Choosing how to approach which customers is a daily challenge for all sales representatives. CWS Boco’s sales representatives meet with thousands of potential customers every year and whether or not they convert the opportunity depends on many factors. Type of product sold, trends, time of meeting, the competition and the customer’s industry among many others determine success or failure. Can an AI clear the fog and uncover what truly matters?
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CWS Boco’s sales representatives visit over 1000 potential customers monthly. These opportunities are created by telesales based on a list of millions of companies which is provided to them. The conversion rate of opportunities is at around 20% but depends heavily on the factors mentioned above. CWS Boco provides over three years of data that can be used to analyze the probability of converting an opportunity into a paying customer.
The goals of this project are threefold:
Identify customer segments with very high/low conversion rates Identify the most influential features for conversion from data Predict conversion probabilities
The best prediction quality will probably be achieved by making individual forecasts for each division. Such an opportunity scoring and recommendations engine could provide tremendous efficiency gains and savings. By approaching potential customers the right way, and avoiding acquisition efforts for the wrong potential customers, yearly savings could be tremendous. The data currently used by CWS Boco should be enhanced to create more accurate forecasts. Therefore, an optional part of this project is to enrich the existing data with external data sources to get additional information about opportunities.
What you bring to the table
● Experience in the field of sales
● In-depth knowledge of classification algorithms and ability to uncover relationships between features/variables and outcome to generate actionable results
● Will to explore alternative datasources to enrich the available information
● Reliable identification of high potential opportunity
● Computation of accurate conversionprobabilities
● Detect factors with highest influence on outcome
The project can be divided into three milestones:
The first milestone is reached when the available data has been enriched with external data sources that extend the number of useful features for prediction. Visualizations and descriptive statistics have been used to understand the problem of predicting conversions. The second milestone is reached when a model/machine has been developed that accurately predicts conversion probabilities and explains the observed outcomes. The third milestone is reached when the algorithm is presented to and accepted by CWS Boco.
Let's talk! Patrick Majunke –Your personal consultant firstname.lastname@example.org GOT ALL THAT? APPLY FOR THE CHALLENGE!
If you are convinced that you can solve the problem together with us and you are up for an intense experience full of opportunities then directly apply for the challenge.
Let's talk! Patrick Majunke –Your personal consultant datahub@ gruenderallianz.ruhr
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