A technological-tylenol hasn’t traditionally been easy to come by. Until recently, complex issues have required complex and human solutions. However, advancements in data unification, artificial intelligence, and automation have made solutions scalable, more secure, less prone to error, and, most importantly, far easier to implement.
Ecommerce Challenges in Manufacturing
Customer satisfaction is a challenge for many manufacturers.
Typically, customers know what they want; they aren’t looking to browse. That being said, the ability to quickly predict and surface what they need is essential. If someone comes to your site looking to buy the same group of products on a monthly or quarterly basis, they shouldn’t have to navigate through ten or twenty pages – and they won’t.
When faced with complexity, they will simply pick up the phone to call customer service, resulting in frustration for them and an added cost-to-serve for you. This costly frustration not only arises when products are difficult to find but also when the products they do find have been labeled improperly.
“Oftentimes, the data that they have about their own products is a mess,” Justin Bradley, Senior Solutions Architect at Rightpoint, explains. “They don’t keep the right data around, so you have the incorrect entitlement management.”
If a customer can’t (easily) find what they need at the price they’ve been promised, they may very well go elsewhere to find the personalized experience that they expect.
Bradley states that the real challenge for manufacturers at this point is distinguishing themselves from others in the same space. To meet that challenge is to ensure that products can be easily found and located, which means manufacturers need to take a good look at the data they have at their disposal.
Cleaning Up Messy Data
Specification data can be your friend when it comes to building a good online experience, but it can also be your worst enemy. It is essential for a company to have standardized specs across a supply chain.
“You have to get the right data for these distributors or these installers,” Bradley emphasizes. “Not only does the pricing have to be correct to their entitlements, but you need to meet the specifications that they need. A few centimeters can make or break whether or not that product could fit.”
A product merely fitting incorrectly can have vast implications. Issues with shipping, communication and even regulations become exacerbated when operations are conducted at the international level.
Making sure that all your data interacts in a cohesive manner can be a daunting task, but it’s critical to delivering a valuable ecommerce experience. If different systems aren’t interacting properly, automation becomes difficult and the whole system can atrophy because of it.
“A product information management system (PIM) is something that we’re just starting to see adopted by major players in this space. But more and more, we’re coming in and recommending that this be an integral piece of their solution,” Bradley highlights. “The products are scattered and if they’re not in the right place, then you’re not meeting standards because there’s no workflow in place. A PIM helps solve this.”
Compiling a better data set is what will allow an organization to start to build out the solutions to the challenges they face, as it serves as the raw material required to deliver the experiences their buyers expect.
Artificial Intelligence and The Value of Relevance
Once a data set is standardized, artificial intelligence can be applied on top to learn buying behaviors on an individual basis and tailor each digital interaction to the individual user. Not only does this allow manufacturers to ascertain what an individual needs in the moment, but the application of AI can also help them predict future needs and adapt to meet them.
“It [AI] reveals insights into the minds of the people that they’re marketing to, allowing a business to change their marketing behavior patterns to match,” Bradley notes.
The ability to tap into historical and in-session user behavior is incredibly valuable for any business. When a customer authenticates themself, it becomes possible to provide an experience so seamless that they can checkout with a single click, as reorder lists and relevant recommendations can be automatically generated.
In addition to using interactional data to help buyers find what they need, AI can also use it to uncover issues that stand in the way of them doing so.
For example, when that replacement part is mislabelled by a few centimeters, AI can alert you after picking up on irregularities in higher than average returns, less sales, or higher bounce rates. Instead of letting these small issues grow into bigger pains, AI allows you to address them as soon as they arise.
According to Intershop, proper digitization of the B2B commerce experience brings a 39% increase in customer satisfaction, a 42% increase in overall efficiencies, and an increase in sales of another 39%. And while this digitization is valuable, it is also essential.
“As more and more people move to online, they’re going to seek out that convenience,” Bradley explains. “It’s just a smarter way of doing business.”
In accelerating their digital transformation efforts, every manufacturer should be cleaning their datasets and relying on AI to deliver the relevant experiences their buyers expect. Laggards risk not only losing business to their more tech-savvy competitors but also losing out on the cost benefits of more efficient processes.
To learn more about the challenges that can be addressed by applying AI to the data at your disposal, read: B2B Ecommerce Challenges: Why Your Catalog Search Is Awful.