Welcome to the revolution. Data sciences have taken the consumer marketing world by storm. Once upon a time, marketing had to rely on limited data to forecast the behavior of customers. Now, data sciences have unlocked the vault containing vast amounts of consumer information, so that it’s now possible to predict a customers’ buying decisions - frequently before the customers realize they’ve made a decision. Data sciences’ ability to analyze that information is the closest thing marketing has yet found to knowing the future, and no company that wants to thrive, or even survive, can do so without partnering data sciences.
Consumer marketing has wholeheartedly embraced data sciences. Industrial marketing, while offering tremendous value – practically everything that is manufactured falls under the umbrella of industry – has been slow to reap the benefits from data sciences. Why?
A direct comparison between marketing in the consumer and industrial spheres – see the figure below – shows some potential barriers to using best practices between the two.
However, the use of fundamental statistical learning and advanced data techniques in marketing, which has served consumer marketing handsomely, can definitely be applied to industrial marketing as well
The most notable and substantial difference is the fact that industrial products tend to serve a multiplicity of purposes while consumer products are more narrow and focused. For example, in plastics, a novel engineering polymer may find its way into consumer electronics, automotive lighting or aircraft interior applications whereas a product directed at the end user will generally be limited to a very specific function. Likewise, while consumer marketing casts a broad net, affecting millions of customers, industrial marketing is more narrowly targeted.
In addition, consumer marketing targets fewer decision makers – usually one – and not having to negotiate the functional and organizational layers of industry leads to a shorter decision cycle time when a consumer is picking out a product. Industrial purchases generally are not made on impulse. Finally, consumer-based businesses, even with a solid value chain, can be at the mercy of the latest fad. Industrial applications, driven more by specifications and less by consumer whim and fashion, tend to be “stickier” and more stable over time.
So, will industrial marketing enable the leap and embrace data sciences? Can it find a way to break down these barriers? Where to begin?
Math and statistics are neutral and don’t take sides in this debate. Machine learning, natural language processing and algorithms like clustering and decision trees, can be applied equally to industrial as well as consumer marketing. Data may be “darker” on the industrial side – in its variety and sources – but can still lend itself to elegant analysis.
Consumer marketing is driven heavily by customer segmentation. Understanding consumer behavior and getting a larger share of wallet are dominant factors in this segmentation. On the other hand, beyond segmenting customers, industrial marketing offers a rich variety of axes for segmentation. Target applications, products, regulatory themes, suppliers, value chain and competitors all offer themselves for analysis and insights. In addition, these aspects of segmentation form a very critical component of business strategy for industrial enterprises.
Marketing leaders of industrial enterprises are (a) striving to create market-facing strategies and (b) aspiring to fully use the ongoing data explosion. Segmentation offers a pathway for making the above-mentioned industrial dreams real. Segmentation occupies considerable amount of time and resources in industrial marketing organizations. Above all, segmentation is critical to industrial enterprises’ growth and success. Given its prominent role, segmentation seems like a natural, feasible and effective starting point to launch the transformation.
It is time for industrial marketing leaders to enable the leap. The other fish bowl is highly valuable. It is substantial. It is vast. It is nearer than it seems. And most importantly, we know where to begin.