Companies provide a huge amount of metrics and a variety of analysis for sales management. There is no shortage of data. But how do you connect the metrics and masses of data with practical sales support measures that produce results below the line?
How to refine numbers into practical measures that boost sales? There is now more data available than ever, but the greatest potential in data is yet to be harnessed in most companies.
Process mining is a method of extracting real gold nuggets: the opportunities and insights that generate business benefits. Successful global companies such as Uber and Amazon already swear by process mining, and in a few years everyone in Finland will too.
So what does process mining mean and what kind of issues can it solve? Here are three real life examples from the world of sales management:
How to make bidding more efficient and improve profit?
The question of how salespeople use their time is always controversial. According to a survey conducted by Salesforce, salespeople spend only 34 per cent of their time on actual sales work. The rest of the time is mainly spent preparing bids, doing background work, and finessing details.
The inefficient use of time easily creates tensions, especially in growth companies that recruit a lot of new salespeople. The way salespeople work is rarely forced to fit into a single mould. However, the aim is to provide salespeople with the conditions to shorten sales cycles and make work more efficient.
When we analysed the activities and timestamps in the CRM of a service company, we found that rapid bidding correlated directly with the probability of profit. Because the data had been mined from our own data sources, no one challenged it with an “another American study, it doesn’t apply to us here in the Nordics” attitude. The company introduced a CPQ tool which automates bidding and therefore frees up salespeople’s time for the most important thing: customer dialogue and sales work.
The salespeople’s use of time became more efficient, and they found their work even more meaningful. There was no return to the old manual bidding process. Feedback from customers sealed the matter: “Your offers are now clear and understandable!” The bid acceptance process speeded up as customers no longer had to ask for more specifics.
How to predict customer departures?
Nothing hurts a sales manager more than the sudden loss of a customer which comes out of nowhere. Like many other companies, an industrial company looked for an answer to this problem. The management wanted to get an alert in the CRM when the risk of customer exit was increasing.
The solution required the data from the ERP and customer relationship management systems to be mined, combined, and utilised intelligently.
The reasons for customers’ declining purchases were directly related to past delivery delays. The same data mining revealed that the reasons for delivery delays originated from repeated delays by a few key suppliers.
Inspired by this precise understanding, the company was quickly able to target development measures and negotiations with the right supplier. At the meetings, they presented the suppliers with diagrams produced by the data mining. These showed the differences in the delivery punctuality of the suppliers and the consequences of the delays on the company’s declining sales on a factual basis. With these facts, negotiations to develop the functions began promptly, and no time was wasted on debates whether these facts were true or not.
With increased understanding, deviations in the supply chain were systematically monitored in customer service and sales. Now their root causes could also be addressed.
Proactive information became a daily part of customer service. Sales began to receive alerts in the CRM system from very early exit risk signals and were able to make appointments with those companies. The meetings consisted of planning a shared future and presenting measures and investments which further developed the supply chain and security. As a result, customer satisfaction and loyalty increased. Exit risk companies also returned as loyal customers.
How to clone your star salespeople?
Sales has always sought to identify the right recipes for profit: what acts and similarities are behind winning bids? The means of understanding the causes of profits and on the other hand, of losses, have traditionally been based on empirical information that sales gather as feedback from their customers or from discussions in team meetings.
Would we be more successful in cloning star salespeople if we could combine empirical knowledge with hard facts? Take, for example, a healthcare company that managed to increase its profit margin through CRM data mining.
The company found that customers of the winning bids had an average of seven times more contacts in the CRM than those who had failed sales projects during the same period. A more detailed view also proved that profitability did not depend solely on the seller’s activity, but marketing activities also played a role. Marketing measures had increased company awareness and a positive image.
This information from their own CRM system triggered the kind of change in the salespeople that had not been seen before. They were motivated to increase the contact area in their customer relationships. Marketing was also excited about the trend, as campaigns could now be targeted at a wider range of decision-makers. The cooperation of marketing and sales intensified and there was no going back to the time of working in silos.
Quick profits – and a lot more!
These three stories show that process mining can offer sales quick wins with immediate results. The rapid benefits are always sweet, but above all it is worthwhile to reflect the process mining in the bigger picture.
A lasting competitive advantage is achieved by those who make process mining the cornerstone of their operational management and development. Process analysis must also be closely linked to the implementation of the strategy.
Want to hear more success stories and see a demo of process mining? Sofigate’s experts Liisi Koivu and Simo Parkkali will help you to consider how to achieve results with process mining. Contact us to arrange a chat session: email@example.com, tel. +358 50 2460.
About the authors
Liisi Koivu works in the Sofigate’s Customer Experience & Interaction business area as a Senior Advisor. She is an expert in customer experience strategy, governance models, customer experience measurement and customer vision management.
Simo Parkkali works at Sofigate as a leading process mining expert and business developer. He is responsible for partnering with process mining technology suppliers and building workable concepts to improve process performance and the competitiveness of his customers.