When it comes to the speed of technology and need for immediate and accurate results, slow and steady does not win the race. With rapid advances in technology, instant access to information has become status quo. B2B and B2C customers alike expect omni-channel ordering based on their preference. They also expect product delivery with speed and pinpoint accuracy. To keep up with fast-changing customer demands, companies are constantly trying to effectively optimize their workflow and productivity. With vast quantities of information pouring in, identifying relevant data can be a daunting task. Strategic analysis and optimization is necessary to effectively perform on all platforms. There is no doubt that data is still king in 2017 and can deliver insights into the efficiency, productivity, and profitability of almost every area of business.
Recent research by Transparency Market Research indicates the growth in industries of finance, manufacturing and distribution at a CAGR (Compound Annual Growth Rate) of 60.5 percent in the coming years. In the competitive commercial landscape, data is becoming more relevant for the success of distribution and manufacturing companies. Innovative businesses are leading the way by improving efficiency and accuracy of product delivery and customer service through automated data handling.
Data analysis takes the guesswork out of a company’s decision making, making every process quicker and more reliable. By tracking and analyzing order data through an automated solution, it is possible to effectively identify key operational trends such as peak order periods, bottlenecks and error rates, as well as customer order placement preferences. Companies can resolve problems efficiently and cost effectively, but also spot opportunities for improvement.
There are some key data trends to identify when analyzing critical business data to stay competitive:
Order Volume by Day/Time
Understanding specific customer preferences and buying habits are essential to customer service and profitability. For example, tactics such as the creation of customer profiles through order data have dramatically improved marketing and inventory control. Identifying which products are in high demand and isolating peak periods of high/low shopper volume can have significant benefits to sales and business operations. For instance, knowing a customer’s buying habits down to the exact time and day of purchases allows for integration with marketing strategy. Companies can time promotions and discounts accordingly to increase sales. Employee errors that stem from an overflow of demand are better targeted and proactively resolved, which in turn improves customer satisfaction.
Order Placement Preferences
While general customer feedback and attention to buying patterns will allow for some insight into customer preferences, it is not enough to rely on this information alone. By digging into empirical data, businesses will be able to view how customers are responding to sales on particular items, and which products tend to be sold online vs through phone sales, for example. While many had hoped that e-commerce would take over for time-intensive order channels, a 2016 study by Modern Distribution Management1 found that 74 percent of buyers very frequently or frequently order by email, in comparison to 60 percent via ecommerce and 58 percent by phone.
With data analysis, companies can correctly pinpoint their most popular sales channels by monitoring and analyzing orders from each simultaneously. This allows for informed decision making on staffing and technology solutions to bolster efficiency, while providing insight on where to focus marketing efforts to optimize sales.
Order Processing Times & Cost
Prior to automation technology, long order cycle times were an accepted part of customer transactions, where traditional order cycles take up to 27 days, with manual processing costs fluctuating between $30 and $60 per order. Today, fast and accurate product delivery is the norm. Analyzing how long it takes a team to process each incoming purchase order and how much it costs to do so can be eye opening. It can reveal bottlenecks and outlays that can hinder profitability and customer satisfaction. By mapping the data and finding key patterns, businesses can take preventive measures to avoid potential pitfalls as well as reduce time and costs spent on order processing.
There are several different ways to evaluate customer satisfaction, but like with any data point, understanding how to transform insights into business decisions is key. Net Promoter Score (NPS) is a metric for assessing customer loyalty used to measure how many customers are recommending a given company or brand. If more customers promote a company than not, the score becomes positive. This generally means that the company is functioning at a high level of customer service. Monitoring NPS on a regular basis can help to alert operational changes and fluctuations that are influencing overall customer satisfaction.
The importance and relevance of data for distributors and manufacturers is critical for profitability and growth. Examining these data trends is beneficial for companies looking to increase customer satisfaction, reduce costs, and maximize the overall productivity and profitability of their business.
Brent Halverson is President & CEO of ecmarket, cloud solutions developer of the patent-pending Conexiom sales order and invoice automation solution. Conexiom allows manufacturers and distributors to focus on serving customers and managing supplier relationships instead of entering data. For more information, visit conexiom.com
1“What Customers Want: A Distributor’s Guide to Customer Buying & Shopping Preferences (2016),” J. Bein & D. Mueller. © 2016 Gale Media, Inc. All rights reserved.