Why IoT is the CRM of Things

As is often the case, innovation borrows from the past. Successful innovators use linkages to prior successes to reduce the perceived risks of adopting something new.

Those involved in and interested in IoT systems can benefit from the comparison of newly emerging IoT systems to mature CRM systems. CRM systems were once where IoT systems are today. Here are few helpful parallels.

Order out of Chaos

Sales data was once the wild, wild West of corporate data. Sales people went about their business without providing much detail on the selling process. Results were all that mattered. Early identification of an issue within the sales process wasn’t something that could be seen from data, it was up to an attentive sales manager.

Devices and systems have been in a similar freestyle mode. There has been minimal data available on the details of device or system performance. Failure and other exception reports are about the only data reviewed. Thus most responses are after the fact, not preventive.

Goals of CRM systems and IoT systems Aligned

The goal of a CRM systems is to sell more effectively by capturing the data that are created during the sales process. These data were once only in the heads (likely) and notebooks (maybe) of the sales team.

CRM systems capture, organize and make sales process data available to the entire organization. The capture and organization of sales process data permits more detailed reporting and analysis. This leads to benchmarks and performance improvement. A well-executed CRM system can help detect and correct potential issues and improve the close rate.

An IoT system has a similar goal; to improve the performance of a device or system by capturing, organizing and sharing detailed data from the operation of the device or system. These data are usually only in the heads of people who work closely with the machines or systems. Sensors, network connectivity, time series databases, rules-based reporting and mobile delivery mechanisms make device and system data available to all in a timely, exception-based manner.

With data from things and systems captured and organized, performance reporting, benchmarks and performance improvement are possible. Rules-based exceptions can create alerts and contribute to the automatic prevention of problems. And like CRM system data, IoT system data delivers additional benefits when integrated with other data sources.

Accountability

Prior to CRM systems, sales people were largely unaccountable for the process they used. There was training and coaching and but not much reporting on the details of the selling process.

It turns out things you during a sales process can have a direct impact on your odds of closing or not closing more deals. A CRM system introduces multiple ways to increase accountability with the objective of improving sales performance.

Devices and systems have typically followed the, “If it works, leave it alone” approach. Scheduled maintenance is undertaken to keep devices working, but not much is done in the way of understanding detailed performance.

Sensors, microprocessor and other IoT system data capture capabilities provide details on what happens second to second or even millisecond to millisecond. A small glitch that used to lead to a major malfunction is now detected and quickly – possibly automatically – corrected.

The telematics systems on commercial vehicles are a great example of this type of greater system accountability. Sensors detect and report small variations in performance to prevent bigger problems before it gets to the point where you, “hear something funny.”

Trends, Benchmarks, Root Causes and Automation

The data captured by a CRM system means detailed metrics can be trended, benchmarked and improved. You can dig into unexpected changes and see what other factors could have led to these changes. What effect did the travel restriction have on client visits, pipeline status and deals closed? Are we being penny wise and pound foolish?

You might see that certain marketing materials, when presented to certain types of prospects, at certain stages in the selling process, increase response rates. You could then automate the process: When client Type A inquires and the sales stage is 2, respond with Marketing Material XYZ.

Data driven marketing automation and sales automation continues to expand as CRM data becomes more reliable and as greater successes are realized. The blending of marketing data with sales data will continue to improve the variety and effectiveness of automated sales and marketing tactics.

Since devices and systems are more compliant with data input quality and frequency than sales people, IoT system data provide even more opportunity to capture and compare trends, establish benchmarks, explore root causes, and use all of the above to develop automated rules.

“When you detect X, take action Y,” is a common capability of an IoT system. Action Y can be a simple text alert sent to a manager or supervisor. Alerts are typical of early stage IoT system implementations where confidence in the IoT system data is low. There may still be nuances to the proper action that the IoT system data does not convey or know.

Over time, the action Y may be an actual adjustment or action that is taken automatically and logged for later review. No one is interrupted with an alert. One example might be: “inventory level of product 12345 at 0.1n = place reorder for n units X sales rate y.”

IoT system data will also suggest new rules-based actions. There will be ongoing refinements to earlier rules-based actions. And finally, other data elements will be integrated with the IoT system data and rules to increase the results that can be achieved by upgrading alerts to automated actions.

If you’re curious about how to make an IoT system work for you, borrow a few notes from a successful CRM system adoption. There are quite a few parallels in how the capture and application of new and more timely and granular data improve the performance of both systems.