 employees spread over 38 countries.
Making sure that these people focus their time on contacting companies who have real potential for Randstad, and enabling them to build conversations based on accurate and relevant information has a direct impact on the success of Randstad in serving its clients and hence realizing its own core ambition.
While Randstad already had the data and insights in the ‘right companies’, with attractive commercial potential, this knowledge was not yet used optimally to steer the activities of the sales organisation on a day-to-day basis.
It therefore embarked on a mission to create an AI enabled solution that allows a better and continuous use of data by proposing concrete, actionable tasks to sales consultants through an intuitive end-user application.
ML6, as AI specialist partner, is driving the setup and training of the data intelligence components of the solution, as well as the implementation of the AI ML Ops architecture to ensure that the solution can easily be scaled across teams and countries, and retraining in a continuous, automated way.
This 'sales effectiveness tool’ consists of four different solution components. Core is the scoring system that assesses the potential of an individual company for Randstad, as well as how easy or difficult it will be to get to that potential. This company segmentation in an attractiveness quadrant is done based on company profile data, but also leveraging market data to predict job needs for the next 12 weeks and to spot company buying signals for Randstad services.
The data-driven territory design aims to make the scoring specific by geographic area, increasing the relevance of the generated insights for local sales teams. The resulting kanban script - an automated and actionable to-do list - provides individual sales consultants on a weekly basis with an overview of companies to focus on; and insights about that company/industry as a basis to structure the customer discussion.Finally, the market insights solution component uses additional external data to provide the sales consultants with further context, for example a valid business reason to call the company, instant customer industry information, supporting data for expectations management/ fillability and pricing negotiations.The 'sales effectiveness tool’ is conceived to formally and continuously leverage data in an outside-in approach, acquiring up to date, factual data from selected, structured external data sources and applying time series based forecasting to generate insights and triggers.
The Dun & Bradstreet company repository serves as the source for a worldview on all companies, existing Randstad clients as well as prospects, including a comprehensive view on their profile features. Job demand data is gathered from multiple sources like textkernel, careerjet, burning glass and jobdigger. Finally, economic data per geography (eg gdp, employment) are acquired from Oxford Economics.Defining and imposing data governance principles like global definitions and data protection (e.g. no manual override) ensure data quality and hence the relevancy of the weekly recommendations for sales consultants.
The onset of the Covid pandemic, as a textbook example of a high-impact, low-frequency (HILF) event, has triggered the addition of Natural Language Processing (NLP) techniques to spot extra data on irregular, unpredictable events.
The hit rate has increased from the historical 25% to 70%, meaning that today the sales consultants using the tool are spending 70% of their time with the right clients - clients who have actual and feasible potential for Randstad.