One often hears from owners and managing directors of renowned companies that one is lucky if incoming inquiries are properly answered. This is because the sales department has little or no capacity to handle inquiries properly. For this reason, an AI-based solution for the evaluation of leads is recommended.
IT companies are overwhelmed by masses of leads/requests via social media or other online advertisements. The number of requests is increasing enormously – but not every request is actually good. Often many companies ask – but in retrospect the costs are too high or the offer is not suitable. Roughly estimated, only about 20% of the company inquiries turn out to be useful. Not all enquiries can be processed intensively because sales resources are very limited – often there is only little staff available or the sales department is generally too busy.
But how is it even possible to keep an overview of the actually useful inquiries, which at best lead to an offer being closed?
We would like to answer this question by means of a project with a software company in a special niche, which cooperates with many major international customers.
The problem of the IT company was that the managing directors were complaining about sales regarding lead processing. Due to the high amount of unfiltered inquiries (approx. 1000 per month), the sales department was overstrained to process every single lead intensively and decently. This overstraining was in turn reflected in a high level of demotivation at work. If the quality of the enquiry was only average, the sales staff assumed that nothing would “get around” anyway.
Predictive analytics for the quality of the leads
For this reason we at Dastani Consulting have developed a procedure – or rather a predictive analytics model – to qualitatively classify a request in advance. This model takes all information about different companies from the web (e.g. industry, employees, turnover), as well as from different reference databases. Subsequently, the interested parties that have been approached are crawled. Based on the historical inquiries, deals and opportunities that have been set, the system is able to judge which of the prospects actually become customers. The AI system learns from the past and can therefore realize considerable learning curve effects.
Using the AI tool
The software company uses the AI-based tool to check older leads on the one hand, where it is still worthwhile to tackle them after a few months. On the other hand, the new requests can be checked every month in a continuous process. Every month, the AI system should decide a priori whether the new request should be forwarded directly to the sales department for intensive processing or whether it should first be answered by office staff, social media or even automatically.
Result: High hit rates
As a result, it turned out astonishingly that the AI system is able to identify a priori with a high hit rate the addresses at which customer inquiries have developed or an opportunity, i.e. ultimately an offer, has been created. In this way it was possible to differentiate between “good” and “bad” prospects. Rather, the factors that ultimately turn a prospect into a customer were also identified. It is precisely these customers that the sales department has to process with the highest priority.
Conclusion: Focus on the important customers
This means that by introducing the predictive analytics model, sales can focus on the really important customers. Thus, the sales resource remains constant, the probability of closing deals increases and efficiency increases. Last but not least, everyone is happy and even the prospects who had an inquiry are happy because they got exactly what they wanted.
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