Marcel Hollerbach is the CMO of Productsup, a SAAS cloud based software provider and partner of EliteSEM. He's also a Partner at Cavalry Ventures.
Tell us about the mission behind Productsup. How are you hoping to help marketers with data management?
It is expected that by 2020, 50 percent of online retail will run through marketplaces. The mission of Productsup is to enable companies to be agile and connected, and to adapt to the ever changing requirements of new and existing marketplaces and marketing channels like Amazon or Google.
How has data management evolved in the past five years? What has made the biggest impact on how brands manage product data?
When Productsup started, a lot of retailers primarily used the platform to manage their products for Google Shopping. Five years later, there was a whole ecosystem of channels that brands could send their data to. Think of Facebook Dynamic Ads, Criteo retargeting, affiliate networks, price comparison sites, B2B syndication, GDSN product information network, etc. The complexity and the amount of partners are increasing on a daily basis. Productsup helps brands and retailers to stay on top of the product data syndication challenge very effectively and hustle free.
What are some of the challenges or pain points your clients come to you with? How do you help them?
Some clients have very messy product data that needs to be cleaned before it can be sent to a marketplace, such as removing HTML tags and consecutive whites paces in product titles. Other retailers need to syndicate one product catalogue to 10-15 partners in 20 different countries. Other customers need to deal with very large product catalogs (50 million units and larger) with a lot of pictures and information. They use delta updates with their partners to keep data volumes low. Where Productsup is of great help is offering 1,000+ channel templates that are already implemented in our software and that are frequently updated. As a Google Shopping partner, Productsup receives changes before they are implemented by Google and we are able to roll out these changes to our customer base making sure everything keeps up and running.
What do you wish more of your clients would do to improve their product data management strategy? What is the payoff of smarter product data management?
In my opinion an online retailer or a brand has two important data sets. First their customer data (CRM) and second their product data (PIM/PCM). Shoppers in the future expect to be able to buy products across multiple platforms (via chatbots, marketplaces, voice, on their smartwatch, etc.). All this requires state-of-the-art syndication capabilities. If you have clean and structured product data and good connectivity (syndication) functionality in place, the payoff is, that you will be:
A. more agile and be able to try out new trends quicker (think of selling products on a new channel like Pinterest and be able to connect within days not weeks) and
B. get more performance out of your channels. An optimized product image and product title might increase your click-through rates by more than 80 percent resulting in a better return on investment.
What are some outdated or obsolete methods of product data management you observe clients using? What should they be doing differently?
Well, good old Excel never dies. We still see customers copying excel lists of their products by hand. Unfortunately, this is extremely prone to error and will lead to weaker performance (fewer sales) in your channels. As Excel has got limited data-handling capabilities, you end up waiting and wasting a lot of time. Excel cannot handle large data sets and sometimes won’t even open, depending on how big your inventory is. What’s more, automated imports and exports or integrations with third-party tools mean that anyone wanting to take their data to the next level can’t.
What brands do you think have done an especially noteworthy job of managing their shopping feeds? What can we learn from them?
Some of our brands really figured out how to create shopping feeds with high relevance for shopping channels like Google. The Productsup platform supports the A/B testing of data feeds that can be used to continuously try new things and evaluate the results. You might discover that a product title at Google performs better if all the products contain the brand name and the color of the product in the title. These optimizations might be different for another brand. The most successful customers we see are those who really play around and test new variants of their shopping feeds to get to the best results.
What should brands be doing today to prepare for consumers shop in the future?
They should be aware that the shopper in the future expects a multichannel experience. He might want to research a product on Google, watch a product video on YouTube, ask a chatbot on the brand’s website about some product details and then order it via voice through Amazons Alexa. To prepare for that, brands need to work on three key areas:
1. Content: Have as much information as possible available on your products (images, videos, product attributes).
2. Data quality: Have your data as structured as possible, so that marketplaces, marketing channels, chatbots, recommendation algorithms, etc. can really make use of it.
3. Connectivity: You need to be agile and able to quickly adapt to changes and new opportunities. Connecting your products with a new channel should be a task takes a few hours, not weeks, if you want to stay competitive.
What trends or innovations in the world of product data are you following today? Why do they interest you?
There is a lot going on in the area of machine learning. I believe that in six to eight years from now there won't be a single area in in retail that won't be influences by artificial intelligence. We already see the first use cases like detecting colors from images, automatic categorization (taxonomies) and enrichment of product data with new and relevant keywords for example. Making use of this development in smart ways will skyrocket the productivity and return on investment of brands and retailers.
We are happy to be a part of that movement with our own machine learning developments.
Get more insight on managing your online store, read eCommerce insight: top 8 shopping and feed tips.