Projekte

Together with the team, we defined benchmarks in terms of activities and conversion steps based on a comprehensive data analysis of historical performance data and current business goals. Alongside the analysis, we adapted and improved processes in the CRM (Salesforce) to ensure clean and consistent tracking of all relevant data points. Introducing Power BI dashboards on key sales metrics containing live views on deviations from benchmarks along the full sales funnel allowed team leaders to intervene and offer individual support at an early stage which led to a significant and sustainable increase in productivity.

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The logic of this approach of defining activity and conversion benchmarks next to the targets in outcome has then also been successfully applied to the company's farming and retention teams.

We started off by designing a qualitative survey among users to get a first grasp of their responsiveness and feedback. Combining this with a quantitative analysis of web tracking data (Google Analytics) as well as available data from the platform such as log-ins, sign-ups for offline events and other engagement metrics, we were able to identify clusters of users. Enriched with available data on user characteristics, we derived personas, which proved invaluable to successfully relaunch the website, improve targeting and ultimately leading to sustainably elevated engagement metrics.

I developed an excel based dynamic revenue forecasting model for a subscription based SaaS company that allows to differentiate growth scenarios based on assumptions in terms of product development, head count growth and performance assumptions in marketing, sales, customer success and retention. The model comprised a detailed 12 month forecast and a higher-level forecasting of a 5 year horizon. Basing the modeling on deep-dive discussions with department heads and engaging them in the process, led to increased accountability and more precise results. 

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In another project, together with the founder and the CFO of a small but strongly growing start-up, we developed an excel model that allowed them to have a live view on their forecasted cash-flows from current projects. 

Together with the sales management, we implemented a forecasting system at the case level that required account executives to provide an estimate about closing probability and details on timing and revenue on each case in their pipeline that reaches a  previously defined sufficiently advanced stage. This information was directly documented in the CRM based on customized properties (HubSpot, Salesforce). This set-up laid not only foundations for discussions on the current forecast but also on forecast accuracy in a timely and case based dimension on across individuals and teams. While a machine learning based forecasting set-up brings the advantage of exploiting a variety of known and unknown influencing factors, having a case based, human forecast on top brings the strong advantage of educating the sales organization by providing transparency on (mis)perceptions of timing and likelihood of success case by case.

Based on first results from an analysis of historical data, tests were implemented to see the effect of changes in wordings and in case of the passive lead scoring weighting of different components. Lead quality on both active and passive leads could be sustainably improved, leading to higher conversion rates and gains in key metrics of the marketing department such as costs per lead. 

In the past, I have been teaching bachelor- and master level courses at the University of Munich and the Hochschule München.

I am happy to discuss individual requirements and help finding the suitable solution for the needs of your business.