How can data and analytics help you accelerate B2B sales and stay ahead of unexpected competitors? Read our practical case example from the manufacturing industry.
The way we do B2B sales challenging enough before the pandemic. Take the manufacturing sector as an example. Over the course of the last few years, Asian competitors have increased their pressure on spare-part replacement sales, capturing more and more revenue from the installed base. As an example of this increased competition, it is not uncommon for cheaper spare-part alternatives to catch your customers’ attention by appearing first in any Google search.
We’re also living in the online world more than ever before. The sales force used to meet and greet their customers face to face, but, as the pandemic has unfolded, we’re are increasingly finding ourselves in remote sales. Customers are also expecting the sales force to contact them with a targeted approach – offering correct products at the right price and the right time.
So, competition is increasing and it’s hard to stand out … but it doesn’t have to be that way!
Manufacturers no longer have the monopoly of selling spares and services across their installed base. The competition has already moved online! It started with search engine optimizations (SEO) a few years ago, and the pandemic has only made matters worse. Typically, half of all quotations never turn into firm orders, so manufacturers are painfully aware that the competition has increased over the last few years.
The winning strategy is to secure sales earlier, contacting customers when the time is right on their side and closing the deal before the customer even starts searching for replacement spare parts.
The solution is to make use of the one asset your competitors don’t have: customer data.
Manufacturing companies have something that their competitors are lacking: years of installed base data. Their experience of Enterprise Resource Planning (ERP) systems is making it possible to analyze years of sales data, to get a very precise view of the materials, quantities and timing of future sales. This is a direct representation of the world using the most refined level of detail. It allows them to anticipate customer needs, secure stock on critical items and conduct relevant discussions up front.
This “algorithm-driven” approach is relatively new to the manufacturing industry, and you may be wondering what to do with all the data. If the hypothesis is that we can indeed predict a customer’s upcoming demand by using existing data, how can we test it to see if the hypothesis is indeed correct?
We ran a short development project with one of our manufacturing customers. Using our agile background, we proposed running through just two sprints: one to prove that the maths actually works, and the other to implement the first activations. It is very important to be ready for the possibility that the maths does not work once you get into it. But, in this particular case, we were able to prove that it worked across two very different types of geographies. This was great news for the developments that followed!
Our customer had several ideas in mind that we described as a business hypothesis, using a standard agile framework:
This most important action you can take is to just get started – and evidence suggests that this is even more critical in times of crisis and uncertainty. Accurate prediction models will help your sales team to focus on the right things. For example, in our pilot project it was possible to predict what customers won’t buy. Further, the accuracy of sales forecasts improved significantly and we were able to forecast sales with 80% accuracy.
The data-driven approach creates new opportunities. By using data you can get quick wins, but becoming a truly data-driven company won’t happen overnight. Rather, it is a long-term transformation where you need the right partner.
Read also: What is data governance and what if it did not exist?
Could the maths also work for you? Would you like to be able to forecast your sales with 80% accuracy and put your salespeople ahead of the competition as soon as the next quarter? Testing hypotheses is the only way to actually become a digital leader and have a truly data-driven business model. The quality of the data used is obviously important – but not as important as the willingness to start before you are ready.
Sounds scary? The good news is that you can have something ready within a couple of months. Start small and pivot during this month, this quarter and this year. Don’t spend your time waiting!
This last paragraph is a call for action – it is also a call succeed – because starting to work with data and artificial intelligence means you are starting to succeed in this new digital world of ours.
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He has been working extensively with business and has deep understanding on how data is created and what it represents. Emmanuel can support customers refining and prioritizing their business needs and driving agile business transformation. He also has experience in key data related topics including data privacy, data protection, ERP transformations, data warehousing, analytics and Artificial Intelligence.