Manufacturing Companies’ Shrinking Margins Put Pressure on Sales Every Day
You hardly hear discussions about sales analytics or big data in the manufacturing industry. The only analytics in a wider scale is done to optimise monthly production capacities or to provide financial reports for analysts and stock holders.
Big data means that you capture, combine, analyse, visualise and share sets of data, which would not be possible with manual or traditional data processing. Big data methods create new information that would not be possible to derive due to lack of ability to combine large set of various data sources, or due to the complexity of data.
The steel industry has traditionally relied on two things: manufacturing and good sales people. An individual sales person only looks at his own customers’ orders and profitability. Nobody is consistently analysing various customer segments’ needs, behaviour, winning ratio, share of wallet or new customer prospects. The manufacturing industry particulary lacks systematic ways to utilise external customer data and to combine that with internal sales and financial data. If some information is collected, it might be in various reports, but not systematically visualised in dashboards.
Examples of Big Data applied to sales analytics
I believe systematic sales analytics that combine also external information sources will have a huge impact on profitability as companies are fighting against shrinking margins.
Example: Pre-sales analytics
Sales spend a lot of time in finding new customers. In some cases even 25-50% of the sale time is used for pre-sales analytics. The work is normally not effective as the data mining is not systematic and sales doesn’t have sufficient tools or skills to match data from different sources.
Market intelligence or market prospecting companies can provide the tools and a systematic process to find new customers and analyse external data. They might utilise free databases, outbound call centres, competitor websites etc. However, market intelligence companies don’t normally have access to the company’s own sales data.
By combining these two different types of data sources you build fully new ways to identify new prospects. And, if you make visual dashboards of the information, you probably start to be one of the sales data leaders in the manufacturing industry.
Example: Sensors and remote monitoring
There are several great examples of using sensors in remote monitoring applications. This data is used to create new services. Crane manufacturer Konecranes collects data from cranes around the world to provide preventive maintenance services. Power plant and marine engine supplier Wärtsilä has provided remote monitoring services for their customers for a long time.
Example: Customer segmentation
There are many ways to do segmentation, but manufacturing companies rarely use segmentation based on customer needs and behaviour. This has not even been considered possible as companies look at data and customer information from the perspective of a single sales person or a sales team.
Segmentation based on customer needs has potential for several percent profitability gains whereas e.g. traditional product segment pricing tactics that don’t consider customers’ behaviour might affect the operating profit by only 0.5%. Customer-based segmentation is the biggest underutilised opportunity in the whole manufacturing industry.
For effective segmentation you need to have data of the customers’ past behaviour and profitability. However, the most important thing is to know the customer’s preferences e.g. related to availability of materials, speed of delivery and requirements for technical support. These can be collected through market research companies.
By combining internal sales and financial data to the systematically collected customer needs and preferences you will create new business models, pricing strategies and services.