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100-Year Tradition Meets Cloud-Based Connectivity between Systems, Tools and Environment

RFID is nothing new when it comes to identifying tools, on the part of manufacturers, retailers and their customers. The same goes for tools that communicate with software applications. There’s no doubt that data will only grow in importance moving forward. The pressure to innovate is growing for tool manufacturers and retailers alike. And the driving force behind all this? Data. The industry is changing and doesn’t look set to stop.

Innovative business models, new ways of interacting, small-business apps and pop-up stores – customer expectations have never changed more. Or faster. These days, success relies so much more on identifying customer needs early on and responding with your own product portfolio. No company can make the right strategic decisions without all the relevant data on customers, customer behavior and market development. Data leadership is a key success factor. The tool for this: company-wide data management.

From Small and Local to Digital Business

Data is changing the whole customer journey. Your customers are digitally connected and want a seamless online and offline shopping experience, from detailed product information to hassle-free procurement and service-focused distribution to handling returns. Data-based added value is pretty much considered a given.

Three Trends Revolutionizing Your Industry

Data-Based Product Innovation

Today, a tool’s sensors and actuators are a valuable source of data – as are direct interactions with consumers and customers. They provide the full picture for your product use and condition. Data analytics open up endless opportunities for optimization and sales, from segment-specific customized offers to proactive product development. And all of this grows customer loyalty.

AI, ML, BI Win the Race

If you want to identify market potential early on and make high-quality decisions, you need to link market, product, customer and supplier data and create market transparency. Strategic data management will allow you to leverage machine learning to improve your forecasting ability, harness artificial intelligence to identify recurring anomalies and utilize business intelligence to visualize outliers.

Simplifying Product Classification

Providing consistent, up-to-date and truly complete technical data and product descriptions across all channels is no easy feat. Software support and classification standards such as proficl@ss, ETIM and eCl@ss minimize complexity and optimize data quality, making life easier for you – whether that means customer communications, catalog creation or online sales channels.

Customer Voices

“In Parsionate, we found a strategic partner and advisor for the Hoffmann Group’s data initiatives. This is a journey we can get excited about – and one that’s become much clearer.”
“Working with Parsionate and Syndigo as experienced and creative partners, we have the confidence to continue our journey into hybrid commerce while we simultaneously manage to successfully scale our business.”
“Digital transformation is, on the one hand, the optimization, automation and transparency of business processes. On the other, it’s the development of innovative, new business models and services for our customers.”
“Our partnership with Parsionate runs like clockwork – not least because the team thinks pragmatically and proactively and can draw on impressive expertise in their domains.”
“Contentserv and Parsionate immediately understood what we needed and were able to propose optimized solutions for our use case.”

Data Leadership For Experts

Thank You, Managers. From Here On, It Gets Boring…

Or simply more specific (however you like to see it).
The experts among you will probably appreciate that we also provide insight here
that goes well beyond the necessary basic understanding. So, let’s go!

Technology Issues in the Industry

For Experts Only: Technology Issues in the Tool Industry

Automated Onboarding

Until recently, onboarding new suppliers, or more specifically their data, was a tedious, manual process involving hours of cumbersome manual import and the manual mapping of un- or semi-structured product lists or supplier provisions. These are exactly the kind of onboarding processes where automation promises incredible efficiency gains. In fact, your suppliers can even handle this process themselves, with data provision based on templates and standards. Not only does this drastically reduce process costs; it also frees up your employees to devote their time to more valuable tasks – think strengthening supplier relationships or optimizing your product range.

The idea of a golden record also plays a key role in automated onboarding. This means you select the attributes from each data suppliers that have the highest quality. And it goes without saying, the best way to do this is automatically, with the help of trained and optimized machine learning models known as matching algorithms. Data lineage, on the other hand, aims to boost transparency and traceability regarding which suppliers you have received which data from and where it’s used. Another valuable tool is automated supplier scoring, where suppliers who provide better and more comprehensive product data are granted better conditions – and greater visibility in your channels and campaigns.

The Growing Importance of Classification Standards

Now and moving forward, implementing classification standards for your products, and then anchoring these standards in your business processes, has a central role to play. We can only recommend using the right tools as well as drawing on expert knowledge, and there are a number of key questions to ask yourselves: How deeply are you looking to anchor these classification and characteristic structures in your product data processes? Which classification standards and versions do your business partners actually support? And which ones do they expect?

How do you deal with a version change to the standards? In other words, how do you map from old to new structures? In this context, it’s important to consider that it’s advisable to have several versions for reasons of backward compatibility. Then, of course, there’s the question of how to initially map from your internal product classification to the classification standard in a particularly efficient manner, remembering that there may well be places where the standard fails to adequately reflect your own products. In turn, that means supplementing and expanding that standard to meet your needs. Implementing classification standards in your own business processes really is no mean feat. But all the hard work does pay off. Especially once your business partners make them a cooperation requirement!

Leveraging Data Insights for Customer Centricity

Data only exerts its full value when you take the time to analyze it for key insights. These data insights can prove truly invaluable in identifying market potential along the customer journey and are driven by a wide range of use cases as well as artificial intelligence and machine learning. The goal here is to perfectly embody the omnichannel concept with cross-channel visibility, proactive recognition of any given customer’s current interests and an automated individual approach.

Automatic observation and analysis of the customer journey could reveal, for example, that a master of crafts is in the process of setting up their own business – if you monitor new company registrations to the Chamber of Crafts business register. This could be the perfect time to approach this master of crafts with an offer for business equipment. Another major benefit of data insights is the ability to strengthen customer loyalty. Imagine a customer wants to reorder a product and they could do so just by sending you a picture. Your AI recognizes the product and triggers the reordering process. Similarly, it’s not hard to imagine using automated order and sales forecasts to reorder consumer goods. But what all these different examples of customer centricity have in common is that the automation used is based on data, data pipelines and AI analytics.   

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